Tool wear monitoring method based on multi-sensor current signals

Abstract

The invention discloses a tool wear monitoring method based on multi-sensor current signals. Firstly, a current sensor is used for collecting non-coaxial motor current signals in the tool machining process, time domain analysis and wavelet packet energy spectrum analysis are adopted to carry out energy character extraction on the multi-sensor current signals, a distance evaluation method is used for calculating characteristic sensitiveness, and according to sorting of sensitiveness, the characteristics with high sensitivity to the tool state changes are selected; through the quantitative series self-adaption scalarization method based on the Lloyds algorithm, the selected characteristics are subject to scalarization treatment; finally, tool primary machining data are used for building a tool normal state DHMM monitoring model, the data under the serious wear state are used for building a tool serious wear state DHMM monitoring model, the monitoring data are input to the two models to obtain the state monitoring results, that is, the maximum likelihood estimates, and through comparison of the maximum likelihood estimates output by the two models, monitoring of the tool wear state is achieved.

Description

technical field [0001] The invention relates to the technical field of tool wear state monitoring, in particular to a tool wear monitoring method based on multi-sensor current signals. Background technique [0002] Machining, as one of the main processing methods in the manufacturing industry, has a significant impact on improving the manufacturing level and efficiency. It is increasingly important to carry out condition monitoring for the cutting process of CNC machine tool automated production lines. As the direct working part of the cutting process, the monitoring of the working state of the tool, that is, the tool wear condition is one of the important tasks in the automated production line. In an automated production line, tool wear will affect the machining accuracy of the workpiece, causing the workpiece to be out of tolerance or scrapped, causing the machine tool to vibrate or be forced to stop, and even cause the entire processing system to malfunction, resulting i...

AI Technical Summary

Problems solved by technology

This will lead to two problems: on the one hand, the tools that have not been scrapped are replaced in advance, resulting in the waste of tools;

Due to quality fluctuations in the tool forming process, tool wear monitoring is required. Tool wear monitoring based on the initial state of the tool can reflect the tool change process, but the initial processing data of the tool is less, how to perform tool wear in the case of a small sample Monitoring is an urgent problem

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