基于响应表面和遗传算法的超声波电机灵活转子优化
Bo Chen1, Jiyue Yang1, Haoyu Tang1
1School of Intelligent Equipment, Shandong University of Science and Technology, Tai'an 271019, China.
Micromachines
|January 25, 2025
概括
这项研究优化了使用Kriging和MOGA的旅行波旋转超声波电机旋转器. 新的设计显著减少了接触应力,提高了电机性能和耐用性.
科学领域:
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 旅行波旋转超声波电机中的灵活旋转机可以减少辐射摩擦,但面临着诸如不均的定子旋转机接触和旋转机变形应力等挑战.
- 现有的设计需要优化,以解决影响发动机性能和寿命的持续问题.
研究的目的:
- 开发和验证TRUM60稳压器的新型转子设计,以最大限度地减少接触应力并提高电机性能.
- 引入一种优化方法,将Kriging响应表面模型和多目标遗传算法 (MOGA) 结合起来.
主要方法:
- 提出了基于现有结构的新型转子设计,以补充改进的TRUM60定位器.
- 利用Kriging响应表面建模与拉丁语超立方采样用于数据生成.
- 采用多目标遗传算法 (MOGA) 来优化Kriging模型并确定最佳设计解决方案.
- 建立了一个目标函数,以定量机-旋转机接触面为目标,以优化.
主要成果:
- 优化过程将目标函数的值从0.631降低到0.036.
- 旋转器内环的最大接触应力从32.77MPa显著降低到9.96MPa.
- 实验验证证了设计的可靠性和性能改进.
结论:
- 集成的Kriging-MOGA优化方法有效地解决了旋转超声波电机的设计挑战.
- 新型转子设计增强了定子-转子相互作用,从而大大减少了接触应力.
- 这种方法显著提高了移动波旋转超声波电机的整体性能和耐用性.
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