通过使用基于轴电流杂乱信号构建的磨损过渡百分比来替换磨损切割机的时间研究
Zhihao Liu1, Min Wang1, Zhishan Wang1
1College of Mechanical & Energy Engineering, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一种新方法,用于使用轴电流信号预测最佳工具更换时间. 这种方法避免了直接测量工具磨损,简化了制造业中的实际应用.
科学领域:
- 制造业 工程 制造工程
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
背景情况:
- 磨损的刀具会降低加工精度,增加表面粗度,需要有效的工具更换策略.
- 确定工具磨损的传统方法需要频繁的操作关闭来进行测量,这阻碍了实际实施.
- 开发用于预测工具磨损的非侵入性方法对于高效的工业生产至关重要.
研究的目的:
- 提出一种新的方法,根据磨损过渡百分比来确定工具更换的最佳时间.
- 开发一种避免直接测量工具磨损的系统,提高在现实世界制造中的应用性.
- 通过传统的侧翼磨损测量来验证拟议的方法.
主要方法:
- 使用Vold-Kalman过器,通过移除旋转频率组件来提取轴心电流杂乱信号 (SCCS).
- 使用卷积神经网络 (CNN) 将SCCS数据分为正常磨损和严重磨损阶段.
- 计算磨损过渡百分比,以正常磨损样本在一定的时间内比率为基础.
主要成果:
- 一个二进制分类CNN模型被成功训练,以识别与不同工具磨损阶段相关的SCCS特征.
- 拟议的磨损过渡百分比方法在预测最佳工具更换时间方面表现出有效性.
- 结果与侧翼磨损的直接测量相比显示出很好的一致性.
结论:
- 新型磨损过渡百分比方法提供了一种实用且非侵入性的方法,以优化机械加工操作中的工具更换.
- 使用CNN来分析SCCS提供了一种强大的方法,可以在没有直接测量的情况下监测工具状况.
- 这种方法适用于各种加工参数,并提高制造过程的效率.
相关概念视频
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01:25Multimachine Stability
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01:18Reducing Line Loss
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