Publications
2017
1.
García-Ordás, María Teresa; Alegre, Enrique; González-Castro, Víctor; Alaiz-Rodríguez, Rocío
A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques Artículo de revista
En: The International Journal of Advanced Manufacturing Technology, vol. 90, pp. 1947–1961, 2017, (Publisher: Springer London).
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, cutting tool wear, machine learning, shape descriptors, wear monitoring automation
@article{garcia-ordas_computer_2017,
title = {A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques},
author = {María Teresa García-Ordás and Enrique Alegre and Víctor González-Castro and Rocío Alaiz-Rodríguez},
url = {https://link.springer.com/article/10.1007/s00170-016-9541-0},
year = {2017},
date = {2017-01-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {90},
pages = {1947–1961},
abstract = {In this paper, we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORCHIZ, a novel shape-based descriptor computed from the wear region image. A new Insert dataset with 212 images of tool wear has been created to evaluate our approach. It contains two subsets: one with images of the main cutting edge and the other one with the edges that converge to it (called Insert-C and Insert-I, respectively). The experiments were conducted trying to discriminate between two (low-high) and three (low-medium-high) different wear levels, and the classification stage was carried out using a support vector machine (SVM). Results show that B-ORCHIZ outperforms other shape descriptors (aZIBO and ZMEG) achieving accuracy values between 80.24 and 88.46 % in the different scenarios evaluated. Moreover, a hierarchical cluster analysis was performed, offering prototype images for wear levels, which may help researchers and technicians to understand how the wear process evolves. These results show a very promising opportunity for wear monitoring automation in edge profile milling processes.},
note = {Publisher: Springer London},
keywords = {Computer vision, cutting tool wear, machine learning, shape descriptors, wear monitoring automation},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORCHIZ, a novel shape-based descriptor computed from the wear region image. A new Insert dataset with 212 images of tool wear has been created to evaluate our approach. It contains two subsets: one with images of the main cutting edge and the other one with the edges that converge to it (called Insert-C and Insert-I, respectively). The experiments were conducted trying to discriminate between two (low-high) and three (low-medium-high) different wear levels, and the classification stage was carried out using a support vector machine (SVM). Results show that B-ORCHIZ outperforms other shape descriptors (aZIBO and ZMEG) achieving accuracy values between 80.24 and 88.46 % in the different scenarios evaluated. Moreover, a hierarchical cluster analysis was performed, offering prototype images for wear levels, which may help researchers and technicians to understand how the wear process evolves. These results show a very promising opportunity for wear monitoring automation in edge profile milling processes.
2.
Ordás, María Teresa García; Alegre, Enrique; Castro, Víctor González; Rodríguez, Rocío Alaiz
A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques Artículo de revista
En: The International Journal of Advanced Manufacturing Technology, vol. 90, pp. 1947–1961, 2017, (Publisher: Springer London).
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, cutting tool wear, machine learning, shape descriptors, wear monitoring automation
@article{garcia_ordas_computer_2017,
title = {A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques},
author = {María Teresa García Ordás and Enrique Alegre and Víctor González Castro and Rocío Alaiz Rodríguez},
url = {https://link.springer.com/article/10.1007/s00170-016-9541-0},
year = {2017},
date = {2017-01-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {90},
pages = {1947–1961},
abstract = {In this paper, we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORCHIZ, a novel shape-based descriptor computed from the wear region image. A new Insert dataset with 212 images of tool wear has been created to evaluate our approach. It contains two subsets: one with images of the main cutting edge and the other one with the edges that converge to it (called Insert-C and Insert-I, respectively). The experiments were conducted trying to discriminate between two (low-high) and three (low-medium-high) different wear levels, and the classification stage was carried out using a support vector machine (SVM). Results show that B-ORCHIZ outperforms other shape descriptors (aZIBO and ZMEG) achieving accuracy values between 80.24 and 88.46 % in the different scenarios evaluated. Moreover, a hierarchical cluster analysis was performed, offering prototype images for wear levels, which may help researchers and technicians to understand how the wear process evolves. These results show a very promising opportunity for wear monitoring automation in edge profile milling processes.},
note = {Publisher: Springer London},
keywords = {Computer vision, cutting tool wear, machine learning, shape descriptors, wear monitoring automation},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORCHIZ, a novel shape-based descriptor computed from the wear region image. A new Insert dataset with 212 images of tool wear has been created to evaluate our approach. It contains two subsets: one with images of the main cutting edge and the other one with the edges that converge to it (called Insert-C and Insert-I, respectively). The experiments were conducted trying to discriminate between two (low-high) and three (low-medium-high) different wear levels, and the classification stage was carried out using a support vector machine (SVM). Results show that B-ORCHIZ outperforms other shape descriptors (aZIBO and ZMEG) achieving accuracy values between 80.24 and 88.46 % in the different scenarios evaluated. Moreover, a hierarchical cluster analysis was performed, offering prototype images for wear levels, which may help researchers and technicians to understand how the wear process evolves. These results show a very promising opportunity for wear monitoring automation in edge profile milling processes.