EPSRC PHD Studentships - University of Huddersfield

Artificial Intelligence (AI) assisted Industrial Symbiosis (IS)

Project Code

EPSRC_2022_01

Supervisory Team

Main Supervisor: Dr Athanasios Angelis Dimakis

Project Introduction

We propose a cross disciplinary project, combining the concepts of Industrial Symbiosis (IS) and Artificial Intelligence (AI), in order to develop an algorithm for the dynamic optimization of IS. The algorithm will be based on an existing approach and an online tool, currently developed by our research group, which will be expanded to incorporate AI-based methods and tools. The developed algorithm will be validated using real-life industrial data, at a regional, national and international level.

Project Details

Industrial Symbiosis (IS) is defined as the development of mutually beneficial relationships between two or more industries, by exchanging/sharing material, energy, services and/or knowledge. Over the last years, the development of such schemes has been halted due to several barriers, mostly financial and social. IS practitioners have agreed that there is need for facilitators that can help overcome those barriers. Computational algorithms, and their application through Information and Communication Technology (ICT) tools, can play this role.

At the same time, the increasing digitalization of industrial ecosystems (Industry 4.0) and the widespread deployment of Internet of Things (IoT) networks, leads to the generation and capture of huge amounts of data. Artificial intelligence can provide a wide range of robust technologies that can deliver intelligence through processing the acquired data and supporting the management of complex and dynamic aspects of IS ecosystems. However, only a limited number of the developed tools have exploited AI tools to address problems related to IS development, mostly focusing on data analysis related to industrial facilities, waste production and supply chain economics.

The objective of this project will be to lay the theoretical foundations and develop an AI framework for the dynamic IS optimization, through opportunity identification, efficiency assessment and control, and assisting in the decision process of the involved stakeholders as the basis towards enhancing the foundation of IS ecosystems. A previously developed tool, which has been developed for the facilitation of IS schemes (based on solid, liquid and gaseous waste streams), will be improved and extended by leveraging on recent advances in the artificial intelligence field. Existing work demonstrated the suitability of a set of machine learning techniques to identify IS opportunities, including recommender algorithms such as association rule mining, case-based reasoning, collaborative filtering, knowledge-based recommendation, and rule-based recommendation.

The developed algorithm will be able to: (i) Detect favourable geographic areas and industrial sectors to establish IS schemes; (ii) Detect anomalies in the flow of materials, waste and energy that need to be treated to optimise the process; (iii) Analyse and predict significant events that may affect the demand and supply of resources providing the basis for predictive demand-supply balancing and logistics optimisation; (iv) Support the optimisation of IS matches based on user-defined preferences; (v) Automatically identify additional suitable waste products to be considered by IS schemes, and suggest an optimized value chain, by considering symbiosis with stakeholders already included in the schemes.

Entry Requirements

The PhD candidate will have a strong first degree in chemical engineering or a closely related discipline. Experience in computer science or mathematics is desirable, as well as basic knowledge in software development.

Developing physiologically responsive smart materials for Drug Delivery

Project Code

EPSRC_2022_02

Supervisory Team

Main Supervisor: Prof Alan Smith

Co-Supervisor: Prof Barbara Conway

Research Institute/Centre

Biopolymer Research Centre

Project Introduction

Recently, we have demonstrated that modifying processing conditions of newly forming biopolymer hydrogels, can cause molecular reorganization of polymer chains producing microstructures that result in physical properties useful for encapsulation and delivery of drugs. Furthermore, different physiological environments can change the physical properties of these materials once in the body, allowing for the design of more efficient targeted drug delivery systems. Here, we will investigate new approaches to designing gel microstructures that improve drug delivery using regulatory approved biopolymer materials.

Project Objectives

Biopolymer hydrogels prepared from polysaccharides and proteins are extensively used in pharmaceuticals due to their diverse and controllable physical characteristics that range from weak spreadable gels to strong self-supporting gels. Importantly, these material properties can be utilised in developing a stable product and to respond to physiological conditions when administered. The physical characteristics of a biopolymer hydrogel are dependent on the polymer chain associations that occur during gelation. Recently, we have shown that by disrupting the gel cross-linking process with shear it is possible to generate suspensions of gelled microparticles rather than a continuous gel network without modifying composition. Once produced these microgel suspensions or “fluid gels” exhibit viscoelastic solid behaviour at rest that can be reverted to a pourable liquid with the application of a small shear force such as shaking with the ability to self-heal back to the viscoelastic solid state post-shearing. These reversible structural properties have been shown to be particularly useful in drug delivery as they can be carefully designed to have physiologically responsive properties such as mucoadhesion or undergo sol gel transitions that can control the release of drugs. Despite the potential of fluid gels these systems are still far from being exploited to their full potential and require considerable refinement before eventually finding widespread use.

This project aims to investigate how microstructure and subsequent bulk physical properties can be controlled in a variety of biopolymer materials and that have differences in chemistry and gelation behaviour. To achieve this, we will investigate using different mechanical and environmental processing parameters to generate new microstructures in different gel forming biopolymers. This will involve mechanical processing techniques and full material characterisation of the produced materials. These properties will then be assessed for functionality as a drug delivery vehicle, developed as eyedrops, nasal and oral spraying devices. As the project develops we will investigate the development of more complex fluid gels systems based on mixed biopolymers whereby the gelled microparticles and the continuous phase consist of different materials that have different material behaviours in various environmental conditions. These complex composite materials will then be investigated as controlled and targeted drug delivery systems.

Techniques will include pharmaceutical encapsulation formulation and analysis, Particle size analysis, rheology, texture analysis, advanced imaging techniques. In addition, training will be provided in experimental design, data collection, data analysis and presentation skills.

Entry Requirements

Successful applicants will have a very good first or upper second degree or Masters degree in a relevant subject (chemistry/physical science, biochemistry, pharmacy, materials science, bio/chemical engineering, or related discipline).

Achieving resilient critical infrastructure that facilitates societal preparedness to hazard events linked to environmental change

Project Code

EPSRC_2022_03

Supervisory Team

Main Supervisor: Prof. Dilanthi Amaratunga

Co-Supervisor: Prof. Richard Haigh

Project Introduction

Reliable, safe and resilient critical infrastructure, which is able to supply water, energy, communications, waste services and transport systems, is essential to society. Society needs to understand how infrastructure will evolve with environmental changes. Our infrastructure is a system of interconnected systems and a strong understanding of the interdependencies between infrastructure assets is essential. This includes the impact of failure due to hazard events linked to environmental change. This project will help to develop adaptation actions towards multi hazard risk reduction, and the cascading and the compound nature of disasters on infrastructure, both locally and across national frontiers.

Project Details

According to a study by the EU Joint Research Centre, damage to infrastructure due to disasters and climate change in Europe currently amounts to approximately €9.3 billion annually. This is expected to soar to €19.3 billion by 2050 and €37 billion by 2080. The energy and transport sectors will be the most affected, with annual expected damages of €8.2 billion by 2080 for the energy sector and €0.8 billion by the end of the century for the transport sector (EU Science Hub, 2017). The Sendai Framework Monitor (SFM) reported that, in 2018 alone, 1,889 infrastructure assets in 20 countries in Europe and Central Asia were damaged or destroyed as a result of disasters, amounting to direct economic losses of over $3 billion (UNDRR SFM report, 2020).

In the face of uncertainties, how do we develop and manage infrastructure that will adapt to a changing climate and hazard events linked to environmental change?

A common set of issues challenge efforts to change these dynamics:

No common understanding of what “resilient infrastructure” constitutes (should go beyond simply recovering and into adapting to changing conditions and transforming for “building for the future”)
Lack of understanding on how our infrastructure will evolve with hazard events linked to environmental change
Lack of disaster loss data and understanding on how cascading hazard risks impact infrastructure systems (linked to the interconnected nature of infrastructure), both locally and across national frontiers
Lack of clarity on the role that governance, the private sector, and the society in enabling infrastructure which are resilient to environmental change

On top of these challenges, the COVID-19 pandemic has painfully shown the breadth of the consequences on cascading hazard risks impact infrastructure systems.

In this context, this PhD project is an effort to address the following:

Improved understanding of “resilient” infrastructure and the metrics and principles of resilience
Improved knowledge of the interdependencies, interactions, and connectedness of infrastructure systems
A better understanding of vulnerabilities of infrastructure to climate change, and how our infrastructure will evolve with hazard events linked to environmental change (including, impact-based forecasting, early warning, and rapid information dissemination in the interests of achieving more resilient infrastructure and societal preparedness, comprehensive models of physical, social, and economic infrastructure)
Greater clarity on the role of governance and policy mechanisms available to make infrastructure resilient, and pathways to deal with complex interdependencies so that society is better prepared

Entry Requirements

This EPSRC DTP studentship is fully funded (fees and maintenance) for an eligible UK student. EU and international students may also be considered for this award.

Candidate should have as a minimum a 2:1 undergraduate degree (or equivalent) in in Engineering, Geography, Environmental Sciences, Built Environment, or related disciplines.

Research experience on disaster resilience, climate change, global environmental landscape and/or infrastructure is desirable.

Heating and electricity production via domestic-scale pyrolysis of wastes to drive micro-gas turbines

Project Code

EPSRC_2022_04

Supervisory Team

Main Supervisor: Professor Grant Campbell

Co-Supervisor: Dr Daya Pandey, Dr Athanasios Angelis Dimakis, Professor John Allport

Project Introduction

We propose to integrate two recent innovations, domestic-scale pyrolysis and micro-gas turbine technology, to recover energy from domestic, commercial and university waste, and to upgrade a portion of the energy to electricity alongside the heat, in order to enhance the benefits and support the introduction of these technologies into homes, businesses and the university’s energy systems. The project will generate pyrolysis gas from a range of feedstocks, characterise the resulting gas mixture, and evaluate its performance in MGTs, in order to understand the operating window for the turbine relative to the sensitivity of the combustion gas to feedstock.

Project Details

The unique Home Energy Recovery Unit (HERU, www.myheru.com) was delivered and commissioned at our university in June 2021. The HERU allows households and businesses to recover energy from their own wastes by pyrolysing these resources into combustible gases that can be burned in hot water boilers, saving money and saving the environment.

Meanwhile, micro-gas turbine technology is well suited to burning gas of variable provenance and offers the opportunity to upgrade some of the recovered energy to more valuable electricity, making these two technologies well matched for synergistic benefits.

The PhD project will encompass three main areas:

1. Pyrolysis of feedstocks and analysis of pyrolysis products.

Pyrolysis gases will be generated from a range of representative feedstocks including typical domestic food wastes (bread, fruit and vegetable wastes), typical packaging associated with these foods, and typical university wastes from catering and laboratories, including coffee cups and PPE.

The constituent makeup of the pyrolysis gas will be modelled empirically in relation to feedstocks and production of hydrocarbons, CO, CO₂ and H₂.

The deliverables from this task will be data on gas production and combustion from a range of feedstocks, presented in a scientific paper that also places the HERU innovation in the wider energy context, and contributing new knowledge about low temperature pyrolysis from these feedstocks and this context.

2. Gas composition envelope for MGT combustion

In this task the sensitivity of the syngas content to feedstock variation will be evaluated, to understand the operating envelope of the turbine. If the variability of the gas components is within a narrow band, this will allow design and use of a tightly optimised combustion system; if not, a system tolerant to a wider range of constituents will be required. The deliverable from this task will be an MGT design suitable for combusting HERU gases, presented in a scientific paper that also describes the integrated system.

3. LCA and techno-economic analyses within the university context

This task will integrate the above tasks to maintain a coherence and integrity to the overall project and to ensure its implementation is informed by a clear understanding of the interactions and synergies between the components. The deliverables from this task will be a report to the university recommending whether and how to integrate larger-scale units into the university’s energy systems in ways that also support teaching and research, and a scientific paper detailing the assessments.

Entry Requirements

The PhD candidate will have a strong first degree in chemical or biochemical engineering, mechanical engineering or a closely related discipline, with excellent practical technical and laboratory skills (ideally including experience in analytical chemistry) and an aptitude for mathematical modelling including kinetic studies and techno-economic and life cycle analyses.

Skin-Inspired Functional Polymer Materials

Project Code

EPSRC_2022_06

Supervisory Team

Main Supervisor: Prof. Laura Waters

Co-Supervisor: Dr Marco Molinari , Dr Leigh Fleming

Project Introduction

Functional polymer materials that mimic the properties of skin have driven research in advanced robotics, health monitoring technologies, and the environment. A major challenge is to control the chemical diversity of the polymer to achieve the best outcome for a specific device need. Imparting skin-like thermal regulation, permeability, self-healing, biodegradability and stretchability is essential for the success and utilisation of skin-inspired materials. This project brings together the materials chemistry and engineering to formulate and functionalise polymers with targeted skin-like properties. This will allow for the development of next-generation materials that can be fully integrated in healthcare and textile technologies.

