Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
Source abstract
Abstract
Abstract Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences, i.e., thermal histories. The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process, such as critical temperature ranges and fundamental thermal frequencies. We systematically compare the developed approach with other machine learning methods. The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data. It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.
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Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Machine Learning in Materials Science · Additive Manufacturing and 3D Printing Technologies
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References (62)
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1. DebRoy, T. et al. Additive manufacturing of metallic components–process, structure and properties. Prog. Mater. Sci. 92, 112–224 (2018).10.1016/j.pmatsci.2017.10.001 ↗
2. Khairallah, S. A., Anderson, A. T., Rubenchik, A. & King, W. E. Laser powder-bed fusion additive manufacturing: physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones. Acta Mater. 108, 36–45 (2016).10.1016/j.actamat.2016.02.014 ↗
3. Cunningham, R. et al. Keyhole threshold and morphology in laser melting revealed by ultrahigh-speed x-ray imaging. Science 363, 849–852 (2019).10.1126/science.aav4687 ↗
4. Ye, J. et al. Energy coupling mechanisms and scaling behavior associated with laser powder bed fusion additive manufacturing. Adv. Eng. Mater. 21, 1900185 (2019).10.1002/adem.201900185 ↗
5. Hojjatzadeh, S. M. H. et al. Pore elimination mechanisms during 3d printing of metals. Nat. Commun. 10, 1–8 (2019).10.1038/s41467-018-07882-8 ↗
6. Zhao, C. et al. Real-time monitoring of laser powder bed fusion process using high-speed x-ray imaging and diffraction. Sci. Rep. 7, 3602 (2017).10.1038/s41598-017-03761-2 ↗
7. Martin, J. H. et al. 3d printing of high-strength aluminium alloys. Nature 549, 365 (2017).10.1038/nature23894 ↗
8. Gray III, G. T. et al. Structure/property (constitutive and spallation response) of additively manufactured 316l stainless steel. Acta Mater. 138, 140–149 (2017).10.1016/j.actamat.2017.07.045 ↗
9. King, W. E. et al. Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges. Appl. Phys. Rev. 2, 041304 (2015).10.1063/1.4937809 ↗
10. Gan, Z. et al. Benchmark study of thermal behavior, surface topography, and dendritic microstructure in selective laser melting of inconel 625. Integr. Mater. Manuf. Innov. 8, 1–16 (2019).10.1007/s40192-019-00130-x ↗
11. Lian, Y. et al. A cellular automaton finite volume method for microstructure evolution during additive manufacturing. Mater. Des. 169, 107672 (2019).10.1016/j.matdes.2019.107672 ↗
12. Herriott, C. F. et al. A multi-scale, multi-physics modeling framework to predict spatial variation of properties in additive-manufactured metals. Model. Simul. Mater. Sci. Eng. 27, 025009 (2018).10.1088/1361-651X/aaf753 ↗
13. Gan, Z., Yu, G., He, X. & Li, S. Numerical simulation of thermal behavior and multicomponent mass transfer in direct laser deposition of co-base alloy on steel. Int. J. Heat. Mass Transf. 104, 28–38 (2017).10.1016/j.ijheatmasstransfer.2016.08.049 ↗
14. Thompson, S. M., Bian, L., Shamsaei, N. & Yadollahi, A. An overview of direct laser deposition for additive manufacturing; part i: transport phenomena, modeling and diagnostics. Addit. Manuf. 8, 36–62 (2015).
