锻压技术

基于响应曲面法同/异种铝合金自冲铆接接头强度预测

英文标题：Strength prediction on self-piercing riveting joint for similar/dissimilar aluminum alloys based on response surface method

作者：雷乐1 2 赵伦1 高山凤2 李承旺1 卢杰豪1 刘高宇1

单位：1.深圳职业技术大学智能制造技术研究院 2.山西大学自动化与软件学院

关键词：铝合金自冲铆接响应面法回归模型强度预测力学性能

分类号：TH131.1

出版年,卷(期):页码：2024,49(4):146-152

摘要：

为寻找自冲铆接试验指标与接头强度的定量规律，找出各因子水平的最佳组合并进行有效预测，基于BoxBehnken Design(BBD)响应面法对Al1060、Al5754、Al6061这3种铝合金薄板进行自冲铆接试验。以搭接上板硬度、下板硬度、铆钉长度为自变量，失效载荷和能量吸收为响应值，建立自冲铆接接头强度预测多元非线性回归模型。试验结果表明，接头强度预测值与试验值之间的误差小于8%，证明了回归模型的可靠性。对模型进行方差分析发现，上下板硬度与接头失效载荷呈正相关性，铆钉长度与接头失效载荷呈负相关性；上板硬度与下板硬度的交互作用对接头失效载荷和能量吸收均有较大影响。

In order to find out the quantitative laws of self-piercing riveting test indexes and joint strength, and the optimal combination of each factor level to make effective prediction, the self-piercing riveting tests for Al1060, Al5754 and Al6061 aluminum alloy thin plates were conducted by Box-Behnken Design (BBD) response surface method. Then, taking the hardness of upper plate, hardness of lower plate and length of rivets as the independent variables, and the failure load and energy absorption value as the response values, a multivariate nonlinear regression model for the strength prediction of self-piercing riveting joint was established. The test results show that the error of joint strength between predicted and test values is within 8%, which proves the reliability of the regression model. The variance analysis of the model shows that the hardnesses of upper and lower plates gave a positive correlation with the failure load of joint, and the length of rivet has a negative correlation with the failure load of joint. The interaction between the hardness of upper plate and the hardness of lower plate has a greater effect on the failure load and energy absorption of joint.

基金项目：

国家自然科学基金资助项目 (12104324)；高层次人才科研启动项目（6022310046k）;深职院-新栋力超声波焊接技术研发中心项目（602331009PQ）; 深圳市优秀科技创新人才培养项目（RCBS20210706092213010）；广东省普通高校特色创新项目（504-6020210006T）;博士后基金启动项目(6022331003K)

作者简介：雷乐(1999-)，男，硕士研究生 E-mail：leile319777@163.com 通信作者：赵伦(1988-)，男，博士，副研究员 E-mail：zhaolun@szpt.edu.cn

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