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Combining shape and contour features to improve tool wear monitoring in milling processes

2024-02-07 22:27:16
M. T. García-Ordás, E. Alegre-Gutiérrez, V. González-Castro, R. Alaiz-Rodríguez

Abstract

In this paper, a new system based on combinations of a shape descriptor and a contour descriptor has been proposed for classifying inserts in milling processes according to their wear level following a computer vision based approach. To describe the wear region shape we have proposed a new descriptor called ShapeFeat and its contour has been characterized using the method BORCHIZ that, to the best of our knowledge, achieves the best performance for tool wear monitoring following a computer vision-based approach. Results show that the combination of BORCHIZ with ShapeFeat using a late fusion method improves the classification performance significantly, obtaining an accuracy of 91.44% in the binary classification (i.e. the classification of the wear as high or low) and 82.90% using three target classes (i.e. classification of the wear as high, medium or low). These results outperform the ones obtained by both descriptors used on their own, which achieve accuracies of 88.70 and 80.67% for two and three classes, respectively, using ShapeFeat and 87.06 and 80.24% with B-ORCHIZ. This study yielded encouraging results for the manufacturing community in order to classify automatically the inserts in terms of their wear for milling processes.

Abstract (translated)

在本文中,根据计算机视觉方法提出了一种新系统,用于根据工件的磨损级别对铣削过程中切削面的插入进行分类。为了描述磨损区域形状,我们提出了一个新的描述符ShapeFeat,并使用BORCHIZ方法对其轮廓进行特征描述。根据我们目前的知识,该方法在计算机视觉方法下对工具磨损监测方面的表现最佳。结果表明,使用晚融合方法将ShapeFeat与BORCHIZ的结合可以显著提高分类性能,获得二分类准确度为91.44%,三分类准确度为82.90%。这些结果超过了使用各自描述符的结果,后者分别获得了88.70%和80.67%的准确度。对于铣削过程中的切削面插入,本研究为制造业界提供了鼓舞人心的结果,以便能够自动对工件的磨损进行分类。

URL

https://arxiv.org/abs/2402.05978

PDF

https://arxiv.org/pdf/2402.05978.pdf


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