锻压技术

基于遗传算法的强力旋压连杆衬套工艺参数多目标优化

英文标题：Multi-objective optimization on process parameters of power spinning for connecting rod bushing based on genetic algorithms

作者：佘勇占刚樊文欣毛卫秀余世捷

单位：贵州电子信息职业技术学院贵州大学中北大学

关键词：强力旋压连杆衬套神经网络遗传算法多目标优化

分类号：TG376

出版年,卷(期):页码：2019,44(12):187-191

摘要：

针对连杆衬套的强力旋压成形工艺参数与力学性能之间的复杂关系，建立了工艺参数（减薄率、进给比）与力学性能（抗拉强度、伸长率）之间的RBF神经网络非线性关系。利用正交试验所得的数据结果对神经网络进行训练和测试，通过实测值与预测值的对比，发现所建立的神经网络模型具有较高的预测精度。并将此非线性关系作为适应度函数，基于遗传算法建立了工艺参数（减薄率、进给比）的多目标（抗拉强度、伸长率）优化模型，得出了多目标Pareto最优解集，并通过试验分析验证了最优解集的可行性，可以有效提高工艺参数的设计效率和产品的力学性能。

For the complex relationship between process parameters and mechanical property of connecting rod bushing formed by power spinning, the non-linear relationship of RBF neural network between process parameters (thinning rate, feeding ratio) and mechanical properties (tensile strength, elongation) was established, and the neural network was trained and tested by the results of orthogonal experiment. Then, comparing the measured values with the predicted values, it was found that the established neural network model had high prediction accuracy. Furthermore, this non-linear relationship was regarded as the fitness function, and the multi-objective optimization model (tensile strength, elongation) of the process parameters (thinning rate, feeding ratio) was established based on the genetic algorithm theory. Finally, the multi-objective Pareto optimal solution set was obtained, and the feasibility of the optimal solution set was verified by experimental analysis to effectively improve the design efficiency of process parameters and the mechanical properties of products.

基金项目：

国家自然科学基金资助项目(51665007)；贵州省经济和信息化委员会资助项目(2017GH063)

佘勇（1990-），男，硕士，助教 E-mail：sy09020641@163.com 通讯作者：占刚（1979-），男，博士，教授 E-mail：zhangangbmw@163.com

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