Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Gunasegaram, Dayalan R.; Murphy, A. B.; Barnard, Amanda; DebRoy, T.; Matthews, M. J.; Ladani, L.; Gu, D.

doi:10.1016/j.addma.2021.102089

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Journal Article · Tue Jun 08 00:00:00 EDT 2021 · Additive Manufacturing

DOI:https://doi.org/10.1016/j.addma.2021.102089· OSTI ID:1881614

Gunasegaram, Dayalan R. ^[1]; Murphy, A. B. ^[2]; Barnard, Amanda ^[3]; DebRoy, T. ^[4]; Matthews, M. J. ^[5]; Ladani, L. ^[6]; Gu, D. ^[7]

CSIRO Manufacturing, Clayton (Australia)
CSIRO Manufacturing, Linfield (Australia)
Australian National Univ., Acton (Australia)
Pennsylvania State Univ., University Park, PA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Arizona State Univ., Tempe, AZ (United States)
Nanjing Univ. of Aeronautics and Astronautics, Jiangsu (China)

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. Here, we argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1881614

Report Number(s):: LLNL-JRNL-838645; 1058737

Journal Information:: Additive Manufacturing, Journal Name: Additive Manufacturing Vol. 46; ISSN 2214-8604

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (67)

Process Monitoring of Laser Beam Melting: Towards in-situ process control for powder bed laser melting Hagedorn, Yves; Pastors, Felix Laser Technik Journal, Vol. 15, Issue 2 https://doi.org/10.1002/latj.201800009	journal	April 2018
A computational framework for scale-bridging in multi-scale simulations: A COMPUTATIONAL FRAMEWORK FOR SCALE-BRIDGING Knap, J.; Spear, C.; Leiter, K. International Journal for Numerical Methods in Engineering, Vol. 108, Issue 13 https://doi.org/10.1002/nme.5270	journal	May 2016
Additive manufacturing methods and modelling approaches: a critical review Bikas, H.; Stavropoulos, P.; Chryssolouris, G. The International Journal of Advanced Manufacturing Technology, Vol. 83, Issue 1-4 https://doi.org/10.1007/s00170-015-7576-2	journal	July 2015
Digital twin-driven product design, manufacturing and service with big data Tao, Fei; Cheng, Jiangfeng; Qi, Qinglin The International Journal of Advanced Manufacturing Technology, Vol. 94, Issue 9-12 https://doi.org/10.1007/s00170-017-0233-1	journal	March 2017
Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities Hu, Zhen; Mahadevan, Sankaran The International Journal of Advanced Manufacturing Technology, Vol. 93, Issue 5-8 https://doi.org/10.1007/s00170-017-0703-5	journal	July 2017
Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks Zhu, Qiming; Liu, Zeliang; Yan, Jinhui Computational Mechanics, Vol. 67, Issue 2 https://doi.org/10.1007/s00466-020-01952-9	journal	January 2021
Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling Wang, Zhuo; Liu, Pengwei; Ji, Yanzhou JOM, Vol. 71, Issue 8 https://doi.org/10.1007/s11837-019-03555-z	journal	June 2019
Integrated Simulation Framework for Additively Manufactured Ti-6Al-4V: Melt Pool Dynamics, Microstructure, Solid-State Phase Transformation, and Microelastic Response Shi, Rongpei; Khairallah, Saad; Heo, Tae Wook JOM, Vol. 71, Issue 10 https://doi.org/10.1007/s11837-019-03618-1	journal	June 2019
Temperature Profile, Bead Geometry, and Elemental Evaporation in Laser Powder Bed Fusion Additive Manufacturing Process Ahsan, Faiyaz; Ladani, Leila JOM, Vol. 72, Issue 1 https://doi.org/10.1007/s11837-019-03872-3	journal	October 2019
Machine Learning in Additive Manufacturing: A Review Meng, Lingbin; McWilliams, Brandon; Jarosinski, William JOM, Vol. 72, Issue 6 https://doi.org/10.1007/s11837-020-04155-y	journal	April 2020
A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring Baumgartl, Hermann; Tomas, Josef; Buettner, Ricardo Progress in Additive Manufacturing, Vol. 5, Issue 3 https://doi.org/10.1007/s40964-019-00108-3	journal	February 2020
A Multiscale Understanding of the Thermodynamic and Kinetic Mechanisms of Laser Additive Manufacturing Gu, Dongdong; Ma, Chenglong; Xia, Mujian Engineering, Vol. 3, Issue 5 https://doi.org/10.1016/J.ENG.2017.05.011	journal	October 2017
Denudation of metal powder layers in laser powder bed fusion processes Matthews, Manyalibo J.; Guss, Gabe; Khairallah, Saad A. Acta Materialia, Vol. 114 https://doi.org/10.1016/j.actamat.2016.05.017	journal	August 2016
