Machine learning based prediction of melt pool morphology in a laser-based powder bed fusion additive manufacturing process

References

• Akbari, Parand, Francis Ogoke, Ning-Yu Kao, Kazem Meidani, Chun-Yu Yeh, William Lee, and Amir Barati Farimani. 2022. “MeltpoolNet: Melt Pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning.” Additive Manufacturing 55: Artlcle ID 102817. doi:10.1016/j.addma.2022.102817.
   PubMed Web of Science ®Google Scholar
• Aoyagi, Kenta, Hao Wang, Hideki Sudo, and Akihiko Chiba. 2019. “Simple Method to Construct Process Maps for Additive Manufacturing Using a Support Vector Machine.” Additive Manufacturing 27: 353–362. doi:10.1016/j.addma.2019.03.013.
   Web of Science ®Google Scholar
• Bertoli, Umberto Scipioni, Alexander J. Wolfer, Manyalibo J. Matthews, Jean-Pierre R. Delplanque, and Julie M. Schoenung. 2017. “On the Limitations of Volumetric Energy Density as a Design Parameter for Selective Laser Melting.” Materials & Design 113: 331–340. doi:10.1016/j.matdes.2016.10.037.
   Web of Science ®Google Scholar
• Cao, Yang, Xin Lin, Nan Kang, Liang Ma, Lei Wei, Min Zheng, Jun Yu, Dongjian Peng, and Weidong Huang. 2021. “A Novel High-Efficient Finite Element Analysis Method of Powder Bed Fusion Additive Manufacturing.” Additive Manufacturing 46: Artlcle ID 102187. doi:10.1016/j.addma.2021.102187.
   Web of Science ®Google Scholar
• Claesen, Marc, and Bart De Moor. 2015. “Hyperparameter Search in Machine Learning.” arXiv preprint arXiv:1502.02127.
   Google Scholar
• Criales, Luis E., Yiğit M. Arısoy, Brandon Lane, Shawn Moylan, Alkan Donmez, and Tuğrul Özel. 2017. “Laser Powder Bed Fusion of Nickel Alloy 625: Experimental Investigations of Effects of Process Parameters on Melt Pool Size and Shape with Spatter Analysis.” International Journal of Machine Tools and Manufacture 121: 22–36. doi:10.1016/j.ijmachtools.2017.03.004.
   Web of Science ®Google Scholar
• Dilip, J. J. S., Shanshan Zhang, Chong Teng, Kai Zeng, Chris Robinson, Deepankar Pal, and Brent Stucker. 2017. “Influence of Processing Parameters on the Evolution of Melt Pool, Porosity, and Microstructures in Ti-6Al-4V Alloy Parts Fabricated by Selective Laser Melting.” Progress in Additive Manufacturing 2 (3): 157–167. doi:10.1007/s40964-017-0030-2.
   Google Scholar
• Gander, Walter, Gene H. Golub, and Rolf Strebel. 1994. “Least-squares Fitting of Circles and Ellipses.” BIT Numerical Mathematics 34 (4): 558–578. doi:10.1007/BF01934268.
   Web of Science ®Google Scholar
• Ghiasian, Seyedeh Elaheh, Prakhar Jaiswal, Rahul Rai, and Kemper Lewis. 2018. “From Conventional to Additive Manufacturing: Determining Component Fabrication Feasibility.” In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 51753, V02AT03A043. American Society of Mechanical Engineers.
   Google Scholar
• Goodfellow, Ian J., Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Networks”.
   Google Scholar
• Gordon, Jerard V., Sneha P. Narra, Ross W. Cunningham, He Liu, Hangman Chen, Robert M. Suter, Jack L. Beuth, and Anthony D. Rollett. 2020. “Defect Structure Process Maps for Laser Powder Bed Fusion Additive Manufacturing.” Additive Manufacturing 36: Artlcle ID 101552. doi:10.1016/j.addma.2020.101552.
   Web of Science ®Google Scholar
• Gupta, Sulabh, and Rahul Rai. 2014. “A Graph Grammar Based Approach to 3D Print and Assemble Furniture.” In 2014 International Solid Freeform Fabrication Symposium, University of Texas at Austin.
