Prediction of Cross-Tension Strength of Self-Piercing Riveted Joints Using Finite Element Simulation and XGBoost Algorithm

Jianping Lin,Junying Min,Hailang Wan,Kai Zhang,Chengwei Qi,Jiajie Chen,Li Zhang

doi:10.1186/s10033-021-00551-w

Abstract

Self-piercing riveting (SPR) has been widely used in automobile industry, and the strength prediction of SPR joints always attracts the attention of researchers. In this work, a prediction method of the cross-tension strength of SPR joints was proposed on the basis of finite element (FE) simulation and extreme gradient boosting decision tree (XGBoost) algorithm. An FE model of SPR process was established to simulate the plastic deformations of rivet and substrate materials and verified in terms of cross-sectional dimensions of SPR joints. The residual mechanical field from SPR process simulation was imported into a 2D FE model for the cross-tension testing simulation of SPR joints, and cross-tension strengths from FE simulation show a good consistence with the experiment result. Based on the verified FE model, the mechanical properties and thickness of substrate materials were varied and then used for FE simulation to obtain cross-tension strengths of a number of SPR joints, which were used to train the regression model based on the XGBoost algorithm in order to achieve prediction for cross-tension strength of SPR joints. Results show that the cross-tension strengths of SPR steel/aluminum joints could be successfully predicted by the XGBoost regression model with a respective error less than 7.6% compared to experimental values.

Highlights

Reduction in weight of automobile parts is a prevailing trend in automobile industry
5 Conclusions In this work, a cross-tension strength prediction method based on finite element (FE) model simulation and XGBoost gradient descent decision tree algorithm is proposed
A 2D FE model of self-piercing riveting (SPR) process was established to acquire the residual mechanical fields including plastic strain and residual stress of rivet and substrate material, which were imported into the 2D FE model of cross-tension testing of SPR joints

Summary

Introduction

Reduction in weight of automobile parts is a prevailing trend in automobile industry. The strengths of SPR joints are generally obtained through conventional mechanical tests, which is costly and time-consuming To solve this problem, Sun and Khaleel [[10]] proposed an analytical model to predict the static strength based on some cross-sectional dimensions of SPR joints and characteristics of substrates. An FE model considering the plastic strain and residual stress induced from SPR process can effectively help the prediction of SPR joint strengths, but it is still a timeconsuming method. Prediction models using machine learning algorithms usually require huge data sets to achieve accurate predictions, and it is not economical to obtain data sets through experiments.

Experimental Details

Numerical Simulation

Finite Element Modeling of Cross‐Tension Testing of SPR Joints

Findings

Conclusions