Prediction of cross-tension strength of self-piercing riveted joints using finite element simulation and XGBoost algorithm

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.

Research on transformer fault diagnosis: Based on improved firefly algorithm optimized LPboost–classification and regression tree

“…Suppose that there are n types of samples in the sample set D, in which the proportion of class i samples is pi , then the Gini index is: Generally speaking, in order to ensure the calculation speed of the ensemble learning model, the weak classifier should be a relatively simple model. Because CART has the advantages of simple structure and fast running speed, it is often used as a weak classifier in ensemble learning algorithms, for example, the weak classifiers in random forest [23] and XGboost [34] are CART. Therefore, in this study, we choose CART as the weak classifier in the transformer fault diagnosis model.…”

Realtime Prediction of Self-Pierce Riveting Joints - Prognosis and Visualization Based on Simulation and Machine Learning

“…The ability to use machine learning algorithms to predict results in mechanical joining technology has already been used widely. For example, artificial neural networks have been used to predict joint strengths [3], to classify defects in radial clinching [8] and rivet head end position in SPR-ST [9], to predict forces in clinching with divided dies [10], to predict punch force [11] and to generally predict joining ability [12] in SPR-ST. Other algorithms were used, for example, in the prediction of loadbearing behavior of clinch joints (k-nearest neighbors) [13], for the determination of failure values in SPR-ST (XG Boost) [14] or joining point prediction of clinching joints [1], lockbolts [15], self-pierce riveting with solid formable rivet [16] or self-flaring rivet [17] (Kriging, moving least squares, polynomial approaches). These works only allow the prediction of discrete values and not the prediction of a complete joining point contour.…”

Mechanical performance and failure modes of self-piercing riveted joints between AA6061 and solution-treated TC4 alloy

“…Steel plates are usually concave, while aluminum plates are usually convex. Lin et al [20] used finite element simulation to study the cross tensile strength of SPR joints, and verify the simulation results with experimental results. Kim et al [21] calculated the formation of the SPR joint with the help of the machine learning method.…”