Automatic non-contact grinding surface roughness measurement based on multi-focused sequence images and CNN

Yupeng Shi,Bing Li, Lei Li,Tongkun Liu,Xiao Du,Xiang Wei


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Microscopic images of surfaces can be used for non-contact roughness measurement by visual methods. However, the images are usually acquired manually and need to be as sharp as possible, which limits the general application of the method. This manuscript provides an automatic roughness measurement method that can apply to automatic industrial sites. This method first automatically acquires the sharpest image and then feeds the image into a convolutional neural network (CNN) model for roughness measurement. In this method, the weighted window enhanced sharpness evaluation algorithm based on the sharpness evaluation function is proposed to automatically extract the sharpest image. Then, a CNN model, CFEN, suitable for the roughness measurement task was designed and pre-trained. The results demonstrate that the measurement accuracy of the method reaches 91.25% and the time is within a few seconds. It is proved that the method has high accuracy and efficiency and is feasible in practical applications.
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Key words
surface roughness,machine vision,convolutional neural network,sharpness function,auto-focusing,grinded surfaces
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