Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading
Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from computer-aided design models. Using two or more constituent materials with different physical and...
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| Veröffentlicht in: | Computational mechanics Jg. 72; H. 3; S. 563 - 576 |
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| Sprache: | Englisch |
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01.09.2023
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| Abstract | Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from computer-aided design models. Using two or more constituent materials with different physical and mechanical properties, it becomes possible to construct interpenetrating phase composites (IPCs) with 3D interconnected structures to provide superior mechanical properties as compared to the conventional reinforced composites with discrete particles or fibers. The mechanical properties of IPCs, especially response to dynamic loading, highly depend on their 3D structures. In general, for each specified structural design, it could take hours or days to perform either finite element analysis (FEA) or experiments to test the mechanical response of IPCs to a given dynamic load. To accelerate the physics-based prediction of mechanical properties of IPCs for various structural designs, we employ a deep neural operator (DNO) to learn the transient response of IPCs under dynamic loading as surrogate of physics-based FEA models. We consider a 3D IPC beam formed by two metals with a ratio of Young’s modulus of 2.7, wherein random blocks of constituent materials are used to demonstrate the generality and robustness of the DNO model. To obtain FEA results of IPC properties, 5000 random time-dependent strain loads generated by a Gaussian process kennel are applied to the 3D IPC beam, and the reaction forces and stress fields inside the IPC beam under various loading are collected. Subsequently, the DNO model is trained using an incremental learning method with sequence-to-sequence training implemented in JAX, leading to a 100X speedup compared to widely used vanilla deep operator network models. After an offline training, the DNO model can act as surrogate of physics-based FEA to predict the transient mechanical response in terms of reaction force and stress distribution of the IPCs to various strain loads in one second at an accuracy of 98%. Also, the learned operator is able to provide extended prediction of the IPC beam subject to longer random strain loads at a reasonably well accuracy. Such superfast and accurate prediction of mechanical properties of IPCs could significantly accelerate the IPC structural design and related composite designs for desired mechanical properties. |
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| AbstractList | Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from computer-aided design models. Using two or more constituent materials with different physical and mechanical properties, it becomes possible to construct interpenetrating phase composites (IPCs) with 3D interconnected structures to provide superior mechanical properties as compared to the conventional reinforced composites with discrete particles or fibers. The mechanical properties of IPCs, especially response to dynamic loading, highly depend on their 3D structures. In general, for each specified structural design, it could take hours or days to perform either finite element analysis (FEA) or experiments to test the mechanical response of IPCs to a given dynamic load. To accelerate the physics-based prediction of mechanical properties of IPCs for various structural designs, we employ a deep neural operator (DNO) to learn the transient response of IPCs under dynamic loading as surrogate of physics-based FEA models. We consider a 3D IPC beam formed by two metals with a ratio of Young's modulus of 2.7, wherein random blocks of constituent materials are used to demonstrate the generality and robustness of the DNO model. To obtain FEA results of IPC properties, 5000 random time-dependent strain loads generated by a Gaussian process kennel are applied to the 3D IPC beam, and the reaction forces and stress fields inside the IPC beam under various loading are collected. Subsequently, the DNO model is trained using an incremental learning method with sequence-to-sequence training implemented in JAX, leading to a 100X speedup compared to widely used vanilla deep operator network models. After an offline training, the DNO model can act as surrogate of physics-based FEA to predict the transient mechanical response in terms of reaction force and stress distribution of the IPCs to various strain loads in one second at an accuracy of 98%. Also, the learned operator is able to provide extended prediction of the IPC beam subject to longer random strain loads at a reasonably well accuracy. Such superfast and accurate prediction of mechanical properties of IPCs could significantly accelerate the IPC structural design and related composite designs for desired mechanical properties. Not provided. |
| Audience | Academic |
| Author | Meng, Zhaoxu Li, Zhen Meng, Xuhui Mohammadi, Ali Li, Gang Lu, Minglei |
| Author_xml | – sequence: 1 givenname: Minglei surname: Lu fullname: Lu, Minglei organization: Department of Mechanical Engineering, Clemson University – sequence: 2 givenname: Ali surname: Mohammadi fullname: Mohammadi, Ali organization: Department of Mechanical Engineering, Clemson University – sequence: 3 givenname: Zhaoxu surname: Meng fullname: Meng, Zhaoxu organization: Department of Mechanical Engineering, Clemson University – sequence: 4 givenname: Xuhui surname: Meng fullname: Meng, Xuhui organization: School of Mathematics and Statistics, Huazhong University of Science and Technology – sequence: 5 givenname: Gang surname: Li fullname: Li, Gang organization: Department of Mechanical Engineering, Clemson University – sequence: 6 givenname: Zhen surname: Li fullname: Li, Zhen email: zli7@clemson.edu organization: Department of Mechanical Engineering, Clemson University |
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| CitedBy_id | crossref_primary_10_1016_j_ijheatmasstransfer_2024_125659 crossref_primary_10_1016_j_ijmultiphaseflow_2024_104959 crossref_primary_10_1016_j_ymssp_2025_112914 crossref_primary_10_1038_s41524_025_01718_y crossref_primary_10_1002_nme_7637 crossref_primary_10_1016_j_biombioe_2025_108217 crossref_primary_10_3390_buildings14113515 crossref_primary_10_1016_j_powtec_2024_119830 crossref_primary_10_1088_2632_2153_ada0a5 crossref_primary_10_1016_j_cma_2024_117229 crossref_primary_10_1016_j_compstruc_2024_107425 |
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| Keywords | Additive manufacturing Neural network Finite element analysis Deep operator network Machine learning |
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| Title | Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading |
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