Effect Analysis for the Uncertain Parameters on Self-Piercing Riveting Simulation Model Using Machine Learning Model 2020-01-0219
Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions). It is found that it is feasible to train surrogate models with high accuracy to replace the time-consuming and computationally expensive CAE simulations with a limited sampling volume. Based on trained surrogate models, an extensive sensitivity study is conducted to thoroughly understand the effect of a set of model parameters. This work provides a solid foundation for data-modelling and CAE model calibration for the SPR insertion process.
Citation: Fang, Y., Huang, L., Zhan, Z., Huang, S. et al., "Effect Analysis for the Uncertain Parameters on Self-Piercing Riveting Simulation Model Using Machine Learning Model," SAE Technical Paper 2020-01-0219, 2020, https://doi.org/10.4271/2020-01-0219. Download Citation
Yudong Fang, Li Huang, Zhenfei Zhan, Shiyao Huang, Weijian Han
Chongqing University, Ford Motor Company, Nanjing Tech University
WCX SAE World Congress Experience
Finite element analysis
CAD, CAM, and CAE
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