Machine-Learning-Based Monitoring of Laser Powder Bed Fusion

Yuan, Bodi; Guss, Gabriel M.; Wilson, Aaron C.; Hau-Riege, Stefan P.; DePond, Phillip J.; McMains, Sara; Matthews, Manyalibo J.; Giera, Brian

doi:10.1002/admt.201800136

Machine-Learning-Based Monitoring of Laser Powder Bed Fusion

Journal Article · Wed Sep 05 00:00:00 EDT 2018 · Advanced Materials Technologies

DOI:https://doi.org/10.1002/admt.201800136· OSTI ID:1481059

Yuan, Bodi ^[1]; Guss, Gabriel M. ^[1]; Wilson, Aaron C. ^[1]; Hau-Riege, Stefan P. ^[1]; DePond, Phillip J. ^[1]; McMains, Sara ^[1]; Matthews, Manyalibo J. ^[1]; ^[1]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

A two-step machine learning approach to monitoring Laser Powder Bed Fusion (LPBF) additive manufacturing is demonstrated that enables on-the-fly assessments of laser track welds. First, in situ video melt pool data acquired during LPBF is labeled according to the (1) average and (2) standard deviation of individual track width and also (3) whether or not the track is continuous, measured post-build through an ex situ height map analysis algorithm. This procedure generates three ground truth labeled datasets for supervised machine learning. Using a portion of the labeled 10-millisecond video clips, a single Convolutional Neural Network architecture is trained to generate three distinct networks. With the remaining in situ LPBF data, the trained neural networks are tested and evaluated and found to predict track width, standard deviation, and continuity without the need for ex situ measurements. This two-step approach should benefit any LPBF system – or any additive manufacturing technology – where height-map-derived properties can serve as useful labels for in situ sensor data.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

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

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1481059

Alternate ID(s):: OSTI ID: 1468826

Report Number(s):: LLNL-JRNL--748383; 933429

Journal Information:: Advanced Materials Technologies, Journal Name: Advanced Materials Technologies Journal Issue: 12 Vol. 3; ISSN 2365-709X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

References (40)

