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

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.

A machine learning-based calibration method for strength simulation of self-piercing riveted joints

This paper presents a new machine learning-based calibration framework for strength simulation models of self-piercing riveted(SPR)joints.Strength simulations were conducted through the integrated modeling of SPR joints from process to performance,while physical quasi-static tensile tests were performed on combinations of DP600 high-strength steel and 5754 aluminum alloy sheets under lap-shear loading conditions.A sensitivity study of the critical simulation parameters(e.g.,friction coefficient and scaling factor)was conducted using the controlled variables method and Sobol sensitivity analysis for feature selection.Subsequently,machine-learning-based surrogate models were used to train and accurately represent the mapping between the detailed joint profile and its load-displacement curve.Calibration of the simulation model is defined as a dual-objective optimization task to minimize errors in key load displacement features between simulations and experiments.A multi-objective genetic algorithm(MOGA)was chosen for optimization.The three combinations of SPR joints illustrated the effectiveness of the proposed framework,and good agreement was achieved between the calibrated models and experiments.

An Overview of Self-piercing Riveting Process with Focus on Joint Failures, Corrosion Issues and Optimisation Techniques

Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has become increasingly popular mainly due to the growing use of lightweight materials in transportation applications.However,SPR joining of these advanced light materials remains a challenge as these materials often lack a good combination of high strength and ductility to resist the large plastic deformation induced by the SPR process.In this paper,SPR joints of advanced materials and their corresponding failure mechanisms are discussed,aiming to provide the foundation for future improvement of SPR joint quality.This paper is divided into three major sections:1)joint failures focusing on joint defects originated from the SPR process and joint failure modes under different mechanical loading conditions,2)joint corrosion issues,and 3)joint optimisation via process parameters and advanced techniques.

Study of galvanic corrosion and mechanical joint properties of AZ31B and carbon-fiber–reinforced polymer joined by friction self-piercing riveting

A new testing methodology was developed to quantitively study galvanic corrosion of AZ31B and thermoset carbon-fiber–reinforced polymer spot-joined by a friction self-piercing riveting process.Pre-defined areas of AZ31B in the joint were exposed in 0.1 M NaCl solution over time.Massive galvanic corrosion of AZ31B was observed as exposure time increased.The measured volume loss was converted into corrosion current that was at least 48 times greater than the corrosion current of AZ31B without galvanic coupling.Ninety percent of the mechanical joint integrity was retained for corroded F-SPR joints to 200 h and then decreased because of the massive volume loss of AZ31B。

A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints

In lightweight automotive vehicles,the application of self-piercing rivet(SPR)joints is becoming increasingly widespread.Considering the importance of automotive service performance,the fatigue performance of SPR joints has received considerable attention.Therefore,this study proposes a data-driven approach to predict the fatigue life and failure modes of SPR joints.The dataset comprises three specimen types:cross-tensile,cross-peel,and tensile-shear.To ensure data consistency,a finite element analysis was employed to convert the external loads of the different specimens.Feature selection was implemented using various machine-learning algorithms to determine the model input.The Gaussian process regression algorithm was used to predict fatigue life,and its performance was compared with different kernel functions commonly used in the field.The results revealed that the Matern kernel exhibited an exceptional predictive capability for fatigue life.Among the data points,95.9%fell within the 3-fold error band,and the remaining 4.1%exceeded the 3-fold error band owing to inherent dispersion in the fatigue data.To predict the failure location,various tree and artificial neural network(ANN)models were compared.The findings indicated that the ANN models slightly outperformed the tree models.The ANN model accurately predicts the failure of joints with varying dimensions and materials.However,minor deviations were observed for the joints with the same sheet.Overall,this data-driven approach provided a reliable predictive model for estimating the fatigue life and failure location of SPR joints.

