A Tool Change Monitoring Method Based on Extended Kalman Filter and Cutting Force Model

Abstract

The invention discloses a tool diagnosis and prediction method based on extended Kalman filter and cutting force model, which can carry out real-time on-line monitoring of machining tools during the work of CNC machine tools, accurately diagnose and predict the wear state of tools, and control the machining system Prompt to replace the tool in time; the invention uses the cutting force as the monitoring signal, which is intuitive, easy to deploy, and less affected by other factors; uses the extended Kalman filter to process the original signal. Compared with the artificial intelligence method, it does not require time to train the model. Parameters can be changed to adapt to other processing materials; use the remaining effective life model combined with the extended Kalman filter algorithm to accurately predict the remaining life of the tool. The invention solves the problems of long training time and poor adaptability of the traditional tool model, can improve the accuracy of diagnosis and prediction, and reduces the loss caused by premature discarding of tools or use of worn tools in production that does not meet the requirements of workpiece accuracy.

Description

technical field [0001] The invention relates to the field of tool wear diagnosis and prediction, in particular to a tool diagnosis and prediction method based on an extended Kalman filter and a cutting force model. Background technique [0002] During the cutting process, the tool wears out because the tool is subjected to continuous impact, huge pressure and extremely high temperature. The continuous increase or even breakage of the tool wear will cause the precision of the workpiece to be processed to decrease, the workpiece to be processed will be scrapped, the tool life will continue to decrease, and even cause damage to the machine tool. The original tool condition judgment relies on skilled workers, who subjectively judge the tool condition by observing chips, judging processing noise, and calculating the length of processing time. This method relies on experience, resulting in large estimation errors. If the tool is replaced too early, the tool will be wasted and the...

AI Technical Summary

Problems solved by technology

[0004] The present invention aims to solve the deficiencies in the physical model method and artificial intelligence method in the indirect detection method, and proposes an extended Kalman filter and cutting method based on extended Kalman filtering and cutting with small error, high precision, low cost, good real-time performance and easy deployment. The tool change monitoring method of the force model is expected to solve the problem that the tool wear cannot be monitored online in real time and the monitoring model needs a lot of time and data training, so as to improve production efficiency and reduce the loss caused by excessive tool wear

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