   Google Scholar
• Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Jaiswal, Prakhar, and Rahul Rai. 2019. “A Geometric Reasoning Approach for Additive Manufacturing Print Quality Assessment and Automated Model Correction.” Computer-Aided Design 109: 1–11. doi:10.1016/j.cad.2018.12.001.
   Web of Science ®Google Scholar
• Kamath, Chandrika, Bassem El-Dasher, Gilbert F. Gallegos, Wayne E. King, and Aaron Sisto. 2014. “Density of Additively-manufactured, 316L SS Parts Using Laser Powder-bed Fusion At Powers Up to 400 W.” The International Journal of Advanced Manufacturing Technology 74 (1-4): 65–78. doi:10.1007/s00170-014-5954-9.
   Web of Science ®Google Scholar
• Kempen, K., L. Thijs, E. Yasa, M. Badrossamay, W. Verheecke, and J. P. Kruth. 2011. “Process Optimization and Microstructural Analysis for Selective Laser Melting of AlSi10Mg.” In Solid Freeform Fabrication Symposium, Austin TX, Vol. 22, 484–495.
   Google Scholar
• Khadilkar, Aditya, Jun Wang, and Rahul Rai. 2019. “Deep Learning–Based Stress Prediction for Bottom-Up SLA 3D Printing Process.” The International Journal of Advanced Manufacturing Technology 102 (5): 2555–2569. doi:10.1007/s00170-019-03363-4.
   Web of Science ®Google Scholar
• Khairallah, Saad A., Andrew T. Anderson, Alexander Rubenchik, and Wayne E. King. 2016. “Laser Powder-Bed Fusion Additive Manufacturing: Physics of Complex Melt Flow and Formation Mechanisms of Pores, Spatter, and Denudation Zones.” Acta Materialia 108: 36–45. doi:10.1016/j.actamat.2016.02.014.
   Web of Science ®Google Scholar
• Khorram Niaki, Mojtaba, and Fabio Nonino. 2017. “Additive Manufacturing Management: A Review and Future Research Agenda.” International Journal of Production Research 55 (5): 1419–1439. doi:10.1080/00207543.2016.1229064.
   Web of Science ®Google Scholar
• Kruth, J.-P., G. Levy, F. Klocke, and T. H. C. Childs. 2007. “Consolidation Phenomena in Laser and Powder-Bed Based Layered Manufacturing.” CIRP Annals 56 (2): 730–759. doi:10.1016/j.cirp.2007.10.004.
   Web of Science ®Google Scholar
• Kunovjanek, Maximilian, and Gerald Reiner. 2020. “How Will the Diffusion of Additive Manufacturing Impact the Raw Material Supply Chain Process?.” International Journal of Production Research 58 (5): 1540–1554. doi:10.1080/00207543.2019.1661537.
   Web of Science ®Google Scholar
• Lane, Brandon, S. Mekhontsev, S. Grantham, Mihaela Vlasea, Justin Whiting, Ho Yeung, Jason Fox, et al. 2016. “Design, Developments, and Results from the NIST Additive Manufacturing Metrology Testbed (AMMT).” In Solid Freeform Fabrication Symposium, Austin, TX, 1145–1160.
   Google Scholar
• Lane, Brandon, and Ho Yeung. 2020. “Process Monitoring Dataset From the Additive Manufacturing Metrology Testbed (AMMT): Overhang Part X4.” Journal of Research of the National Institute of Standards and Technology 125: 1–18. doi:10.6028/jres.125.027.
   Web of Science ®Google Scholar
• Liao, Kang, Chunyu Lin, Yao Zhao, and Moncef Gabbouj. 2019. “DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time.” IEEE Transactions on Circuits and Systems for Video Technology 30 (3): 725–733. doi:10.1109/TCSVT.76.
   Web of Science ®Google Scholar
• Liu, Ying, Rahul Rai, Anurag Purwar, Bin He, and Mahesh Mani. 2020. “Machine Learning Applications in Manufacturing.” Journal of Computing and Information Science in Engineering 20 (2): Artlcle ID 020301. doi:10.1115/1.4046427.
