提高制造精度:利用电机电流数据 计算机数控机器通过机器学习进行几何精度预测
Lucijano Berus1,2, Jernej Hernavs1, David Potocnik1
1Intelligent Manufacturing Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了一种使用计算机数控 (CNC) 机器电机电流数据和机器学习的间接测量方法,以实时预测零件的几何精度,减少生产时间和成本.
科学领域:
- 制造业 工程 制造工程
- 机器学习应用 机器学习应用
- 计量学 计量学 计量学
背景情况:
- 机械加工零件的直接几何验证通常需要加工后检查,增加生产时间和成本.
- 目前的方法,如坐标测量机 (CMM) 和光学扫描仪是连续的,在制造过程中造成瓶.
研究的目的:
- 开发一种新的间接测量方法,用于在CNC加工过程中实时预测几何精度.
- 减少对加工后检查的依赖,从而优化生产效率和成本效益.
主要方法:
- 利用来自数控机器控制器的电机电流数据作为间接测量信号.
- 应用机器学习算法,包括随机森林 (RF),k-最近邻居 (k-NN) 和决策树 (DT),用于预测建模.
- 使用 Tsfresh 和 ROCKET 来从电机电流数据中提取特征,以与几何特征相关联.
主要成果:
- 成功预测了安装轨道的三个几何特征,平均绝对百分比误差 (MAPE) 低于0.61% (学习) 和0.64% (测试).
- 从加工操作数据直接证明了实时几何精度预测的可行性.
结论:
- 拟议的间接测量方法大大减少了后续CMM或光学扫描检查的需要.
- 这种方法为大幅减少制造时间和成本提供了一条途径,同时保持质量标准.
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