Building blocks for a digital twin of additive manufacturing Knapp, G. L.; Mukherjee, T.; Zuback, J. S. Acta Materialia, Vol. 135 https://doi.org/10.1016/j.actamat.2017.06.039	journal	August 2017
Phase field simulation of powder bed-based additive manufacturing Lu, Liang-Xing; Sridhar, N.; Zhang, Yong-Wei Acta Materialia, Vol. 144 https://doi.org/10.1016/j.actamat.2017.11.033	journal	February 2018
Transient dynamics of powder spattering in laser powder bed fusion additive manufacturing process revealed by in-situ high-speed high-energy x-ray imaging Guo, Qilin; Zhao, Cang; Escano, Luis I. Acta Materialia, Vol. 151 https://doi.org/10.1016/j.actamat.2018.03.036	journal	June 2018
Microstructural control in metal laser powder bed fusion additive manufacturing using laser beam shaping strategy Shi, Rongpei; Khairallah, Saad A.; Roehling, Tien T. Acta Materialia, Vol. 184 https://doi.org/10.1016/j.actamat.2019.11.053	journal	February 2020
A digital twin for rapid qualification of 3D printed metallic components Mukherjee, T.; DebRoy, T. Applied Materials Today, Vol. 14 https://doi.org/10.1016/j.apmt.2018.11.003	journal	March 2019
Characterising the Digital Twin: A systematic literature review Jones, David; Snider, Chris; Nassehi, Aydin CIRP Journal of Manufacturing Science and Technology, Vol. 29 https://doi.org/10.1016/j.cirpj.2020.02.002	journal	May 2020
Real time monitoring and control of friction stir welding process using multiple sensors Mishra, Debasish; Gupta, Abhinav; Raj, Pranav CIRP Journal of Manufacturing Science and Technology, Vol. 30 https://doi.org/10.1016/j.cirpj.2020.03.004	journal	August 2020
An adaptive strategy for the control of modeling error in two-dimensional atomic-to-continuum coupling simulations Prudhomme, Serge; Chamoin, Ludovic; Dhia, Hachmi Ben Computer Methods in Applied Mechanics and Engineering, Vol. 198, Issue 21-26 https://doi.org/10.1016/j.cma.2008.12.026	journal	May 2009
Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models Sahli Costabal, Francisco; Perdikaris, Paris; Kuhl, Ellen Computer Methods in Applied Mechanics and Engineering, Vol. 357 https://doi.org/10.1016/j.cma.2019.112602	journal	December 2019
Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods Herriott, Carl; Spear, Ashley D. Computational Materials Science, Vol. 175 https://doi.org/10.1016/j.commatsci.2020.109599	journal	April 2020
Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison Tao, Fei; Qi, Qinglin; Wang, Lihui Engineering, Vol. 5, Issue 4 https://doi.org/10.1016/j.eng.2019.01.014	journal	August 2019
Recent advances in Big Data Analytics, Internet of Things and Machine Learning Martis, Roshan Joy; Gurupur, Varadraj Prabhu; Lin, Hong Future Generation Computer Systems, Vol. 88 https://doi.org/10.1016/j.future.2018.07.057	journal	November 2018
Patterns for High Performance Multiscale Computing Alowayyed, S.; Piontek, T.; Suter, J. L. Future Generation Computer Systems, Vol. 91 https://doi.org/10.1016/j.future.2018.08.045	journal	February 2019
The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms Makridakis, Spyros Futures, Vol. 90 https://doi.org/10.1016/j.futures.2017.03.006	journal	June 2017
Understanding powder degradation in metal additive manufacturing to allow the upcycling of recycled powders Powell, Daniel; Rennie, Allan E. W.; Geekie, Louise Journal of Cleaner Production, Vol. 268 https://doi.org/10.1016/j.jclepro.2020.122077	journal	September 2020
Accelerated scale bridging with sparsely approximated Gaussian learning Wang, Ting; Leiter, Kenneth W.; Plecháč, Petr Journal of Computational Physics, Vol. 403 https://doi.org/10.1016/j.jcp.2019.109049	journal	February 2020
Identification of critical factors affecting shrinkage porosity in permanent mold casting using numerical simulations based on design of experiments Gunasegaram, D. R.; Farnsworth, D. J.; Nguyen, T. T. Journal of Materials Processing Technology, Vol. 209, Issue 3 https://doi.org/10.1016/j.jmatprotec.2008.03.044	journal	February 2009
