Publications

@article{alegre_tool_2005,

title = {Tool insert wear classification using statistical descriptors and neuronal networks},

author = {Enrique Alegre and Rocío Aláiz-Rodríguez and Joaquín Barreiro and M Viñuela},

url = {https://link.springer.com/chapter/10.1007/11578079_82},

year  = {2005},

date = {2005-01-01},

journal = {Progress in Pattern Recognition, Image Analysis and Applications: 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, November 15-18, 2005. Proceedings 10},

pages = {786–793},

abstract = {This work proposes an automated method to determine tool insert wear levels using image analysis. Images of tungsten carbide inserts were acquired during machining of AISI SAE 1045 and 4140 steel bars. After pre-processing and wear area segmentation, statistical moment-based descriptors were extracted. Two classification experiments (binary and three-class) were conducted using Lp2, k-NN, and neural networks. Zernike and Legendre descriptors achieved the best results with a multilayer perceptron (MLP) neural network.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {image processing, machine vision, Manufacturing, neural networks, Tool wear},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_tool_nodate,

title = {Tool wear classification using texture descriptors based on Local Binary Pattern},

author = {Óscar García-Olalla and Enrique Alegre and Joaquín Barreiro and Laura Fernández-Robles and Marıa Teresa García-Ordás},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=opCbArQAAAAJ&cstart=100&pagesize=100&sortby=title&citation_for_view=opCbArQAAAAJ:RHpTSmoSYBkC},

abstract = {This paper presents a new approach for tool wear identification in metal milling using texture descriptors LBP and ALBP, enhanced with LOSIB (Local Oriented Statistical Information Booster). Two datasets are considered: Cutting Edges (gray-scale images of worn cutting edges) and Edge Wear (cropped worn areas). Both datasets are labeled into two (low/high wear) and three (low/medium/high wear) classes. Classification using Support Vector Machine with Least Squares training achieved a 74.05% accuracy for two classes and 53.77% for three classes in the Edge Wear dataset.},

keywords = {Machine Learing, Manufacturing, SVM, texture analysis, Tool wear},

pubstate = {published},

tppubtype = {article}

}