锻压技术

基于克里金模型和遗传算法的铝合金高筋薄壁锻件多目标优化

英文标题：Multiobjective optimization on thin-walled forgings with high rib of aluminum

作者：骆静尹小燕

单位：成都理工大学

分类号：TG316

出版年,卷(期):页码：2020,45(10):20-26

摘要：

以数值模拟和智能算法为手段，以某高筋、薄壁、带有纵横内筋的铝合金锻件为研究对象，将其成形过程中的坯料高宽比、坯料温度、成形速度以及摩擦系数作为设计变量，将填充性能、组织流线、成形均匀性作为优化目标，利用克里金模型构建坯料高宽比、坯料温度、成形速度、摩擦系数与填充性能、组织流线、成形均匀性之间的高度非线性关系。利用多目标遗传算法（NSGAII），对克里金模型进行寻优得到Pareto解集，均衡考虑实际工艺条件，选取了一组比较均衡的最优解进行工艺试制。结果表明：优化结果能够显著地提升应变均匀性和填充性能，并改善流线分布。

By means of numerical simulation and intelligent algorithms, for thin-walled forgings with vertical and horizontal high ribs of aluminium alloy, taking the ratio of blank height to width, the blank temperature, the forming speed and the friction coefficient in the forming process as the design variables, and taking the filling performance, the organization streamline and the forming uniformity as the optimization objectives, the highly nonlinear relationships between the design variables and the optimization objectives were constructed by the Kriging model. Then, the Kriging model was optimized to obtain Pareto solution set by the multi-objective genetic algorithm (NSGA-II). Furthermore, considering the actual process conditions, a group of relatively balanced optimal solutions were selected for the trial production. The results show that the optimized results significantly improve strain uniformity, filling performance and streamline distribution.

基金项目：

四川省教育厅自然科学研究项目(17ZB0061)；成都理工大学工程技术学院科研基金（C122018011）

骆静(1983-)，男，硕士，讲师 E-mail：luojing19831219@163.com

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