深度学习工具 基于切断力信号的磨损状态识别方法
Shuhang Li1, Meiqiu Li1, Yingning Gao1
1School of Mechanical Engineering, Yangtze University, Jingzhou 434023, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了一种改进的MobileViT网络,用于精确识别削刀具磨损状态. 该方法提高了实时工业应用的准确性和效率.
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:
- 精确识别削刀具磨损对于优化加工流程和防止工具故障至关重要.
- 现有的方法可能缺乏实时工业监控所需的效率和准确性.
研究的目的:
- 开发一种准确,快速和高效的方法来识别削刀具的磨损状态.
- 提高用于工具磨损监控的轻量级神经网络的性能.
主要方法:
- 提出了一种结合连续波形变换 (CWT) 和改进的MobileViT轻量级网络的新方法.
- CWT将切割力信号转化为时间频率图像.
- 移动ViT网络增强了一个上下文变压器模块和一个全球注意力机制模块.
主要成果:
- 与原来的MobileViT相比,增强的MobileViT模型在准确性 (1.58%),精度 (1.23%),回忆 (1.92%) 和F1得分 (1.57%) 中显示出显著的改进.
- 拟议的模型在内存占用和预测准确度方面比VGG16,ResNet18和Pool Former有优势.
- 该方法可以近乎实时识别工具磨损状态.
结论:
- 拟议的方法提供了一种高效和准确的方法,用于削刀具磨损状态的识别.
- 增强的MobileViT网络为工具磨损监控提供了卓越的性能.
- 这种技术在工业生产环境中具有很强的应用潜力.
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