Publications
2009
1.
Alegre, Enrique; Alaiz-Rodríguez, Rocío; Barreiro, Joaquín; Ruiz, Jonatan
Use of contour signatures and classification methods to optimize the tool life in metal machining Artículo de revista
En: Estonian Journal of Engineering, vol. 15, no 1, pp. 3–12, 2009, (Publisher: Estonian Academy publishers).
Resumen | Enlaces | BibTeX | Etiquetas: Contour Signature, Neural Network Classification, Tool Life, Tool wear
@article{alegre_use_2009,
title = {Use of contour signatures and classification methods to optimize the tool life in metal machining},
author = {Enrique Alegre and Rocío Alaiz-Rodríguez and Joaquín Barreiro and Jonatan Ruiz},
url = {https://d1wqtxts1xzle7.cloudfront.net/114640679/eng-2009-1-3-12-libre.pdf?1715926032=&response-content-disposition=inline%3B+filename%3DUse_of_contour_signatures_and_classifica.pdf&Expires=1741355794&Signature=W4qnk8wYuY5QBKx9kYhr2GtRSqNq8u6UgJRYHxZs6disLI2R-vaIE3KgdorgyM1jKiIDXR3~tATngNgrzXJ2oEthp-FHV422v0pr5zeCTesWB4csarChn4DGqetjDWO~pOfCIY6wIThNAk~IIeOBkncxP6GjWzUyvjeK-jOcfJ1qHHCggKtWkGIdGeDQQmQubImeJt55o0STKFrHMRHTqitie08gYt93GuyH~x7rCIP08mEVqt2srrVEsW2fCt7a7dxp5RMuh-gn~t–~zF29SuzG0hEotF-8HDXw4ZJoWv8VKYtZdI2hU3x0e1OTgiomaqW4Sdm6PuYJ8tSpM9rLw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
journal = {Estonian Journal of Engineering},
volume = {15},
number = {1},
pages = {3–12},
abstract = {This work aims to improve decision-making regarding tool replacement times, which significantly impacts the cost of machined parts. The approach utilizes digital images of the cutting edge, focusing on contour signatures of the wear region. Two classification techniques, k-nearest neighbor and a neural network, are used to analyze these signatures. The study also examines the effect of the signature size vector on classification performance. A total of 1383 flank wear images were acquired, with an error rate estimation of about 5%.},
note = {Publisher: Estonian Academy publishers},
keywords = {Contour Signature, Neural Network Classification, Tool Life, Tool wear},
pubstate = {published},
tppubtype = {article}
}
This work aims to improve decision-making regarding tool replacement times, which significantly impacts the cost of machined parts. The approach utilizes digital images of the cutting edge, focusing on contour signatures of the wear region. Two classification techniques, k-nearest neighbor and a neural network, are used to analyze these signatures. The study also examines the effect of the signature size vector on classification performance. A total of 1383 flank wear images were acquired, with an error rate estimation of about 5%.
2008
2.
Alegre, Enrique; Aláiz-Rodríguez, Rocío; Barreiro, Joaquín; Ruiz, Jonatan
Tool wear classification using shape signatures from digital images and neural networks Miscelánea
2008.
Resumen | Enlaces | BibTeX | Etiquetas: Contour Signature, Neural Network Classification, Tool Life, Tool wear
@misc{alegre_tool_2008,
title = {Tool wear classification using shape signatures from digital images and neural networks},
author = {Enrique Alegre and Rocío Aláiz-Rodríguez and Joaquín Barreiro and Jonatan Ruiz },
url = {https://d1wqtxts1xzle7.cloudfront.net/44449799/TOOL_WEAR_CLASSIFICATION_USING_SHAPE_SIG20160405-3188-z6pcbc-libre.pdf?1459894371=&response-content-disposition=inline%3B+filename%3DTool_Wear_Classification_Using_Shape_Sig.pdf&Expires=1739807418&Signature=EvzaKBJe-t9uYSU0VHYfIsHLonQIni35AX6MmZfYX-OGZpEp8t2BJx27qJd32s9rE2Cc9pvkE7~9h~snb0YBv54nDj8YR4vULhHEIdJeYDH3ggm0seHqVpOg0pkE5KRCJByFN6SpvJdwTX6zzuuuXMbbQ0kTnZ0a~pzRBxr4yuCiHStUIcT7gpfiHcYVtBgznT0XiC~AM1vif5Wed6giDaCYv3YT7bs2Hn8h3DYoE0VJGSpQwIe8rhe9GBeQE0gJmfRsc4hacjCdf2ROSX-TJqo4CEPlVk97SWMlmOoEGa3ZfDGfaIMozuJUFUptedp7W8U-0-bsAIQJQihJHdch7g__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
publisher = {na},
abstract = {This paper presents a computer vision-based method with a neural network classifier to estimate wear in metal cutting inserts and determine replacement timing. A supervised classification approach distinguishes between low and excessive wear to ensure timely tool replacement. A dataset of 1,383 wear flank images was processed into binary images, and the wear perimeter was described using a normalized shape signature resized to 40 and 100 values. Classification using k-NN and MLP achieved error rates of 5.5% and 5.1%, respectively.},
keywords = {Contour Signature, Neural Network Classification, Tool Life, Tool wear},
pubstate = {published},
tppubtype = {misc}
}
This paper presents a computer vision-based method with a neural network classifier to estimate wear in metal cutting inserts and determine replacement timing. A supervised classification approach distinguishes between low and excessive wear to ensure timely tool replacement. A dataset of 1,383 wear flank images was processed into binary images, and the wear perimeter was described using a normalized shape signature resized to 40 and 100 values. Classification using k-NN and MLP achieved error rates of 5.5% and 5.1%, respectively.