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Machine Learning in AM
The inestimable value of AI: How Machine Learning can help AM project teams achieve their goals and beyond They each have similar two-letter acronyms, and, for both technologies, it can be hard to separate hype from reality. But Artificial Intelligence (AI) and Additive Manufacturing also overlap in interesting and beneficial ways. In this article, Stephen Warde of Intellegens considers how AI methods such as Machine Learning (ML) could help AM to deliver against expectations – and at the very least, to meet more realistic and commercially essential objectives, such as consistently delivering lighter, stronger components and supporting on-demand manufacturing.
It’s possible to think of many applications in which Machine Learning can be applied to Additive Manufacturing, from generative design of part geometries to defect detection in manufactured components. In this article, we’ll focus on how the metal AM development process moves from the design or selection of powders towards the successful, repeatable manufacture of parts. Anything that makes this workflow faster and more reliable will be of enormous value, particularly in sectors such as aerospace, where new materials and parts must go through numerous certification cycles, often taking years and costing millions of dollars. The project teams engaged in this area have what appears to be an ideal application for Machine Learning: they want to optimise a series of processes. Material composition, operating conditions, and process settings all interact with one another and impact the outcomes of these processes in complex and subtle ways. Even
Vol. 7 No. 4 © 2021 Inovar Communications Ltd
though we can describe and understand individual effects through the laws of physics, establishing all of the factors at play and grasping how they interact in multi-dimensional space is beyond straightforward human comprehension. But Machine Learning doesn’t care about under-
standing those physical laws; it should be able to take data on the inputs and outputs of any process and ‘train’ models to capture how the inputs give rise to the outputs. Such a model could then, for example, predict which inputs will deliver optimal outputs. Can’t we simply apply this to AM?
Fig. 1 Machine Learning has proved to be invaluable in the development of new AM alloys for aerospace applications
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