2,579
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Predictions of in-situ melt pool geometric signatures via machine learning techniques for laser metal deposition

ORCID Icon, , , , ORCID Icon, ORCID Icon & show all
Pages 1345-1361 | Received 21 Aug 2021, Accepted 21 Feb 2022, Published online: 14 Mar 2022

References

  • Ansari, M., R. Shoja Razavi, and M. Barekat. 2016. “An Empirical-statistical Model for Coaxial Laser Cladding of NiCrAlY Powder on Inconel 738 Superalloy.” Optics & Laser Technology 86: 136–144. doi:10.1016/j.optlastec.2016.06.014.
  • Awad, M., and R. Khanna. 2015. “Support Vector Regression,” In Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, edited by Pepper, J., Weiss, S., Hauke, P., 67–80, Berkeley, CA: Apress. doi:10.1007/978-1-4302-5990-9_4.
  • Bax, B., R. Rajput, R. Kellet, and M. Reisacher. 2018. “Systematic Evaluation of Process Parameter Maps for Laser Cladding and Directed Energy Deposition.” Additive Manufacturing 21: 487–494. doi:10.1016/j.addma.2018.04.002.
  • Brian, G., Y. Kumar Bandari, B. Richardson, A. Roschli, B. Post, M. Borish, A. S. Thornton, W. C. Henry, M. D. Lamsey, and L. Love. 2019. “Melt pool monitoring for control and data analytics in large-scale metal additive manufacturing.“ 2019 International Solid Freeform Fabrication Symposium, August 12-14, 2019, Austin, Texas, USA.
  • Caiazzo, F., and A. Caggiano. 2018. “Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning.” Materials (Basel) 11 (3): 444. doi:10.3390/ma11030444.
  • Cao, L., S. Chen, M. Wei, Q. Guo, J. Liang, C. Liu, and M. Wang. 2019. “Effect of Laser Energy Density on Defects Behavior of Direct Laser Depositing 24CrNiMo Alloy Steel.” Optics & Laser Technology 111: 541–553. doi:10.1016/j.optlastec.2018.10.025.
  • Chan, S. L., L. Yanglong, and Y. Wang. 2018. “Data-driven Cost Estimation for Additive Manufacturing in Cybermanufacturing.” Journal of Manufacturing Systems 46: 115–126. doi:10.1016/j.jmsy.2017.12.001.
  • Colton, J. A., and K. M. Bower. 2002. “Some Misconceptions about R2.” accessed October 2021.
  • Daiki, I., A. Vargas-Uscategui, W. Xiaofeng, and P. C. King. 2019. “Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing.” Materials (Basel, Switzerland) 12 (17). doi:10.3390/ma12172827.
  • DebRoy, T., H. L. Wei, J. S. Zuback, T. Mukherjee, J. W. Elmer, J. O. Milewski, A. M. Beese, A. Wilson-Heid, A. De, and W. Zhang. 2018. “Additive Manufacturing of Metallic Components – Process, Structure and Properties.” Progress in Materials Science 92: 112–224. doi:10.1016/j.pmatsci.2017.10.001.
  • Ding, Y., J. Warton, and R. Kovacevic. 2016. “Development of Sensing and Control System for Robotized Laser-based Direct Metal Addition System.” Additive Manufacturing 10: 24–35. doi:10.1016/j.addma.2016.01.002.
  • Errico, V., S. L. Campanelli, A. Angelastro, M. Dassisti, M. Mazzarisi, and C. Bonserio. 2021. “Coaxial Monitoring of AISI 316L Thin Walls Fabricated by Direct Metal Laser Deposition.” Materials (Basel) 14 (3): 673. doi:10.3390/ma14030673.
  • Feenstra, D. R., A. Molotnikov, and N. Birbilis. 2021. “Utilisation of Artificial Neural Networks to Rationalise Processing Windows in Directed Energy Deposition Applications.” Materials & Design 198: 109342. doi:10.1016/j.matdes.2020.109342.
  • Géron, A. 2019. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Sebastopol, California, United States: O’Reilly Media, Inc.
  • Guijun, B., A. Gasser, K. Wissenbach, A. Drenker, and R. Poprawe. 2006. “Identification and Qualification of Temperature Signal for Monitoring and Control in Laser Cladding.” Optics and Lasers in Engineering 44 (12): 1348–1359. doi:10.1016/j.optlaseng.2006.01.009.
  • Gunasegaram, D. R., A. B. Murphy, A. Barnard, T. DebRoy, M. J. Matthews, L. Ladani, and D. Gu. 2021. “Towards Developing Multiscale-multiphysics Models and Their Surrogates for Digital Twins of Metal Additive Manufacturing.” Additive Manufacturing 46 (102089): 102089. doi:10.1016/j.addma.2021.102089.
  • Hofman, J. T., B. Pathiraj, J. van Dijk, D. F. de Lange, and J. Meijer. 2012. “A Camera Based Feedback Control Strategy for the Laser Cladding Process.” Journal of Materials Processing Technology 212 (11): 2455–2462. doi:10.1016/j.jmatprotec.2012.06.027.
  • Jiang, J., and F. Yun-Fei. 2020. “A Short Survey of Sustainable Material Extrusion Additive Manufacturing.” Australian Journal of Mechanical Engineering 1–10. doi:10.1080/14484846.2020.1825045.
  • Jingchao, J., S. T. Newman, and R. Y. Zhong. 2020. “A Review of Multiple Degrees of Freedom for Additive Manufacturing Machines.” International Journal of Computer Integrated Manufacturing 34 (2): 195–211. doi:10.1080/0951192x.2020.1858510.
