数控机床刀具磨损监测实验数据处理方法  

Experimental data processing method for monitoring tool wear of computer numerical control machine tools

作　　者：陈宁宁[1] CHEN Ning-Ning(Jiangsu Vocational and Technical College of Finance and Economics,Huai’an 223003,China)

机构地区：[1]江苏财经职业技术学院,淮安223003

出　　处：《实验室检测》2024年第10期107-109,共3页Laboratory Testing

基　　金：淮安市自然科学研究计划:基于EMD的机床刀具寿命预测方法的研究与应用(HAB202160)

摘　　要：文章深入探讨了数控机床刀具磨损监测中的实时数据处理方法,期望能够提升刀具监测系统的整体精度。文章还系统回顾了当前主流的几种数据处理方法,包括功率谱分析法、小波变换、人工神经网络技术以及多传感器信息融合技术。尽管这些方法各有优缺点,但单一方法在处理复杂多变的加工环境下的数据时,往往难以达到理想效果。为克服这一挑战,本文提出了一种基于混合智能算法的多传感器信息融合数据处理方法。The article delves into real-time data processing methods for monitoring tool wear in computer numerical control(CNC)machine tools,with the aim of improving the overall accuracy of the tool monitoring system.The article also systematically reviews several mainstream data processing methods,including power spectrum analysis,wavelet transform,artificial neural network technology,and multi-sensor information fusion technology.Although these methods have their own advantages and disadvantages,a single method often fails to achieve ideal results when dealing with data in complex and changing processing environments.To overcome this challenge,this paper proposes a multi-sensor information fusion data processing method based on hybrid intelligent algorithms.

关 键 词：数控机床 刀具磨损 数据处理 智能制造 多传感器融合 

分 类 号：TG659[金属学及工艺—金属切削加工及机床]