卷积神经网络用于CNC转过程监控中的原始信号分类
Emmanuel Stathatos1, Evangelos Tzimas1, Panorios Benardos1
1Manufacturing Technology Laboratory, School of Mechanical Engineering, National Technical University of Athens, Heroon Polytechniou 9, GR15772 Athens, Greece.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究引入了一种新的机器学习方法,用于实时监控制造过程. 开发的模型自主地从原始传感器数据中提取特征,以高精度预测零件质量.
科学领域:
- 制造业 工程 制造工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 智能制造需要先进的过程监控来控制质量.
- 传统方法依赖于手动的特征提取,这可能耗时且效率较低.
- 几乎实时预测零件质量对于优化生产至关重要.
研究的目的:
- 开发一种机器学习方法,用于近乎实时的CNC制部件质量预测.
- 通过使用新型深度学习架构,从原始过程信号中实现自主功能提取.
- 用单一高效的模型监测多个质量指标 (平均粗度,峰值到谷间粗度,直径偏差).
主要方法:
- 使用一维卷积神经网络 (1D-CNN) 架构进行自主特征提取.
- 来自多个信号通道的集成数据:振动,电机转速和电机扭矩.
- 采用滑动窗技术进行持续监控,并采用分类机制来区分切割和非切割阶段.
主要成果:
- 在所有三种质量指标 (平均粗度,高峰至低谷粗度,直径偏差) 中,F1平均得分超过0.97.
- 在各种训练配置和设计参数中表现出强大而一致的性能.
- 验证了模型使用自主提取的特征准确预测零件质量的能力.
结论:
- 拟议的1D-CNN方法为智能制造过程监控提供了高效和准确的解决方案.
- 从原始信号中自主提取特征显著提高了机器学习模型在这个领域的能力.
- 开发的模型提供了一个紧而有效的分类器,用于近实时的质量预测,提高制造效率.
相关概念视频
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01:30Classification of Signals
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