Publications

@article{fernandez-robles_tool_2018,

title = {Tool wear estimation and visualization using image sensors in micro milling manufacturing},

author = {Laura Fernández-Robles and Noelia Charro and Lidia Sánchez-González and Hilde Pérez and Manuel Castejón-Limas and Javier Alfonso-Cendón},

url = {https://link.springer.com/chapter/10.1007/978-3-319-92639-1_33},

year  = {2018},

date = {2018-01-01},

journal = {Hybrid Artificial Intelligent Systems: 13th International Conference, HAIS 2018, Oviedo, Spain, June 20-22, 2018, Proceedings 13},

pages = {399–410},

abstract = {This paper introduces a reliable machine vision system for automatically estimating and visualizing tool wear in micro milling. The system aims to ensure high-quality machining and prevent tool failure by accurately determining when a tool needs replacement. The process involves defining the area of interest and identifying the worn area using morphological operations and the k-means algorithm. The system's performance, evaluated with precision and recall (harmonic mean of 90.24 ± 2.78%), demonstrates its effectiveness and suitability for on-line integration in micro milling machines. Other tested methods include pure morphological operations and Otsu multi-threshold algorithms.},

note = {Publisher: Springer International Publishing},

keywords = {Micro Milling, Tool wear, Wear Estimation, Wear Visualization},

pubstate = {published},

tppubtype = {article}

}