Innovation Fund Projects

AMPI Innovation Fund: Fives Open Access CNC

(Innovate UK – Grant Ref: S26447)

In collaboration with Fives Landis Ltd and the Universities of Huddersfield & Manchester, this project aims to revolutionise industrial CNC (Computer Numerical Control) machining systems by incorporating real-time machine learning capabilities using the Python programming language. The activity builds upon previous work involving the Fives CNC6400 control system and aims to embed Python directly into candidate CNC machine core architecture, enabling dynamic, user-written machine learning algorithms to operate in real time. 

This type of Python integration in industrial CNCs represents a significant technical and commercial innovation. The open architecture will allow researchers, developers, and companies to experiment and innovate without the constraints of proprietary systems. 

The project’s outcomes will be a unique selling proposition for Fives Landis’ CNC systems, as well as enhancing collaboration between academia and industry, and developing a platform for future research and commercial applications. It is expected to lead to broader adoption of advanced manufacturing technologies by other companies. The long-term vision includes enabling adaptive machine behaviour and expanding the CNC’s use into areas like robotics and metrology. 

Ultimately, this project positions Fives Landis and the Universities of Huddersfield & Manchester as leaders in next-generation CNC technology, fostering sustainability, innovation, and economic growth in the advanced machinery sector. 

Find out more about this collaboration.

AMPI Innovation Fund: Fives Adaptive Machining

(Innovate UK – Grant Ref: S26447)

This project, funded by the Advanced Machinery and Productivity Institute (AMPI), focuses on enhancing control over multiple process and machine parameters to meet stringent precision requirements. In collaboration with Fives Landis Ltd (FLL), the project will introduce innovations in high-precision temperature and strain measurement. These measurements, along with spindle dynamometer data, will be employed in a feedback loop to optimize process parameters and therefore improve product quality and overall machine productivity. 

Three key innovations in this project are required to achieve the objective capability. The first is to improve existing technology used to control high flow rate coolant temperature to within a very challenging +/-0.01°C, using a new multi-sensor control algorithm embedded in the NC. The second is integration of a spindle mounted dynameter with direct feedback control to adjust the process parameters; and finally, the integration of the University of Huddersfield strain sensing framework for thermal monitoring/control. 

These innovations will be deployed for use on the Fives TTG. By implementing these on FLL’s Open Access CNC platform – with systems programming in Python developed by the University of Huddersfield’s Engineering Control and Machine Performance Group - This initiative will support the growth of the UK’s advanced machinery sector by expanding FLL’s market reach and strengthening academia-industry collaboration, which is vital for sustained innovation. 

Find out more about Adaptive Machining.

AMPI Innovation Fund: Craftsman Live Driven Tooling 

(Innovate UK – Grant Ref: S26447)

This project will design and develop a range of power-driven smart toolholders to suit CNC lathes. This innovative design will include a modular mounting system enabling it to be mounted on a variety of different lathe types and brands. This project will help re-shore this category of live tooling that is solely manufactured abroad.  

The innovation of the project is composed in two parts: 

1.  The development of a modular design which can be adapted to suit several different brands and types of machine tools. Presently, most live-driven tooling units are only accepted by the machine tool for which they are designed.  As such, companies sell them as additional components. Exceptions can be made in the form of custom units; however, these are inherently expensive to procure. This project aims to develop a generic live-driven tool system with an interchangeable mount that can be configured to suit any lathe turret. 
2. Further design innovation is needed to enable the embedding of sensors in the product that will provide diagnostic feedback to the end user on cutting performance, maintenance issues and toolholder usage history. This will allow for machine tool path, and cutting routine optimisation, as well as predictive maintenance, both of which will improve productivity, part quality and reduce machine downtime. 

Find out more about Live Driven Tooling.

Halifax Numerical Controls – Metrology Enhanced Roller/Grinder

(Innovate UK – Grant Ref: S26447)

In collaboration with Halifax Numerical Controls (HNC) and their state-of-the art roller/grinder for rubber and composite rolls. The machine setup is quick and easy for any roll profile and pattern, with a "load once" philosophy and simple user controls eliminate the need for highly skilled machinists. This multi-faceted project comprises three main phases that are critical to HNCs next generation of rubber roller and groover machines:  

The first multi-point, in-situ roll diameter and roundness measurement. 

Machine and product thermal management through tuned error avoidance (by design), a cost-effective embedded network of sensors for feedback to CNC compensation and auto-cut correction. 

Efficient and rapid rubber swarf extraction to reduce non-productive time associated with waiting for particulates to settle before operators can access the machine (H&S). 

Find out more about rollers/grinders.

Holdson Ltd: Electroform Project (PLC Integration and Software) 

(Innovate UK – Grant Ref: S26447)

Project Electroform aims to augment a Holdson Ltd. proprietary electrochemical process that selectively enhances surface material properties, while maintaining the component’s original shape and structural integrity. This technology is specifically designed to overcome surface finishing limitations commonly associated with additive manufacturing and was initially developed to improve fuel efficiency in diesel and hydrogen systems. 

Our input to the project plays a pivotal role in this effort, focusing on the integration of a Programmable Logic Controller (PLC) and the development of a comprehensive software ecosystem to support the electrochemical polishing machine tool. In collaboration with the manufacturer (Holdson Ltd), this project ensures seamless coordination between the machine’s physical components and its digital intelligence. 

The engineering goals include the integration of hardware and drive systems, along with the implementation of in-process monitoring and machine learning algorithms powered by a large language model (LLM). Electroform machines generate substantial volumes of high-resolution operational data. The project will enable users to interact with this data through a Large Language Model (LLM), applying statistical analysis and machine learning techniques to enhance machine accessibility and usability. This capability will also support real-time process control and optimization, ensuring consistent surface quality and operational efficiency. 

Ultimately the project underpins the creation of a robust, scalable, and intelligent machine tool architecture, aligning with the broader vision of delivering a UK-built, globally competitive solution for advanced surface finishing. 

This project has been made possible by the Advanced Machinery & Productivity Institute funded “Innovation for Machinery (I4M)” Programme.

Find out more about the Electroform Project.