Project Details

This project will involve the chemical modification of relevant polymers including polydimethylsiloxane (PDMS), to produce a variety of bespoke functional materials. We will use a combination of chemical synthesis, plasma treatment and analytical techniques to create skin-inspired materials with unique properties. The process will require the development of a number of modified polymer surfaces, and the integration of inorganic materials. The main aim is to generate composite materials that optimise thermal regulation, permeability, self-healing, biodegradability and stretchability. This study will be harnessing the properties of the chemical diversity of polymers and the peculiar properties of 2D inorganic materials to generate functional skin-like composite materials. Complimentary analytical techniques will be used to characterise these composite polymers and help understand how their performance relates to their structure and composition. The main analysis will be surface metrology, by using both optical interferometry and atomic force microscopy (AFM) to characterise modified surfaces and determine function from a topographical perspective. The AFM will also be used to develop a methodology for the characterisation of mechanical properties at the nanoscale.

Where relevant to the project, computational analysis will be considered to provide nanoscale insights into the local structures of these materials, and to predict more useful modifications. This work is therefore an exciting collaborative project across three research areas incorporating chemical modification, surface metrology and computational analysis.

The potential applications of the composite skin-like materials are extensive with our main initial intentions for their uses as a medical device to be applied on the skin as a biosensor, a treatment opportunity if used as a wound dressing or device to preserve skin integrity, and a replacement for animal skin in the analysis of drug permeability for the pharmaceutical industry. We envisage that an extension for the application of these composite materials would naturally be clothing, such as ‘smart’ sportswear.

Following collation of data after the first year of the project, it is envisaged that two significant grant applications will be submitted using the results as ‘proof-of-concept’ data. One of these applications will be to EPSRC in the manufacturing of functional materials for healthcare technologies, and the second to a charity or group such as DEBRA (debra.org.uk) that allows more bespoke medically-oriented proposals, in our case on skin mimics.

Entry Requirements

A suitable candidate should have (or be expected to soon obtain) a degree in a STEM subject including Chemical/Pharmaceutical Sciences, Biochemistry, Biological Sciences, Physical Sciences, Materials Science, Chemical Engineering and any other engineering degrees or related subject areas that are willing to engage with Materials Science.

Bridging the gap in forensic toolmark evidence using advanced correlation methods

Project Code

EPSRC_2022_05

Supervisory Team

Main Supervisor: Dr Katie Addinall

Co-Supervisor: Dr Marco Molinari , Prof. Liam Blunt

Project Introduction

A tool used against a surface, for example in criminal activity, may leave an impression in the substrate material. Currently forensic investigation relies upon the visual comparison of 2D images of the impressions by expert examiners who base their conclusions on opinion. With the advancement of measurement technologies and comparison techniques, there is a gap between current capabilities in research and their use in criminal proceedings, which this project seeks to address. An interdisciplinary approach to areal measurement and correlation will be applied to toolmark investigation to achieve an objective, repeatable approach to the forensic comparison of toolmark evidence.

Project Details

When a tool comes into contact with a relatively softer substrate material, plastic deformation is caused whereby a permanent impression of the tool is imparted. Toolmarks are vital in within the forensic field for the determination of events at crime scenes, for example in instances of burglary or even when a tool leaves an impression in bone. While the comparison of toolmark evidence has been used in criminal proceedings for several decades, the methods that are accepted in court have not changed. This has resulted in methods being deemed as subjective and unrepeatable in scientific reports. While other evidence types have been able to apply modern measurement techniques, resulting in objective methods which are based on scientific standards, the same cannot currently be stated for impression evidence. Therefore, there is a gap between the methods available and those currently being used in forensic comparison.

The application of modern techniques will result in a change from 2D to areal measurement, and with this comes the acquisition of significantly more data with regards to topography of the toolmark. This ensures that the salient data has been captured, however with a larger amount of data comes the need to determine the correct processing methods for efficient correlation. Therefore, this research will focus on the application of processing methods to achieve a computational comparison, which has not been achieved yet. An interdisciplinary approach will be applied in which both scientific and engineering techniques will be utilised to acquire the knowledge currently needed in the field.

This project relies on a stepwise approach to build a method for the areal measurement and comparison of toolmark information. The first step will create a library of toolmarks in which variables in tool, operation and substrate material have been considered. This will allow forensically relevant controlled testing of the creation of the toolmark. Using advanced measurement techniques such as focus variation, interferometric techniques and profilometry, topography of the toolmark will be acquired. Computational processing will then be applied to separate the salient information for correlation, resulting in a method that can bridge the gap between current methods and the court room.

Entry Requirements

A suitable candidate should have (or be expected to soon obtain) a degree in a STEM subject including Forensic Sciences, Analytical Sciences, Chemical Sciences, Physical Sciences, Materials Sciences, and/or any other Engineering degrees or related subject areas.

Development of novel sustainable materials for healthcare applications

Project Code

EPSRC_2022_07

Supervisory Team

Main Supervisor: Dr Muhammad Usman Ghori

Co-Supervisor: Dr Shan Lou , Prof Xiangqian Jane Jiang

Project Introduction

Developing new materials based on environment-friendly bio-circular economy and sustainability protocols is challenging. However, the invention and implementation of artificial intelligence and machine learning methods promise to accelerate and improve the traditional complex pathways. This project will develop new green sustainable materials with net-zero carbon footprint using machine-learning approaches while underpinning the novel bio-circular economy protocols for healthcare applications.

Project Details

New sustainable materials are strategic drivers for societal advantage as they reinforce the use of methods and technologies that positively impact the global challenges associated with climate change, resource and economic sustainability. This avenue has opened up some new scientific fronts that require a methodical approach for understanding, development and translation. To develop new materials using net carbon zero sustainable bio-circular economy approaches necessitate a negotiation with a complex space of instrumental engineering and chemical sciences while underpinning the different experimental conditions require for preparing these materials. Hence, developing, characterising and scaling up new material production is challenging, but artificial intelligence and machine learning promise to accelerate and improve the traditional complex pathways. Therefore, this project is aimed at developing a class of green sustainable materials with net-zero carbon footprint using machine-learning approaches while underpinning the novel bio-circular economy protocols for healthcare applications. The key deliverables of this project are divided into the following stages;

Artificial intelligence-based machine-learning method development using MATLAB to
prepare new materials.
Development of an experimental protocol for scaling up the material production.
The materials' functional, structural and chemical properties will be evaluated using a
range of characterisation techniques, for example, XRD, FTIR, DSC, SEM, TEM, AFM, SAXS,
and hot stage microscopy, white light interferometry and different microbial,
chromatographic and wet chemistry assays.
Finally, the materials will be investigated for their potential healthcare applications
(e.g. drug delivery and wound dressings)

Entry Requirements

Basic degree in relevant subject including chemistry, chemical engineering, materials sciences, pharmacy, pharmaceutical sciences or pharmaceutical chemistry. Previous working or general knowledge of machine learning techniques would be advantageous

Multi-metasurface optical instrumentation designed using AI approaches

Project Code

EPSRC_2022_09

Supervisory Team

Main Supervisor: Dr Andrew Henning

Co-Supervisor: Prof Paul Scott, Prof Xiangqian "Jane" Jiang

Project Introduction

Metasurfaces are nanostructured surfaces which have shown exceptional ability to manipulate the phase, amplitude and polarisation of incident light, offering an exceptionally compact and lightweight alternative to traditional optics. However, the use of a single metasurface in an optical instrument does limit the manipulations that can be achieved. This could be overcome by using multiple metasurface elements to perform the manipulations but the design of such systems becomes far more complex. AI approaches have been successfully used as a design tool for single metasurfaces, and this project will look to adapt these methods for the multi-surface case.

Project Details

Over the previous few years the exceptional control over light that metasurfaces offer has been widely demonstrated, and these structures are now rapidly approaching the point where they are ready for deployment in real world applications. Metasurfaces are surfaces covered with precisely defined structures, arranged with sub-wavelength spacing between the elements, allowing the phase, amplitude and polarisation of incident light to be manipulated in very precise ways. By designing the metasurface correctly it can affect incident light in a way that would traditionally be achieved using one or more refractive glass elements, but without the need for large and heavy shaped blocks of glass. This offers a path by which a step change in the size and weight of optical instrumentation can be achieved, precisely what is needed in order for it to be suitable for integrating in manufacturing processes for in-line and in-process measurements.

While a metasurface offers a route to highly compact instrumentation, the fact that all of the manipulations of the light are carried out at one plane in space does still limit what can be achieved, and while a metasurface could replace one (or a few) traditional elements in a system, if it needs to be combined with other traditional refractive elements the advantages of a metamaterial based system are not fully realised. However, once we start looking at systems where multiple metasurfaces are used many new questions start to arise, for instance, how should we break up the optical manipulations between the different elements, does our choice affect the sensitivity of the system to misalignment, is one set of surfaces more resilient to manufacturing errors and so on. Many of these are optimisation problems, where we not only want to find the mimima of a cost function in the parameter space, but we wish to consider the surrounding locality, identifying solutions where the cost function varies slowly over a region (perhaps even avoiding the minima in the cost function and simply identifying reasonable solutions but that are stable.) AI approaches (such as Generative Adversarial Networks) have been shown to be successful in the design of single metasurface elements for broadband applications, a task where too many degrees of freedom are present to use traditional metasurface design methods. Approaches such as these appear to be a promising route by which multisurface optical systems can also be designed, and realise far more of the potential metasurfaces offer.

Entry Requirements

The candidate is expected to have a first or upper second degree in mathematics, physics, computer science, or a related subject.

Waste heat recovery potential in energy-intensive industries for sustainable development

Project Code

EPSRC_2022_10

Supervisory Team

Main Supervisor: Dr Faisal Asfand

Co-Supervisor: Prof Artur Jaworski

Project Introduction

In UK industry, the majority of waste heat sources are below 250 °C [1], with a waste heat potential of 24 TWh/year [2]. Efficient and cost-effective heat recovery technologies, such as, an Organic Rankine Cycle (ORC) cannot only recover low grade waste heat but can help in reducing the energy consumption of the industry thereby reducing carbon emissions [3]. This study aims to assess the feasibility of advanced two-phase expansion ORC technology to recuperate waste heat in energy intensive industries (Cement, Steel, Glass, and Paper industry). To achieve this aim, data will be collected from the industrial sector and literature to assess and characterise the waste heat potential based on source type, quality, and quantity. A two-phase expansion ORC will be design and optimised considering potential organic working fluids to recuperate waste heat at low temperatures. Moreover, a techno-economic and environmental assessment of the ORC technology for waste heat recovery will be performed.

Project Details

Industries are one of the main consumers of electricity which is in most cases delivered through the utilization of fossil fuels. In addition, the most significant amounts of waste heat are produced and being lost in the industrial and thermal processes. It is estimated that in EU only the total industrial waste heat potential is about 304 TWh/year, with temperatures ranging from 100°C to over 1000°C. This is equivalent to 16.7% of the industrial consumption for process heat whereas represents 9.5% of the total industrial energy consumption [2]. One third of the total industrial waste heat is released at a temperature level of below 200 °C. In many processes, this low temperature waste heat is not recuperated and is released to the environment. Better use of low-grade waste heat represents a significant source of energy savings and provide an affordable and reliable technical solution to increase the efficiency of energy intensive industrial sector by enhancing self-production of electricity. This can help in mitigating the increase of electricity consumption due to industrial electrification and thereby reducing the load on the power grids. Moreover, waste heat recovery can substantially reduce carbon emissions and contribute to the challenge of combat against global warming.

Waste heat occurs in almost all mechanical and thermal processes which is either released in the form of hot combustion gases discharged to the atmosphere, low-quality steam or hot water released into the environment, and heat transfer from hot equipment surfaces. Moreover, in some cases heat can be retained in the products exiting industrial processes, such as hot steel [4]. As such, waste heat sources differ regarding the aggregate state (mainly fluid and gaseous), temperature range, and frequency of their occurrence. Moreover, many processes in the industry are batch-based and the quantities and qualities/temperatures of waste energy fluctuates. This study will focus on the different processes involved in the energy intensive industries, such as, cement, glass, paper and steel industry, with an aim to characterise the waste heat potential based on the quality and quantity of waste heat sources.

Furthermore, a two-phase expansion ORC system will be designed and optimised to assess the performance and feasibility of the advanced ORC to recover waste heat at different temperature levels. ORC technologies for waste heat recovery, are one of the most suitable technologies to boost sustainable transition of the industrial sector. This study will provide knowledge on the design criteria, achievable performance and cost of the components paving the way for the advanced two-phase expansion ORCs for waste heat recovery in industrial sector, supporting their market penetration and enhancing their role in the fight against climate change.

Entry Requirements

MEng degree (Energy Systems, Thermal Engineering, Chemical Engineering, Mechanical Engineering or closely related field) with expertise in process modelling and simulations. Basic knowledge of MATLAB and process modelling simulations tool such as EES or ASPEN Plus. Prior knowledge of thermodynamic cycles and waste heat recovery is desirable.

