数控机床刀具磨损在线监测与预警技术

CNC Machine Tool Wear Online Monitoring and Early Warning Technology Research

刀具磨损是影响数控机床加工质量和效率的主要因素之一,为了实现数控机床刀具磨损的在线监测与预警,该文基于数字孪生技术,构建数控机床刀具磨损在线监测与预警技术体系,采用神经网络技术进行海量数据特征提取与降维,并选取三种模型评价指标(MAPE、RMSE、MAE)对该模型监测及预测精度进行评价。结果表明,该技术在刀具磨损监测和预警方面表现出较高的准确性和可靠性,能够及时发现刀具磨损并提前预警,从而避免因刀具磨损而引发的加工质量下降和生产事故。

Tool wear is one of the main factors affecting the quality and efficiency of CNC machine tool processing,in order to realize the online monitoring and early warning of CNC machine tool wear,this paper is based on the digital twin technology,constructs the online monitoring and early warning technology system of CNC machine tool wear,adopts the neural network technology for feature extraction and dimension reduction of the massive data,and selects the three kinds of model evaluation indexes(MAPE,RMSE,and MAE)for the model monitoring and prediction accuracy is evaluated.The results show that the technology shows high accuracy and reliability in tool wear monitoring and early warning,and can detect tool wear in time and warn in advance,so as to avoid machining quality degradation and production accidents caused by tool wear.