Development of a cascaded multitask physics-informed neural network (CM-PINN) to construct the muti-physical field model of rubber bushing press fitting

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yiru Chen, Jianfu Zhang, Pingfa Feng, Zhongpeng Zheng, Xiangyu Zhang, Jianjian Wang
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引用次数: 0

Abstract

The real-time and accurate prediction of the stress–strain and deformations field of material is a vital function for the intelligent press fitting system of the rubber bushing. The physics-informed neural network (PINN) provide an efficient approach to constructing physical fields with high robustness and interpretability in real time. However, currently, PINN usually solves problems under known boundary conditions, which are not given explicitly in most realistic engineering problems. This study proposes a cascaded multitask PINN (CM-PINN) that divides the problem solving of rubber bushing interference fit into two phases: boundary computation and forward solving of the physical field. In CM-PINN, one sub-network is used for boundary computation, while two other sub-networks are used for computing the physical fields of hyperelastic material, rubber. In both stages, physical constraints are incorporated into the sub-networks. These subnetworks are trained hybridly through the cascaded framework using data obtained from the finite element model (FEM), which was verified by experimental results. In order to validate the CM-PINN model, FEM data are used as a reference solution for comparison with conventional PINN. To evaluate the advantages of CM-PINN, ablation tests are conducted by randomly selecting training samples with different sizes. It is found that CM-PINN has higher accuracy and convergence compared to hybrid output PINNs. CM-PINN shows remarkable improvement in its generalization ability in the case of small sample size, underscoring its robust applicability across different data scenarios.

Abstract Image

开发级联多任务物理信息神经网络(CM-PINN)以构建橡胶衬套压配的多物理场模型
实时准确地预测材料的应力应变和变形场是橡胶衬套智能压配系统的一项重要功能。物理信息神经网络(PINN)为实时构建具有高鲁棒性和可解释性的物理场提供了一种有效方法。然而,目前 PINN 通常是在已知边界条件下求解问题,而大多数现实工程问题并没有明确给出边界条件。本研究提出了一种级联多任务 PINN(CM-PINN),它将橡胶衬套干涉配合的问题求解分为两个阶段:边界计算和物理场的前向求解。在 CM-PINN 中,一个子网络用于边界计算,另外两个子网络用于计算超弹性材料橡胶的物理场。在这两个阶段,子网络中都包含物理约束。利用从有限元模型(FEM)中获得的数据,通过级联框架对这些子网络进行混合训练,并通过实验结果进行验证。为了验证 CM-PINN 模型,有限元模型数据被用作与传统 PINN 比较的参考解决方案。为了评估 CM-PINN 的优势,通过随机选择不同大小的训练样本进行了烧蚀测试。结果发现,与混合输出 PINN 相比,CM-PINN 具有更高的精度和收敛性。在样本量较小的情况下,CM-PINN 的泛化能力也有显著提高,这突出表明了它在不同数据场景下的强大适用性。
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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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