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Machine Learning and AM
Machine Learning and Additive Manufacturing: What does the future hold? As industry marches toward automation, networked communication and robotics, Additive Manufacturing has a unique advantage. No other production technology has been designed, from its inception, to enable connectivity and communication; AM machines around the world are already producing more build data than any other manufacturing technology. If used properly, this data will provide the foundation for the development of Machine Learning tools that can improve and industrialise the AM process at nearly every point in the workflow. In this article, Chelsea Cummings and John Barnes, from The Barnes Global Advisors, discuss the present and future of Machine Learning in AM.
Manufacturing is on an accelerating march towards automation, networked communication, and robotics. The internet collapses great geographical distances so that immense amounts of data created through manufacturing processes can be shared easily and quickly. This assemblage of different disciplines then feeds into what is called Industry 4.0 – the Fourth Industrial Revolution, which picks up from the Digital Revolution (Industry 3.0) that gave us the unimaginable computing power we rely on today. With Industry 4.0, anything that generates signals or data can be connected to the internet, making that item more efficient or more useful to more people. Generating data is only useful to a society if we can balance the quantity of data created with the ability to turn it into usable, actionable information. Additive Manufacturing, having been born with the internet, has always enjoyed the connectivity and communication potential that its manufacturing brethren did not
Vol. 6 No. 4 © 2020 Inovar Communications Ltd
have. AM, being a digital process, can generate terabytes of data during a build. The value of AM lies in the design of performanceoptimised components that can only be built with AM technology. By enabling components which would usually be made of multiple parts to be produced in one piece, AM also contributes to the minimisation of
assembly and with it, touch labour. With Artificial Intelligence (AI) and Machine Learning (ML), this data or digital input can then be used to optimise the physical output. We hope this translates to increased productivity, higher yield and the ability to close the loop on quality control rather than relying on costly ‘after the fact’ inspection processes.
Fig. 1 The evolution of AI and ML over the decades
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