中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

重庆交通大学学报(自然科学版) ›› 2018, Vol. 37 ›› Issue (11): 127-132.DOI: 10.3969/j.issn.1674-0696.2018.11.20

• 载运工具与机电工程 • 上一篇    下一篇

基于响应面法的制动盘多目标及稳健性优化设计

谷 婷 婷   

  1. (泛亚汽车技术中心有限公司,上海 201201)
  • 收稿日期:2017-03-26 修回日期:2017-11-16 出版日期:2018-11-19 发布日期:2018-11-19
  • 作者简介:谷婷婷(1979—),女,湖北南漳人,工程师,主要从事汽车制动系统开发方面的研究。E-mail:tingting_gu@patac.com.cn。

Multi-objective and Robust Optimization Design of Brake Disc Based on Response Surface Method

GU Tingting   

  1. (Pan-Asia Technical Automotive Center Co., Ltd., Shanghai 201201, P. R. China)
  • Received:2017-03-26 Revised:2017-11-16 Online:2018-11-19 Published:2018-11-19

摘要: 针对某款制动盘的设计要求,提出了一种基于响应面法的多目标和稳健性优化设计方法。建立了制动盘多工况下的有限元模型;以制动盘结构参数作为设计变量,目标性能参数为响应,设计正交试验,建立了制动盘的Kriging响应面模型;以制动盘冷却系数最大、质量最小为优化目标,其它性能参数为约束条件,以制动盘结构尺寸公差为噪声因子,将多目标遗传算法与蒙特卡洛方法相结合,对制动盘结构进行了多目标/稳健性优化设计;获得了制动盘冷却系数和质量指标的Pareto最优解。采用有限元仿真和试验相结合的方法对优化结果的可靠性进行验证,结果表明提出的优化设计方法有效可靠。

关键词: 车辆工程, 制动盘, Kriging响应面, 多目标遗传算法, 蒙特卡洛方法, 稳健性

Abstract: A multi-objective with robust optimization design method was proposed to satisfy the brake disc design requirements based on response surface method. Finite element model of the brake disc with multiple conditions was established. Taking the brake disc structural parameters and performance as design variables and responses, a Kriging response surface model was developed based on orthogonal design. To maximize cooling factor and minimize brake disc mass, taking other performance indices as constraints, choosing structural dimensional tolerance as noise factor, multi-objective with robust optimization design of brake disc structural parameters was carried out using combination of multi-objective genetic algorithm (MOGA) and Monte Carlo method. The pareto optimal solutions for cooling performance and mass were obtained. The result was verified by finite element simulation and experiment test. The study shows the proposed optimization method is effective and reliable.

Key words: vehicle engineering;brake disc, Kriging response surface method, multi-objective genetic algorithm, Monte Carlo method, robust

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