基于CNN的金刚石砂轮激光修锐参数优化

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

采用正交试验法对青铜金刚石砂轮进行激光修锐试验,并对其激光修锐参数进行优化。通过卷积神经网络(convolutional neural network,CNN)对砂轮表面图片进行像素级的金刚石磨粒识别,提取磨粒面积信息,求出磨粒突出高度,利用统计分布规律得到突出高度得分和最佳区间比率2个激光修锐质量评价指标。利用提出的评价指标对试验得到的砂轮激光修锐图片进行质量评价,并进行极差分析。结果表明:平均功率是影响修锐质量最大的因素。最优的修锐工艺参数为:平均功率,35 W;重复频率,100 kHz;转速,300 r/min;扫描速度,1.0 mm/min。

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 is100 kHz;the rotational speed is 300 r/min;the scanning speed is 1.0 mm/min.