LS-DYNA Machine Learning–Based Multiscale Method for Nonlinear Modeling of Short Fiber–Reinforced Composites
Publication: Journal of Engineering Mechanics
Volume 149, Issue 3
Abstract
Short fiber–reinforced composites (SFRCs) are high-performance engineering materials for lightweight structural applications in the automotive and electronics industries. Typically, SFRC structures are manufactured by injection molding, which induces heterogeneous microstructures, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical analyses. In this work, we present a machine learning–based multiscale method by integrating injection molding–induced microstructures, material homogenization, and deep material network (DMN) in the finite-element simulation software LS-DYNA for structural analysis of SFRC. DMN is a physics-embedded machine learning model that learns the microscale material morphologies hidden in representative volume elements of composites through offline training. By coupling DMN with finite elements, we have developed a highly accurate and efficient data-driven approach that predicts nonlinear behaviors of composite materials and structures at a computational speed orders of magnitude faster than the high-fidelity direct numerical simulation. To model industrial-scale SFRC products, transfer learning is utilized to generate a unified DMN database, which effectively captures the effects of injection molding–induced fiber orientations and volume fractions on the overall composite properties. Numerical examples are presented to demonstrate the promising performance of this LS-DYNA machine learning–based multiscale method for SFRC modeling.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. The training data and trained deep material network models are confidential, and the source code of LS-DYNA is proprietary of Ansys Inc.
Acknowledgments
The authors would like to acknowledge Zeliang Liu, Tianyu Huang, Dandan Lyu, Yong Guo, Kai Wang, and Philip Ho for their great help with the multiscale method development, and we would also like to thank Madhu Keshavamurthy for continuously supporting this research work. The first author is thankful to Xiaolong He for in-depth discussions on machine learning methods. In addition, we sincerely thank the anonymous reviewers for their constructive comments.
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Journal of Engineering Mechanics
Volume 149 • Issue 3 • March 2023
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© 2023 American Society of Civil Engineers.
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Received: Aug 31, 2022
Accepted: Nov 1, 2022
Published online: Jan 5, 2023
Published in print: Mar 1, 2023
Discussion open until: Jun 5, 2023
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