锻压技术

铝合金板件自冲铆接性能的响应面-遗传算法优化

英文标题：Optimization on self-piercing riveted performance of aluminum alloy plate part based on response surface and genetic algorithm

作者：卢秋霞刘洁王志鹏

单位：山东劳动职业技术学院海军航空大学

关键词：自冲铆接极差分析主效应图二阶响应面模型存档微遗传算法抗拉剪能力

分类号：TG386.1

出版年,卷(期):页码：2021,46(5):57-63

摘要：

为了改善自冲铆接的接头性能，提出了融合二阶响应面模型和存档微遗传算法的多目标优化方法。介绍了自冲铆接原理和铆接过程，建立了提高接头底切量、剩余厚度、底部厚度的多目标优化模型。选择了待优化的模具参数，基于极差分析和主效应图对模具参数的灵敏度进行了分析。基于中心复合实验设计法与二阶响应面法，对模具参数和质量参数之间的函数关系进行了拟合，经决定系数和修正决定系数验证，二阶响应面的拟合精度较高。使用存档微遗传算法对多目标优化模型进行求解，获得了优化的模具参数。经生产验证并与工厂的自冲铆接接头相比较可知，优化的接头底切量提高了10.00%，底部厚度提高了8.61%，剩余厚度提高了3.11%，抗拉剪能力提高了5.68%。以上数据表明，经过优化后，自冲铆接的接头性能得到了有效提高。

In order to improve the performance of self-piercing riveting joint, a multi-objective optimization method combining the second-order response surface model and the archive micro genetic algorithm was proposed. Then, the principle and process of self-piercing riveting were introduced, and a multi-objective optimization model was established to improve undercut amount, residual thickness and bottom thickness of joint. Furthermore, the mold parameters to be optimized were selected, and the sensitivity of mold parameters was analyzed based on range analysis and main effect diagram. Based on the central composite experimental design method and the second-order response surface method, the functional relationship between mold parameters and quality parameters was fitted, and the fitting accuracy of the second-order response surface was higher through the verification of determination coefficient and modified determination coefficient. Finally, the multi-objective optimization model was solved by the archive micro genetic algorithm, and the optimized mold parameters were obtained. Compared with the factory self-piercing riveting joint, the optimized undercut amount of joint increases by 10.00%, the bottom thickness increases by 8.61%, the residual thickness increases by 3.11%, and the tensile shear capacity increases by 5.68%. Thus, the above data show that the performance of self-piercing riveting joint is effectively improved after optimization.

基金项目：

中国交通教育研究会教育科学研究课题（交教研1802-129）

卢秋霞（1983-），女，硕士，讲师 E-mail：luqiuxia_123@163.com

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