机械加工表面粗糙度的视觉检测
The Automated Visual Inspection of Machined Surface Coarseness
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摘要: 为了快速地进行机械加工表面的粗糙度的检测, 提高检测自动化水平, 本文提出了一种视觉检测的方法, 该方法通过对不同类型不同粗糙度的表面功率谱的比较研究, 提取机械加工表面的纹理分布信息特征, 再把特征矢量输入神经网络进行训练及测试, 结果表明, 该方法能成功地实现机械加工表面的粗糙度的检测。该方法与一些采用图像统计特征进行粗糙度检测相比较, 具有快速, 识别误差小的特点。Abstract: The coarseness inspection of machined surface plays a important role in machine industry for it influence the machine 's life-span and performance. It aims to enhance inspection automation performance. In our paper, a method of automated visual inspection is proposed. By analyzing various machined surface's power spectrum, we abstract the feature vector of machined surface. Then feature is inputted into networks for training and recognition. The experiment shows the method can evaluate machined surface's coarseness successfully. Compared with some other classic method, this method is more rapid and automation has higher precision.