Smart multi-sensor high dynamic in-situ measurement platform for layer monitoring of EBAM process

Project Code

EPSRC_2022_11

Supervisory Team

Main Supervisor: Dr Feng Gao

Co-Supervisor: Dr Shan Lou, Prof Liam Blunt

Project Introduction

A multi-sensor measurement platform, comprising an Infrared (IR) thermal system and an intelligent 3D optical fringe projection measurement system, will be developed for layer monitoring of Electron-Beam Additive Manufacturing (EBAM) process. The EBAM built surface geometry is a challenge for optical measurement techniques due to their complexity and high optically dynamic range as the powder bed is normally diffuse and the resolidified metal material is highly reflective to visible light. The project will develop smart digital fringe projection technologies and machine learning (ML) algorithms to create an AI enabled in-situ 3D optical sensing system for high dynamic additive manufactured surface inspection.

Project Details

In recent years, in-situ sensing and monitoring technologies for additive manufacturing (AM) processes to enhance the quality of builds. The motivation has been to support the production of complex parts while benefiting from the decrease in scrap rate, cost reduction and post-built metrology requirement for quality qualification. Many in-situ sensing technologies monitoring melt pool development, which is a main feature of interest in every additive process that requires the energy beam-powder interaction intended at achieving a local fusion of the material. Many of these monitoring systems are used to provide visual information to operators with no or limited 3D surface information and active feedback. This project aims to develop a multi-sensor measurement platform, comprising an Infrared (IR) thermal system and an intelligent 3D optical fringe projection measurement system for layer monitoring of Electron-Beam Additive Manufacturing (EBAM) process. The EBAM built surface geometry is a challenge for optical measurement techniques due to their complexity and high optically dynamic range as the powder bed is normally diffuse and the resolidified metal material is highly reflective to visible light. In this research project a multi-sensor measurement platform, comprising an Infrared (IR) thermal system and an intelligent 3D optical fringe projection measurement system, will be developed for layer monitoring. It will employ smart digital fringe projection technologies and machine learning (ML). The system will be trained on the simulation of, pre-test results from selected reflective and diffuse materials used in EBAM. The fully trained model will be able to directly predict the surface defect information from fewer fringe images or even a single fringe image which will greatly reduce the sampling time due to minimising the multiple phase-shifting process of the fringe projection system. The IR sensor which has been pre-calibrated will monitor the melt pool temperature distribution during the fusion process. Both systems combined will enable layer thermal signatures and physical defects to be correlated to locate layer anomalies. The research will include research and development and construction of a multi-sensor in-situ EBAM surface inspection experimental system. System calibration methods to build the coordination system of fringe projection sensors and IR sensor will be developed. ML algorithm for highly dynamic EBAM manufactured surface measurement will be investigated. Simulation and training on selected materials will be conducted for the developed ML model for image analysis and post image processing.

Entry Requirements

2:1 degree or above in Physics, Mathematics, Engineering or a related discipline, and with interest in the areas of artificial intelligence and precision metrology. Knowledge and skills in Matlab and C++ programming is preferred.

Robotic Processing of Complex Surfaces from Additive Manufacturing

Project Code

EPSRC_2022_12

Supervisory Team

Main Supervisor: Dr Guoyu Yu

Co-Supervisor: Prof Andrew Longstaff, Prof David Walker

Project Introduction

This project is to process complex surfaces that are produced from additive manufacturing (3D printing). The student will be based at Huddersfield’s well-equipped Laboratory for Ultra Precision Surfaces (L-UPS) at the National Daresbury Science and Innovation Campus, giving access to the associated industry-cluster and the UK Science Base. Freeform surfaces are big challenges for traditional methods. 3D printing has the advantage to produce complex base shapes and we propose to process these surfaces to high-precision optical surfaces with robotic polishing. The scope can be tuned to a particular student’s skills and interests, from hands-on experimental work, to numerical simulations.

Project Details

The project aims to fabricate a ‘freeform’ surface to ultra-precision on additively manufactured components. Additive manufacturing (AM) is a very promising process that can form complex component with high efficiency and fast delivery. To achieve ultra-precision, successive processing is required. The starting point of this project is to define surfaces to be processed. The considerations of the design will include the surface’s shape, finish quality, size and materials to be used. This will involve the design of structures and surfaces using commercial CAD software (e.g. Solidwords). The student will also be required to liaise with technical staff at Daresbury to discuss the technical contents regarding 3D printing.

The aim of this project is to be able to perform corrective polishing directly after 3D printing, although intermediate smoothing process can be applied to achieve the starting condition of the polishing. The characterisation of the surfaces will be carried out regarding their form, mid-spatial frequency error and roughness. The student will liaise with the co-supervisor, Prof. Andrew Longstaff regarding metrology programs. Complex surfaces will be characterised with different instruments, such as PGI profilometer, CMMs, white-light interferometers, digital fringe projection and phase measurement deflectometry. The venue of metrology will be both at Huddersfield and Daresbury campus depending on the availability of instruments.

Polishing experiments will be carried out at the University’s Laboratory for Ultra Precision Surfaces at Daresbury. The laboratory is equipped with cutting-edge CNC polishing machines and robots to enable processing experiments of multiple options regarding blank material, toolpath, tooling and polishing media. To demonstrate that ultra-precision polishing is a competitive process, multiple processing experiments will be carried out on samples of varied blank material, surface shape and size considering the applications in different sectors. Taguchi methods will be utilised to optimise the reduction of the amount of experiments. The results from every stage will be collected to access the suitability of polishing of different materials and their input surface quality. Comparison experiments will also be conducted on similar characteristic samples of traditional optical materials. This will provide a quantitative evaluation of the surface quality vs cost of manufacturing.

Trainings will be provided to the student by appropriate supervisors and technical staff. The student will start by literature research regarding the state of art additive manufacturing, metrology and polishing technologies. He/she will also be provided with supervision to be familiarise instruments that are necessary for subsequent experiments.

Entry Requirements

2:1 or a Master’s degree in a suitable Physics or Engineering subject
Willingness to conduct experiments using robots or CNC machines
Experience of CAD software

Experimental investigation of Li₂O₂ carbothermic reaction

Project Code

EPSRC_2022_13

Supervisory Team

Main Supervisor: Dr Hui Cao

Co-Supervisor: Prof. Artur Jaworski

Project Introduction

The proposed research will explore the feasibility of Li₂O₂ carbothermic reaction, which potentially will offer an novel approach to integrate solid-state Li-O₂ battery in a large-scale thermal-chemical-electrical energy storage and conversion system. The key concept in the energy system is to use recycled carbon to constantly reduce the battery discharging product Li₂O₂ to anode material lithium metal that will be reused in the battery for discharging. The project will investigate the thermodynamics of Li₂O₂ carbothermic reaction along with the scientific challenge of collecting the highly reactive lithium vapor from it.

Project Details

To support the UK towards net-zero emissions by 2050, increasing interests have been paid in enhancing the interaction and integration between the constituent parts of energy systems. The solid-state Li-O₂ battery (LOB) has a great potential to be integrated with existing renewable power plants to buffer their intermittency. However, to charge a LOB one needs electricity, and the charging/discharging process can only be done in consequence. The proposed research will explore the feasibility of charging LOB via carbothermic reaction of battery discharging product Li₂O₂ on site.

The project will focus on the following objectives:

1. a lab scale carbothermic reaction of Li₂O₂: step 1 is to oxidise Li₂O₂ to Li₂O under inert gas using commercially purchased Li₂O₂ powder; step 2 is to reduce Li₂O to Li using high purity graphite either commercially purchased or synthesised in the lab. The thermodynamics of this reaction will be systematically investigated to retard side reactions.

2. condensation of lithium vapor: the collection of reaction product lithium vapor from the second step is the most challenging part of the project as lithium vapor is highly reactive. Feasible approach will be carefully assessed. A potential method is to condense the lithium vapor on a ceramic surface with the help of a cooler inert gas. Systematic study on a selection of candidate gases at different temperature will be conducted to find the optimal condition.

Success of this project will significantly broader LOB’s intergratability with diverse energy sources e.g. industrial waste heat, renewable power plant and off-peak electricity. It will also potentially offer an alternative approach to cost-effectively recover lithium from end-of-life Li-ion batteries.

Entry Requirements

This project aims to study the feasibility of chemically reducing Li2O2 to lithium at an elevated temperature. To undertake this research, a motivated candidate is required with a first-class degree or upper second or a combination of qualification and professional experience equivalent to that level, in materials science & engineering, energy and power engineering, chemical engineering or other related science and engineering disciplines. The candidate must show evidence of motivation for and understanding of the proposed area of study and preliminary knowledge of research techniques.

Independent work, self-motivation, good team spirit and excellent communication skills are important assets of the successful candidate. The candidate must demonstrate proof of proficiency in English if applicable. Candidate with some knowledge and research experience in chemical reaction thermodynamics or chemical looping materials will be preferred.

Miniaturising focus variation sensors for optical metrology using metalenses

Project Code

EPSRC_2022_14

Supervisory Team

Main Supervisor: Dr Haydn Martin

Co-Supervisor: Prof Xiangqian (Jane) Jiang, Dr Andrew Henning

Project Introduction

This research will assess the potential of optical metamaterials (artificial materials that have properties beyond those produced in nature) in the form of metalenses (ultracompact, lightweight, planar optical elements) to be used as the basis for enhancing optical sensors in terms of their size, weight and speed.

The research will address the engineering challenges associated with harnessing metalens technology to build optical systems that have a substantial performance advantage over conventional optical systems.

Successful outcomes from this research will illuminate future developments in metrology and will inform the development of new optical metrology tools for smart and autonomous manufacturing applications.

Project Details

Aim

To explore how metalens technology can be adapted for application as a component in a focus variation (FV) sensor to enable a level of miniaturisation and speed that has not previously been possible using conventional optical systems.

Objectives

Design and fabrication of a metalens having the specific performance requirements for FV applications;
First demonstration of a metalens as an optical element in a FV sensor;
Realisation of an ultra-compact FV sensors based on the integration of metalens technology;
Performance verification of an ultra-compact FV sensor and development of associated calibration procedures.

Methodology

Focus variation (FV) microscopy systems are a common tool for assessing the surface topography of high/ultra-precision manufactured parts. For on-machine (in-situ) measurement applications, miniaturisation and weight reduction is limited by the ability to reduce the size/weight of conventional optical elements (glass/metal). In fact, this a problem for any sensor system based on microscopy. Additionally, the weight of the components limits the attainable measurement rate where optics must be scanned mechanically to facilitate data acquisition.

This research will investigate a FV concept sensor based upon the replacement of the conventional microscope objective with a metalens. A compact planar metalens can reduce the size of the optical chain substantially and thus the overall sensor size/weight by at least an order of magnitude. A reduction in the size of the kinematic stages is also possible with a fast lightweight electromagnetic ‘voice coil' type drive, thereby increasing the measurement rate through an improvement in mechanical dynamics.

Within the research programme the following steps will be taken:

A metalens design which will concentrate on single-wavelength off-axis performance validated through modelling with appropriate software e.g. Lumerical.
Fabrication of the metalens with design of optimisation cycles as required by a third-party.
Characterisation of the metalens performance, leading to the development of a detailed focus variation sensor design including kinematics and image detection.
Realisation of the metalens-enhanced focus variation (FV) sensor as a benchtop apparatus
Benchmarking of the metalens-enhanced FV sensor and generation of appropriate calibration methods.

A key challenge for this type of research is the fabrication of metalenses which require nanofabrication facilities. The supervisory team have established a relationship with Kelvin Nanotechnology (Glasgow) and worked with them to successfully develop an e-beam and etch process for fabricating nano-pillars (80-200 nm radii) in gallium nitride which will be used as the basis for metalenses operating at visible wavelengths.

Entry Requirements

BEng (Hons) in a related degree – Physics or Engineering (optical, electrical, mechanical)

Some knowledge/experience in one or more of the following areas:

Optical modelling/simulation
Optical systems engineering

Micro-hydro power from redundant river weirs

Project Code

EPSRC_2022_15

Supervisory Team

Main Supervisor: Prof John Allport

Co-Supervisor: Dr John Lever

Project Introduction

Across the UK, there are over 17000 weirs built prior to and during the Industrial Revolution to provide water power to mills and factories via water wheels. Many of these weirs could still be used to generate significant amounts of power if a cost effective way of utilising them could be found. Most current micro-hydro schemes require significant civil engineering works and costly changes to the weirs, involving embedded carbon and environmental disruption. This proposal is to develop low-cost, environmentally friendly generation systems that can be retrofitted to weirs without detriment to either the weir or its environs.

Project Details

The proposal is to develop micro-hydro generator systems for retrofitting to existing weirs. The systems need to be compatible with the many regulations and legislative requirements that exist. Systems are required that can be customised to fit a wide range of weirs, without the need to adapt or modify the weir. This is generally a requirement, as many of the weirs are critical as part of local flood management and so cannot be changed. This in itself is at odds with a requirement of the Environment Agency, which is to enable passage of fish upstream, a process which often necessitates the removal of weirs.

To address these complex, and sometimes contradictory requirements, it is proposed to work with the Environment Agency, the Canal and Rivers Trust, Kirklees Council, EPIKS (Environmental Projects in Kirklees) and Hallidays Hydropower to develop both a prototype system and a test / demonstration site at Snow Island alongside the University.