Flexible servo riveting system control strategy based on the RBF network and self-pierce riveting process

As more and more composite materials are used in lightweight vehicle white bodies,self-pierce riveting(SPR)technology has attracted great attention.However,the existing riveting tools still have the disadvantages of low efficiency and flexibility.To improve these disadvantages and the riveting qualification rate,this paper improves the control scheme of the existing riveting tools,and proposes a novel controller design approach of the flexible servo riveting system based on the RBF network and SPR process.Firstly,this paper briefly introduces the working principle and SPR procedure of the servo riveting tool.Then a moving component force analysis is performed,which lays the foundation for the motion control.Secondly,the riveting quality inspection rules of traditional riveting tools are used for reference to plan the force-displacement curve autonomously.To control this process,the riveting force is fed back into the closed-loop control of the riveting tool and the riveting speed is computed based on the admittance control algorithm.Then,this paper adopts the permanent magnet synchronous motor(PMSM)as the power of riveting tool,and proposes an integral sliding mode control approach based on the improved reaching law and the radial basis function(RBF)network friction compensation for the PMSM speed control.Finally,the proposed control approach is simulated by Matlab,and is applied to the servo riveting system designed by our laboratory.The simulation and riveting results show the feasibility of the designed controller.

Influence of die geometry on self-piercing riveting of aluminum alloy AA6061-T6 to mild steel SPFC340 sheets

The self-piercing riveting (SPR) process was used to join 2.0-mm-thick aluminum alloy 6061-T6 and 1.2-mm-thick mild steel SPFC340 sheets. SPR joints produced with a conventional flat-bottom die and conicalsection dies were investigated both experimentally and numerically. Lap shear tests were conducted under quasistatic conditions to evaluate the load-carrying capability of these SPR joints. The effect of variation in die geometry (such as variation in the die groove shape, cone height, and die radius) on the main mechanical response of the joints, namely the peak load and energy absorption, was discussed. The results showed that SPR joints produced with the conical-section dies exhibited a failure mode similar to those produced with a conventional die. All the joints failed by tearing of the top steel sheet. Cracks that occurred in the bottom aluminum alloy 6061-T6 sheet around the rivet leg were a result of tangential tensile stress. The cone height of a conical-section die is the most important parameter affecting the surface quality of Al/steel SPR joints. Conical-section dies with a moderate convex can ensure a good surface quality during the SPR process. In addition, SPR joints with single conical-section die allow higher tensile strength and energy absorption compared to those with double conical-section die.

基于试验的自冲铆接头失效机理分析

通过试验对比分析了自冲铆接头在静态力学性能测试和疲劳试验中的拉伸剪切和剥离失效机理。结果表明:母材强度和厚度、内锁长度、母材与铆钉之间的接触摩擦系数决定着自冲铆接头的静载强度和失效形式;板材轧制方向、材料缺陷、铆接成形残余应力以及接触界面摩擦系数是影响自冲铆接头疲劳寿命和疲劳失效的主要因素。

钉脚张开度对自冲铆构件机械内锁刚度的影响

利用数字图像分析技术研究了铆钉张开度对自冲铆接头机械内锁结构刚度的影响.借助材料性能试验机对具有不同铆钉张开度的5052铝合金板自冲铆接头进行单向静拉伸试验,加载过程中利用CCD获取自冲铆机械内锁结构形变的图像序列,利用图像相关分析计算不同载荷水平下铆钉脚与底板的滑移错动量,建立滑移载荷曲线,并据此评估结构件刚度大小;建立自冲铆接头有限元模型,对比分析不同钉脚张开度的自冲铆机械内锁的失效过程.结果表明,在受载状态下,自冲铆接头存在一个临界滑移启动载荷值,当外载荷小于此临界值时,机械内锁结构保持相对完整,处于弹性变形阶段;大于此临界值时,滑移错动量明显增大,结构发生塑性大变形,并最终呈现脱离失效模式.其次,钉脚张开度对自冲铆构件的强度和刚度影响较大,提高钉脚张开度有助于增大构件整体刚度.

铝锂合金T型自冲铆接头疲劳特性及失效机理

为研究T型自冲铆接头的疲劳特性,以铝锂合金(AL1420)同种及其与铜合金(H62)和不锈钢(410)异种组合制备的AA、HA和SA三组T型自冲铆接头为研究对象,基于其静力学试验采用正弦波进行拉-拉加载疲劳试验,用二参数威布尔分布对所得疲劳数据的有效性进行验证,采用最小二乘拟合直线得到拟合的F-N曲线,并采用SEM扫描电镜对各组典型疲劳失效断口进行微观分析。结果表明:SA组疲劳寿命对载荷变化最敏感,并且疲劳寿命随相对滑移量的增加而减小;由三组T型接头失效形式可看出接头薄弱环节有由铆钉向板材转移的趋势;从微观分析疲劳断口可知裂纹萌生区多为形貌特征较为平坦的准解理,裂纹瞬断区形貌更加突出,多为沿晶断裂或韧窝形貌。

