Publications

@article{garcia-ordas_tool_2018,

title = {Tool wear monitoring using an online, automatic and low cost system based on local texture},

author = {María Teresa García-Ordás and Enrique Alegre and Rocío Alaiz-Rodríguez and Víctor González-Castro},

url = {https://www.sciencedirect.com/science/article/pii/S088832701830236X},

year  = {2018},

date = {2018-01-01},

journal = {Mechanical systems and signal processing},

volume = {112},

pages = {98–112},

abstract = {This work presents a new, cost-effective, and fast approach for determining whether cutting tools in edge profile milling processes are serviceable or disposable, based on wear levels. A new dataset of 254 images of edge profile cutting heads was created, with 577 images of segmented cutting edges, classified as either functional (301) or disposable (276). The proposed method involves dividing the cutting edge into regions (Wear Patches, WP), characterizing them using texture descriptors based on Local Binary Patterns (LBP), and using a Support Vector Machine (SVM) with an intersection kernel to classify the patches. The best configuration achieved an accuracy of 90.26% in detecting disposable cutting edges, showing great potential for automatic wear monitoring in milling.},

note = {Publisher: Academic Press},

keywords = {Patches, texture description, Tool wear, Wear Region},

pubstate = {published},

tppubtype = {article}

}