Internally Funded Projects

Multi-faceted and scaled advanced manufacturing facility

This University Research Fund (URF) project supports cross-disciplinary research aimed at removing barriers to accessing high-value manufacturing equipment. The Scaled ModUlar Reconfigurable Factory (SMURF), developed through this initiative, enables researchers and students to explore cutting-edge topics such as Industry 4.0, system autonomy, and prognostics—areas where computer science and engineering intersect. 

With the SMURF facilities, the modularity and reconfigurability of the equipment makes it possible to build custom manufacturing cells and obtain useful data which would have been relatively more difficult to obtain on a full-size machine. It is also providing students with the opportunities to learn on relatively easier-to-access machines which enhance safety of personnel and equipment which do not have the practical limitations for training and development present on industrial machinery due to their design, health and safety considerations and availability.

Smart Workshop

Our Manufacturing Research Workshop focuses on collecting and analysing real-time sensor data to better understand the performance and condition of machine tools. We gather a range of data including acceleration, electrical current, machine temperature, and ambient environmental conditions. This data is used to study how machines behave under different operating and environmental conditions.  

We are also examining how environmental factors, particularly air quality, affect not only machine performance but also the health and safety of machinists. In addition, we also monitor the air and coolant that our machines use, with smart controls to turn off the air when machine is not in use, which saves energy, and allows us to better evaluate the coolant properties when machining. 

We apply both deterministic methods and machine learning techniques to identify patterns, detect anomalies, and explore relationships between variables. This helps us investigate issues such as tool wear, energy consumption, and environmental impact, with the aim of improving reliability, efficiency, and sustainability in manufacturing processes. 

This work contributes to broader Industry 4.0 goals by integrating data analytics and intelligent systems into traditional manufacturing environments. Through this research, we aim to support the development of smarter, more adaptive machine tools and provide insights that can inform predictive maintenance, process optimisation, and digital manufacturing strategies.

Quality planning and process research

The team has been advancing quality planning and process research, engaging with industry partners to better understand CNC machine-building processes and product development challenges. This work positions the group to provide informed practical insights to industrial collaborators and other researchers in the field of manufacturing machinery. Contributions from current and former researchers, alongside our AMPI industrial partners, continue to strengthen these initiatives, bridging academic research and real-world application.