Why Beam Analysis is Crucial for Additive Manufacturing: Reproducible laser beam parameters are the key to success in selective laser manufacturing processes Dini, Christian Laser Technik Journal, Vol. 15, Issue 1 https://doi.org/10.1002/latj.201800004	journal	January 2018
In Situ and Real-Time Monitoring of Powder-Bed AM by Combining Acoustic Emission and Artificial Intelligence Wasmer, K.; Kenel, C.; Leinenbach, C. Industrializing Additive Manufacturing - Proceedings of Additive Manufacturing in Products and Applications - AMPA2017 https://doi.org/10.1007/978-3-319-66866-6_20	book	September 2017
Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W Kamath, Chandrika; El-dasher, Bassem; Gallegos, Gilbert F. The International Journal of Advanced Manufacturing Technology, Vol. 74, Issue 1-4 https://doi.org/10.1007/s00170-014-5954-9	journal	May 2014
Data mining and statistical inference in selective laser melting Kamath, Chandrika The International Journal of Advanced Manufacturing Technology, Vol. 86, Issue 5-8 https://doi.org/10.1007/s00170-015-8289-2	journal	January 2016
Quality assessment in laser welding: a critical review Stavridis, John; Papacharalampopoulos, Alexios; Stavropoulos, Panagiotis The International Journal of Advanced Manufacturing Technology, Vol. 94, Issue 5-8 https://doi.org/10.1007/s00170-017-0461-4	journal	May 2017
Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel Tapia, Gustavo; Khairallah, Saad; Matthews, Manyalibo The International Journal of Advanced Manufacturing Technology, Vol. 94, Issue 9-12 https://doi.org/10.1007/s00170-017-1045-z	journal	September 2017
Metal Additive Manufacturing: A Review Frazier, William E. Journal of Materials Engineering and Performance, Vol. 23, Issue 6 https://doi.org/10.1007/s11665-014-0958-z	journal	April 2014
Effect of Laser Power and Scan Speed on Melt Pool Characteristics of Commercially Pure Titanium (CP-Ti) Kusuma, Chandrakanth; Ahmed, Sazzad H.; Mian, Ahsan Journal of Materials Engineering and Performance, Vol. 26, Issue 7 https://doi.org/10.1007/s11665-017-2768-6	journal	June 2017
Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks DeCost, Brian L.; Jain, Harshvardhan; Rollett, Anthony D. JOM, Vol. 69, Issue 3 https://doi.org/10.1007/s11837-016-2226-1	journal	December 2016
Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones Khairallah, Saad A.; Anderson, Andrew T.; Rubenchik, Alexander Acta Materialia, Vol. 108 https://doi.org/10.1016/j.actamat.2016.02.014	journal	April 2016
Flaw detection in powder bed fusion using optical imaging Abdelrahman, Mostafa; Reutzel, Edward W.; Nassar, Abdalla R. Additive Manufacturing, Vol. 15 https://doi.org/10.1016/j.addma.2017.02.001	journal	May 2017
Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm Scime, Luke; Beuth, Jack Additive Manufacturing, Vol. 19 https://doi.org/10.1016/j.addma.2017.11.009	journal	January 2018
Parametric analysis of the selective laser melting process Yadroitsev, I.; Bertrand, Ph.; Smurov, I. Applied Surface Science, Vol. 253, Issue 19 https://doi.org/10.1016/j.apsusc.2007.02.088	journal	July 2007
Single track formation in selective laser melting of metal powders Yadroitsev, I.; Gusarov, A.; Yadroitsava, I. Journal of Materials Processing Technology, Vol. 210, Issue 12 https://doi.org/10.1016/j.jmatprotec.2010.05.010	journal	September 2010
Defect generation and propagation mechanism during additive manufacturing by selective beam melting Bauereiß, A.; Scharowsky, T.; Körner, C. Journal of Materials Processing Technology, Vol. 214, Issue 11 https://doi.org/10.1016/j.jmatprotec.2014.05.002	journal	November 2014
Mesoscopic simulation model of selective laser melting of stainless steel powder Khairallah, Saad A.; Anderson, Andy Journal of Materials Processing Technology, Vol. 214, Issue 11 https://doi.org/10.1016/j.jmatprotec.2014.06.001	journal	November 2014
Experimental analysis of spatter generation and melt-pool behavior during the powder bed laser beam melting process Gunenthiram, V.; Peyre, P.; Schneider, M. Journal of Materials Processing Technology, Vol. 251 https://doi.org/10.1016/j.jmatprotec.2017.08.012	journal	January 2018
Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing Everton, Sarah K.; Hirsch, Matthias; Stravroulakis, Petros Materials & Design, Vol. 95 https://doi.org/10.1016/j.matdes.2016.01.099	journal	April 2016
Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing Li, Jingran; Jin, Ran; Yu, Hang Z. Materials & Design, Vol. 139 https://doi.org/10.1016/j.matdes.2017.11.028	journal	February 2018