The proposed project will establish the technical feasibility of such a system, whilst incorporating a fish pass and relevant environmental protections to ensure that the system meets all the current regulations and licensing requirements. It will include identifying the most appropriate type of fish pass and its location on the weir; and details of how the fish pass can be constructed. The micro-hydro system will consider the type, design, output and most appropriate location for the micro-hydro unit, the cost of installing the micro-hydro unit and associated infrastructure the lower and upper scenarios for generating electricity and the likely income derived from such an output, the likely maintenance requirements of the installation and liabilities associated with this and a full cost benefit analysis of the economics of installing micro-hydro, including any regulatory or other requirements/benefits. It is also proposed to assess any potential direct or indirect community benefits, by establishment of a public viewing area to develop the educational value of site in understanding about, biodiversity, climate change and renewables. Other targets are improved value of the river for biodiversity, benefits from researching the micro-hydro installation and its potential replication in communities elsewhere (local and international), and the contribution of the overall scheme to Huddersfield’s Sustainable Town’s Development Goals and the Kirklees Climate target of net zero carbon emission by 2038. The ideal final outcome will be a customisable system which meets all regulatory requirements, and is certified as acceptable for application on any similar weir.

Entry Requirements

Suitable candidates will have a strong first degree in Energy Engineering, Mechanical Engineering or a closely related discipline, with excellent practical technical and laboratory skills (ideally including experience in hydro generation) and an aptitude for mathematical modelling. An understanding of the regulations relating to hydropower development would be a distinct advantage.

Next generation nano-photonics driven multi-functional metasurfaces for micro-deflectometry application

Project Code

EPSRC_2022_16

Supervisory Team

Main Supervisor: Prof. X. Jiang

Co-Supervisor: Dr F. Gao

Project Introduction

A single metasurface can change a wavefront in a manner that would normally require several optical elements e.g. lenses, filters etc., yielding compact systems requiring minimal alignment. Conventional micro-deflectometry using an electronically controllable light modulator, a beam splitter and an objective lens to project an aerial image at a distance d from the object. A multi-functional metasurfaces could be possible to integrate the functions of the above components into one metasurfaces component. This will greatly reduce the size of the system and make it possible to be used for in-situ surface inspection. The project will focus on the investigation of feasibility, design, simulation and explore the application of multi-functional metasurfaces in the direction of micro-deflectometry.

Project Details

Optical metrology plays a vital role in a wide range of research areas and applications from basic science discovery to material processing, medicine, healthcare, energy, manufacturing and engineering. However, the form of current optical instrumentation (e.g. its size and weight) has been a longstanding barrier to its use in many desirable applications, for instance an instrument cannot be used to monitor a manufacturing process if it was to get in the way of it. This is a barrier to developing the type of measurement systems that would enable the smart and autonomous manufacturing processes to be developed that are envisioned to be used in the 'factories of the future', however, such considerations of size and weight are also true for many other applications, e.g. robotic arm mounted instrumentation, surgical applications, space science applications. While traditional methods have only yielded incremental improvements, the advances that have been made in the area of nanophotonics offer a route to achieve a step change to overcome these problems. These nanophotonic elements, such as metasurfaces (surfaces structured with precise arrays of subwavelength elements) provide a method to achieve exquisite control of light, but without needing to use large blocks of material such as glass to achieve the effect. A single metasurface can change a wavefront in a manner that would normally require several optical elements e.g. lenses, filters etc., yielding compact systems requiring minimal alignment. These offer an alternative to traditional refractive optical elements in optical instrumentation and would allow ultracompact and lightweight instrumentation to be developed, as well as open up the possibility of developing novel measurement techniques going beyond what is currently achievable. With the impressive developments in this field over the last few years, the technology is reaching a point where it is ready to transition from lab-based demonstrators to real world applications.

Microdeflectometry is a technique which utilise the principle of optical reflection for measuring the microtopography of specular surfaces by observing the sample patterns projected at a distance to the object surface via a specular surface under test to acquire surface gradients and shape. Conventional micro-deflectometry using an electronically controllable light modulator, a beam splitter and an objective lens to project an aerial image at a distance from the object. A multi-functional metasurfaces could be possible to integrate the functions of the electronically controllable light modulator, the beam splitter and the objective lens into one metasurfaces component. This will greatly reduce the size of the system and make it possible to be used for in-situ surface inspection. The project will focus on the investigation of feasibility, design, simulation and explore the application of multi-functional metasurfaces in the application of micro-deflectometry.

Entry Requirements

2:1 degree or above in Physics, Mathematics, Engineering or a related discipline, and with interest in the areas of optical metamaterials/nanophotonics and optical instrumentation.

Additive Manufacturing of Ti Alloys Using NeuBeam Technology

Project Code

EPSRC_2022_17

Supervisory Team

Main Supervisor: Prof Liam Blunt

Co-Supervisor: Dr Paul Bills, Dr Ahmed Tawfik

Project Introduction

Additive manufacturing (AM) is a technology rapidly expanding across a range of industrial sectors. AM provides design freedom and environmental/ecological advantages. AM facilitates a direct route from essentially design files to fully functional products. However, it is still hampered by low productivity, poor quality and uncertainty of final part mechanical properties. The root cause of undesired effects lies in the control aspects of the process. Metal additive manufacturing is dominated by two processes laser powder bed fusion AM and electron beam powder bed fusion AM and it is a new EBeam technology study that form the basis of this PhD

Project Details

Through previous collaborative research a novel electron beam, powder bed Additive Manufacturing (AM) process has been developed. The new process has significant commercial (mass of material processed per hour) and technical (very broad process window allowing material microstructure control and in-process monitoring for quality assurance) benefits over current e-beam and laser based powder bed systems available on the market today. The new technology patented as NeuBeam is capable of neutralizing the charge accumulation that tends to occur with other electron beam technologies such as electron beam melting (EBM). This results in potentially a wider range of viable print parameters when compared to other electron beam or laser-based PBF technologies.

The NeuBeam Technology is new and its full potential for processing Ti alloys has yet to be established. In order for it to become accepted in the market, material studies are needed to validate the claims and explore the boundaries and capabilities of this exciting technology. This validation work will be a core part of the proposed PhD. The novelty of the work lies in the application of the new processing technology to Ti structures with complex geometries and lattice structures as well as the prospect of manipulation of the AM part microstructure. End users (in the aerospace and medical implant fields) are very interested but need to see validation of the technology in terms of material properties.

The program of work will cover exploring the optimum process parameters of the NeuBeam technology to:

Optimise NeuBeam processing rates for Ti alloys
Optimising NeuBeamTi part density as assessed using X-ray Computer Tomography
Controlling part surface topography
Demonstrating control of Ti part microstructure and minimising thermal cracking using NeuBeam techniques
Investigate the mechanical properties of part obtained by NeuBeam AM
Exploring the production of fine lattice structures beyond the scope of conventional E-Beam AM using NeuBeam methods.

This project is a partnership between the EPSRC Future Metrology Hub and Wayland Additive Ltd. Wayland Additive is a very young start-up company that has formed from seed R&D carried out by Reliance Precision Limited. Significant periods of study will be spent at the sponsoring company which is less than 5km from the University The company will provide an additional top up to any bursary associated with the project to the tune of £1000 pa.

Entry Requirements

Prospective candidates should have a minimum of a Bachelors level degree in mechanical/production engineering or materials science at a 1^st or 2:1 level.

AI designed robust-Metasurfaces for future manufacturing applications

Project Code

EPSRC_2022_18

Supervisory Team

Main Supervisor: Dr Mauro Vallati

Co-Supervisor: Dr Andrew Henning

Project Introduction

This project will investigate the use of AI techniques to support the design process of metasurface based instrumentation, with the surfaces being specifically designed to be robust when subjected to real world environments. Metasufaces, nanostructured surfaces that can manipulate light akin to traditional refractive elements, are often designed with only a couple of degrees of freedom in the design parameters. This limits what can be achieved but makes the design process tractable. Recently researchers have demonstrated AI approaches in order to search more complex parameter spaces, making devices with more complex behaviour, or that are stable to perturbation, possible.

Project Details

Metasurfaces are nanostructured surfaces that can manipulate light and produce effects traditionally achieved using glass elements, but in a dramatically more compact and lightweight form. The exceptional control over light that metasurfaces offer has been demonstrated widely over the last few years. One application where these structures can make a significant difference is in the development of miniaturised sensors that can be integrated into manufacturing processes to provide in-line and in-process measurements and underpin the type of smart manufacturing processes envisaged by ‘Industry 4.0’. The ‘factories of the future’ are expected to deliver bespoke ‘one-off’ high value objects, manufactured using autonomous processes, while achieving ‘right-first-time’ production, reducing waste both in terms of energy used to produce the items and scrappage due to manufacturing errors. The current form of optical instrumentation, relying on traditional refractive elements, is not suitable for such tasks as the large size and the weight of the instrumentation means it cannot be simply integrated with current manufacturing tools. Metasurface based instrumentation offers an alternative, where instead of shaped blocks of glass controlling light, planar surfaces with sub-wavelength structures are used, reproducing the effect of a single, or even multiple, traditional optical elements in one surface. This offers a path by which a step change in instrumentation size and weight can be achieved, creating precisely the type of instrumentation needed to be integrated with, and provide the data to control, these manufacturing processes.

The design of metamaterial based instrumentation and the analysis of its expected performance is extremely challenging. A large number of parameters, such as the shape, location, and size of each element have to be taken into account, not only to achieve well defined effects across a broad range of incident wavelengths, but also to minimise the variation of performance with changes in temperature, humidity and slight distortions of the surface. The large number of parameters and their complex interactions make it an almost impossible task to design metamaterial based instrumentation using simple design methods. Recent approaches based on AI techniques, such as Generative Adversarial Networks, suggest that AI can be effective at exploring the vast parameter space to develop a metasurface that provides the desired performance. This project will advance the state of the art by investigating the use of AI-based approaches to find the most suitable parameters to enable the realisation of stable ultra-compact optical instrumentation ready for real world use.

Entry Requirements

The candidate is expected to have a first or upper second degree in computer science, mathematics, physics, or a related subject. Experience in software development.

Characterization of wear in additively manufactured dental implants through fusion of multiscale metrological techniques

Project Code

EPSRC_2022_19

Supervisory Team

Main Supervisor: Dr Paul Bills

Co-Supervisor: Prof Liam Blunt

Prof Mazen Ahmed (external – Beni Suef University Dental School)

Project Introduction

Additive manufacturing is experiencing exponential growth across most sectors of engineering. High impact is felt in medical engineering where personalised devices have great benefit to patients.

One area that is taking the lead in this respect is dentistry. For any advantages to be fully exploited the performance of AM dental implants needs to be understood. Creation of a closed loop process in which wear performance can be analysed will allow for further optimisation of the implant manufacturing process and allow for the development of suitable laboratory testing and screening protocols. Creation of this closed loop process which feeds back to both manufacturers and clinicians is the basis of this PhD.

Project Details

on developing wear measurement methods that can be used for analysis of retrieved implants where no pre wear data is available which will be verified through traceable metrology. The methods will allow for a better understanding of the wear process and will enable the visualization of the wear area, quantification of volume as well as surface changes. A range of dimensional and surface measurement instruments will be used to gather data from the implant surfaces with bespoke analysis procedures being developed to address the particular geometry of the dental surface.

This project is supported by the Dental School at Beni-Suef University in Egypt where the main supervisor is a Visiting Professor.

Outcomes:

Measurement methods for evaluating wear of dental implant

Retrieval analysis of dental implant to evaluate mode of failure and quantify wear.

Contribution towards developing standards for measurement of dental implant wear. Dissemination through high-impact journals such as Journal of Dental Research, Biomaterials and Wear.

Impact:

Providing the first published methods for quantifying wear in retrieved AM dental implants. This research is directly translatable to post-market surveillance of implants, helping to inform patients and regulators. Through these efforts and by creating a feedback loop to manufacturers it is envisaged that revision rates can be more closely monitored and recognized at an early stage.

Entry Requirements

Prospective candidates should have a minimum of a Bachelors level degree in mechanical/biomedical engineering or materials science at a 1^st or 2:1 level.

A general framework to support dynamic information flow in digital manufacturing

Project Code

EPSRC_2022_21

Supervisory Team

Main Supervisor: Dr Qunfen Qi

Co-Supervisor: Prof Paul J Scott, Prof Dame Xiangqian Jiang

Project Introduction

Data/information flow in manufacturing systems is complex and dynamic in nature. Developing a multi-level semantic structure can greatly reduce the complexity, but it does not automatically promise that the dynamic change of data will be preciously synchronised to the whole system. The proposed research aims to create formal foundations to enable dynamic information flow across multi-level digital models in an efficient, coherent, and stable manner. The focus will be on the development of foundations and tools to support rigorous bidirectional transitions between models on different levels and facilitate integration of different tools to enable multi-ary transitions.

Project Details

Digital twin technologies enable manufacturing systems to become ‘smart’, gaining system insights and analytics to predict and control the whole production system in an optimal way, by exploiting a real time synchronisation of the sensed data originating from the physical system. This results in a higher efficiency and accuracy in manufacturing execution, at the cost of increased design complexity in dealing with the big volume of sensed data, and complicated IT infrastructure to manage and process them.

