基于局部纹理的在线、自动、低成本刀具磨损监测系统
Tool wear monitoring using an online, automatic and low cost system based on local texture
AI总结：
提出一种基于局部纹理和机器学习的在线低成本刀具磨损监测方法，通过划分磨损补丁并分类，在自建数据集上达到90.26%的报废检测准确率。
摘要：
在这项工作中，我们提出了一种基于计算机视觉和机器学习的新型在线、低成本且快速的方法，根据磨损程度来确定边缘轮廓铣削过程中使用的切削刀具是可用的还是应报废的。我们创建了一个包含254张边缘轮廓铣刀头图像的新数据集，据我们所知，这是第一个公开可用的、质量足以满足此目的的数据集。所有刀片均被分割，其切削刃被裁剪，得到577张切削刃图像：其中301张为功能可用，276张为应报废。所提出的方法基于：(1) 将切削刃图像划分为不同区域，称为磨损补丁(WP)；(2) 使用基于局部二值模式(LBP)不同变体的纹理描述符，将每个区域表征为磨损或可用；(3) 根据这些磨损补丁的状态，确定切削刃（因此刀具）是可用的还是应报废的。我们提出并评估了五种不同的补丁划分配置。单个磨损补丁由带有交集核的支持向量机(SVM)进行分类。最佳的补丁划分配置和纹理描述符在检测应报废切削刃方面达到了90.26%的准确率。这些结果表明，在边缘轮廓铣削过程中实现自动磨损监测具有非常有前景的机会。
英文摘要：
In this work we propose a new online, low cost and fast approach based on computer vision and machine learning to determine whether cutting tools used in edge profile milling processes are serviceable or disposable based on their wear level. We created a new dataset of 254 images of edge profile cutting heads which is, to the best of our knowledge, the first publicly available dataset with enough quality for this purpose. All the inserts were segmented and their cutting edges were cropped, obtaining 577 images of cutting edges: 301 functional and 276 disposable. The proposed method is based on (1) dividing the cutting edge image in different regions, called Wear Patches (WP), (2) characterising each one as worn or serviceable using texture descriptors based on different variants of Local Binary Patterns (LBP) and (3) determine, based on the state of these WP, if the cutting edge (and, therefore, the tool) is serviceable or disposable. We proposed and assessed five different patch division configurations. The individual WP were classified by a Support Vector Machine (SVM) with an intersection kernel. The best patch division configuration and texture descriptor for the WP achieves an accuracy of 90.26% in the detection of the disposable cutting edges. These results show a very promising opportunity for automatic wear monitoring in edge profile milling processes.
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