A review on physics-informed machine learning for  monitoring metal additive manufacturing process

Shoulan Yang; Shitong Peng; Jianan Guo; Fengtao Wang

doi:10.55092/am20240008

Article

Open Access

A review on physics-informed machine learning for monitoring metal additive manufacturing process

Shoulan YangShitong Peng^∗Jianan GuoFengtao Wang^∗

Department of Mechanical Engineering, Shantou University, Shantou, 515063, China

* shtpeng@stu.edu.cn; ftwang@stu.edu.cn

Volume
Volume 1 Issue 2, 2024
Citation
Yang S, Peng S, Guo J, Wang F. A review on physics-informed machine learning for monitoring metal additive manufacturing process. Adv. Manuf. 2024(2):0008, https://doi.org/10.55092/am20240008.
DOI
10.55092/am20240008
Copyright
Copyright2024 by the authors. Published by ELSP.
Special Issue
Multi-material Additive Manufacturing

Abstract

The traditional data-driven models and pure physics models have been widely employed in quality prediction for additive manufacturing (AM). However, data-driven models rely on a large amount of labeled data, while pure physics models suffer from lower computational efficiency and accuracy. The Physics-Informed Neural Network (PINN) model has emerged as a hybrid data-driven paradigm that imbues data-driven models with physical domain knowledge. To refrain from the inherent “black box” or inefficiency of AM process prediction or monitoring, this paper discusses the pros and cons of traditional data driven methods and pure physics models and further elaborates on the principles and architecture of the PINN model along with its applications in AM research. We review and analyze current state-of-the-art PINN applications to AM, focusing on temperature field prediction, fluid dynamics issues, fatigue life prediction, accelerated finite element simulation, and process characteristics prediction. The corresponding embedded physical knowledge, either integrated into loss function or data preprocessing, is also summarized for these applications. Based on this review, we identify the challenges of PINN and provide an outlook for further research of its AM applications.

Keywords

Data-driven models; physics models; additive manufacturing; physics-informed neural network

