Life Prediction Model of Machine Tool based on Deep Learning

Abstract

In view of the shortage of traditional life prediction methods for machine tools, such as low accuracy of life prediction and few samples basis attributes, a life prediction model of machine tools combined with machine tool attributes is proposed. The life prediction model of machine tool adopts KL dispersion distribution theory, uses modal superposition method to carry out machine tool life analysis, calculates the theoretical life of machine tool, and then carries on the simulation, obtains the machine tool life prediction value. Compared with the traditional method of machine tool life prediction, the model is based on the application life fatigue damage model, which superimposes the service times and maintenance cycle of the machine tool, derives the influence factor of machine tool life, and obtains the linear relationship between the influence factor of machine tool life and the life of machine tool. The influence factor of machine tool life is introduced as the life prediction parameter of machine tool. The data transformation relationship of HT300 parts is constructed. The original part data is enhanced. The effective training set is obtained. The life prediction model of machine tool based on deep learning is completed. The quantitative analysis of machine tool life is carried out. The experiment of machine tool life prediction using training data set proves the validity of the model. Regression test was carried out on the training data set to reflect the robustness of the model. The prediction accuracy of the model is further verified by Weibull test.