Such complexity can be reduced first by designing the twin under a ladder of abstraction, where each abstraction represents a level of focus (or fidelity). The challenge is to go up and down the ladder in a steady and secure manner. We propose to develop formal foundations to guide the system while manoeuvring safely and efficiently on the digital ladder. Such tools enable the digital twin to zoom in and out between two models in a rigorous manner, while the change of one will be synchronised to the other. The key advantage of these tools is that multiple tools can be composed together in various ways, making the information flows across multi-level efficient and easy to manage even in the most complex cases. Under such supporting mechanism of information flow, the complexity of designing and operating a digital twin will be greatly reduced, whilst efficiency and accuracy can be improved, therefore saving both labour and computational costs. It will also give us insight into how to build a digital ladder which is both strong and easy to use.

The research challenge is how to enable such multi-level modelling, and most importantly, how to implement information flows between models with different levels of fidelities in an efficient, coherent and stable manner.

This project is ambitious to enable the dynamic information flows in manufacturing, using the state-of-the-art inherently hierarchical language CSL (Category Semantic Language, developed in the Hub) as the language and foundation. Objectives of the project will include:

Explore a set of formal structures (mathematical lenses) to build formal foundations for the bidirectional transitions;
Develop a common resolution framework;
Develop a lens tool which integrates different lenses structures with the resolution package;
Test and validate the formal structure and tools on real industrial cases.

This exciting research project is of great scientific and industrially interest and therefore will offer the candidate the possibility to establish successful academic and industrial collaborations.

Entry Requirements

The applicants should have study background in, Mathematics, or Engineering (mechanical/computing), or a related discipline, and with interest in the areas of Artificial Intelligence, informatics/knowledge representation, and smart manufacturing. The applicants are expected with advanced level of programming, and solid mathematical foundation.

Development of a novel integrated modelling process of pressure, shear, and friction in relation to device related pressure injury.

Project Code

EPSRC_2022_22

Supervisory Team

Main Supervisor: Prof. Rakesh Mishra

Co-Supervisor: Leigh Fleming/Karen Ousey

Project Introduction

Pressure Ulcers (PU) occur when skin is subjected to pressure shear and friction in certain conditions. These pressure ulcers can occur when there is contact between any surface and an area of skin, usually, but not always a bony prominence. Pressure ulcers are skin damage which cause a loss of integrity at the skin interface and can give rise to serious complications such as necrosis and infection. This project seeks to provide a fundamental understanding of interactions at device skin interfaces to provide a tool for development of interventions and therapeutics.

Project Details

One area of PU formation which is less understood is device related pressure injury, where the prolonged application of pressure through a medical or therapeutic device causes skin damage. An example of this is when patients are treated in a prone position due to severe covid, the respirator mask can be put under pressure through the weight of the patients face resting on it, this in turn can give rise to pressure damage. Having the ability to model what occurs at the skin device interface and how this contributes to a lack of Oxygen perfusion at the skin interface through Finite Element Analysis and Computational Fluid Dynamics will expand the fundamental knowledge and enable device developers to use this information to ensure safety in their design process. This in turn will lead to developments in digitisation of testing for new product development for interventions which are likely to contact with skin and may also be used to inform clinical practice where changes to patient position are required.

Phase one of the project will build on previous work to establish mechanical properties and physical interactions through pressure mapping and tribological analysis.

Phase two of the project will seek to integrate modelling of flow such that a better understanding of perfusion at the device skin interface is established.

Phase three will see the integration of the models to provide an holistic evaluation of the system by which pressure damage occurs in device related pressure ulcer formation.

The total cost of pressure ulcers to the NHS is around 4% of its total annual expenditure, which amounts to approximately 2.1 billion pounds annually demonstrating that there is not only a human cost in terms of health related quality of life, but also financial gains to be made from addressing the issue of avoidable pressure ulcers.

Entry Requirements

A suitable candidate should have (or be expected to soon obtain) a degree in a STEM subject including Chemical Sciences, Physical Sciences, Materials Science, Chemical Engineering, Mechanical Engineering and any other Engineering degrees or related subject areas with some experience of FEA and CFD with the ability to learn experimental techniques including surface metrology and mechanical testing.

Modelling railway ballast using physics engines

Project Code

EPSRC_2022_23

Supervisory Team

Main Supervisor: Dr Minsi Chen

Co-Supervisor: Dr. Samuel Hawksbee

Project Introduction

The majority of rail track worldwide is supported by a ballast layer, consisting of large particles of crushed stone. The performance of this layer is crucial to the maintaining track geometry, needed for safe running of trains. However, the large particle sizes of the ballast are not well represented in normal simulation packages. The interlocking mechanics between reinforcing geogrids sheets and ballast particles is specially challenging to model. Therefore, alternative methods are need for accurate predictions of track behaviour including distribution of train loads to the subsoil, the lateral resistance of the track and permanent deformation. Accurate predictions are needed to assess different design options and plan for future maintenance.

Project Details

A holistic approach to modelling of the ballast layer can be considered as a multiscale problem. It needs to account for both the mechanical interaction amongst the ballast stones and their combined resistance to track loads. A Discrete Element Methods (DEM) is needed to model the dynamic interaction amongst ballast stones. DEM simulations are extremely computational expensive and only small systems can be practically modelled (e.g. three-sleepers). In parallel, physics engines (often associated with computer games) have been developed to provide realistic approximations of particle interactions. Already, researchers have explored the transfer of physics engines to modelling of granular materials with promising results (https://doi.org/10.1016/j.cma.2019.01.017). While some loss of simulation accuracy is expected, the optimised, highly parallelised solvers available in physics engines could allow for much reduced computational times.

The proposed multi-disciplinary PhD research project will develop and validate efficient physic engines codes for simulating ballasted track. The research will be structured in four work packages.

Create adequate solid representations for the ballast stones.
Develop a stable DEM code using an off-the-shelf physics engine.
Validation of the method against published small scale test.
Apply the new method to a complex track phenomenon.

Initially, it is proposed to apply the method to model the complex interaction between ballast stones and geogrid sheet reinforcement. Additionally, the potential for applying pretension to the geogrid sheet during ballast compaction will be explored. The use of pretension is thought to mobilise beneficial confining pressure, preventing permanent settlement, earlier in the lifecycle. However, its application to ballast track has yet to be investigated.

The proposed PhD research project will allow the supervisory team to develop a new multi-disciplinary collaboration and a new avenue for their research. It is anticipated that the research project will produce several high-quality journal articles and conference presentations in both Engineering and Computational Science domains as well as dissemination to industrial stakeholders. In future, the method could be applied model other ballast track problems, requiring longer track lengths to be modelled. For example, lateral track buckling could be modelled. (Buckling is an increasing concern to the UK rail industry due to increasing temperatures arising from climate change.) Alternatively, the method could be used to model track transitions, a research interest of Dr. Hawksbee.

Entry Requirements

The candidate should hold a good degree in a relevant engineering, science, or computer science discipline. Good mathematical skills and excellent coding skills including experience in at least one coding language such as C/C++ are essential.

A STEP-NC Enabled Digital Thread for Closed-loop Quality Control of Additive Manufacturing

Project Code

EPSRC_2022_24

Supervisory Team

Main Supervisor: Dr Shan Lou

Co-Supervisor: Dr Xianzhi Zhang, Dr Wenhan Zeng, Prof. Xiangqian Jane Jiang

Project Introduction

Additive manufacturing, also commonly called 3D printing, is shaping the future of manufacturing towards a flexible and on-demand approach and accelerating the transformation from the conventional manufacturing industry to Industry 4.0. The quality control of additively manufactured components is critical for many industrial applications, especially aerospace and healthcare. The conventional digital solution (e.g. STL and G-codes), however, does not fully promote the digital thread of additive manufacturing, particularly those relevant to quality control. This project will construct a digital thread model based on STEP-NC with an aim to facilitate the closed-loop quality control of AM products.

Project Details

Additive manufacturing (AM) is significantly shaping the future of manufacturing towards a flexible and on-demand approach and accelerating the transformation from the conventional manufacturing industry to Industry 4.0. To facilitate the uptake of AM technologies into a wider range of applications and foster AM’s full commercialisation, there must be focused attempts to overcome existing technical barriers. A major challenge is the quality control of AM products, which is particularly important for key industrial sectors, e.g. aerospace and healthcare. To enable the close-loop AM quality control, the digital thread throughout various stages of AM production must secure the bi-directional data flow to avoid redundancy and loss of digital information. The majority of current AM digital threads is still based on the conventional solution, e.g. STL and G-code. These technologies, however, cannot facilitate the smooth and reliable digital flow of the CAD-CAM-CAPP-CNC-Inspection chain. For instance, STL is commonly used in AM for the exchange of data between the CAD model and the CAM and/or hardware of the AM system. It enables an approximation of CAD, but does not provide the information of material, tolerance, manufacturing process, and inspection. G-code is a well-established standard to specify elementary actions and tool movements. However, it does not promote the mutual communication between CNC and CAD/CAM systems. In comparison, the new STandard for Exchange of Product data model compliant NC (STEP-NC) provides a high-level data model to represent not only toolpath information but also all the information related to product, process, resource, control, and inspection.

This PhD project will construct a digital thread platform based on STEP-NC with an aim to facilitate the closed-loop quality control of AM products. A major concern will be placed to the optimisation of AM layer-wise build. This will require the development of STEP-NC model to accommodate AM process simulation and optimisation, AM tool path generation, and part geometry inspection.

Entry Requirements

Hold a high-grade qualification, at least the equivalent of a UK First or 2:1 class degree or MSc in engineering or related disciplines
Be proficient in both written and spoken English, and possess excellent presentation and communication skills
Possess basic experience and skills in additive manufacturing/3D printing, CNC, and programming

An eXplainable Artificial Intelligence (XAI) Technique for Diagnosing Mental Health

Project Code

EPSRC_2022_25

Supervisory Team

Main Supervisor: Dr Tianhua Chen

Co-Supervisor: Prof Grigoris Antoniou

Project Introduction

As a significant area of computation intelligence, the linguistically inspired fuzzy system is an ideal eXplainable Artificial Intelligence (XAI) technique to design intelligent and interpretable models that would allow non-technical healthcare professionals to interrogate, understand, debug and perhaps, improve the underlying systems employed. This project aims to design an explainable fuzzy systems, for the diagnosis of mental health disorders, with the ultimate aim to improve clinicians’ acceptability and confidence for wider clinical deployment of such AI techniques. The project will work with real-world clinical data and involve collaboration with NHS professionals from the South-West Yorkshire Partnership NHS Trust (SWYPFT).

Project Details

This project aims to explore eXplainable Artificial Intelligence (XAI) techniques, fuzzy systems in particular, for the diagnosis of mental health disorders such as Attention deficit hyperactivity disorder (ADHD) and Autism Spectrum Disorder (ASD), with the ultimate aim to improve clinicians’ acceptability and confidence for wider clinical deployment of AI techniques.

As a significant area of computation intelligence, the linguistically inspired fuzzy system is known for its capability in dealing with imprecision and uncertainty as well as the approximate inference framework that mimic human reasoning. These characteristics prompt fuzzy system to play an important role for the emerging area of trustworthy and eXplainable Artificial Intelligence while making it an ideal tool to design interpretable models for intelligent healthcare that would allow non-technical healthcare professionals to interrogate, understand, debug and perhaps, improve the underlying systems employed.

The aim of this PhD project is to develop fuzzy rule-based systems with a particular focus on scenarios of diagnosing mental health disorders. Despite being a largely application-oriented project, significant underlying theoretical investigations are necessary. At the initial phase, the project will explore a comprehensive set of recent XAI approaches for healthcare applications in general, and then identifies unique characteristics of fuzzy systems in relation to alternative XAI approaches. This underpins the core part of the project, which will involve the design and implementation of a specific fuzzy rule-based model that fits the underlying clinical requirement. The implemented system will be evaluated with real-world clinical data, followed by a close examination of how such a system may perform in collaboration with medical doctors when applied to the diagnostic problem of realistic complexity. The project will collaboration with NHS professionals from the South-West Yorkshire Partnership NHS Trust (SWYPFT).

Entry Requirements

We are seeking a highly motivated individual with a strong academic background, as demonstrated with a 1st class degree, or equivalent, in a Computer Science or Health Informatics. The candidate will have demonstrable knowledge in machine learning and possibly experience working with health data. The candidate should also have the proficiency in the use of mainstream data analytics platforms such as Python and Matlab. Good professional writing skills are also expected.

For informal enquiries please contact Dr Tianhua Chen.

3D Volumetric Filtration in XCT Metrology for Additively Manufactured Parts

Project Code

EPSRC_2022_26

Supervisory Team

Main Supervisor: Dr. Wenhan Zeng

Co-Supervisor: Dr. Shan Lou, Prof. Paul Scott, Prof. Xiangqian Jiang

Project Introduction

X-ray computed tomography (XCT) has proven to be an effective inspection tool for non-destructive measure parts with complex geometry, such as the internal/external dimension/geometry and surface texture with re-entrant features that conventional instruments find difficult/impossible to measure due to the limitations of feature accessibility. To accurately quantify the dimension and surface features, filtration is a key process to improve the quality of the 3D volumetric data and then to ensure good segmentation. This project aims to develop practical, robust and efficient 3D volumetric filtration for improved surface determination to improve the accuracy of the dimensional/integrity metrology through industrial-XCT.