Preview

view pdf

References

[1]Abdulhameed O, Al-Ahmari A, Ameen W, et al. Additive manufacturing: Challenges, trends, and applications. Adv. Mech. Eng. 2019, 11(2): 168781401882288.
[2]Frazier W E. Metal additive manufacturing: a review. J. Mater. Eng. Perform. 2014, 23(6): 1917–1928.
[3]Wong KV, Hernandez A. A review of additive manufacturing. ISRN Mech. Eng. 2012, 2012: 1–10.
[4]Guo S, Agarwal M, Cooper C, et al. Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm. J. Manuf. Syst. 2022, 62: 145–163.
[5]Roy M, Wodo O. Data-driven modeling of thermal history in additive manufacturing. Addit. Manuf. 2020, 32: 101017.
[6]Mozaffar M, Paul A, Al-Bahrani R, et al. Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manuf. Lett. 2018, 18: 35–39.
[7]Mozaffar M, Liao S, Lin H, et al. Geometry-agnostic data-driven thermal modeling of additive manufacturing processes using graph neural networks. Addit. Manuf. 2021, 48: 102449.
[8]Wang Z, Yang W, Liu Q, et al. Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions. J. Manuf. Process. 2022, 77: 13–31
[9]Zhang Z, Liu Z, Wu D. Prediction of melt pool temperature in directed energy deposition using machine learning. Addit. Manuf. 2021, 37: 101692.
[10]Ye J, Bab-Hadiashar A, Hoseinnezhad R, et al. Predictions of in-situ melt pool geometric signatures via machine learning techniques for laser metal deposition. Int. J. Comput. Integr. Manuf. 2023, 36(9): 1345–1361.
[11]Parsazadeh M, Sharma S, Dahotre N. Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution. Prog. Mater. Sci. 2023, 135: 101102.
[12]Mukherjee T, Wei H L, De A, et al. Heat and fluid flow in additive manufacturing—Part I: Modeling of powder bed fusion. Comput. Mater. Sci. 2018, 150: 304–313.
[13]Bhatti M M, Marin M, Zeeshan A, et al. Editorial: Recent Trends in Computational Fluid Dynamics. Front. Phys. 2020, 8: 593111.
[14]Liao S, Xue T, Jeong J, et al. Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification. Comput. Mech. 2023, 72(3): 499–512.
[15]Gao W, Zhao S, Wang Y, et al. Numerical simulation of thermal field and Fe-based coating doped Ti. Int. J. Heat Mass Transfer. 2016, 92: 83–90.
[16]Li S, Wang G, Di Y, et al. A physics-informed neural network framework to predict 3D temperature field without labeled data in process of laser metal deposition. Eng. Appl. Artif. Intell. 2023, 120: 105908.
[17]Raissi M, Perdikaris P, Karniadakis G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 2019, 378: 686–707.
[18]Ness K L, Paul A, Sun L, et al. Towards a generic physics-based machine learning model for geometry invariant thermal history prediction in additive manufacturing. J. Mater. Process. Technol. 2022, 302: 117472.
[19]Kats D, Wang Z, Gan Z, et al. A physics-informed machine learning method for predicting grain structure characteristics in directed energy deposition. Comput. Mater. Sci. 2022, 202: 110958.
[20]Zagoruyko S, Komodakis N. Wide Residual Networks. arXiv 2017, arXiv:1605.07146.
[21]Raissi M, Yazdani A, Karniadakis G E. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Sci. 2020, 367(6481): 1026–1030.
[22]Luo S, Vellakal M, Koric S, et al. High-Performance Computing. Cham: Springer International Publishing, 2020.pp. 137–149.
[23]Peng B, Panesar A. Predicting Temperature Field for Metal Additive Manufacturing using PINN. Solid Freeform Fabrication 2023.
[24]Sajadi P, Dehaghani M R, Wang G G, et al. Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks. arXiv 2024,arXiv:2401.02403.
[25]Hosseini E, Scheel P, Müller O, et al. Single-track thermal analysis of laser powder bed fusion process: Parametric solution through physics-informed neural networks. Comput. Methods Appl. Mech. Eng. 2023, 410: 116019.
[26]Xie J, Chai Z, Xu L, et al.3D temperature field prediction in direct energy deposition of metals using physics informed neural network. Int. J. Adv. Manuf. Technol. 2022, 119(5–6): 3449–3468.
[27]Zobeiry N, Humfeld K D. A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications. Eng. Appl. Artif. Intell. 2021, 101: 104232.
[28]Ren K, Chew Y, Zhang Y F, et al. Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning. Comput. Methods Appl. Mech. Eng. 2020, 362: 112734.
[29]Zhu Q, Liu Z, Yan J. Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Comput. Mech. 2021, 67(2): 619–635.
[30]Yan J, Yan W, Lin S, et al. A fully coupled finite element formulation for liquid–solid–gas thermo-fluid flow with melting and solidification. Comput. Methods Appl. Mech. Eng. 2018, 336: 444–470.
[31]Zhang G-T, Cheng C, Liu S, et al. Deep learning based on mixed-variable physics informed neural. arXiv 2021, arXiv:2111.09086.
[32]Bararnia H, Esmaeilpour M. On the application of physics informed neural networks (PINN) to solve boundary layer thermal-fluid problems. Int. Commun. Heat Mass Transf. 2022, 132: 105890.
[33]Cai S, Mao Z, Wang Z, et al. Physics-informed neural networks (PINNs) for fluid mechanics: a review. Acta Mech. Sin. 2021, 37(12): 1727–1738.
[34]Salvati E, Tognan A, Laurenti L, et al. A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing. Mater. Des. 2022, 222: 111089.
[35]Jiang L, Hu Y, Liu Y, et al. Physics-informed machine learning for low-cycle fatigue life prediction of 316 stainless steels. Int. J. Fatigue 2024, 182: 108187.
[36]Peng X, Wu S, Qian W, et al. The potency of defects on fatigue of additively manufactured metals. Int. J. Mech. Sci. 2022, 221: 107185.
[37]H. Wang, S.L. Gao, et al. Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review. J. Mater. Sci. Technol. 2024,1005-0302.
[38]Wang L, Zhu S-P, Luo C, et al. Defect driven physics-informed neural network framework for fatigue life prediction of additively manufactured materials. Philos. Trans. R. Soc. A 2023, 381(2260): 20220386.
[39]Ciampaglia A, Tridello A, Paolino D S, et al. Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts. Int. J. Fatigue 2023, 170: 107500.
[40]Luo Z, Zhao Y. A survey of finite element analysis of temperature and thermal stress fields in powder bed fusion Additive Manufacturing. Addit. Manuf. 2018, 21: 318–332.
[41]Ekanayaka V, Hürkamp A. Modeling of additive manufacturing processes with time‐dependent material properties using physics‐informed neural networks. PAMM 2023, 23(4): e202300265.
[42]Zhou M, Mei G, Xu N. Enhancing Computational Accuracy in Surrogate Modeling for Elastic–Plastic Problems by Coupling S-FEM and Physics-Informed Deep Learning. Mathematics 2023, 11(9): 2016.
[43]Zhou M, Mei G. Transfer Learning-Based Coupling of Smoothed Finite Element Method and Physics-Informed Neural Network for Solving Elastoplastic Inverse Problems. Mathematics 2023, 11(11): 2529.
[44]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. 2019, 25: 151–165.
[45]Mahmoud D, Magolon M, Boer J, et al. Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review. Appl. Sci. 2021, 11(24): 11910.
[46]Jiang F, Xia M, Hu Y. Physics-Informed Machine Learning for Accurate Prediction of Temperature and Melt Pool Dimension in Metal Additive Manufacturing. 3D Print. Addit. Manuf. 2023: 3dp.2022.0363.
[47]Kapusuzoglu B, Mahadevan S. Physics-informed and hybrid machine learning in additive manufacturing: application to fused filament fabrication. Jom 2020, 72(12): 4695-4705.
[48]Wenzel S, Slomski-Vetter E, Melz T. Optimizing System Reliability in Additive Manufacturing Using Physics-Informed Machine Learning. Machines 2022, 10(7): 525.
[49]Lawal Z K, Yassin H, Lai D T C, et al. Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis. BDCC 2022, 6(4): 140.
[50]Pu J, Li J, Chen Y. Solving localized wave solutions of the derivative nonlinear Schrodinger equation using an improved PINN method. arXiv 2021, arXiv:2101.08593.
[51]Brunton S L, Proctor J L, Kutz J N. Discovering governing equations from data: Sparse identification of nonlinear dynamical systems. Proc. Natl. Acad. Sci. USA 2016, 113(15): 3932–3937.
[52]Raissi M, Perdikaris P, Karniadakis G E. Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations. arXiv 2017, arXiv:1711.10566.