   PubMed Web of Science ®Google Scholar
• Lu, Yan, Zhuo Yang, Douglas Eddy, and Sundar Krishnamurty. 2018. “Self-Improving Additive Manufacturing Knowledge Management.” In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 51739, V01BT02A016. American Society of Mechanical Engineers.
   Google Scholar
• Mani, Mahesh, Brandon M. Lane, M. Alkan Donmez, Shaw C. Feng, and Shawn P. Moylan. 2017. “A Review on Measurement Science Needs for Real-Time Control of Additive Manufacturing Metal Powder Bed Fusion Processes.” International Journal of Production Research 55 (5): 1400–1418. doi:10.1080/00207543.2016.1223378.
   Web of Science ®Google Scholar
• Mertens, Raya, Sasan Dadbakhsh, J. Van Humbeeck, and J.-P. Kruth. 2018. “Application of Base Plate Preheating During Selective Laser Melting.” Procedia CIRP 74: 5–11. doi:10.1016/j.procir.2018.08.002.
   Google Scholar
• Mirza, Mehdi, and Simon Osindero. 2014. “Conditional Generative Adversarial Nets.” arXiv preprint arXiv:1411.1784.
   Google Scholar
• Mower, Todd M., and Michael J. Long. 2016. “Mechanical Behavior of Additive Manufactured, Powder-Bed Laser-Fused Materials.” Materials Science and Engineering: A 651: 198–213. doi:10.1016/j.msea.2015.10.068.
   Web of Science ®Google Scholar
• Ocylok, Sörn, Eugen Alexeev, Stefan Mann, Andreas Weisheit, Konrad Wissenbach, and Ingomar Kelbassa. 2014. “Correlations of Melt Pool Geometry and Process Parameters During Laser Metal Deposition by Coaxial Process Monitoring.” Physics Procedia 56: 228–238. doi:10.1016/j.phpro.2014.08.167.
   Google Scholar
• Pal, Deepankar, Nachiket Patil, Kai Zeng, and Brent Stucker. 2014. “An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling.” Journal of Manufacturing Science and Engineering 136 (6). doi:10.1115/1.4028580.
   Web of Science ®Google Scholar
• Petridis, Stavros, Zuwei Li, and Maja Pantic. 2017. “End-to-End Visual Speech Recognition With LSTMs.” In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2592–2596. IEEE.
   Google Scholar
• Rai, Rahul, and Prakhar Jaiswal. 2021. “Method for Automated 3D Print Quality Assessment and Redesign.” Oct. 21. US Patent App. 17/267,430.
   Google Scholar
• Ramos-Grez, Jorge Andrés, and Mihir Sen. 2019. “Analytical, Quasi-stationary Wilson-Rosenthal Solution for Moving Heat Sources.” International Journal of Thermal Sciences 140: 455–465. doi:10.1016/j.ijthermalsci.2019.03.017.
   Web of Science ®Google Scholar
• Rubenchik, Alexander M., Wayne E. King, and Sheldon S. Wu. 2018. “Scaling Laws for the Additive Manufacturing.” Journal of Materials Processing Technology 257: 234–243. doi:10.1016/j.jmatprotec.2018.02.034.
   Web of Science ®Google Scholar
• Salomon, David. 2004. Data Compression: The Complete Reference. Springer Science & Business Media.
   Google Scholar
• Scime, Luke, and Jack Beuth. 2018a. “Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process Using a Trained Computer Vision Algorithm.” Additive Manufacturing 19: 114–126. doi:10.1016/j.addma.2017.11.009.
   Web of Science ®Google Scholar
• Scime, Luke, and Jack Beuth. 2018b. “A Multi-Scale Convolutional Neural Network for Autonomous Anomaly Detection and Classification in a Laser Powder Bed Fusion Additive Manufacturing Process.” Additive Manufacturing 24: 273–286. doi:10.1016/j.addma.2018.09.034.
