Prediction method of cutting tool fatigue strength based on artificial intelligence
Traditional methods have poor ability to handle raw signal noise and outliers.Poor quality signals affect the integrity of feature extraction results,affect the prediction of tool wear,and cause the prediction of cutting tool fatigue strength to be inconsistent with the actual situation.Research is being conducted on an artificial intelligence based method for predicting cutting tool fatigue strength.Using wavelet denoising method to filter signal noise and remove contained outliers;Targeting time-domain and frequency-domain features,extract more complete signal features,use Pearson coefficient method and MIC coefficient method to sort the feature signals,complete signal feature fusion through kernel principal component analysis,train different feature signals using generalized regression neural network,and predict the fatigue strength of cutting tools based on the obtained tool wear amount.The experimental results show that the traditional method of using BPNN to obtain wear accuracy is low,and the predicted crack width of the tool rapidly expands from the 45th hour onwards.After working for more than 60 hours,the tool head will break;The research method processed the head and tail noise and mid section outliers in the initial stage,and the wear amount obtained by GRNN was more accurate.It predicted that the crack width of the tool would expand from the 75th hour,and the tool head would only break after working for more than 80 hours.The difference between this prediction and the actual results was minimal,indicating that the prediction of the method in this paper is more accurate.
万方数据
维普
