A method for measuring the roughness of ground gear tooth surface based on machine vision

Through multi-angle image acquisition and processing technology based on machine vision, the problem of long and high cost measurement in traditional tooth grinding processing is solved, and fast, accurate and low-cost grinding tooth surface roughness measurement is achieved, which is suitable for grinding tooth processing produced by different processes.

CN119559229BActive Publication Date: 2025-03-28XIAN WEIERXIN PRECISION INSTR CO LTD
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Patent Information

Application Number
CN202510113523.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-01-24
Publication Date
2025-03-28
Estimated Expiration
2045-01-24

AI Technical Summary

Technical Problem

During the existing tooth grinding process, the traditional tooth surface roughness measurement method has problems such as long contact measurement, which may damage the tooth surface, and the equipment is expensive and complex to operate. It lacks a non-contact, fast, accurate and low-cost measurement method.

Method used

Using machine vision-based grinding tooth surface roughness measurement method, the grinding tooth surface roughness measurement method is used to generate a complete tooth surface grayscale image and extract the tooth surface roughness value through multi-angle image acquisition and processing, including grayscale processing, pixel point energy analysis, adaptive Gaussian filtering, feature point matching and splicing, and deep learning model construction.

Benefits of technology

It realizes non-contact, rapid and accurate measurement of the roughness of the tooth grinding surface, reduces the measurement cost, improves production efficiency, and can adapt to the tooth grinding processing produced in different batches and processes.

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Abstract

The invention discloses a method for measuring the roughness of a grinding tooth surface based on machine vision, and relates to the technical field of gear grinding processing quality detection. The method comprises the following steps: performing grayscale processing on multi-angle tooth surface images to obtain the multi-angle tooth surface grayscale images, dividing each pixel point in the multi-angle tooth surface grayscale images into a first feature point and a featureless point; performing regional clustering on each featureless point to obtain a plurality of local regions, and performing an adaptive Gaussian filtering operation on the multi-angle tooth surface grayscale images; performing feature point extraction on the tooth surface grayscale images at each angle in the multi-angle tooth surface grayscale images after noise removal, then performing feature point matching on the tooth surface grayscale images at each angle, splicing the tooth surface grayscale images at each angle, and generating a complete tooth surface grayscale image; constructing a tooth surface roughness measurement model, performing texture feature extraction on the complete tooth surface grayscale image, and obtaining the tooth surface roughness value of the complete tooth surface grayscale image, thereby significantly improving the accuracy and efficiency of the tooth surface roughness value measurement.
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