A multiscale modeling approach for fast prediction of part distortion in selective laser melting Li, C.; Fu, C. H.; Guo, Y. B. Journal of Materials Processing Technology, Vol. 229 https://doi.org/10.1016/j.jmatprotec.2015.10.022	journal	March 2016
Distributed multiscale computing with MUSCLE 2, the Multiscale Coupling Library and Environment Borgdorff, J.; Mamonski, M.; Bosak, B. Journal of Computational Science, Vol. 5, Issue 5 https://doi.org/10.1016/j.jocs.2014.04.004	journal	September 2014
Multiscale computing in the exascale era Alowayyed, Saad; Groen, Derek; Coveney, Peter V. Journal of Computational Science, Vol. 22 https://doi.org/10.1016/j.jocs.2017.07.004	journal	September 2017
Accelerated scale-bridging through adaptive surrogate model evaluation Leiter, Kenneth W.; Barnes, Brian C.; Becker, Richard Journal of Computational Science, Vol. 27 https://doi.org/10.1016/j.jocs.2018.04.010	journal	July 2018
Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing Everton, Sarah K.; Hirsch, Matthias; Stravroulakis, Petros Materials & Design, Vol. 95 https://doi.org/10.1016/j.matdes.2016.01.099	journal	April 2016
Additive manufacturing of metallic components – Process, structure and properties DebRoy, T.; Wei, H. L.; Zuback, J. S. Progress in Materials Science, Vol. 92 https://doi.org/10.1016/j.pmatsci.2017.10.001	journal	March 2018
Optimisation of blade type spreaders for powder bed preparation in Additive Manufacturing using DEM simulations Haeri, S. Powder Technology, Vol. 321 https://doi.org/10.1016/j.powtec.2017.08.011	journal	November 2017
Multi-material modelling for selective laser melting Sorkin, A.; Tan, J. L.; Wong, C. H. Procedia Engineering, Vol. 216 https://doi.org/10.1016/j.proeng.2018.02.088	journal	January 2017
Dynamics of pore formation during laser powder bed fusion additive manufacturing Martin, Aiden A.; Calta, Nicholas P.; Khairallah, Saad A. Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-10009-2	journal	April 2019
Scientific, technological and economic issues in metal printing and their solutions DebRoy, T.; Mukherjee, T.; Milewski, J. O. Nature Materials, Vol. 18, Issue 10 https://doi.org/10.1038/s41563-019-0408-2	journal	July 2019
Metallurgy, mechanistic models and machine learning in metal printing DebRoy, T.; Mukherjee, T.; Wei, H. L. Nature Reviews Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41578-020-00236-1	journal	October 2020
Metal vapor micro-jet controls material redistribution in laser powder bed fusion additive manufacturing Ly, Sonny; Rubenchik, Alexander M.; Khairallah, Saad A. Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-04237-z	journal	June 2017
Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance Shevchik, Sergey; Le-Quang, Tri; Meylan, Bastian Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-60294-x	journal	February 2020
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences Alber, Mark; Buganza Tepole, Adrian; Cannon, William R. npj Digital Medicine, Vol. 2, Issue 1 https://doi.org/10.1038/s41746-019-0193-y	journal	November 2019
Roadmap on multiscale materials modeling van der Giessen, Erik; Schultz, Peter A.; Bertin, Nicolas Modelling and Simulation in Materials Science and Engineering, Vol. 28, Issue 4 https://doi.org/10.1088/1361-651X/ab7150	journal	March 2020
Multiscale modelling and simulation: a position paper Hoekstra, Alfons; Chopard, Bastien; Coveney, Peter Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 372, Issue 2021 https://doi.org/10.1098/rsta.2013.0377	journal	August 2014
Performance of distributed multiscale simulations Borgdorff, J.; Ben Belgacem, M.; Bona-Casas, C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 372, Issue 2021 https://doi.org/10.1098/rsta.2013.0407	journal	August 2014
Mastering the scales: a survey on the benefits of multiscale computing software Groen, Derek; Knap, Jaroslaw; Neumann, Philipp Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 377, Issue 2142 https://doi.org/10.1098/rsta.2018.0147	journal	February 2019
Data-Driven Optimization Based on Random Forest Surrogate Zheng, Yuan; Fu, Xiaogang; Xuan, Yanwen 2019 6th International Conference on Systems and Informatics (ICSAI) https://doi.org/10.1109/ICSAI48974.2019.9010547	conference	November 2019
