基于深度学习PSD-CVT模型的智能工具磨损预测.
Sumei Si1, Deqiang Mu2, Zekai Si3,4
1College of Electromechanical Engineering, Changchun University of Technology, Changchun, 130012, China.
Scientific reports
|September 5, 2024
概括
准确的工具磨损预测对于加工质量至关重要. 一个新的深度学习模型,PSD-CVT,将功率光谱密度 (PSD) 与卷积神经网络 (CNN) 和视觉变压器 (ViT) 结合起来,用于更优质的工具磨损预测.
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:
- 可靠的加工质量取决于准确的工具磨损预测.
- 现有的方法可能无法完全捕获复杂的信号特征,以监测磨损.
研究的目的:
- 提出一种新的深度学习模型,PSD-CVT,用于增强工具磨损预测.
- 在一个统一的框架中利用功率光谱密度 (PSD),卷积神经网络 (CNN) 和视觉变压器模型 (ViT) 的优势.
主要方法:
- 开发了PSD-CVT模型,将PSD地图集成用于光谱分析,CNN用于本地特征提取,ViT用于全球依赖.
- 利用两个完全连接的层,并具有ReLU激活功能,用于预测工具磨损值.
- 对PHM 2010数据集的模型进行了评估.
主要成果:
- 与独立的CNN或ViT模型相比,PSD-CVT模型的预测准确性更高.
- 拟议的模型在工具磨损预测准确性方面超过了现有的几种方法.
- 实验结果验证了综合方法的有效性.
结论:
- PSD-CVT模型为工具磨损预测提供了强大而准确的解决方案.
- 这种新的深度学习方法可以应用于各种加工领域,以提高质量控制.
- PSD,CNN和ViT的合成为预测性维护提供了一个强大的工具.
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