一个智能热补偿系统使用边缘计算为机床工具
Endah Kristiani1,2, Lu-Yan Wang1, Jung-Chun Liu1
1Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
智能制造使用时间序列热补偿来预测和防止车床切割工具的热位移错误. 该GA-LSTM模型实现了超过90%的精度,使边缘设备能够有效的进行热补偿.
科学领域:
- 制造业 工程 制造工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 车床切削机器在长时间运行时容易发生热变形,导致位移错误和产品损坏.
- 智能制造为实时监控和控制提供解决方案,以减轻这些问题.
研究的目的:
- 利用时间序列热补偿,开发一套用于机床热位移的预测系统.
- 应用边缘计算技术用于预测系统的工业实施.
主要方法:
- 从机床工具中收集时间,转速,扭矩和温度的时间序列数据.
- 训练并比较了七个机器学习模型 (WNN,LSTNet,TPA-LSTM,XGBoost,BiLSTM,CNN,GA-LSTM) 用于温度预测和五轴位移预测.
- 通过实验分析优化温度预测模型和验证预测准确性.
主要成果:
- GA-LSTM模型表现出卓越的性能,平均精度超过90%.
- GA-LSTM的性能优于其他领先型号,包括LSTM,GRU和XGBoost.
- 分析考虑了模型精度和实践应用的培训时间.
结论:
- 该GA-LSTM模型是高效的热位移预测在机床.
- 建议使用LSTM,GRU和XGBoost模型的系统进行热补偿.
- 像Raspberry Pi这样的边缘设备适合在工业环境中实施这种热补偿系统.
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