Publications

@article{garcia-ordas_tool_2024,

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://arxiv.org/abs/2402.05977},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {arXiv preprint arXiv:2402.05977},

abstract = {This work presents a fast and cost-effective method using computer vision and machine learning to assess cutting tool wear in edge profile milling. A new dataset of 577 images was created, including functional and disposable cutting edges. The method divides the edges into regions (Wear Patches) and classifies them using texture descriptors (LBP). A Support Vector Machine (SVM) achieved 90.26% accuracy in detecting worn tools, demonstrating strong potential for automatic wear monitoring in milling.},

keywords = {Computer vision, machine learning, Milling, Tool wear},

pubstate = {published},

tppubtype = {article}

}