Optimization of laser sharpening parameters for diamond grinding wheel based on CNN

Abstract

To optimize laser sharpening parameters for bronze diamond grinding wheels, the laser sharpening test was carried out on the bronze diamond grinding wheel using the orthogonal test method.The convolutional neural network (CNN) is used to identify the diamond abrasive grains at the pixel level. The protruding height of abrasive grains is obtained by extracting the area information of abrasive grains. Two laser sharpening quality evaluation indicators, the protruding height score and the optimal interval ratio, are obtained by using the statistical distribution law. The quality of the grinding wheel laser sharpening effect obtained by the test is evaluated by the evaluation index proposed and the range method is performed. The results show that the average power is the biggest factor affecting the quality of trimming. The optimal trimming process parameters are as follows: the average power is 35 W; the repetition frequency is 100 kHz; the rotational speed is 300 r/min; the scanning speed is 1.0 mm/min