Application of Machine Learning to Monitor Metal Powder-Bed Fusion Additive Manufacturing Processes
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Published:2023
Abstract
The use of additive manufacturing (AM) is increasing for high-value, critical applications across a range of disparate industries. This article presents a discussion of high-valued engineering components predominantly used in the aerospace and medical industries. Applications involving metal AM, including methods to identify pores and voids in AM materials, are the focus. The article reviews flaw formation in laser-based powder-bed fusion, summarizes sensors used for in situ process monitoring, and outlines advances made with in situ process-monitoring data to detect AM process flaws. It reviews investigations of ML-based strategies, identifies challenges and research opportunities, and presents strategies for assessing anomaly detection performance.
Edward Reutzel, Jan Petrich, David Jeffrey Corbin, Zackary Snow, Application of Machine Learning to Monitor Metal Powder-Bed Fusion Additive Manufacturing Processes, Additive Manufacturing Design and Applications, Vol 24A, ASM Handbook, Edited By Mohsen Seifi, David L. Bourell, William Frazier, Howard Kuhn, ASM International, 2023, p 360–373, https://doi.org/10.31399/asm.hb.v24A.a0006992
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