Multisensor Data Fusion for Additive Manufacturing Process Control Vandone, Ambra; Baraldo, Stefano; Valente, Anna IEEE Robotics and Automation Letters, Vol. 3, Issue 4 https://doi.org/10.1109/LRA.2018.2851792	journal	October 2018
A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing Tapia, Gustavo; Elwany, Alaa Journal of Manufacturing Science and Engineering, Vol. 136, Issue 6 https://doi.org/10.1115/1.4028540	journal	October 2014
Thermomechanical Modeling of Additive Manufacturing Large Parts Denlinger, Erik R.; Irwin, Jeff; Michaleris, Pan Journal of Manufacturing Science and Engineering, Vol. 136, Issue 6 https://doi.org/10.1115/1.4028669	journal	October 2014
Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations Huang, Yong; Leu, Ming C.; Mazumder, Jyoti Journal of Manufacturing Science and Engineering, Vol. 137, Issue 1 https://doi.org/10.1115/1.4028725	journal	February 2015
A Review of Machine Learning Applications in Additive Manufacturing Razvi, Sayyeda Saadia; Feng, Shaw; Narayanan, Anantha ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 1: 39th Computers and Information in Engineering Conference https://doi.org/10.1115/DETC2019-98415	conference	November 2019
Controlling interdependent meso-nanosecond dynamics and defect generation in metal 3D printing Khairallah, Saad A.; Martin, Aiden A.; Lee, Jonathan R. I. Science, Vol. 368, Issue 6491 https://doi.org/10.1126/science.aay7830	journal	May 2020
Multiscale Modeling of Powder Bed–Based Additive Manufacturing Markl, Matthias; Körner, Carolin Annual Review of Materials Research, Vol. 46, Issue 1 https://doi.org/10.1146/annurev-matsci-070115-032158	journal	July 2016
Uncertainty Quantification in Multiscale Simulation of Materials: A Prospective Chernatynskiy, Aleksandr; Phillpot, Simon R.; LeSar, Richard Annual Review of Materials Research, Vol. 43, Issue 1 https://doi.org/10.1146/annurev-matsci-071312-121708	journal	July 2013
Multiphysics simulations: Challenges and opportunities Keyes, David E.; McInnes, Lois C.; Woodward, Carol The International Journal of High Performance Computing Applications, Vol. 27, Issue 1 https://doi.org/10.1177/1094342012468181	journal	February 2013
Overview of modelling and simulation of metal powder bed fusion process at Lawrence Livermore National Laboratory King, W.; Anderson, A. T.; Ferencz, R. M. Materials Science and Technology, Vol. 31, Issue 8 https://doi.org/10.1179/1743284714Y.0000000728	journal	November 2014
The Creation of Surrogate Models for Fast Estimation of Complex Model Outcomes Pruett, W. Andrew; Hester, Robert L. PLOS ONE, Vol. 11, Issue 6 https://doi.org/10.1371/journal.pone.0156574	journal	June 2016
Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning Han, Yi; Griffiths, R. Joey; Yu, Hang Z. Journal of Materials Research, Vol. 35, Issue 15 https://doi.org/10.1557/jmr.2020.120	journal	June 2020
Heat transfer and fluid flow in additive manufacturing Raghavan, A.; Wei, H. L.; Palmer, T. A. Journal of Laser Applications, Vol. 25, Issue 5 https://doi.org/10.2351/1.4817788	journal	November 2013
Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models Pathmanathan, Pras; Cordeiro, Jonathan M.; Gray, Richard A. Frontiers in Physiology, Vol. 10 https://doi.org/10.3389/fphys.2019.00721	journal	June 2019
Digital Twins for Additive Manufacturing: A State-of-the-Art Review Zhang, Li; Chen, Xiaoqi; Zhou, Wei Applied Sciences, Vol. 10, Issue 23 https://doi.org/10.3390/app10238350	journal	November 2020
Predictive Simulation of Process Windows for Powder Bed Fusion Additive Manufacturing: Influence of the Powder Bulk Density Rausch, Alexander; Küng, Vera; Pobel, Christoph Materials, Vol. 10, Issue 10 https://doi.org/10.3390/ma10101117	journal	September 2017
Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants Bevilacqua, Maurizio; Bottani, Eleonora; Ciarapica, Filippo Emanuele Sustainability, Vol. 12, Issue 3 https://doi.org/10.3390/su12031088	journal	February 2020
Standardization of Additive Manufacturing for Oil and Gas Applications Vendra, Lakshmi; Malkawi, Ameen; Avagliano, Aaron Day 3 Wed, May 06, 2020 https://doi.org/10.4043/30533-MS	conference	May 2020

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