基于自我愈合模型的多刚体系统的在线振动状态识别
Guoyu Wang1, Yang Tian2, Chunming Hou1
1School of Mechanical Engineering and Automation, Shenyang Institute of Technology, Fushun, 113122, Liaoning, People's Republic of China.
Scientific reports
|December 31, 2024
概括
本研究介绍了多刚体系统的自我修复模型,以提高CNC机床的精度. 该方法使用加速数据进行实时识别,提高可靠性并实现补偿.
科学领域:
- 机械工程 机械工程
- 控制系统工程 控制系统工程
背景情况:
- 对于精密制造,数控机床需要高精度和可靠性.
- 传统的状态识别方法与复杂的多刚体动态和参数变化作斗争.
研究的目的:
- 为CNC机床开发一个准确可靠的多刚体系统状态识别方法.
- 为实时参数识别建立一个自我修复的机械模型.
- 为机床精度补偿和健康估计提供数据基础.
主要方法:
- 使用拉格朗奇法建立机械模型,考虑关节表面的影响.
- 在线实时识别关节表面参数通过二次调制函数.
- 使用卡尔曼波器与加速度输入的系统状态方程公式.
主要成果:
- 成功建立了多刚体系统的自愈机械模型.
- 使用加速数据实现系统状态的完整信息识别.
- 在VMC850垂直加工中心验证高精度.
结论:
- 拟议的在线状态识别方法提高了CNC机床的性能.
- 自愈模型提高了动态系统的准确性和可靠性.
- 这种方法支持实时准确性补偿和健康监测.
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