An Evolutionary Learning Method for Intelligent Monitoring of Tool Condition

Abstract

An evolutionary learning method for intelligent monitoring of tool state, using three-way acceleration sensors and microphones to collect vibration signals and acoustic signals, smoothing the signals, and dividing them into training sets and test sets; using stacked autoencoders to analyze dynamic signals Automatically extract deep-level features and classify the extracted features; assign weights to each algorithm according to the accuracy of the training set model, obtain the final predicted tool state through weighted average, and save the relevant parameters of the model; the actual processing process The real-time vibration signal and acoustic signal are input into the saved monitoring model after data preprocessing, and the tool state of the corresponding signal is obtained, the data label with a high level of confidence is saved, and the network parameters are updated, so as to realize the intelligent monitoring of the tool state evolutionary learning. This method can avoid manual participation, reduce computational complexity, and can weaken the impact of machine tool performance degradation on the prediction accuracy of the tool state monitoring model.

Description

technical field [0001] The invention belongs to the technical field of tool state monitoring, in particular to an evolutionary learning method for tool state monitoring. Background technique [0002] In the field of mechanical processing, the state of the tool has an important impact on production efficiency and the machining accuracy and surface quality of the machined parts. When the tool wears seriously, it may even damage the machine tool, posing a threat to the personal safety of the operator. At present, the wear of the tool is generally judged by experienced workers based on the cutting vibration, cutting noise and chip color at the processing site. The cost increases, and it may also lead to the scrapping of parts and even the downtime inspection of equipment due to untimely tool change. Therefore, in the automatic NC machining process, the research on the accurate monitoring and identification of the tool state is of great significance. [0003] At present, resear...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide an evolutionary learning method for intelligent monitoring of tool state, to solve the problem that the existing tool state monitoring model cannot adapt to the degradation process of CNC machine tools, and to realize the evolutionary learning of the monitoring model

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