面向机械加工过程的状态监测技术研究现状分析

A Survey of Condition Monitoring Technology for Mechanical Machining Process

机械加工过程状态监测对于提高工件加工质量,确保系统可靠性以及减少机床停机时间具有重要的实践意义。但是当前的研究对于机械加工过程状态监测技术的相关理论和方法缺乏总体性概括、分析和比较,从而难以为机械加工过程状态监测技术的后续研究提供可以参考的整体理论框架。为此,本文以当前表面粗糙度和刀具状态监测两大研究热点为研究对象,基于以下三点视角:(1)基于人工智能的状态监测策略;(2)基于统计学习的状态监测策略;(3)基于多传感器信息融合的状态监测策略,较为全面地介绍和论述了用于机械加工过程状态监测的相关理论和方法,并对其优缺点进行了对比分析,最后给出机械加工过程状态监测技术研究未来所面临的挑战。

Condition monitoring in mechanical machining process has important practical significance for improving workpiece quality, ensuring system reliability and reducing machine downtime. However, the current research lacks a general overview, analysis, and comparison of relevant theories and methods, making it difficult to provide a comprehensive theoretical framework for follow-up research of the condition monitoring technology during machining processes. Therefore, this paper takes the current two research hotspots of surface roughness and tool condition monitoring as the research object, based on the following three perspectives： （1） Artificial Intelligence-based condition monitoring strategy; （2） Statistical learning-based condition monitoring strategy; （3） Multi-sensor information fusion-based condition monitoring strategy. The related theories and methods used for monitoring machining process were introduced and discussed in a comprehensive way and their advantages and disadvantages were compared and analyzed. Finally, the challenges faced by the condition monitoring technology in the future are presented.