Publications
2018
1.
Fernández-Robles, Laura; Charro, Noelia; Sánchez-González, Lidia; Pérez, Hilde; Castejón-Limas, Manuel; Alfonso-Cendón, Javier
Tool wear estimation and visualization using image sensors in micro milling manufacturing Artículo de revista
En: Hybrid Artificial Intelligent Systems: 13th International Conference, HAIS 2018, Oviedo, Spain, June 20-22, 2018, Proceedings 13, pp. 399–410, 2018, (Publisher: Springer International Publishing).
Resumen | Enlaces | BibTeX | Etiquetas: Micro Milling, Tool wear, Wear Estimation, Wear Visualization
@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}
}
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.