基于转移学习的球形剪刀连接的刚度建模 结构优化远程运动中心机制
Baoyue Lu1,2, Sihan Wang1,2, Shuxin Wang1,2
1School of Mechanical Engineering, Tianjin University, Tianjin, China.
概括
这项研究开发了一种精确的硬度模型,用于使用虚拟联合方法和转移学习的球形剪刀连接远程运动中心 (SSL-RCM) 机制. 优化的SSL-RCM机制显示了性能和承载能力的提高.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机械工程 机械工程
- 生物力学 生物力学
背景情况:
- 球形剪刀连接远程运动中心 (SSL-RCM) 机制的刚度较低,影响承载能力和运动精度.
- 准确的刚性建模对于有效的SSL-RCM机制设计和优化至关重要.
研究的目的:
- 为SSL-RCM机制开发一个准确的刚性模型.
- 引导SSL-RCM机制的结构优化和设计.
主要方法:
- 使用虚拟关节方法建立了刚性模型,其中包括被动关节.
- 转移学习被用来扩展有限的有限元素数据,创建一个高精度的刚度模型.
- 一个多目标的遗传算法被用于基于度因子的结构优化.
主要成果:
- 与基线模型相比,开发的刚性模型显示,与基线模型相比,适配精度有12.2%的改善.
- 优化的SSL-RCM机制显示出增强的整体性能和承载能力.
结论:
- 刚度模型为SSL-RCM机制提供了几何尺寸和纠正的刚度之间的分析关系.
- 该模型是SSL-RCM机制开发中的结构优化和设计指导的宝贵工具.
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