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.
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[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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