Tool Wear Prediction Based on Noise Reduction Processing for Cutting Region Temperature Data
Tool wear prediction is a crucial issue in the manufacturing industry.Predicting tool wear in ad-vance and replacing it in a timely manner can reduce production costs and improve production efficiency.This article selects cutting area temperature data to predict tool wear,while considering the impact of cut-ting chips during the data collection process,a noise reduction algorithm is designed to remove the interfer-ence of cutting chips.Specifically,we constructed a convolutional long and short term memory neural net-work to extract features from temperature data in the cutting area and predict tool wear.The shedding of cutting chips can generate noise,and we use the idea of frame difference method to remove the influence of cutting chips.Finally,the effectiveness of the method was verified through experiments.The experimental results show that the noise reduction algorithm can effectively remove the noise generated by cutting chips,and The proposed network model has improved prediction accuracy compared to the traditional BP neural network model,and the average root mean square error of prediction results under different working condi-tions has decreased by 0.017 1.
万方数据
维普
