A machine learning recognition and process parameter optimization method for abrasive belt abrasion

Abstract

The invention discloses a machine learning recognition and process parameter optimization method for abrasive belt abrasion. The method comprises the following steps: S1, making a training set and a test set required by convolutional neural network training; S2, training a machine learning classification model based on a neural network; S3, an abrasive particle abrasion image on the surface of theabrasive belt is obtained; S4, identifying and distinguishing a wear region, an unworn region and a blocked region in the abrasive belt wear image through a machine learning classification model; S5,calculating the area and the area rate of each area; and S6, judging whether the process parameters are reasonable or not according to the area ratio of each part, and optimizing the existing parameters by adopting a basic particle swarm optimization algorithm. According to the method, the abrasion condition is identified through the model obtained through machine learning, and the process parameter optimization direction is predicted. The abrasive belt abrasion measuring and calculating process is simplified, intelligent image detection of the abrasive belt abrasion degree is achieved, the abrasive belt abrasion condition can be accurately, rapidly and conveniently measured, and good measuring precision is achieved.

Description

technical field [0001] The invention belongs to the technical field of grinding processing and engineering testing, and in particular relates to a method for machine learning identification and process parameter optimization of abrasive belt wear. Background technique [0002] With the development of machinery manufacturing industry, abrasive belt grinding has become one of the effective methods for precision machining, and abrasive belt grinding has also been used more and more widely. However, in the process of abrasive belt grinding, the abrasive coated on the surface of the abrasive belt rubs against the surface of the workpiece, plowing and cutting, and continuous fracture and wear. The wear process can be divided into crushing wear, abrasion wear and clogging adhesion. . The wear and tear of the abrasive grains of the abrasive belt is caused by the friction between the abrasive grains and the workpiece when they move relative to each other, just like the tip is ground...

AI Technical Summary

Problems solved by technology

However, so far, no relatively mature detection method has been popularized and applied in actual production, and the above method is proposed for the grin

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