  • Jingchao, J., Y. Xiong, Z. Zhang, and D. W. Rosen. 2020. “Machine Learning Integrated Design for Additive Manufacturing.” Journal of Intelligent Manufacturing. doi:10.1007/s10845-020-01715-6.
  • Li, J., H. Ren, C. Liu, and S. Shang. 2019. “The Effect of Specific Energy Density on Microstructure and Corrosion Resistance of CoCrMo Alloy Fabricated by Laser Metal Deposition.” Materials (Basel) 12 (8). doi:10.3390/ma12081321.
  • Matthew, D., M. A. Wonders, M. Flaska, and A. T. Lintereur. 2021. “K-Nearest Neighbors Regression for the Discrimination of Gamma Rays and Neutrons in Organic Scintillators.” Nuclear Instruments Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors Associated Equipment 987: 164826. doi:10.1016/j.nima.2020.164826.
  • Meng, L., B. McWilliams, W. Jarosinski, H.-Y. Park, Y.-G. Jung, J. Lee, and J. Zhang. 2020. “Machine Learning in Additive Manufacturing: A Review.” Jom 72 (6): 2363–2377. doi:10.1007/s11837-020-04155-y.
  • Mukherjee, T., and T. DebRoy. 2019. “A Digital Twin for Rapid Qualification of 3D Printed Metallic Components.” Applied Materials Today 14: 59–65. doi:10.1016/j.apmt.2018.11.003.
  • Mukherjee, T., J. S. Zuback, A. De, and T. DebRoy. 2016. “Printability of Alloys for Additive Manufacturing.” Scientific Reports 6 (1): 1–8. doi:10.1038/srep19717.
  • Myers, J. L., A. Well, and R. Frederick Lorch. 2010. Research Design and Statistical Analysis. New York, New York, United States: Routledge.
  • Ocylok, S., E. Alexeev, S. Mann, A. Weisheit, K. Wissenbach, and I. 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.
  • Paturi, U. M. R., and S. Cheruku. 2021. “Application and Performance of Machine Learning Techniques in Manufacturing Sector from the past Two Decades: A Review.” Materials Today: Proceedings 38:2392–2401. doi: 10.1016/j.matpr.2020.07.209.
  • Song, L., W. Huang, X. Han, and J. Mazumder. 2017. “Real-time Composition Monitoring Using Support Vector Regression of Laser-induced Plasma for Laser Additive Manufacturing.” IEEE Transactions on Industrial Electronics 64 (1): 633–642. doi:10.1109/tie.2016.2608318.
  • Spearman, C. 1961. “The Proof and Measurement of Association between Two Things.” In Studies in Individual Differences: The Search for Intelligence, edited by J. J. Jenkins and D. G. Paterson, 45–58. New York, New York, United States: Appleton-Century-Crofts.
  • Tang, L., and R. G. Landers. 2011. “Layer-to-layer Height Control for Laser Metal Deposition Process.” Journal of Manufacturing Science and Engineering 133 (2): 021009. doi:10.1115/1.4003691.
  • Tang, Z.-J., W.-W. Liu, L.-N. Zhu, Z.-C. Liu, Z.-R. Yan, D. Lin, Z. Zhang, and H.-C. Zhang. 2021. “Investigation on Coaxial Visual Characteristics of Molten Pool in Laser-based Directed Energy Deposition of AISI 316L Steel.” Journal of Materials Processing Technology 290: 116996. doi:10.1016/j.jmatprotec.2020.116996.
  • Tang, C., J. L. Tan, and C. H. Wong. 2018. “A Numerical Investigation on the Physical Mechanisms of Single Track Defects in Selective Laser Melting.” International Journal of Heat and Mass Transfer 126: 957–968. doi:10.1016/j.ijheatmasstransfer.2018.06.073.
  • Wang, C., X. P. Tan, S. B. Tor, and C. S. Lim. 2020. “Machine Learning in Additive Manufacturing: State-of-the-art and Perspectives.” Additive Manufacturing 36: 101538. doi:10.1016/j.addma.2020.101538.
  • Wolff, S. J., H. Wu, N. Parab, C. Zhao, K. F. Ehmann, T. Sun, and J. Cao. 2019. “In-situ High-speed X-ray Imaging of Piezo-driven Directed Energy Deposition Additive Manufacturing.” Scientific Reports 9 (1). doi:10.1038/s41598-018-36678-5.
  • Xie, X., J. Bennett, S. Saha, Y. Lu, J. Cao, W. Kam Liu, and Z. Gan. 2021. “Mechanistic Data-driven Prediction of As-built Mechanical Properties in Metal Additive Manufacturing.” Npj Computational Materials 7 (1). doi:10.1038/s41524-021-00555-z.
  • Xinbo, Q., G. Chen, L. Yong, X. Cheng, and L. Changpeng. 2019. “Applying Neural-network-based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives.” Engineering 5 (4): 721–729. doi:10.1016/j.eng.2019.04.012.
  • Yun-Fei, F., B. Rolfe, L. N. S. Chiu, Y. Wang, X. Huang, and K. Ghabraie. 2019. “Design and Experimental Validation of Self-supporting Topologies for Additive Manufacturing.” Virtual and Physical Prototyping 14 (4): 382–394. doi:10.1080/17452759.2019.1637023.
  • Yun-Fei, F., B. Rolfe, L. N. S. Chiu, Y. Wang, X. Huang, and K. Ghabraie. 2020. “Parametric Studies and Manufacturability Experiments on Smooth Self-supporting Topologies.” Virtual and Physical Prototyping 15 (1): 22–34. doi:10.1080/17452759.2019.1644185.