Publications
2018
1.
García-Ordás, María Teresa; Alegre, Enrique; González-Castro, Víctor; Alaiz-Rodríguez, Rocío
Combining shape and contour features to improve tool wear monitoring in milling processes Artículo de revista
En: International Journal of Production Research, vol. 56, no 11, pp. 3901–3913, 2018, (Publisher: Taylor & Francis).
Resumen | Enlaces | BibTeX | Etiquetas: B-ORCHIZ, Contour Features, Feature Fusion, Shape Description, ShapeFeat, Tool wear
@article{garcia-ordas_combining_2018,
title = {Combining shape and contour features to improve tool wear monitoring in milling processes},
author = {María Teresa García-Ordás and Enrique Alegre and Víctor González-Castro and Rocío Alaiz-Rodríguez},
url = {https://www.tandfonline.com/doi/abs/10.1080/00207543.2018.1435919},
year = {2018},
date = {2018-01-01},
journal = {International Journal of Production Research},
volume = {56},
number = {11},
pages = {3901–3913},
abstract = {This paper proposes a vision-based system combining ShapeFeat and BORCHIZ descriptors to classify milling tool wear. Using late fusion, the approach improves classification accuracy, achieving 91.44% for binary classification and 82.90% for three wear levels, outperforming individual descriptors. The method offers a promising solution for automated tool wear monitoring.},
note = {Publisher: Taylor & Francis},
keywords = {B-ORCHIZ, Contour Features, Feature Fusion, Shape Description, ShapeFeat, Tool wear},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a vision-based system combining ShapeFeat and BORCHIZ descriptors to classify milling tool wear. Using late fusion, the approach improves classification accuracy, achieving 91.44% for binary classification and 82.90% for three wear levels, outperforming individual descriptors. The method offers a promising solution for automated tool wear monitoring.