Freeman, F.S.H.B. orcid.org/0000-0002-7402-1790, Chechik, L. and Todd, I. (2020) Beat the machine (learning) : metal additive manufacturing and closed loop control. Physics Education, 55 (5). 055012. ISSN 0031-9120
Abstract
3D printing (additive manufacturing) is an emerging technology with the ability to make complex, free-form shapes from materials including plastics, metals and ceramics. While additive manufacturing has many advantages over more traditional processes, it can be difficult to control, which can then lead to defects in the finished part. Closed-loop control is a key part of most modern manufacturing and household processes, improving efficiency and reducing variation. Machine learning is an extension of this, where the controller learns how changes in the input variables affect the output. Here we provide an overview of the different types of metal additive manufacturing processes, and their relative strengths and weaknesses. We also describe how closed-loop control and thermal cameras are being used to improve these processes. Finally, we provide a link to a free-to-download app which allows students to control their own simulation of an additive manufacturing build, and see first-hand the need for control algorithms. Pseudo-code is provided in an appendix to help students who wish to take this further by building their own control algorithms.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. https://creativecommons.org/licenses/by/4.0/ Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
Keywords: | additive manufacturing; simulation; closed-loop control; educational game |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Jul 2020 13:39 |
Last Modified: | 13 Jul 2020 13:39 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1361-6552/ab9957 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163209 |