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

A process parameter optimization and machine learning technology, applied in character and pattern recognition, instruments, biological models, etc., can solve problems such as the inapplicability of abrasive belts, and achieve the effect of convenient measurement, good measurement accuracy, and good prediction effect.

Inactive Publication Date: 2019-03-01
成都极致智造科技有限公司
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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 grinding wheel. The abrasive belt is a flexible grinding tool, which is different from the grinding wheel. Many methods are not applicable to the abrasive belt.

Method used

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

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Embodiment Construction

[0047] The present invention designs a method for machine learning recognition and process parameter optimization of abrasive belt wear. The method of the present invention is executed through image recognition technology, and the image of the worn area and the image of the unworn area in the image are obtained by this method. Calculate the edges of different regions and use color information to distinguish different regions. And use the new particle swarm optimization algorithm to optimize the experimental parameters.

[0048] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0049] like figure 1 As shown, the machine learning identification and process parameter optimization method of abrasive belt wear of the present invention comprises the following steps:

[0050] S1. Make a label map through the existing photos of abrasive belt wear, and then output an index map to make a training set and a ...

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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...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/62G06K9/62G06N3/04G06N3/00
CPCG06N3/006G06T7/0004G06T7/62G06T2207/30108G06T2207/20081G06N3/045G06F18/241
Inventor 黄智董华章宋瑞吴湘赵燎魏鹏轩王静怡
Owner 成都极致智造科技有限公司
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