Quality control of laser- and powder bed-based Additive Manufacturing (AM) technologies Berumen, Sebastian; Bechmann, Florian; Lindner, Stefan Physics Procedia, Vol. 5 https://doi.org/10.1016/j.phpro.2010.08.089	journal	January 2010
Optical In-Process Temperature Monitoring of Selective Laser Melting Chivel, Y. Physics Procedia, Vol. 41 https://doi.org/10.1016/j.phpro.2013.03.165	journal	January 2013
Monitoring and Adaptive Control of Laser Processes Purtonen, Tuomas; Kalliosaari, Anne; Salminen, Antti Physics Procedia, Vol. 56 https://doi.org/10.1016/j.phpro.2014.08.038	journal	January 2014
Additive manufacturing of metallic components – Process, structure and properties DebRoy, T.; Wei, H. L.; Zuback, J. S. Progress in Materials Science, Vol. 92 https://doi.org/10.1016/j.pmatsci.2017.10.001	journal	March 2018
In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume Grasso, M.; Demir, A. G.; Previtali, B. Robotics and Computer-Integrated Manufacturing, Vol. 49 https://doi.org/10.1016/j.rcim.2017.07.001	journal	February 2018
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Metal vapor micro-jet controls material redistribution in laser powder bed fusion additive manufacturing Ly, Sonny; Rubenchik, Alexander M.; Khairallah, Saad A. Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-04237-z	journal	June 2017
Optical diagnostics of selective laser melting and monitoring of single-track formation Gusarov, Andrey; Kotoban, Dmitriy; Zhirnov, Ivan MATEC Web of Conferences, Vol. 129 https://doi.org/10.1051/matecconf/201712901037	journal	January 2017
Review of selective laser melting: Materials and applications Yap, C. Y.; Chua, C. K.; Dong, Z. L. Applied Physics Reviews, Vol. 2, Issue 4 https://doi.org/10.1063/1.4935926	journal	December 2015
A fuzzy pattern recognition based system for monitoring laser weld quality Park, Hyunsung; Rhee, Sehun; Kim, Dongcheol Measurement Science and Technology, Vol. 12, Issue 8 https://doi.org/10.1088/0957-0233/12/8/345	journal	July 2001
A review on quality control in additive manufacturing Kim, Hoejin; Lin, Yirong; Tseng, Tzu-Liang Bill Rapid Prototyping Journal, Vol. 24, Issue 3 https://doi.org/10.1108/RPJ-03-2017-0048	journal	April 2018
The Unreasonable Effectiveness of Data Halevy, Alon; Norvig, Peter; Pereira, Fernando IEEE Intelligent Systems, Vol. 24, Issue 2 https://doi.org/10.1109/MIS.2009.36	journal	March 2009
Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing Yao, Bing; Imani, Farhad; Sakpal, Aniket S. Journal of Manufacturing Science and Engineering, Vol. 140, Issue 3 https://doi.org/10.1115/1.4037891	journal	January 2018
ImageNet classification with deep convolutional neural networks Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E. Communications of the ACM, Vol. 60, Issue 6 https://doi.org/10.1145/3065386	journal	May 2017
Direct measurements of temperature-dependent laser absorptivity of metal powders Rubenchik, A.; Wu, S.; Mitchell, S. Applied Optics, Vol. 54, Issue 24 https://doi.org/10.1364/AO.54.007230	journal	January 2015
Metal powder absorptivity: modeling and experiment Boley, C. D.; Mitchell, S. C.; Rubenchik, A. M. Applied Optics, Vol. 55, Issue 23 https://doi.org/10.1364/AO.55.006496	journal	January 2016
Evaluation of Powder Layer Density for the Selective Laser Melting (SLM) Process Choi, Joon-Phil; Shin, Gi-Hun; Lee, Hak-Sung MATERIALS TRANSACTIONS, Vol. 58, Issue 2 https://doi.org/10.2320/matertrans.M2016364	journal	January 2017
Elucidation of melt flows and spatter formation mechanisms during high power laser welding of pure titanium Nakamura, Hiroshi; Kawahito, Yousuke; Nishimoto, Koji Journal of Laser Applications, Vol. 27, Issue 3 https://doi.org/10.2351/1.4922383	journal	August 2015
Determination of True Temperature in Selective Laser Melting of Metal Powder Using Infrared Camera Doubenskaia, Maria Andreyevna; Zhirnov, Ivan Vladimirovich; Teleshevskiy, Vladimir Ilyich Materials Science Forum, Vol. 834 https://doi.org/10.4028/www.scientific.net/MSF.834.93	journal	November 2015
Deep Learning Ganatra, Sanket Zenodo https://doi.org/10.5281/zenodo.1189359	text	January 2018
Characterization of Metal Powders Used for Additive Manufacturing Slotwinski, J. A.; Garboczi, E. J.; Stutzman, P. E. Journal of Research of the National Institute of Standards and Technology, Vol. 119 https://doi.org/10.6028/jres.119.018	journal	January 2014

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Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults Gaikwad, Aniruddha; Yavari, Reza; Montazeri, Mohammad IISE Transactions, Vol. 52, Issue 11 https://doi.org/10.1080/24725854.2019.1701753	journal	January 2020