INSPHERE formalises partnership with AMRC to drive intelligent automation in UK manufacturing
"We look forward to continuing to use IONA datasets to show the potential for machine learning and AI."
INSPHERE has announced a formalisation of its partnership with the UK's University of Sheffield Advanced Manufacturing Research Centre (AMRC) network to drive intelligent automation and improved robotics with metrology-grade sensor technology.
INSPHERE, which specialises in technology to capture, analyse and utilise data to drive advanced, automated production, has previously worked with the AMRC on I-UK funded UMAC and ACCUFAS projects. According to Craig Davey, COO, INSPHERE, this partnership will allow the company to “build even stronger links with relevant industrial partners” and provide “continued access to the skills and facilities at AMRC” to conduct combined research.”
Davey said: “It’s fantastic to have our IONA technology at both AMRC Cymru and AMRC Factory 2050 sites, and to see how these installations have contributed to events at the AMRC and have been successfully used by other members (e.g. BAE Systems) to test advanced manufacturing processes.”
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INSPHERE’s IONA technology is a scalable network of sensors which are geared to deliver metrology-grade positional data to automated manufacturing processes. The data is used to improve the accuracy and performance of industrial robots, including identifying and correcting errors on the production line. The company says IONA ‘unlocks the true potential of industrial robots, flexible automation, and virtual commissioning.’ Last year, it secured £3.7 million in funding to help push IONA into German and US markets, specifically in the aerospace, automotive, and composites sectors.
Tom Hodgson, Head of Research at AMRC’s Factory 2050 added: “The AMRC is a globally important venue for advanced manufacturing research. Partnering with INSPHERE who are developing cutting-edge technology is very exciting, and we look forward to continuing to use IONA datasets to show the potential for machine learning and AI in accurate robotic machining, as well as supporting the development of the technology.”