AMFG receives Innovate UK funding to develop AI for Additive Manufacturing
January 22, 2019
AMFG, London, UK, has received funding from Innovate UK, the UK’s Research and Innovation agency, to further the development of its AI and machine learning technology for Additive Manufacturing. The Innovate UK funding builds on AMFG’s comprehensive workflow management/MES software, which includes request submission, production management and post-processing management.
With a focus on the production of end-use parts, the funding will be put towards two key areas of AM production:
- Improving quality assurance: AMFG will be developing methods to improve the analysis of parts during the quality assurance stage, including the use of wide-ranging data sets
- Optimising production scheduling: AMFG will further develop its machine learning technology to optimise production scheduling. This will, for example, enable users to accurately predict failures before they occur
As the industry matures, the production of end parts is fast becoming a key application of AM. However, there remain barriers to scaling AM for production, including quality concerns and lack of efficiency. These are issues AMFG aims to address through its workflow software. Keyvan Karimi, AMFG’s CEO, explained, “Currently, Additive Manufacturing still needs to prove that end-part production is viable at scale. Software will be a vital piece of this puzzle.”
“We’re thrilled to put Innovate UK’s funding towards further enhancing our machine learning technology and helping manufacturers manage — and scale — their AM operations effectively,” he concluded. As part of the project, AMFG will be working with key partners, including the University of Nottingham’s Centre for Additive Manufacturing (CfAM).
Dr Martin Baumers of the CfAM stated, “The Centre [for AM] is one of the leading research groups in the field of AM technology across the globe and is conducting research to advance the productivity, scalability and industrial adoptability of the technology. AM technology is currently at a stage where it needs to demonstrate its stability and controllability in real manufacturing environments. Innovative software approaches will be central in achieving this.”