复杂工况条件下刀具磨损状态监测与剩余寿命预测研究进展

doi:10.3901/JME.2025.19.299

摘要/Abstract

摘要： 当前制造企业面临着需要迅速响应市场多样化和不确定性需求的挑战。小批量个性化产品机械加工任务越来越多，传统的刀具磨损状态监测技术与刀具管理模式不再适用于工况条件频繁变化的制造环境。现存的刀具磨损状态监测与剩余寿命预测的文献综述大都聚焦于单一定工况。为弥补上述缺陷，系统地分析归纳了国内外复杂工况条件下刀具磨损状态监测及剩余寿命预测的最新研究成果，并提出了进一步的研究方向。根据国内外学者近年来的研究工作，首先详细地论述了基于传感技术的间接刀具磨损状态监测方法的研究进展。分别从单传感器监测与多传感器联合监测角度综述了信号处理与特征提取、监测模型的选择在复杂工况条件下的应用现状。其次，总结了迁移学习方法在复杂工况条件下刀具磨损状态监测技术中的应用；此外，从基于磨损退化模型、数据驱动模型以及混合模型角度概括了复杂工况条件下剩余寿命预测的研究现状；最后，归纳了复杂工况条件下刀具磨损状态监测及剩余寿命预测的研究重点和发展趋势。对未来的研究工作具有一定的理论参考与启示作用。

关键词: 复杂工况, 刀具磨损, 刀具状态监测, 剩余寿命预测

Abstract: Manufacturers are now faced with the challenge of responding quickly to diverse markets and uncertain demands. Since there are more and more processing tasks for small batches of personalized products, the traditional tool condition monitoring technology and tool management mode is no longer applicable to the manufacturing environment with frequent changes in working conditions. Existing literature review on tool condition monitoring and remaining life prediction mostly focuses on a single fixed working condition. The latest achievements on tool condition monitoring and remaining life prediction under complex working conditions at home and abroad are systematically analyzed and summarized to bridge above limitations, and the further research direction is put forward. According to the progress of domestic and foreign scholars in recent years, the advances of investigation on sensor-based indirect tool condition monitoring method are firstly discussed in detail. The advance on applications of the signal processing and feature extraction, monitoring model selection under complex working conditions are reviewed from the perspective of single-sensor monitoring and multi-sensor joint monitoring respectively. Next, the current status of the application of the transfer learning method in complex working conditions tool wear condition monitoring technology are summarized; Then, the development status of remaining life prediction under complex working conditions is summarized from wear degradation model, data-driven model and hybrid model; Finally, the focus and development trend of tool wear monitoring and residual life prediction under complex working conditions in the future are generalized. It has certain theoretical reference and enlightenment for future research work.

Key words: complex working conditions, tool wear, tool condition monitoring, remaining useful life

中图分类号:

TG506

王民, 车昌家, 高相胜, 昝涛, 高鹏, 张云飞. 复杂工况条件下刀具磨损状态监测与剩余寿命预测研究进展[J]. 机械工程学报, 2025, 61(19): 299-326.

WANG Min, CHE Changjia, GAO Xiangsheng, ZAN Tao, GAO Peng, ZHANG Yunfei. Tool Wear Condition Monitoring and Remaining Life Prediction under Complex Working Conditions：A Review[J]. Journal of Mechanical Engineering, 2025, 61(19): 299-326.

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