小样本磨削表面粗糙度测量方法研究

Research on Measurement Method of Grinding Surface Roughness for Small Samples

基于机器视觉的表面粗糙度测量方法主要通过图像特征信息与粗糙度的关联指标建立预测模型,但是样本量不足往往难以训练出有效的模型,导致测量准确率较低。针对以上问题,提出一种小样本磨削表面粗糙度测量方法。建立图像采集系统,采集不同粗糙度等级磨削表面图像作为原始样本;通过虚拟样本生成算法扩充样本量,采用灰度共生矩阵提取样本纹理特征;最后,通过神经网络建立预测模型。试验结果表明:样本量扩充后,表面粗糙度测量的准确率从80.4%提升到97.2%,证明了此方法的可行性,为小样本磨削表面粗糙度在机检测提供理论基础。

For surface roughness measurement methods based on machine vision,prediction models are mainly established through image feature information and roughness correlation indicators,but the lack of sample size often makes it difficult to train an effective model,resulting in low measurement accuracy.In view of the above problems,a small sample grinding surface roughness measurement method was proposed.The image acquisition system was established to collect images of grinding surfaces with different roughness levels as the original samples.The virtual sample generation algorithm was used to expand the sample size.The gray level co-occurrence matrix was used to extract the sample texture features.Finally,the prediction model was established through the neural network.The experimental results show that after the sample size is expanded,the accuracy of surface roughness measurement increases from 80.4% to 97.2%,which proves the feasibility of this method and provides a theoretical basis for the on-machine inspection of small sample grinding surface roughness.