Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing

Gunasegaram, D. R.; Barnard, A. S.; Matthews, M. J.; Jared, B. H.; Andreaco, A. M.; Bartsch, K.; Murphy, A. B.

doi:10.1016/j.addma.2024.104013

Title: Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing

Journal Article · 31 January 2024 · Additive Manufacturing

DOI: https://doi.org/10.1016/j.addma.2024.104013 · OSTI ID:2305413

Gunasegaram, D. R.; Barnard, A. S.; Matthews, M. J.; Jared, B. H.; Andreaco, A. M.; Bartsch, K.; Murphy, A. B.

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Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 2305413

Journal Information:: Additive Manufacturing, Journal Name: Additive Manufacturing Journal Issue: C Vol. 81; ISSN 2214-8604

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

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Closed-loop control of microstructure and mechanical properties in additive manufacturing by directed energy deposition Farshidianfar, Mohammad H.; Khodabakhshi, Farzad; Khajepour, Amir Materials Science and Engineering: A, Vol. 803 https://doi.org/10.1016/j.msea.2020.140483	journal	January 2021
Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning Günther, Johannes; Pilarski, Patrick M.; Helfrich, Gerhard Mechatronics, Vol. 34 https://doi.org/10.1016/j.mechatronics.2015.09.004	journal	March 2016
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A review of the parameter-signature-quality correlations through in situ sensing in laser metal additive manufacturing Ye, Jiayu; Bab-hadiashar, Alireza; Alam, Nazmul The International Journal of Advanced Manufacturing Technology, Vol. 124, Issue 5-6 https://doi.org/10.1007/s00170-022-10618-0	journal	December 2022
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Acoustic emission for in situ process monitoring of selective laser melting additive manufacturing based on machine learning and improved variational modal decomposition Wang, Haijie; Li, Bo; Xuan, Fu-Zhen The International Journal of Advanced Manufacturing Technology, Vol. 122, Issue 5-6 https://doi.org/10.1007/s00170-022-10032-6	journal	September 2022
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Kriging with trend functions nonlinear in their parameters: Theory and application in enzyme kinetics Freier, Lars; Wiechert, Wolfgang; von Lieres, Eric Engineering in Life Sciences, Vol. 17, Issue 8 https://doi.org/10.1002/elsc.201700022	journal	August 2017
Review on additive manufacturing and non-destructive testing Segovia Ramírez, Isaac; García Márquez, Fausto Pedro; Papaelias, Mayorkinos Journal of Manufacturing Systems, Vol. 66 https://doi.org/10.1016/j.jmsy.2022.12.005	journal	February 2023
Predictions of Additive Manufacturing Process Parameters and Molten Pool Dimensions with a Physics-Informed Deep Learning Model Zhao, Mingzhi; Wei, Huiliang; Mao, Yiming Engineering, Vol. 23 https://doi.org/10.1016/j.eng.2022.09.015	journal	April 2023
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Metal-based additive manufacturing condition monitoring methods: From measurement to control Lin, Xin; Zhu, Kunpeng; Fuh, Jerry Ying Hsi ISA Transactions, Vol. 120 https://doi.org/10.1016/j.isatra.2021.03.001	journal	January 2022
Optimal Design for Metal Additive Manufacturing: An Integrated Computational Materials Engineering (ICME) Approach Motaman, S. Amir H.; Kies, Fabian; Köhnen, Patrick JOM, Vol. 72, Issue 3 https://doi.org/10.1007/s11837-020-04028-4	journal	January 2020
Reinforcement Learning for Laser Welding Speed Control Minimizing Bead Width Error Kaneko, Toshimitsu; Minamoto, Gaku; Hirose, Yusuke 2023 IEEE International Conference on Robotics and Automation (ICRA) https://doi.org/10.1109/ICRA48891.2023.10161334	conference	May 2023
(Re)Designing for Part Consolidation: Understanding the Challenges of Metal Additive Manufacturing Schmelzle, John; Kline, Eric V.; Dickman, Corey J. Journal of Mechanical Design, Vol. 137, Issue 11 https://doi.org/10.1115/1.4031156	journal	October 2015
Using Unsupervised Learning for Regulating Deposition Speed During Robotic Wire Arc Additive Manufacturing Kulkarni, Ashish; Bhatt, Prahar M.; Kanyuck, Alec Volume 2: 41st Computers and Information in Engineering Conference (CIE) https://doi.org/10.1115/DETC2021-71865	conference	August 2021
A Deep Learning Approach for the Identification of Small Process Shifts in Additive Manufacturing using 3D Point Clouds Ye, Zehao; Liu, Chenang; Tian, Wenmeng Procedia Manufacturing, Vol. 48 https://doi.org/10.1016/j.promfg.2020.05.112	journal	January 2020

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