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.
2015
2.
García-Ordás, María Teresa; Alegre, Enrique; González-Castro, Víctor; Olivera, Óscar García-Olalla; Barreiro, Joaquín; Fernández-Abia, Ana Isabel
aZIBO shape descriptor for monitoring tool wear in milling Artículo de revista
En: Procedia Engineering, vol. 132, pp. 958–965, 2015, (Publisher: No longer published by Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: image processing, machine learning, metal machining, shape descriptors, tool wear detection
@article{garcia_ordas_azibo_2015,
title = {aZIBO shape descriptor for monitoring tool wear in milling},
author = {María Teresa García-Ordás and Enrique Alegre and Víctor González-Castro and Óscar García-Olalla Olivera and Joaquín Barreiro and Ana Isabel Fernández-Abia},
url = {https://www.sciencedirect.com/science/article/pii/S1877705815044951},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Procedia Engineering},
volume = {132},
pages = {958–965},
abstract = {This paper proposes an automated method for estimating insert wear in metal machining to optimize tool replacement. The aZIBO shape descriptor (absolute Zernike moments with Invariant Boundary Orientation) is used for wear characterization. A dataset of 577 wear regions was classified into two (Low-High) and three (Low-Medium-High) classes using kNN and SVM classifiers. aZIBO outperformed traditional shape descriptors, achieving success rates of 91.33% for two-class and 90.12% for three-class classification.},
note = {Publisher: No longer published by Elsevier},
keywords = {image processing, machine learning, metal machining, shape descriptors, tool wear detection},
pubstate = {published},
tppubtype = {article}
}
This paper proposes an automated method for estimating insert wear in metal machining to optimize tool replacement. The aZIBO shape descriptor (absolute Zernike moments with Invariant Boundary Orientation) is used for wear characterization. A dataset of 577 wear regions was classified into two (Low-High) and three (Low-Medium-High) classes using kNN and SVM classifiers. aZIBO outperformed traditional shape descriptors, achieving success rates of 91.33% for two-class and 90.12% for three-class classification.
2014
3.
García-Ordás, Marïa Teresa; Alegre, Enrique; González-Castro, Víctor; García-Ordás, Diego
aZIBO: a new descriptor based in shape moments and rotational invariant features Artículo de revista
En: 2014 22nd International Conference on Pattern Recognition, pp. 2395–2400, 2014, (Publisher: IEEE).
Resumen | Enlaces | BibTeX | Etiquetas: EGCM, Image classification, machile learning, shape descriptors, zernike moments
@article{garcia-ordas_azibo_2014,
title = {aZIBO: a new descriptor based in shape moments and rotational invariant features},
author = {Marïa Teresa García-Ordás and Enrique Alegre and Víctor González-Castro and Diego García-Ordás},
url = {https://ieeexplore.ieee.org/abstract/document/6977127},
year = {2014},
date = {2014-01-01},
journal = {2014 22nd International Conference on Pattern Recognition},
pages = {2395–2400},
abstract = {This work introduces a new shape descriptor called ZIBO (absolute Zernike moments with Invariant Boundary Orientation), combining global Zernike moments and a rotationally invariant version of the Edge Gradient Co-occurrence Matrix (EGCM). The descriptors were applied to three datasets (Kimia99, MPEG2, MPEG7) and evaluated using kNN with City block and Chi-square distance metrics. The combination of global and local descriptors achieved better results than the baseline ZMEG method. Specifically, the ZIBO descriptor obtained success rates of 78.29% on MPEG7 and 81.00% on MPEG2, outperforming ZMEG by 2.43% and 3.75%, respectively.},
note = {Publisher: IEEE},
keywords = {EGCM, Image classification, machile learning, shape descriptors, zernike moments},
pubstate = {published},
tppubtype = {article}
}
This work introduces a new shape descriptor called ZIBO (absolute Zernike moments with Invariant Boundary Orientation), combining global Zernike moments and a rotationally invariant version of the Edge Gradient Co-occurrence Matrix (EGCM). The descriptors were applied to three datasets (Kimia99, MPEG2, MPEG7) and evaluated using kNN with City block and Chi-square distance metrics. The combination of global and local descriptors achieved better results than the baseline ZMEG method. Specifically, the ZIBO descriptor obtained success rates of 78.29% on MPEG7 and 81.00% on MPEG2, outperforming ZMEG by 2.43% and 3.75%, respectively.