Printed sensing technology for metal tooling applications

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Innovate UK has formed a UK based collaboration to develop sensing technologies for the real time monitoring of machined metal parts. The ‘Intelligent Tooling’ project is developing embedded sensors and electronic components within high value machining applications in manufacturing sectors including aerospace, rail, automotive, marine and energy.

The project brings together end users and partners to integrate research, technology and industrial scale manufacturing, with the Centre for Process Development (CPI), BAE Systems, Element Six, The Advanced Manufacturing Research Centre, Advanced Manufacturing Ltd, Printed Electronics Limited, The National Physical Laboratory and DMG Mori Seiki involved.

“CPI’s role in the project is to design and print the electronic sensors, providing expertise in the integration of conventional and printable electronics,” said Dr Peter Tune, business manager at CPI. “Printing will be used where its flexible properties provide great impact. Here it is used to apply sensing functionality close to the cutting edge of the tooling inserts.”

Small variation in input parameters, such as material and tooling properties, are often only observed in the final inspection of products. Within the high value manufacturing sector, this often leads to conservative parameters or conservative tool lives being enforced. The ability to obtain data on the machining process at the time of cutting, at a lower cost and higher resolution than before allows these small changes to be diagnosed and managed within the process, leading to better tool utilisation and improved processing times.

The Intelligent Tooling project will seek to develop a prototype tooling insert with embedded sensing capability, designed to withstand and exceed the harsh environmental conditions that are present in metal machining. Further developmental focus will be to upscale the prototype to derive the data needed for commercial market adoption.

Dr Tune added: “This project represents a tough challenge for printed sensing and the learning will be directly transferrable to other embedded sensor applications where there are similar challenges.”