A Machine Learning-Based Model for Multiple Material Density Prediction Developed by Powder Bed Fusion Additive Manufacturing
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OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Welding Techniques and Residual Stresses
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References (20)
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1. H.R. Kotadia, G. Gibbons, A. Das, and P.D. Howes, A Review of Laser Powder Bed Fusion Additive Manufacturing of Aluminium Alloys: Microstructure and Properties, Addit. Manuf., 2021, 46, 102155.
2. B. Amir, E. Kochavi, S. Gruntman, Y. Gale, S. Samuha, and O. Sadot, Experimental Investigation on Shear Strength of Laser Powder Bed Fusion AlSi10Mg under Quasi-Static and Dynamic Loads, Addit. Manuf., 2021, 46, 102150.
3. P. Wang, Y. Yang, and N.S. Moghaddam, Process Modeling in Laser Powder Bed Fusion towards Defect Detection and Quality Control via Machine Learning: The State-of-the-Art and Research Challenges, J. Manuf. Process., 2022, 73, p 961–984.10.1016/j.jmapro.2021.11.037 ↗
4. T.P. Moran, D.H. Warner, A. Soltani-Tehrani, N. Shamsaei, and N. Phan, Spatial Inhomogeneity of Build Defects across the Build Plate in Laser Powder Bed Fusion, Addit. Manuf., 2021, 47, 102333.
5. T. Moges, G. Ameta, and P. Witherell, A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations, J. Manuf. Sci. Eng., 2019, 141(4), p 40801.10.1115/1.4042789 ↗
6. K.L. Raju, S. Thapliyal, S. Sigatapu, A.K. Shukla, G. Bajargan, and B. Pant, “Process Parameter Dependent Machine Learning Model for Densification Prediction of Selective Laser Melted Al-50Si Alloy and Its Validation, J. Mater. Eng. Perform., 2022, 31, p 1–8.10.1007/s11665-022-06831-3 ↗
7. C. Wang, X.P. Tan, S.B. Tor, and C.S. Lim, Machine Learning in Additive Manufacturing: State-of-the-Art and Perspectives, Addit. Manuf., 2020, 36, 101538.
8. T. DebRoy, 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, Additive Manufacturing of Metallic Components – Process, Structure and Properties, Prog. Mater. Sci., 2018, 92, p 112–224.10.1016/j.pmatsci.2017.10.001 ↗
9. T. DebRoy, T. Mukherjee, H.L. Wei, J.W. Elmer, and J.O. Milewski, Metallurgy, Mechanistic Models and Machine Learning in Metal Printing, Nat. Rev. Mater., 2021, 6(1), p 48–68.10.1038/s41578-020-00236-1 ↗
10. Y. Du, T. Mukherjee, and T. DebRoy, Physics-Informed Machine Learning and Mechanistic Modeling of Additive Manufacturing to Reduce Defects, Appl. Mater. Today, 2021, 24, 101123.10.1016/j.apmt.2021.101123 ↗
11. D.K. Dwivedi, Adhesive Wear Behaviour of Cast Aluminium-Silicon Alloys: Overview, Mater. Des., 2010, 31(5), p 2517–2531.10.1016/j.matdes.2009.11.038 ↗
12. J. Chi, X. Huang, D. He, Y. Wang, G. Zhang, H. Zhang, W. Guo, and H. Guo, Obtaining Strength and Ductility Synergy for Directed Energy Deposited Ti17 Alloys by Machine Learning, Mater. Lett., 2024, 356, 135537.10.1016/j.matlet.2023.135537 ↗
13. Y. Fu, A.R.J. Downey, L. Yuan, T. Zhang, A. Pratt, and Y. Balogun, Machine Learning Algorithms for Defect Detection in Metal Laser-Based Additive Manufacturing: A Review, J. Manuf. Process., 2022, 75, p 693–710.10.1016/j.jmapro.2021.12.061 ↗
14. G.O. Barrionuevo, J.A. Ramos-Grez, M. Walczak, and C.A. Betancourt, Comparative Evaluation of Supervised Machine Learning Algorithms in the Prediction of the Relative Density of 316L Stainless Steel Fabricated by Selective Laser Melting, Int. J. Adv. Manuf. Technol., 2021, 113(1), p 419–433.10.1007/s00170-021-06596-4 ↗
15. I.G. Ian Gibson, Additive Manufacturing Technologies 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing. 2015.10.1007/978-1-4939-2113-3 ↗
16. J. Chan, R. Chung, and J. Huang, Python API Development Fundamentals: Develop a Full-Stack Web Application with Python and Flask, Packt Publishing Ltd. 2019.
17. B.B. Ravichander, A. Amerinatanzi, and N. Shayesteh Moghaddam, Study on the Effect of Powder-Bed Fusion Process Parameters on the Quality of as-Built IN718 Parts Using Response Surface Methodology, Metals (Basel), 2020, 10(9), p 1180.10.3390/met10091180 ↗
18. J.P. Oliveira, A.D. LaLonde, and J. Ma, Processing Parameters in Laser Powder Bed Fusion Metal Additive Manufacturing, Mater. Des., 2020, 193, 108762.10.1016/j.matdes.2020.108762 ↗
19. A. Leicht, M. Rashidi, U. Klement, and E. Hryha, Effect of Process Parameters on the Microstructure, Tensile Strength and Productivity of 316L Parts Produced by Laser Powder Bed Fusion, Mater. Charact., 2020, 159, 110016.10.1016/j.matchar.2019.110016 ↗
20. K.G. Prashanth, S. Scudino, T. Maity, J. Das, and J. Eckert, Is the Energy Density a Reliable Parameter for Materials Synthesis by Selective Laser Melting?, Mater. Res. Lett., 2017, 5(6), p 386–390.10.1080/21663831.2017.1299808 ↗