   Web of Science ®Google Scholar
• Sharma, Ajit, Zhibo Zhang, and Rahul Rai. 2021. “The Interpretive Model of Manufacturing: A Theoretical Framework and Research Agenda for Machine Learning in Manufacturing.” International Journal of Production Research 59 (16): 4960–4994. doi:10.1080/00207543.2021.1930234.
   Web of Science ®Google Scholar
• Shevchik, Sergey A., Christoph Kenel, Christian Leinenbach, and Kilian Wasmer. 2018. “Acoustic Emission for in Situ Quality Monitoring in Additive Manufacturing Using Spectral Convolutional Neural Networks.” Additive Manufacturing 21: 598–604. doi:10.1016/j.addma.2017.11.012.
   Web of Science ®Google Scholar
• Singh, Shubhendu Kumar, Ruoyu Yang, Amir Behjat, Rahul Rai, Souma Chowdhury, and Ion Matei. 2019. “PI-LSTM: Physics-Infused Long Short-Term Memory Network.” In 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), 34–41. IEEE.
   Google Scholar
• Spierings, Adriaan B., Thomas L. Starr, and Konrad Wegener. 2013. “Fatigue Performance of Additive Manufactured Metallic Parts.” Rapid Prototyping Journal. 19 (2): 88–94. doi:10.1108/13552541311302932.
   Web of Science ®Google Scholar
• Stanisavljevic, Darko, David Cemernek, Heimo Gursch, Günter Urak, and Gernot Lechner. 2020. “Detection of Interferences in an Additive Manufacturing Process: An Experimental Study Integrating Methods of Feature Selection and Machine Learning.” International Journal of Production Research 58 (9): 2862–2884. doi:10.1080/00207543.2019.1694719.
   Web of Science ®Google Scholar
• Thijs, Lore, Frederik Verhaeghe, Tom Craeghs, Jan Van Humbeeck, and Jean-Pierre Kruth. 2010. “A Study of the Microstructural Evolution During Selective Laser Melting of Ti–6Al–4V.” Acta Materialia 58 (9): 3303–3312. doi:10.1016/j.actamat.2010.02.004.
   Web of Science ®Google Scholar
• Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik. 2003. “Multiscale Structural Similarity for Image Quality Assessment.” In The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Vol. 2, 1398–1402. IEEE.
   Google Scholar
• Weisstein, Eric W. 2005. “Moore Neighborhood.” Wolfram MathWorld. https://mathworld.wolfram.com/MooreNeighborhood.html.
   Google Scholar
• Wolterink, Jelmer M., Tim Leiner, Max A. Viergever, and Ivana Išgum. 2017. “Generative Adversarial Networks for Noise Reduction in Low-Dose CT.” IEEE Transactions on Medical Imaging 36 (12): 2536–2545. doi:10.1109/TMI.2017.2708987.
   PubMed Web of Science ®Google Scholar
• Yadroitsau, Ihar. 2008. “Direct Manufacturing of 3D Objects by Selective Laser Melting of Metal Powders.” PhD diss., Saint-Etienne.
   Google Scholar
• Yang, Ruo-Yu, and Rahul Rai. 2019. “Machine Auscultation: Enabling Machine Diagnostics Using Convolutional Neural Networks and Large-Scale Machine Audio Data.” Advances in Manufacturing 7 (2): 174–187. doi:10.1007/s40436-019-00254-5.
   Web of Science ®Google Scholar
• Yang, Ruoyu, Shubhendu Kumar Singh, Mostafa Tavakkoli, Nikta Amiri, Yongchao Yang, M. Amin Karami, and Rahul Rai. 2020. “CNN-LSTM Deep Learning Architecture for Computer Vision-Based Modal Frequency Detection.” Mechanical Systems and Signal Processing 144: Artlcle ID 106885. doi:10.1016/j.ymssp.2020.106885.
   Web of Science ®Google Scholar
• Yang, Zhuo, Yan Lu, Ho Yeung, and Sundar Krishnamurty. 2019. “Investigation of Deep Learning for Real-Time Melt Pool Classification in Additive Manufacturing.” In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 640–647. IEEE.