15. Markl, M. & Körner, C. Multiscale modeling of powder bed–based additive manufacturing. Annu. Rev. Mater. Res. 46, 93–123 (2016).10.1146/annurev-matsci-070115-032158 ↗
16. Bayat, M., Mohanty, S. & Hattel, J. H. Multiphysics modelling of lack-of-fusion voids formation and evolution in in718 made by multi-track/multi-layer l-pbf. Int. J. Heat. Mass Transf. 139, 95–114 (2019).10.1016/j.ijheatmasstransfer.2019.05.003 ↗
17. Wei, H. et al. Mechanistic models for additive manufacturing of metallic components. Prog. Mater. Sci. 116, 100703 (2020).10.1016/j.pmatsci.2020.100703 ↗
18. DebRoy, T., Mukherjee, T., Wei, H., Elmer, J. & Milewski, J. Metallurgy, mechanistic models and machine learning in metal printing. Nat. Rev. Mater. 2, 1–21 (2020).10.1038/s41578-020-00236-1 ↗
19. Goh, G. D., Sing, S. L. & Yeong, W. Y. A review on machine learning in 3d printing: applications, potential, and challenges. Artif. Intell. Rev. 54, 1–32 (2020).10.1007/s10462-020-09876-9 ↗
20. Johnson, N. et al. Invited review: machine learning for materials developments in metals additive manufacturing. Addit. Manuf. 36, 101641 (2020).10.1016/j.addma.2020.101641 ↗
21. Popova, E. et al. Process-structure linkages using a data science approach: application to simulated additive manufacturing data. Integr. Mater. Manuf. Innov. 6, 54–68 (2017).10.1007/s40192-017-0088-1 ↗
22. Rodgers, T. M., Madison, J. D. & Tikare, V. Simulation of metal additive manufacturing microstructures using kinetic monte carlo. Comput. Mater. Sci. 135, 78–89. (2017).10.1016/j.commatsci.2017.03.053 ↗
23. Du, Y., Mukherjee, T. & DebRoy, T. Conditions for void formation in friction stir welding from machine learning. npj Comput. Mater. 5, 1–8 (2019).10.1038/s41524-019-0207-y ↗
24. Li, J., Jin, R. & Hang, Z. Y. Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing. Mater. Des. 139, 473–485 (2018).10.1016/j.matdes.2017.11.028 ↗
25. Zhang, W., Mehta, A., Desai, P. S. & Higgs, C. Machine learning enabled powder spreading process map for metal additive manufacturing (am). In Solid Freeform Fabrication 2017: Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium, 1235–1249. University of Texas at Austin (2017).
26. Gan, Z. et al. Data-driven microstructure and microhardness design in additive manufacturing using a self-organizing map. Engineering 5, 730–735 (2019).10.1016/j.eng.2019.03.014 ↗
27. Lu, Y., Jones, K. K., Gan, Z. & Liu, W. K. Adaptive hyper reduction for additive manufacturing thermal fluid analysis. Comput. Methods Appl. Mech. Eng. 372, 113312 (2020).10.1016/j.cma.2020.113312 ↗
28. Wang, Z. et al. Uncertainty quantification and reduction in metal additive manufacturing. npj Comput. Mater. 6, 1–10 (2020).10.1038/s41524-019-0267-z ↗
29. Scime, L. & Beuth, J. Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit. Manuf. 25, 151–165 (2019).
30. Zhang, B., Liu, S. & Shin, Y. C. In-process monitoring of porosity during laser additive manufacturing process. Addit. Manuf. 28, 497–505 (2019).10.1016/j.addma.2019.05.030 ↗
31. Zhang, B., Hong, K.-M. & Shin, Y. C. Deep-learning-based porosity monitoring of laser welding process. Manuf. Lett. 23, 62–66 (2020).10.1016/j.mfglet.2020.01.001 ↗
32. Wolff, S. J. et al. Experimentally validated predictions of thermal history and microhardness in laser-deposited inconel 718 on carbon steel. Addit. Manuf. 27, 540–551 (2019).
33. Lewis, R. J. An introduction to classification and regression tree (cart) analysis. In Annual Meeting of the Society for Academic Emergency Medicine in San Francisco, California (2000).
34. Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning vol. 1 (Springer, 2001).10.1007/978-0-387-21606-5_1 ↗
35. Irwin, J., Reutzel, E. W., Michaleris, P., Keist, J. & Nassar, A. R. Predicting microstructure from thermal history during additive manufacturing for ti-6al-4v. J. Manuf. Sci. Eng. 138, 11107 (2016).10.1115/1.4033525 ↗
36. Yan, W. et al. An integrated process–structure–property modeling framework for additive manufacturing. Comput. Methods Appl. Mech. Eng. 339, 184–204 (2018).10.1016/j.cma.2018.05.004 ↗
37. Wei, Y. et al. Evading the strength–ductility trade-off dilemma in steel through gradient hierarchical nanotwins. Nat. Commun. 5, 1–8 (2014).
