Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading
Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading
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DOI：
10.1007/s00466-023-02343-6
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发表时间：
2023-03
影响因子：
4.1
通讯作者：
Minglei Lu;Ali Mohammadi;Zhaoxu Meng;Xuhui Meng;Gang Li;Zhen Li
中科院分区：
文献类型：
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作者：
Minglei Lu;Ali Mohammadi;Zhaoxu Meng;Xuhui Meng;Gang Li;Zhen Li
增材制造已经被认为是制造业的工业技术革命，其允许直接从计算机辅助设计模型制造具有复杂三维（3D）结构的材料。使用具有不同物理和机械性能的两种或更多种组成材料，可以构造具有3D互连结构的互穿相复合材料（IPC），以提供与具有离散颗粒或纤维的常规增强复合材料相比上级的机械性能。IPC的力学性能，特别是对动态载荷的响应，高度依赖于其三维结构。一般来说，对于每个指定的结构设计，可能需要数小时或数天来执行有限元分析（FEA）或实验，以测试IPC对给定动态载荷的机械响应。为了加速对各种结构设计的IPC的机械性能进行基于物理的预测，我们采用了深度神经运算符（DNO）来学习IPC在动态载荷下的瞬态响应，作为基于物理的FEA模型的替代。我们考虑由两种金属形成的3D IPC梁的杨氏模量比为2.7，其中随机块的组成材料被用来证明的DNO模型的通用性和鲁棒性。为了获得IPC性能的有限元分析结果，由高斯过程核产生的5000个随机时变应变载荷被施加到3D IPC梁，并且收集在各种载荷下IPC梁内部的反力和应力场。随后，DNO模型使用增量学习方法进行训练，并在JAX中实现序列到序列的训练，与广泛使用的vanilla deep算子网络模型相比，速度提高了100倍。离线训练后，DNO模型可以作为基于物理的FEA的替代品，以98%的准确度在一秒内预测IPC对各种应变载荷的反作用力和应力分布方面的瞬态机械响应。此外，学习的操作员能够以相当好的精度提供对经受较长随机应变载荷的IPC梁的扩展预测。IPC的机械性能的这种超快速和准确的预测可以显著加速IPC结构设计和相关的复合材料设计，以获得所需的机械性能。
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.