@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}
}
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.