38. Bennett, J. L. et al. Cooling rate effect on tensile strength of laser deposited inconel 718. Proced. Manuf. 26, 912–919 (2018).10.1016/j.promfg.2018.07.118 ↗
39. MatWeb, L. Material property data. MatWeb [Online]. http://www.matweb.com (2016).
40. Paulonis, D., Oblak, J. & Duvall, D. Precipitation in nickel-base alloy 718. Technical Report (Pratt and Whitney Aircraft, 1969).
41. Knapp, G. et al. Building blocks for a digital twin of additive manufacturing. Acta Mater. 135, 390–399 (2017).10.1016/j.actamat.2017.06.039 ↗
42. Keiser, D. & Brown, H. Review of the physical metallurgy of alloy 718. Technical Report (Idaho National Engineering Lab, 1976).10.2172/4016087 ↗
43. Devore, J. L. Probability and Statistics for Engineering and the Sciences (Cengage Learning, 2011).
44. Bennett, J., Glerum, J. & Cao, J. Relating additively manufactured part tensile properties to thermal metrics. CIRP Ann. 70 (2021).10.1016/j.cirp.2021.04.053 ↗
45. Glerum, J., Bennett, J., Ehmann, K. & Cao, J. Mechanical properties of hybrid additively manufactured inconel 718 parts created via thermal control after secondary treatment processes. J. Mater. Process. Technol. 291, 117047 (2021).10.1016/j.jmatprotec.2021.117047 ↗
46. He, X., Fuerschbach, P. & DebRoy, T. Heat transfer and fluid flow during laser spot welding of 304 stainless steel. J. Phys. D 36, 1388 (2003).10.1088/0022-3727/36/12/306 ↗
47. Elmer, J., Palmer, T., Babu, S., Zhang, W. & DebRoy, T. Phase transformation dynamics during welding of ti–6al–4v. J. Appl. Phys. 95, 8327–8339 (2004).10.1063/1.1737476 ↗
48. Brunton, S. L. & Kutz, J. N. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (Cambridge University Press, 2019).10.1017/9781108380690 ↗
49. Olhede, S. C. & Walden, A. T. Generalized morse wavelets. IEEE Trans. Signal Process. 50, 2661–2670 (2002).10.1109/TSP.2002.804066 ↗
50. Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).10.1038/s41586-020-2649-2 ↗
51. Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).10.1145/3065386 ↗
52. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778. IEEE (2016).10.1109/CVPR.2016.90 ↗
53. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. https://arxiv.org/1412.6980 (2014).
54. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).10.1023/A:1010933404324 ↗
55. Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. Classification and Regression Trees (CRC Press, 1984).
56. Benesty, J., Chen, J., Huang, Y. & Cohen, I. Noise Reduction in Speech Processing 1–4 (Springer, 2009).10.1007/978-3-642-00296-0_5 ↗
57. Svozil, D., Kvasnicka, V. & Pospichal, J. Introduction to multi-layer feed-forward neural networks. Chemom. Intell. Lab. Syst. 39, 43–62 (1997).10.1016/S0169-7439(97)00061-0 ↗
58. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 58, 267–288 (1996).10.1111/j.2517-6161.1996.tb02080.x ↗
59. Shi, Q., Abdel-Aty, M. & Lee, J. A bayesian ridge regression analysis of congestion’s impact on urban expressway safety. Accid. Anal. Prev. 88, 124–137 (2016).10.1016/j.aap.2015.12.001 ↗
60. Fukunaga, K. & Narendra, P. M. A branch and bound algorithm for computing k-nearest neighbors. IEEE Trans. Comput. 100, 750–753 (1975).10.1109/T-C.1975.224297 ↗
61. Saha, S. et al. Hierarchical deep learning neural network (hidenn): an artificial intelligence (ai) framework for computational science and engineering. Comput. Methods Appl. Mech. Eng. 373, 113452 (2020).10.1016/j.cma.2020.113452 ↗
62. Lecun, Y. & Bengio, Y. Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw. 3361, 1995 (1995).