Method for recognizing tool abrasion degree of large numerical control milling machine

Abstract

The invention provides a method for recognizing the tool abrasion degree of a large numerical control milling machine. The method includes the steps of obtaining a vibration time domain signals in the running state of the large numerical control milling machine, obtaining frequency domain distribution of the vibration signals through fast Fourier transform, selecting multiple statistical characteristic parameters in the time domain and the frequency domain for conducting dimensionality reduction of tool abrasion characteristic parameters on the basis of a diffusion mapping method, selecting scale parameters of diffusion mapping through the leave-one-out cross validation algorithm and the optimization search algorithm, and recognizing the abrasion degree of an unknown tool to be measured through the combination with Nystrom expansion and the kernel regression algorithm. By means of the method, the disadvantage that a tool abrasion sample of the large numerical control milling machine is missed can be effectively overcome, the recognition accuracy of the tool abrasion degree of the large numerical control milling machine can be improved, and maintenance cost and time caused by untimely tool abrasion recognition can be reduced.

Description

technical field [0001] The invention belongs to the field of large-scale numerically controlled milling machines, and in particular relates to a method for identifying the degree of tool wear of large-scale numerically controlled milling machines. Background technique [0002] In addition to milling planes, grooves, gear teeth, threads and spline shafts, CNC milling machines can also process more complex profiles, with high production efficiency, and are widely used in the machinery manufacturing industry. Especially large-scale CNC milling machines (such as gantry milling machines), because of their high machining accuracy and production efficiency, are often used in the batch production of large workpieces. [0003] As the most vulnerable parts of large CNC milling machines, cutting tools are particularly important for timely and effective state identification and monitoring. According to statistics, tool wear is the primary cause of milling machine failure, and the resul...

AI Technical Summary

Problems solved by technology

However, for large-scale CNC milling machines, the identification of the tool wear degree still faces the following problems: (1) The processing objects of large-scale CNC milling machines are generally large, which makes it difficult to collect data under different tool wear degrees, and the cost is relatively high. (2) Most of the research methods need to manually determine the value of key parameters (such as the penalty function and scale factor of support vector machine), which is highly subjective. The misdiagnosis rate is high in the identification of the wear degree of CNC milling machine tools

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