Project Details

The layer-by-layer approach offered by additive manufacturing (AM) allows for the creation of complex geometries that are not possible with traditional manufacturing processes, reducing the need for assembly and increasing design freedom. For the quality control of the AM parts with complex geometry (for example, having internal structures and surface with re-entrant features), conventional instruments such as CMM and optical profiler are not able to measure internal or complex external features due to limitations imposed by feature accessibility. X-ray computed tomography (XCT) is currently the only valid inspection tool that can non-destructively measure AM internal dimension and surface texture.

The typical workflow of a CT measurement includes: (1) scan for 2D projected images; (2) reconstruction to 3D volumetric image; (3) surface determination to find the surface/boundary of the object; and then (4) use the generated surface/boundary to quantify the dimension and surface features. During these steps, filtration acts as one of the critical techniques. Much research has been done on filtration on 2D projected images to remove noise, filtration for the volume reconstruction and filtration on segmentation. However, there is much less research on the filtration of 3D volumetric data, which is a crucial step to ensure good surface determination. This project aims to develop practical, robust and efficient 3D volumetric filtration for improved surface determination to improve the accuracy of the dimensional/integrity metrology through XCT. This project covers, in detail:

(1) Sparse representation of the 3D volumetric data

Modern CT devices produce a volumetric dataset of approximately 30GB size, which is difficult to store and process. The research will use a sparse volume data structure to convert a dense volume into a lighter representation.

(2) 3D volumetric data filtration techniques

Current widely used filtration techniques for XCT volumetric data in research and commercial software are simply using the mean/median/Gaussian filter to remove noise. These filters are not robust against outliers, have boundary problems, and cannot remove uneven backgrounds. This research is to develop a robust and efficient filter capable of removing the background.

(3) Virtual 3D volumetric dataset

Design a set of digital 3D volumetric datasets with defined dimension, geometry and controlled surface texture, with the exchangeable format, for the testing the effectiveness of the proposed filters.

(4) Case studies

Case studies to demonstrate the capability of the filters with recommended parameters setting for the user-led applications.

Entry Requirements

Hold a high-grade qualification, at least the equivalent of a UK First or 2:1 class degree or MSc in engineering or related disciplines.
Be familiar with programming languages such as MATLAB, C++ or Python.
Basic knowledge of image processing and data analytics.
Be proficient in both written and spoken English, and possess excellent presentation and communication skills.

Knowledge capture from manufacturing big data using a deep learning method

Project Code

EPSRC_2022_27

Supervisory Team

Main Supervisor: Dr Xianzhi Zhang

Co-Supervisor: Prof. Andrew Longstaff, Dr Simon Fletcher

Project Introduction

Computer Numerical Control (CNC) machines are a major contributor to the production capacity of the advanced manufacturing industry today. Part programs used on CNC machines, usually referred to as G&M codes, are the final and most accurate representation of process plans, which is based on the previous knowledge, experience and innovation of process planning engineers and designers. However, the information flow from CAD/CAM to CNC machine is unidirectional. The CNC machines on the shop floor have been isolated from the manufacturing chain and become an information island of big data in manufacturing. The valuable Knowledge contained in part programs is disregarded.

Project Details

In the era of the fourth industrial revolution (Industry 4.0), data generation will become enormous, and data analytics are becoming increasingly important towards decision making. Manufacturing stores more data than any other sectors. From big data to useful knowledge, the manufacturing industry will benefit greatly via knowledge-based decision making. In CNC manufacturing, a successful piece of part program is the result of combined effort of process planners, shop floor engineers via iterative endeavour of calculation, simulation and tests, which contains valuable process knowledge and know-how. The valuable Knowledge contained in part programs is not included in the loop of knowledge management. Researchers have sought to maximize the utilization of part programs. The early effort was to reuse these part programs directly on different CNC machines, without separate knowledge from product data, which was proven not effective.

Focusing on the big data of Computer Numerical Control (CNC) machines on the shop floor, this PhD project will investigate the mechanism of knowledge capture from CNC part programs used in production and the method to reuse the knowledge to support new product development. Deep learning approaches, including Deep Belief Network (DBN), will be employed to capture knowledge, realise knowledge fusion and innovation in digital manufacturing. An automatic knowledge feedback will be achieved from the shop floor to feedback to the process planning stage to form a closed knowledge loop. This proposed deep learning approach will enable manufacturing companies to capture and accumulate process knowledge, maintain product quality and consistency, reduce the leading time for new products and boost innovation to gain competitive advantages.

Project aim: develop a deep learning method for knowledge capture from CNC part programs.

Challenges and objectives to achieve the aim of the project will be:

1) Develop interpretation methods of part programs (big data) in different dialects. The challenge will be converting the unstructured part programs in different languages into semantical and machining feature-based formats.

2) Design and implement deep learning approach based on prepared big data for knowledge capture, discovery and innovation.

Entry Requirements

UK First or 2:1 class degree or MSc in engineering or related disciplines

2) Be proficient in both written and spoken English, with excellent presentation and communication skills.

3) Knowledge in CNC machining, CAD/CAM systems and programming skills in high-level languages such as Java, Python, C#.

Machine learning-assisted ultra-precision machining of functional structured surfaces

Project Code

EPSRC_2022_28

Supervisory Team

Main Supervisor: Dr Zhen Tong

Co-Supervisor: Prof. Dame Xiangqian Jiang

Project Introduction

The importance of big data and artificial intelligence is increasingly emphasized in future manufacturing. It offers a tremendous opportunity to transform today’s manufacturing paradigm to the data-driven intelligent manufacturing, which allow continuously development of cost-effective manufacturing technologies with improved product quality. The aim of this PhD research project is to develop machine learning-assisted micro and nanomanufacturing technologies for high value-added functional surfaces. The research work will enable improved machining efficiency, surface quality and lead to the development of future self-adaptive and high automated ultra-precision micro/nanomachining system.

Project Details

This PhD project will work on the development of a machine learning-based system to predict and optimise ultra-precision surface structuring. The system will be integrated into our current ultra-precision manufacturing CAD/CAM framework for process simulation and parameters optimisation. The focus of the research is on the development of core machine learning-based data analysis modules to address the link between processing parameters and machined surface quality of micro/nano-structured surfaces. Systematic research work will be conducted to acquire, classify and analyse data measured by embedded sensor nets (dynamometer, accelerometer, optical probe, capacitive sensor etc) in micromachining environment. The developed system will be evaluated against agreed partner-led case studies on creating complex and multiple functional structured surfaces.

This is a highly industrially relevant project with great scientific interest. Success in this project will allow designers to consider foreseeable machining errors at the design stage and provide recommendations to engineers in selecting the appropriate process parameters to achieve specified surface functionalities. It will offer the candidate the possibility to establish successful industrial and academic collaborations.

Entry Requirements

The student must have a high-grade qualification, at least the equivalent of a UK 1st or 2:1 class degree or MSc with distinction in Computing Engineering, Applied Mathematics, Mechanical Engineering and Process Control or related disciplines. The student must be proficient in both written and spoken English, and possess excellent presentation and communication skills.

Structured-light Enhanced Stereo Vision System for AR Spatial Mapping

Project Code

EPSRC_2022_29

Supervisory Team

Main Supervisor: Prof Zhijie Xu

Co-Supervisor: Dr. Wenhan Zeng

Project Introduction

Spatial mapping is a key task in Augmented Reality systems. It attempts to create a 3D map of the environment for virtual and real object alignment, and to enable user interaction with the real world. It is vital for collision avoidance, motion planning, and realistic blending of the real and virtual world. Current vision based passive technologies suffer from complex real-world conditions such as low-light and featureless 3D scene for accurate 3D reconstruction. This project will investigate effective models for integrating modern structured-light technologies for rapid and precision AR mapping.

Project Details

Stereo vision simulates the binocular vision of humans, which results in slight image location disparity when each eye views the same object. Stereo cameras usually have two lenses, apart with a fixed distance to capture slightly different images that can be computed to generate depth map. As a passive technique it does not require external light source other than ambient light in the environment. However, in low light conditions or the scene has few textures, it would be hard for the cameras to extract stereo features for 3D reconstruction or establish camera motions.

As an active technique, structured light camera overcome this shortcoming through projecting modulated patterns to the surface of an object or a scene. It then calculates the disparity between the original projected pattern and the observed pattern deformed by the surface of the scene. The modulated light pattern generates higher accuracy in short range that can reach submillimetre level that is ideal for AR spatial mapping. Several pilot products have been released to market including Intel’s RealSense depth camera series, and Revopoint’s Acusense 3D depth camera. With a working envelop offers “RGB resolution up to 8 megapixels and accuracy up to 0.1mm, it is ideal for cases requiring high accuracy in short range, such as face recognition, gesture recognition and industrial inspection - Revpoint3D.” [https://3dcamera.revopoint3d.com/html/acusense/index.html].

This project aims at integrating the optical sensor and vision-oriented technologies for enhancing AR spatial mapping accuracy. The proposed investigation covers multiple aspects, namely, modelling camera calibration using structured light-boosted features of the scene; followed by accurate matching of extracted image features for 3D scene reconstruction and motion tracking; and then, integrate the developed techniques using Micro-Electronic-Mechanical-System (MEMS); before testing and validating the apparatus in a digitised manufacturing environment, where manufacturing process can be mapped and visualised for aiding the discovery, design and development of new products.

With the improved accuracy and automated AR spatial mapping abilities, it is envisaged that this research will bring significant contributions to the UK 21^st century products initiative through allowing AR-based rapid prototyping with potential scale-up capability embedded. The wider applications in digital manufacturing will extend to optimising the design process, simulation and visualisation of operations, and enabling rapid and responsive control and connectivity of manufacturing systems.

Entry Requirements

An ideal candidate for the proposed project should possess a recent undergraduate or master’s degree (1st or distinction) in computer science or electronic engineering. The candidate needs to have good knowledge in computer graphics, visual signal processing, and software development. The candidate is expected to be competent in modelling tools such as MatLab (or LabView), programming languages such as Python (or C++), and had developed software product independently using 3rd-party libraries such as OpenCV and OpenGL.

Engineered fibrous architectures for developing healthcare technologies

Project Code

EPSRC_2022_30

Supervisory Team

Main Supervisor: Dr Anke Brüning-Richardson

Co-Supervisor: Professor Parik Goswami

Project Introduction

Novel healthcare approaches for the improved management of difficult to treat diseases such as high-grade gliomas are urgently required. Recently, we have engineered suitable fibrous architectures for the time-released treatment of tumours at time of surgery. We have developed novel 3D bioprinted glioma models to assess the effect of such timed-release of drugs on brain tumour cells and the microenvironment; for clinical validation we have established a collaborative link with the Neuropathologist Dr Azzam Ismail (St. James’s Leeds). This project proposes to thoroughly investigate the time release of drugs for informed and improved patient treatment. This technology could also be used for transdermal delivery of drugs and aligns with EPSRC’s Healthcare Technology Themes.

Project Details

This PhD project combines the areas of ‘biomaterials’ and ‘manufacturing of the future’ as we aim to develop novel engineered materials for application in the management of difficult to treat diseases such as high-grade brain tumours (GBM) and test their dispersal and effectiveness eventually developing predictive mathematical models.

GBM is one of the cancers with worst outcomes in terms of survival and loss of life due to a cancer. Treatment options remain extremely limited based around surgery, followed by chemo and radiotherapy. Despite these interventions due to the highly invasive nature of the tumours GBMs will eventually recur within 18 months of original treatment, when the disease becomes incurable. In this project, you will:

Investigate novel materials as potential carriers for the timed-release of drug combinations to target cancer dissemination and promote cell death
Establish 3D models of GBM to investigate the materials in a tumour-like microenvironment and
Determine the effectiveness of candidate materials and the timed release and dispersal of combination drugs in other difficult to treat diseases.

This project relies on the combined expertise of Dr Anke Brüning-Richardson (GBM biology, School of Applied Sciences) and Professor Parik Goswami (Textiles, School of Art and Humanities). The appointed candidate will be based at the University of Huddersfield where he/she will spend time in the laboratory of Professor Goswami (for the engineering component) and Dr Brüning-Richardson (for the health care device and treatment component). As proof of principle, we will be using three cell lines of GBM representative of the three subtypes of GBM to investigate if treatment responses differ according to the biological background of the cells. We will generate 3D tumour spheroids, treat them with fibrous architectures impregnated with drugs and then follow their fate by confocal microscopy. An in-silico model (agent-based) of glioma progression in a 3D physical environment will be developed. Key characteristics of the in-vitro system will be mimicked in-silico (well-geometry, ECM composition, etc.), while model reproducibility, accuracy and fidelity will be validated against the laboratory findings. Validation of drug dispersion and activity via the engineered fibrous architectures on cancer cells will allow informed treatment regimens for patients with GBM and open avenues for other cancer types. The analysis will provide information that will be leveraged using the modelling software, BioDynaMo (www.biodynamo.org.) via our links with the University of Surrey.

