基于XGBoost的数字双胞胎模型用于预测六足脚协调加工系统中的轨迹错误,使用定位精度和振动数据
Kanglin Xing1, Miao Feng2, Ilian A Bonev2
1Department of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre-Dame St W, Montreal, QC H3C 1K3, Canada.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究介绍了一种数字双胞胎,用于使用球杆和振动数据预测机器人加工错误. 该XGBoost模型实现了微米级准确度,使得改善零件质量的实际补偿成为可能.
科学领域:
- 机器人和制造业 机器人和制造业
- 数据驱动建模数据驱动建模
- 计量学 计量学 计量学
背景情况:
- 机器人加工中的动态错误会降低零件质量,特别是在易受干扰的灵活系统中.
- 准确预测这些错误对于保持高精度制造至关重要.
研究的目的:
- 开发一个数据驱动的数字双胞胎,用于预测六足动物加工单元中的圆形轨迹错误.
- 使用紧的传感器配置,将球杆和振动数据结合起来,用于错误预测.
主要方法:
- 同步处理球杆偏差,加速度数据和CMM配置文件.
- 使用滑动窗振动统计和球杆路径错误的特征工程.
- 机器学习模型 (XGBoost,MLP,随机森林) 用于定点错误预测.
主要成果:
- 在未见的数据上,XGBoost模型实现了微米级准确性 (RMSE ~5 μm,R2 > 0.80).
- 该模型显示在±20μm宽容带内几乎完全覆盖.
- 混合功能集结合球杆数据和振动描述器保持了高的预测准确性.
结论:
- 数据驱动的数字双胞胎有效地预测机器人加工中的圆形轨迹错误.
- 球杆路径错误是几何错误的关键,而振动数据捕获动态方面.
- 这种方法使得实际的离线补偿能够提高零件质量.
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