Publications
2016
Alegre, Enrique; Ruiz, Jonatan; Barreiro, Joaquín; Castejón-Limas, Manuel; Hernández, LK
Automatic segmentation of the wear region in cutting tools Artículo de revista
En: 2016.
Resumen | Enlaces | BibTeX | Etiquetas: automatic segmentation, cutting tool, extended-maxima transform, machining
@article{alegre_automatic_nodate,
title = {Automatic segmentation of the wear region in cutting tools},
author = {Enrique Alegre and Jonatan Ruiz and Joaquín Barreiro and Manuel Castejón-Limas and LK Hernández},
url = {https://d1wqtxts1xzle7.cloudfront.net/44449821/Automatic_segmentation_of_the_wear_regio20160405-32089-t0uk07-libre.pdf?1459894370=&response-content-disposition=inline%3B+filename%3DAutomatic_segmentation_of_the_wear_regio.pdf&Expires=1739456394&Signature=O9SQuejVUFfkNXaJaH-Rd1mgX1dE0MfJ9xge3ri6ekr1I71KojNIII7qijC511wXXymUmYaQ-VrMMO-dSYBRVfr8cNcqykaUiNZOb~KAfrMM9ewkyHV-di20qDSZ4nLw-Ij-eFk48OorcP5L8PdZ3CsvnFrsvtnidpUszpwZL9NZo1A49e1q05cXM2SJwQOUbmkHWQDsLmJ8WOh1bawMgkzWNIKImlI5wWwqwa86g0j4~nlHw3NnFgvrDkVMPer-3vj2yWLyDkoKxjwxVSWAAotMWJPseED8RwZV6KAEU5YxWO~bL2IgSidjRBU3zvoL5Neff4wwOEnqpLKrXomX2A__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2016},
date = {2016-04-05},
abstract = {This paper presents a methodology for segmenting wear regions in cutting tools using decision rules and image processing techniques. The approach adapts to different wear levels and achieved 90% accuracy on 625 samples through visual validation.},
keywords = {automatic segmentation, cutting tool, extended-maxima transform, machining},
pubstate = {published},
tppubtype = {article}
}
2012
Morala-Argüello, Patricia; Barreiro, Joaquín; Alegre, Enrique; García-Ordás, Maite; Olivera, Óscar García-Olalla; González-Madruga, Daniel
Surface roughness prediction from combination of cutting forces, turning vibrations and machining conditions using artificial neural networks Artículo de revista
En: AIP Conference Proceedings, vol. 1431, no 1, pp. 510–517, 2012, (Publisher: American Institute of Physics).
Enlaces | BibTeX | Etiquetas: Artificial Neural Networks, Industry, machining, Signal Processing
@article{morala-arguello_surface_2012,
title = {Surface roughness prediction from combination of cutting forces, turning vibrations and machining conditions using artificial neural networks},
author = {Patricia Morala-Argüello and Joaquín Barreiro and Enrique Alegre and Maite García-Ordás and Óscar García-Olalla Olivera and Daniel González-Madruga},
url = {https://pubs.aip.org/aip/acp/article-abstract/1431/1/510/874033/Surface-roughness-prediction-from-combination-of},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {AIP Conference Proceedings},
volume = {1431},
number = {1},
pages = {510–517},
note = {Publisher: American Institute of Physics},
keywords = {Artificial Neural Networks, Industry, machining, Signal Processing},
pubstate = {published},
tppubtype = {article}
}
2009
Morala-Argüello, Patricia; Barreiro, Joaquín; Alegre, Enrique; González-Castro, Víctor
Application of textural descriptors for the evaluation of surface roughness class in the machining of metals Artículo de revista
En: 2009.
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, machine learning, machining, quality control, surface roughness
@article{morala-arguello_application_2009,
title = {Application of textural descriptors for the evaluation of surface roughness class in the machining of metals},
author = {Patricia Morala-Argüello and Joaquín Barreiro and Enrique Alegre and Víctor González-Castro},
url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=opCbArQAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=opCbArQAAAAJ:UebtZRa9Y70C},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
abstract = {Surface roughness measurement has been a key topic in metal machining research for decades. Traditional methods rely on tactile devices providing 2D profiles, but advances in computer vision now enable 3D surface characterization. This paper proposes a computer vision-based method to evaluate machined part quality using five feature vectors: Hu, Flusser, Taubin, Zernike, and Legendre moments. Images were classified into low and high roughness using k-NN and neural networks. Results show that Zernike and Legendre descriptors perform best, achieving a 6.5% error rate with k-NN classification.},
keywords = {Computer vision, machine learning, machining, quality control, surface roughness},
pubstate = {published},
tppubtype = {article}
}