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Título
Combining shape and contour features to improve tool wear monitoring in milling processes
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
International Journal of Production Research
Número de la revista
11
Cita Bibliográfica
García-Ordás, M. T., Alegre-Gutiérrez, E., González-Castro, V., & Alaiz-Rodríguez, R. (2018). Combining shape and contour features to improve tool wear monitoring in milling processes. International Journal of Production Research, 56(11), 3901-3913. https://doi.org/10.1080/00207543.2018.1435919
Editorial
Taylor and Francis Group
Fecha
2018-01-13
ISSN
0020-7543
Resumen
[EN] 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.
Materia
Palabras clave
Peer review
SI
ID proyecto
- info:eu-repo/grantAgreement/MINECO/Programa Nacional de Investigación Fundamental/DPI2012-36166
URI
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