Publications
2015
1.
González-Laguna, Adalberto; Barreiro, Joaquín; Fernández-Abia, Ana Isabel; Alegre, Enrique; González-Castro, Víctor
Design of a TCM system based on vibration signal for metal turning processes Artículo de revista
En: Procedia engineering, vol. 132, pp. 405–412, 2015, (Publisher: No longer published by Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: Monitoring, TCM, Tool wear, Turning, Vibration
@article{gonzalez-laguna_design_2015,
title = {Design of a TCM system based on vibration signal for metal turning processes},
author = {Adalberto González-Laguna and Joaquín Barreiro and Ana Isabel Fernández-Abia and Enrique Alegre and Víctor González-Castro},
url = {https://www.sciencedirect.com/science/article/pii/S1877705815044239},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Procedia engineering},
volume = {132},
pages = {405–412},
abstract = {This paper investigates the identification of cutting tool wear in steel dry turning operations using vibration signal analysis. By analyzing RMS value evolution and FFT frequency spectra, an on-line tool condition monitoring system was developed to determine when the tool condition becomes unacceptable, affecting machining quality. The study concludes that both RMS values and specific frequency amplitude ranges are correlated with tool wear.},
note = {Publisher: No longer published by Elsevier},
keywords = {Monitoring, TCM, Tool wear, Turning, Vibration},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the identification of cutting tool wear in steel dry turning operations using vibration signal analysis. By analyzing RMS value evolution and FFT frequency spectra, an on-line tool condition monitoring system was developed to determine when the tool condition becomes unacceptable, affecting machining quality. The study concludes that both RMS values and specific frequency amplitude ranges are correlated with tool wear.
2.
Olivera, Óscar García-Olalla; Alegre, Enrique; Barreiro, Joaquín; Fernández-Robles, Laura; García-Ordás, María Teresa
Tool wear classification using LBP-based descriptors combined with LOSIB-based enhancers Artículo de revista
En: Procedia engineering, vol. 132, pp. 950–957, 2015, (Publisher: No longer published by Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: LBP, LOSIB, Monitoring, TCM, texture description, Tool wear
@article{garcia-olalla_tool_2015,
title = {Tool wear classification using LBP-based descriptors combined with LOSIB-based enhancers},
author = {Óscar García-Olalla Olivera and Enrique Alegre and Joaquín Barreiro and Laura Fernández-Robles and María Teresa García-Ordás},
url = {https://www.sciencedirect.com/science/article/pii/S187770581504494X},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Procedia engineering},
volume = {132},
pages = {950–957},
abstract = {This paper presents an automatic tool wear detection method using computer vision and texture recognition. Two LBP-based methods combined with the LOSIB texture booster were evaluated on a dataset of 577 images. Binary (Low-High) and ternary (Low-Medium-High) classifications were performed, achieving 80.58% and 67.76% accuracy, respectively. Results highlight the potential for cost and time savings in industrial tool condition monitoring systems (TCMS).},
note = {Publisher: No longer published by Elsevier},
keywords = {LBP, LOSIB, Monitoring, TCM, texture description, Tool wear},
pubstate = {published},
tppubtype = {article}
}
This paper presents an automatic tool wear detection method using computer vision and texture recognition. Two LBP-based methods combined with the LOSIB texture booster were evaluated on a dataset of 577 images. Binary (Low-High) and ternary (Low-Medium-High) classifications were performed, achieving 80.58% and 67.76% accuracy, respectively. Results highlight the potential for cost and time savings in industrial tool condition monitoring systems (TCMS).
2007
3.
Castejón-Limas, Manuel; Alegre, Enrique; Barreiro, Joaquín; Hernández, LK
On-line tool wear monitoring using geometric descriptors from digital images Artículo de revista
En: International Journal of Machine Tools and Manufacture, vol. 47, no 12-13, pp. 1847–1853, 2007, (Publisher: Pergamon).
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, Image classification, Monitoring, Tool wear
@article{castejon-limas_-line_2007,
title = {On-line tool wear monitoring using geometric descriptors from digital images},
author = {Manuel Castejón-Limas and Enrique Alegre and Joaquín Barreiro and LK Hernández},
url = {https://www.sciencedirect.com/science/article/pii/S0890695507000892},
year = {2007},
date = {2007-01-01},
journal = {International Journal of Machine Tools and Manufacture},
volume = {47},
number = {12-13},
pages = {1847–1853},
abstract = {A computer vision and statistical learning system is proposed to estimate wear levels in cutting inserts and determine the optimal replacement time. Using a CNC lathe and vision system, 1383 flank images were processed, extracting nine geometrical descriptors. Linear Discriminant Analysis identified three key descriptors—eccentricity, extent, and solidity—capturing 98.63% of relevant information. A finite mixture model classified wear into three levels: low, medium, and high. The monitoring approach tracks tool wear evolution, ensuring replacement before reaching high wear, optimizing performance and preventing failures.},
note = {Publisher: Pergamon},
keywords = {Computer vision, Image classification, Monitoring, Tool wear},
pubstate = {published},
tppubtype = {article}
}
A computer vision and statistical learning system is proposed to estimate wear levels in cutting inserts and determine the optimal replacement time. Using a CNC lathe and vision system, 1383 flank images were processed, extracting nine geometrical descriptors. Linear Discriminant Analysis identified three key descriptors—eccentricity, extent, and solidity—capturing 98.63% of relevant information. A finite mixture model classified wear into three levels: low, medium, and high. The monitoring approach tracks tool wear evolution, ensuring replacement before reaching high wear, optimizing performance and preventing failures.