Entry Requirements

First or 2.1 in relevant life sciences

IT skills

Tissue culturing

3D modelling

Knowledge of drug development in cancer research

Biomaterials for Alzheimer's disease modelling, prediction and intervention

Project Code

EPSRC_2022_31

Supervisory Team

Main Supervisor: Dr Bushra Almari

Co-Supervisor: Professor Liam Blunt Dr Kofi Asare-Addo

Project Introduction

Alzheimer's disease (AD) is a chronic, progressive disease characterised by a loss of neurons, amyloid plaques and neurofibrillary tangles. Preclinical models are useful for understanding the underlying pathology, identification of potential therapeutic targets, and screening of drug candidates to treat AD. Bioengineered microbeads carrying cell lines that deliver AD-related proteins can be utilised in preclinical models to produce a long and sustainable secretion of relevant proteins. In this project, amyloid-secreting microbeads will be manufactured and characterised in in vitro and in vivo environments. Their potential to develop a preclinical animal model of relevance to AD will be explored.

Project Details

The project will combine cell biology, pharmacology and bioengineering techniques to achieve its final aims. The model will be characterised and developed in 4 main stages:

Part 1 – Bioengineering: Characterisation of the biomaterials to be used to encapsulate amyloid-secreting cells. This involves the assessing of biocompatibility in simulated in vitro Fabrication of 3D protein delivery system and analysing its characteristics in vitro. This includes surface-measurements, size-distribution studies, stability studies, cell-viability studies and exploration of protein-release profile. Techniques: 2D and 3D cell culture, biomaterial manipulation/microbead fabrication. Focus variation microscope measurements will be used to assess how the measurements affect performance.
Part 2 – in vitro studies: Analysis of long-term (3+ months) cell viability in bioengineered 3D system. This includes MTS/MTT assays and protein analysis. Analysis of long-term (3+ months) protein release profile. Analysis of long-term (3+ months) 3D co-culture. This includes studying inflammatory and neuronal markers in existing in vitro models of AD (e.g., neuronal systems, microglial systems, astrocyte systems, etc.). Techniques: cell culture, protein analysis, Western blotting, enzyme-linked immunosorbent assay, flow cytometry, UV imaging (used to visualise the protein release from the beads to ensure success and underpin the mechanisms behind it).
Part 3 – in vivo studies: Engraftment of bioengineered 3D systems in vivo (collaboration with University of Manchester Biological Services Facility, hosted by Dr Michael Harte). Assessment of cognitive changes in vivo using a behavioural test battery (novel object recognition, Y-maze, Barnes maze, etc.) followed by assessment of pathological changes ex vivo (including synaptic markers, inflammatory markers, neuronal markers, astrocytic markers, microglial activation).
Part 4 – Therapeutics: Collaborating with colleagues in SAS currently investigating therapeutic agents for AD using in vitro Therapeutics tested successfully in vitro can be tested in our in vivo model. This will enable us to publish in high-calibre, high impact factor journals, further our work and attract funding. Success of therapeutic drug candidates in vivo will be great news for the field, as it will mean potential collaboration with clinical partners in order to conduct clinical trials in the future.

Entry Requirements

Candidates are expected to hold (or be about to obtain) a minimum upper second-class honours degree (or equivalent) in a related area/subject. Areas may include (but are not limited to): Biology, Pharmacology, Neuroscience, Pharmacy, Medicine, Tissue Engineering, Regenerative Medicine. Candidates with previous laboratory experience are particularly encouraged to apply.

The student will undertake Modules 1-4 of the Home Office training for working with animals. They will receive training in conducting and analysing different cognitive tasks alongside post-mortem analysis of rodent and (possibly) human tissue for different neuroinflammatory and neuronal markers.

Training will also be provided in methods of data collection, statistical analysis and graphical presentation. Oral presentation skills will be developed through presentation of the results at internal laboratory meetings and both national and international conferences.

Biomaterials for Alzheimer's disease modelling, prediction and intervention

Project Code

EPSRC_2022_32

Supervisory Team

Main Supervisor: Prof Craig Rice

Co-Supervisor: Prof Roger Phillips, Dr Simon Allison and Dr Martina Whitehead

Project Introduction

We have previously demonstrated metallo-architectures that are spontaneously generated from preprogramed sub-assemblies. These self-assemblies contain a cavity that bind anions (e.g. Brˉ, Iˉ, CO₃²ˉ, EO₄²ˉ (E = W, Cr, S and Se), and EO₄³ˉ (E = V, As and P)) and as such can remove phosphate and arsenate from eurotrophic/polluted water sources. Furthermore, the self-assemblies can act as an artificial phosphatase leading to potent and selective anti-cancer activity both in vitro and in ovo (Nature Communications, 2021). The objective of this proposal is the synthesis of novel self-assemblies and their ability to encapsulate/hydrolyse anions and the implications for biological systems.

Project Details

We have recently shown that tripodal ligands, upon reaction with metal cations, form self-assembled cryptates that can not only bind and sequester different anionic species from aqueous systems but also hydrolyse phosphate esters. One of the unique features of cryptands is their ability to self-assemble making the construction of cryptands with different anion binding properties inherently flexible. Via the self-assembly process varying the ligand (L), metal (M) can generate different cryptands and each different assembly can possess markedly different anion encapsulation or hydrolytic properties. For example, larger constructed cavities may be more suitable for larger anions (c.f. Brˉ vs SiF₄²ˉ) whereas different shapes of cavities would potentially change the preference for binding (c.f. trigonal planar EO₃ⁿˉ vs tetrahedral EO₄ⁿˉ).

Whilst we have shown that these compounds possess interesting properties there is still a large volume of chemical space to be explored and changing the shape and nature of the ligand L and the metal ion M would produce an array of compounds which would all have different properties and bind to and/or sequester different anions.

Furthermore, these self-assembled chemical systems can either bind (in the case of Cu²⁺) or hydrolyse (in the case of Zn²⁺ and Mn²⁺) phosphate esters and are the first example of a self-assembled phosphatase. This ability is directly responsible for their biological properties and the inhibition of enzymes (kinases) that play an important role in cancer biology. The compounds can selectively kill cancer cell lines in vitro (as opposed to non-cancer cells) and they have anti-tumour activity in the industry recognised chick embryo model at doses that are not toxic to the chick embryo.

One unexplored aspect of the chemistry is whether the phosphatase activity can be manipulated by changing the shape and binding nature of the cavity. Resultantly a library of compounds would be synthesised with varying ligand and metal ions and the rate of hydrolysis (phosphatase activity) determined by UV-Vis kinetics.

Subsequent biological evaluation would enable the determination of the relationship between phosphatase activity and potency and selectivity in vitro. Understanding the chemical principles that govern phosphatase activity and how this influences potency/selectivity would generate a structure activity relationship that will enable the more rational design and synthesis of novel cryptands. This would allow the synthetic generation of novel phosphatases that could target hard-to-treat cancers and a reduction in deleterious side effects common with current treatment.

Entry Requirements

Degree in Chemistry with interest in synthetic and coordination chemistry.

Nature based solutions in urban and agricultural landscapes: assessing the use of artificial small waterbodies for sustainable wastewater treatment, flood mitigation and biodiversity.

Project Code

EPSRC_2022_33

Supervisory Team

Main Supervisor: Dr Matthew Hill

Co-Supervisor: Dr Tory Milner, Dr Thomas Smyth

External partners: Yorkshire Water

Project Introduction

Urban and agricultural land-use is predicted to increase significantly in the future, associated with biodiversity loss, increased run-off and pollution, and the replacement of complex natural ecosystems by built environments. Traditionally, flood management and wastewater has been undertaken using constructed infrastructure (sewage treatment plants, river modification). Recently, more ‘natural’ solutions to flood management and wastewater treatment are being used to provide ecosystem services and support biodiversity in rapidly changing environments. Ponds and Integrated Constructed Wetlands (ICWs) are being employed to collect stormwater/sediment and improve water quality before it reaches riverine habitats. However, assessments of the dual role of these habitats, providing key ecosystem services and supporting biodiversity are lacking.

Project Details

Flood management and wastewater treatment has traditionally been undertaken using constructed infrastructure (sewage treatment plants, river modification). Recently, more ‘natural’ solutions such as stormwater ponds and Integrated Constructed Wetlands (ICWs) are being employed to ensure the sustainable and long-term management of the water system, by collecting stormwater/sediment and treating wastewater before it reaches river habitats. Despite their anthropogenic origins and purposes, there may be opportunities for these waterbodies to support significant freshwater biodiversity, ensure a sustainable supply of water and increase resilience to environmental change (addressing the Ecosystem, Resources and Infrastructure challenge areas). However, research has historically focused on the contribution of small waterbodies to ecosystem services or biodiversity separately. There have been few studies that have considered their contribution to providing key ecosystem services, improving water infrastructure, and supporting biodiversity together. This has likely constrained the development of appropriate conservation and management strategies, that can maximise the effectiveness of small waterbodies to manage wastewater/mitigate flooding and support wildlife.

This interdisciplinary project will assess the multidimensional role of small waterbodies (stormwater ponds and ICWs) for sustainable water management (flood mitigation and wastewater treatment), increasing resilience to environmental change and to support biodiversity in a changing world.

Specifically, this project aims to:

Quantify water storage capacity of stormwater ponds and ICWs for flood management across two urban and agricultural landscapes. This will be undertaken at a pond-network and city-wide scale. Through flood modelling, this project will identify locations stormwater ponds can be constructed in urban areas to reduce flood risk further.
Examine the water quality of stormwater ponds and ICWs, and of the river immediately downstream across two urban and agricultural landscapes.
Quantify the biodiversity of stormwater ponds and ICWs at local and landscape scales. Aquatic macroinvertebrate and macrophyte diversity will be compared to ‘semi-natural’ ponds in proximal nature reserves.
Identify priorities for the conservation and management of urban ponds and ICWs that ensures the sustainable and long-term management of the water system and freshwater biodiversity in changing environments. In particular, small amendments to current management strategies will be identified that can improve biodiversity while not reducing the effectiveness of the primary function of the waterbodies.

The project will involve environmental and ecological field surveys of stormwater ponds across an urban landscape (Leeds/Manchester), and of the newly built ICWs at Clifton Wastewater Treatment works (owned by Yorkshire Water). There will also be significant spatial and statistical modelling during this project, to quantify the water storage capacity of waterbodies, map flood risk, examine wastewater treatment, and to help understand the contribution of ICWs and stormwater ponds to biodiversity.

This project will advance our (1) theoretical understanding of the importance of small waterbodies in urban and agricultural landscapes to the sustainable management of the water system (flood mitigation and wastewater treatment) and wildlife, and (2) applied management and conservation of these systems to maximise ecosystem service provision and biodiversity.

Entry Requirements

Qualification

Minimum of a high 2:1

Skills and Experience

Univariate and multivariate statistical analysis

Experience using GIS

Field experience: Environmental and ecological sampling of freshwaters

Aquatic macrophyte and/or macroinvertebrate identification

A UK driving licence and an ability and willingness to drive to different fieldwork locations

Knowledge

Ecosystem services provided by urban and agricultural freshwaters

Freshwater ecology (particularly within urban and agricultural landscapes).

Freshwater conservation (applied and policy) in human dominated landscapes

Integration of artificial intelligence (AI) and robotics for smart maintenance of rolling stock

Project Code

EPSRC_2022_34

Supervisory Team

Main Supervisor: Prof. Adam Bevan

Co-Supervisor: Prof. Gareth Tucker, Dr Hassna Louadah

Project Introduction

The cost of maintaining rail vehicles can be up to 40% of the life cycle and is estimated to cost over £700m per year to maintain the GB mainline fleet (made up of over 14,000 vehicles, maintained at 96 depots). Train operators are also under increasing pressure to reduce costs, particularly during the post-COVID recovery. A key enabler to improving the efficiency of rolling stock maintenance is the use of ‘smart technologies’ to optimise and automate maintenance activities. This research will aim to build on existing work being undertaken within IRR to apply robotics technologies to rolling stock maintenance through the integration of artificial intelligence (AI) planning and control techniques.

Project Details

The future vision for smart maintenance of rolling stock includes the use of robotics and drones to execute maintenance tasks both autonomously and jointly with the workforce. Whilst some research is on-going to automate a range of depot-based and overhaul rolling stock maintenance activities, using industrial robots or bespoke solutions, the integration of these technologies with advanced analytical and artificial intelligence (AI) techniques to automatically schedule and execute tasks has not been considered. The integration of these techniques will allow for better scheduling of maintenance activities and faster execution of maintenance, improving network capacity by increasing the amount of time that rolling stock can be in operation.

