Tool condition monitoring and identifying method based on signal fusion and multi-fractal spectrum algorithm

Abstract

The invention relates to a tool condition monitoring and identifying method based on a signal fusion and multi-fractal spectrum algorithm. The tool condition monitoring and identifying method based onthe signal fusion and multi-fractal spectrum algorithm comprises the following steps of (1) in the cutting process, acquiring a cutting force signal and a vibrating signal; (2) denoising the cuttingforce signal and the vibrating signal acquired in the step 1; (3) for a denoised signal sequence, analyzing multi-fractal characteristics of the signals, searching the relation between the signals andthe tool wear through the multi-fractal characteristics, extracting relevant characteristic vectors from a multi-fractal spectrum obtained through calculation, and representing the relation between the signals and the tool wear through the characteristic vectors; and (4) merging the characteristic vectors extracted in the step 3 into a characteristic matrix, using as an input parameter variation,building a support vector machine model for tool wear condition monitoring, and utilizing the optimized support vector machine model for diagnosing the unknown tool condition. By adopting the methodprovided by the invention, the high identification rate of the tool condition is achieved.

Description

Technical field [0001] The invention relates to the technical field of mechanical processing and advanced manufacturing, in particular to a tool condition monitoring and recognizing method based on signal fusion and multifractal spectrum algorithm. Background technique [0002] Tool status intelligent monitoring technology is a fusion of technologies including sensor application, digital signal recognition and analysis, computer programming, and artificial intelligence machine learning. It has a significant role in promoting the automation of mechanical processing and the unmanned manufacturing process. It is used in intelligent manufacturing. The field occupies an increasingly important position. [0003] The current intelligent monitoring of tool status mostly uses a single signal for monitoring, including cutting force signals, vibration signals, acoustic emission signals and other signals. Although single-signal monitoring has the advantages of simple and easy operation, it ha...

AI Technical Summary

Problems solved by technology

Time-domain analysis and frequency-domain analysis are the most commonly used analysis methods for signal analysis. The information of the signal is analyzed from two different angles. The inventors found that the extracted features are mostly based on the feature quantities obtained from statistics. However, for the condition monitoring in the field of tool wear, it is impossible to accurately establish the relationship between the signal and tool wear by using only these characteristics for various related signals generated in the cutting process, and then judge the tool state.

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