钛合金同种自冲铆接头的拉伸与剥离性能

为了分析钛合金(TA1)同种自冲铆单搭和T型两种接头的静力学性能,分别对两种接头进行了拉伸和剥离试验,并通过SEM扫描电镜对两种接头的失效部位的微观形貌进行了观察。结果表明:TA1+TA1的T型接头剥离时所承受最大载荷均值(1211.0 N)小于单搭接头拉伸时所承受最大载荷均值(4706.5 N)。单搭接头和T型接头的主要失效形式皆为下板拉脱使机械内锁失效,且下板皆有不同程度的刮擦损伤痕迹;特别是TA1+TA1单搭接头的铆钉管腿处出现不同程度的断裂,铆钉主要断口微观形貌为典型的沿晶断裂;T型接头下板存在很大的刮擦损伤。

TA1钛合金异种板自冲铆接头剪切-剥离性能研究

采用自冲铆接技术(SPR)对厚度1.5 mm的钛合金(TA1)与1420铝锂合金(1420)以及铜合金(H62)异种板材组合进行了连接试验,并对制成的单搭和T型接头进行了剪切与剥离试验,分析了各组接头的失效模式以及静力学性能。结果表明:单搭接头组的静失效载荷均值明显高于T型接头组,但前者的能量吸收均值小于后者,接头形式及失效模式对能量吸收值有很大影响;TA1钛合金异种自冲铆接头拉伸剪切的失效模式取决于塑性变形后铆钉脚的强度、铆扣强度及下板接头处的材料强度。

钛合金异质T型自冲铆接头的剥离性能研究

对TA1钛合金板及与1420铝锂合金板(AL1420)、5052铝合金板(AA5052)、镀锌钢板(Q215)和铜合金板(H62)的异质T型自冲铆接头进行剥离试验,观察了各组接头的成形质量。分析了接头的力学性能和能量吸收值。结果表明:自冲铆接技术能够实现TA1异质板材的有效连接,其中TA1-Q215接头的最大剥离载荷高于其余3组接头,TA1-AL1420接头能量吸收性能最优。

几种金属材料自冲铆接头的静态失效机理

利用自冲铆连接系统、材料试验机及扫描电子显微镜等设备来研究钛合金、铝锂合金及铝合金自冲铆接头的力学性能与静态失效机理。结果表明:TA1钛合金接头平均最大拉剪载荷(6 285.0 N)在3种接头中最大,8090铝锂合金接头(5 478.3 N)次之,5052铝合金接头(3 217.7 N)最小;不同金属材料自冲铆接头的静态失效模式有很大的区别,TA1钛合金接头出现铆钉从沿晶断裂向韧性断裂转变的失效过程,8090铝锂合金接头出现材料被擦伤与解理断裂的失效过程,5052铝合金接头仅出现材料被擦伤的失效过程。

Numerical Study on Die Design Parameters of Self-Pierce Riveting Process Based on Orthogonal Test

The concave die design of self-pierce riveting(SPR) is of critical importance for product quality. The optimization of concave die parameters based on orthogonal test is proposed to explore the relationship between self-pierce riveted joint quality and die parameters. There are nine independent die parameter factors in orthogonal test and each factor has 4 levels. In order to evaluate the interlock and neck thickness, we carry out numerical simulations by the software DEFORM-2D. Then, the primary and secondary factors that affect the joint quality have been found out by means of range analysis. Finally, an optimization scheme is brought forward to design concave die in SPR process, which indicates that the joint has higher quality than that of former orthogonal tests.This work can be extended by a detailed mechanical and fatigue analysis for the joint quality of SPR process.

基于力与位移信号的自冲铆接质量在线监测

基于Labview软件建立了自冲铆接过程信号动态测量系统,研究了自冲铆接过程力和位移信号随时间的变化规律以及接头成形过程中力和位移的演化特征,提出以力和位移信号的特征量在线评估自冲铆接接头质量的方法,并重点研究了不同板件间隙、铆接力和凹模尺寸下力及位移信号的演化规律,并结合接头的剖面形态和准静态拉伸强度,分析了通过力和位移信号在线监测铆接接头质量的可行性。