The research project aims to investigate the feasibility of using AI techniques for the planning, control and execution of rolling stock maintenance activities using robotics (or a combination of robotics and humans). This will include the development of an AI system which integrates with the virtual depot (a digital twin of a railway depot currently under development within the IRR), current automated inspection and prediction systems, and parts management to optimise rolling stock maintenance. To achieve this aim, the following objectives are proposed:

Review application of AI in other industry sectors, with particular focus on digital maintenance.
Identify key inputs to an AI system that could be used to control robotic train maintenance, e.g., train condition data, train usage, parts availability, task prioritisation and integration with other work in a depot.
Identify how AI can be integrated with robotics to autonomously undertake different maintenance activities.
Define the most appropriate AI techniques and functional requirements of the proposed AI system
Development of AI system for planning, control and execution of depot maintenance activities
Link the developed AI system with the virtual depot and robotic simulation software (Octopuz) to demonstrate the feasibility of the integrated techniques.
Live demonstration and dissemination of the integrate techniques to industry stakeholders.

The results from the research can be demonstrated within the robotic test cell located in the IRRs Smart Rolling Stock Maintenance Research Facility, which includes several industrial robotic arms in a realistic depot environment.

Entry Requirements

The candidate should hold a good degree in a relevant engineering or science discipline. Good mathematical skills are essential. Knowledge of the railway engineering and experience in AI would also be beneficial to the project.

ARCTURUS: Architecture for Sustainable Industry 4.0

Project Code

EPSRC_2022_35

Supervisory Team

Main Supervisor: Dr Colin C. Venters

Co-Supervisor: Dr Gary Allen

Advisor: Professor Elisa Yumi Nakagawa, Universidade de São Paulo, Brazil
Advisor: Professor Patricia Lago, Vrije Universiteit Amsterdam, Netherlands

Project Introduction

Industry 4.0 (I4.0) is a new paradigm of smart manufacturing that requires the intelligent networking of machines and processes with information and communication technologies (ICT) in order to obtain greater efficiency, quality and productivity. Digital technology is the backbone of I4.0 and underpins the U.K Governments Green Industrial Revolution. However, interoperability is the key challenge in achieving smart manufacturing. In addition, while modern industrial development aims to be more environmentally sustainable, it is estimated the ICT sector’s carbon footprint accounts for 1.4% of overall global emissions, and that by 2040 is expected to account for 14% of the world’s carbon footprint.

Project Details

The future of I4.0 is highly dependent on a resilient and sustainable eco-system of software systems. In this new environment, standardization, interoperability, and sustainability are critical factors in achieving the I4.0 vision, which paves the way for leveraging the Industrial Internet of Things, Big Data analysis, simulation, Cloud Computing, and augmented reality. The first step towards achieving system integration is to reason about the elements, their properties and relationships that make up the eco-system. Software system design is a key component, which starts with software architecture as it lays the foundation for the successful implementation, maintenance and evolution in a continually changing execution environment by providing a mechanism for reasoning about core system quality requirements including interoperability and sustainability. Reference Architectures have been used for the aggregation of knowledge in a range of specific domains, promoting the reuse of design expertise and facilitating the development, standardization, and evolution of software systems. Examples include AUTOSAR for the automotive sector (AUTOSAR, 2021), ARC-IT for transportation systems (U.S. Department of Transportation, 2019), EIRA for interoperable e-Government systems (Joinup, 2021), and SOA RA for service-oriented systems (Open Group, 2021). The main benefits of these architectures include increased interoperability among systems and subsystems, reduction of development costs and time by enabling reuse, reduction of risks in software projects, improvement in communication, and adoption of best practices. While a limited number I4.0 reference architectures have emerged, i.e., Reference Architectural Model Industrie (RAMI) and Industrial Internet Reference Architecture (IIRA), their suitability in addressing interoperability and sustainability remain an open research challenge. The overall aim of this project is to advance software architectural-level reasoning for pre-system understanding and post-system maintenance and evolution through the development of a Data- Driven Reference Architecture for Sustainable Industry 4.0. The key research objectives of this project are:

R1: To understand the effectiveness of existing reference architectures in quantifying the
impact of architectural effects on interoperability and sustainability;
R2: To demonstrate the efficacy and limitations of existing reference architectures in presenting
relevant architectural views, including architectural design decisions;
R3: To explore the fitness of architectural-level metrics in identifying architectural flaws in the
quantification of architectural interoperability and sustainability;
R4: To design and develop a reference architecture, which integrates architectural-level metrics
to quantify the interoperability and sustainability of the designed software architecture;
R5: To demonstrate the effectiveness of the reference architecture through the analysis and
conceptual refactoring of industrial domain case studies.

Entry Requirements

For this project, we are looking for an enthusiastic individual with an outstanding Software Engineering or equivalent Computing degree with excellent problem solving and strong, demonstrable software development and programming skills. Ideally, candidates should have a background in software architecture and software design or demonstrate a willingness to acquire the foundational knowledge. In addition, candidates should have a strong interest in at least two of the following areas: requirements engineering, software metrics, software testing, reverse engineering, Service-Oriented Computing, Industrial Internet of Things, Ultra-Large Scale Systems, Fog Computing, Edge Computing, Cloud Computing, or Systems of Systems. Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK. If English is not your first language you will need to have achieved at least 6.0 in IELTS and no less than 6.0 in any section by the start of the project.

Radiation response of advanced nuclear fuel cladding materials coated with MAX phases

Project Code

EPSRC_2022_36

Supervisory Team

Main Supervisor: Prof. Konstantina Lambrinou

Co-Supervisor: Prof. Jonathan A. Hinks

Project Introduction

Nuclear fuel cladding materials are the first containment of nuclear fuel (fissile material) and, as such, must demonstrate a reliable performance during in-reactor service. They should demonstrate a good compatibility with the (reactor-specific) primary coolant and are called to withstand high temperatures and radiation damage doses, due to their residence in the reactor core. This project assesses the potential of innovative MAX phase-coated fuel cladding materials (zircaloys, stainless steels) in terms of their response to irradiation. The MAX phases are special ceramic materials with exceptional resistance to corrosive coolants (liquid metals, molten salts) and radiation damage, esp. at temperatures >600°C.

Project Details

This project is a systematic investigation of the radiation response of innovative MAX phase-coated fuel cladding material concepts intended for advanced nuclear systems. The MAX (M_n+1AX_n) phases are nanolayered ternary carbides/nitrides, where M is an early transition metal, A is an A-group element, X is C or N, and n = 1, 2, 3. Due to their unique properties, some of which are characteristic of ceramics (thermal stability, corrosion resistance) and some of metals (damage tolerance, thermal conductivity, machinability), the MAX phases are candidate materials for various applications that demand reliable performance in extreme environments.

A highly demanding application is that of a nuclear fuel cladding material, which is typically shaped as tubes containing the reactor fuel (e.g., UO₂, MOX). Fuel cladding materials must survive taxing service conditions (high temperatures and radiation damage doses) in contact with corrosive coolants, while their limitations can affect the efficiency of advanced nuclear systems or delay their deployment. Failure of the zirconium-based (zircaloy) fuel cladding materials used in Gen-II/III light water reactors during a loss-of-coolant accident caused the Fukushima Daiichi event in 2011. Today a huge effort is undertaken on a global scale to develop accident-tolerant fuel (ATF) cladding materials that can outperform the commercial zircaloy fuel claddings, esp. in transient/accidental conditions.

An ATF cladding material concept studied within the H2020 project IL TROVATORE is the MAX phase-coated zircaloy fuel cladding concept. The development of such innovative nuclear material requires the determination of the appropriate coating composition (phase-pure ternary MAX phase compounds or solid solutions thereof) and microstructure (grain size, texture), as well as the identification of a diffusion barrier layer between coating and substrate to prevent high-temperature reactivity in case of an accident. The optimised ATF cladding material concept will be irradiated at the MIAMI facility to assess its radiation behaviour under both nominal and accidental conditions. The MIAMI facility allows the in-situ ion irradiation of materials in the transmission electron microscope (TEM), permitting the follow-up of the evolution of irradiation-induced damage and providing unique insights into (material-specific) damage mechanisms.

Different MAX phase coatings will be deposited on stainless steel fuel cladding materials for Gen-IV lead-cooled fast reactors to prevent liquid metal corrosion effects. These fuel cladding materials will also be irradiated in the MIAMI facility, while the variety of ion irradiation conditions and MAX phase compounds will establish an in-depth understanding of the radiation response of this promising family of nanolaminated ceramics.

Entry Requirements

The candidate must hold a Bachelor’s or Master’s degree in Materials Science, Physics, Chemistry, Nuclear Engineering or related discipline.

Ideally, the candidate should be familiar with:

Basic principles of materials science and/or solid state physics
Basic principles of radiation damage in solids
Basic principles of materials characterisation by means of electron microscopy techniques and, in particular, transmission electron microscopy (TEM)

In-line Surface Metrology for Responsive Roll to Roll (R2R) Manufacturing of Flexible Electronics.

Project Code

EPSRC_2022_37

Supervisory Team

Main Supervisor: Dr Hussam Muhamedsalih

Co-Supervisor: Prof Liam Blunt, Prof. Jane Jiang, Dr. Dawei Tang

Project Introduction

The University of Huddersfield’s team from CPT and the UK national catapult Centre for Process Innovation (CPI) will collaborate to develop a metrology optical sensor and software to visualize measurement data so that industrial operators of the roll-to-roll production machines can make the right decisions on the coating’s quality and stability. This project runs in parallel with a newly awarded “Responsive Manufacturing of High Value Thin to Thick Films” project EP/V051261/1. The EPSRC project will focus on the enabling CPI’s roll-to-roll production lines to be responsive to disruptions in the substrate or coating material being processed.

Project Details

Surface science has become an important part of the wide engineering and physical science landscape and has received significant funding from the EPSRC since 2011. This investment reflects the importance of surface science in many areas of which ‘digital manufacturing’ is one. Nowadays, the roadmap of manufacturing surface metrology aims to integrate in-line sensors within of manufacturing processes to guarantee the specification of every part. As such, direct interaction between measurements and manufacture is essential in production process, for delivery of industry 4.0 compatible processes, and leads to significant savings in both time and cost.

However, most commercial-use sensors are still laboratory-based and need further development to be used for in-line measurement applications. Real topography measurement for precision surfaces is usually achieved optically by capturing multiple frames to cover a vertical range of several microns or more. The scanning time for methods such as coherence scanning interferometry (CSI) and wavelength scanning interferometry (WSI) is considered a limiting factor for using such interferometry systems for in-line measurement of moving surfaces^3-4. For example, roll-to-roll processing, which is a fundamental approach in meeting the demand for very large numbers of functional flexible electronic devices, needs metrology systems that can cope with the manufacturing throughput (typically > 80 meters/min) and have the ability to handle and process very large amounts of data. There are many optical systems that can only inspect the surfaces visually without producing surface metrics but none that deliver quantitative information.

In addition to the aforementioned challenges in sensors’ development, one of the main challenges for in process metrology is how to assess large and multiple measurement data sets. Many measurement files (typically larger than 800 Mbyte) will be produced over a single 500 mm sheet width. Therefore, a new paradigm in data handling methods is necessary for roll-to-roll process.

The aim of this proposal is to provide a metrological technology for large area precision surfaces produced by roll-to-roll processes. The technology will consist of a novel fast in-line interferometry sensor, a method to handle large amounts of data without interaction from the inspector, and a calibration routine for the sensor. The sensor will be used for detecting and measuring micro/nano-scale defects and critical dimensions on large-area thin film layers, for example flexile photovoltaics (PV) and aspects will be demonstrated at the partner organisation, the UK Catapult; The Centre for Process Innovation (CPI).

Entry Requirements

BSc/BEng/MSc in the following fields (optics, electronics, or mechanics) – 2:1 degree or above
Optics knowledge (Desirable)
Programming skills, MATLAB and C/C++ (Desirable)

Type of Award:	Doctor of Philosophy (PhD)
Eligibility:	UK applicants only First Class or Upper Second Class Honours degree or equivalent in a relevant subject area, please refer to the entry requirements on the specific projects being advertised
Location:	Huddersfield
Funding:	3 years full time research covering tuition fees and a tax free bursary (stipend) starting at £16,062 for 2022/23 and increasing in line with the EPSRC guidelines for the subsequent years. Funded via the Engineering and Physical Sciences Research Council Doctoral Training Programme
Duration:	3 years Full-time plus 12 months writing up (please note that no funding is available for the writing up period)
Closing date:	11 July 2022
Start date:	1 October 2022

EPSRC Funded Studentships

Multi-metasurface optical instrumentation

Smart multi-sensor

Robotic Processing of Complex Surfaces

Focus Variation Sensors for optical metrology

Multi-functional metasurfaces

Additive Manufacturing of Ti Alloys

AI designed robust-Metasurfaces

Dynamic information flow in digital manufacturing

Novel integrated modelling process

Modelling railway ballast using physics engines

STEP-NC Enabled AM Digital Thread

Artificial Intelligence (XAI) Technique

3D Volumetric Filtration in XCT Metrology

Knowledge capture from manufacturing big data

Ultra-precision machining

Structured-light Enhanced Stereo Vision System

Smart maintenance of rolling stock

Nuclear fuel cladding materials

In-line Surface Metrology