   Google Scholar
• Yang, Zhuo, Yan Lu, Ho Yeung, and Sundar Kirshnamurty. 2020. “3D Build Melt Pool Predictive Modeling for Powder Bed Fusion Additive Manufacturing.” In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 83983, V009T09A046. American Society of Mechanical Engineers.
   Google Scholar
• Yang, Zhuo, Yan Lu, Ho Yeung, and Sundar Krishnamurty. 2020. “From Scan Strategy to Melt Pool Prediction: A Neighboring-Effect Modeling Method.” Journal of Computing and Information Science in Engineering 20 (5). doi:10.1115/1.4046335.
   PubMed Web of Science ®Google Scholar
• Yeung, H., B. M. Lane, M. A. Donmez, J. C. Fox, and J. Neira. 2018. “Implementation of Advanced Laser Control Strategies for Powder Bed Fusion Systems.” Procedia Manufacturing 26: 871–879. doi:10.1016/j.promfg.2018.07.112.46th SME North American Manufacturing Research Conference, NAMRC 46, Texas, USA.
   Google Scholar
• Yeung, Ho, Zhuo Yang, and L. Yan. 2020. “A Meltpool Prediction Based Scan Strategy for Powder Bed Fusion Additive Manufacturing.” Additive Manufacturing 35: Artlcle ID 101383. doi:10.1016/j.addma.2020.101383.
   Web of Science ®Google Scholar
• Yuan, Bodi, Brian Giera, Gabe Guss, Ibo Matthews, and Sara Mcmains. 2019. “Semi-Supervised Convolutional Neural Networks for In-Situ Video Monitoring of Selective Laser Melting.” In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 744–753. IEEE.
   Google Scholar
• Zhang, Baicheng, Lucas Dembinski, and Christian Coddet. 2013. “The Study of the Laser Parameters and Environment Variables Effect on Mechanical Properties of High Compact Parts Elaborated by Selective Laser Melting 316L Powder.” Materials Science and Engineering: A 584: 21–31. doi:10.1016/j.msea.2013.06.055.
   Web of Science ®Google Scholar
• Zhang, Bin, Shunyu Liu, and Yung C. Shin. 2019. “In-Process Monitoring of Porosity During Laser Additive Manufacturing Process.” Additive Manufacturing 28: 497–505. doi:10.1016/j.addma.2019.05.030.
   Web of Science ®Google Scholar
• Zhang, Binbin, Prakhar Jaiswal, Rahul Rai, Paul Guerrier, and George Baggs. 2019. “Convolutional Neural Network-Based Inspection of Metal Additive Manufacturing Parts.” Rapid Prototyping Journal. 25 (3): 530–540. doi:10.1108/RPJ-04-2018-0096.
   Web of Science ®Google Scholar
• Zhang, Binbin, Prakhar Jaiswal, Rahul Rai, and Saigopal Nelaturi. 2018. “Additive Manufacturing of Functionally Graded Material Objects: A Review.” Journal of Computing and Information Science in Engineering 18 (4): 041002. doi:10.1115/1.4039683.
   Web of Science ®Google Scholar
• Zhang, Yaqi, Vadim Shapiro, and Paul Witherell. 2020. “A Neighborhood-Based Neural Network for Melt Pool Prediction and Control.” In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 83983, V009T09A026. American Society of Mechanical Engineers.
   Google Scholar
• Zhang, Yi, and Jing Zhang. 2019. “Modeling of Solidification Microstructure Evolution in Laser Powder Bed Fusion Fabricated 316L Stainless Steel Using Combined Computational Fluid Dynamics and Cellular Automata.” Additive Manufacturing 28: 750–765. doi:10.1016/j.addma.2019.06.024.
   Web of Science ®Google Scholar
• Zhang, Zhibo, Prakhar Jaiswal, and Rahul Rai. 2018. “Featurenet: Machining Feature Recognition Based on 3D Convolution Neural Network.” Computer-Aided Design 101: 12–22. doi:10.1016/j.cad.2018.03.006.
   Web of Science ®Google Scholar