Publications

@article{riego_strong_2021,

title = {Strong classification system for wear identification on milling processes using computer vision and ensemble learning},

author = {Virginia Riego and Manuel Castejón-Limas and Lidia Sánchez-González and Laura Fernández-Robles and Hilde Perez and Javier Díez-González and Ángel-Manuel Guerrero-Higueras},

url = {https://www.sciencedirect.com/science/article/pii/S0925231220316155},

year  = {2021},

date = {2021-01-01},

journal = {Neurocomputing},

volume = {456},

pages = {678–684},

abstract = {This paper proposes a machine-vision-based method for evaluating the texture of the inner and outer surfaces of machined metallic pieces with cylindrical holes. Using a microscope camera connected to a rigid industrial boroscope, images of the hole surface are captured. The texture descriptors extracted from these images are analyzed, and a significant correlation is found. Feature reduction is performed, followed by classification using various algorithms with exhaustive grid search and 10-fold cross-validation. The best results are obtained with the Extremely Randomized Trees classifier, achieving a mean test score of 92.98%, surpassing previous research and meeting industry requirements.},

note = {Publisher: Elsevier},

keywords = {Haralick Descriptors, Milling Machined Parts, Quality Estimation, Wear Detection},

pubstate = {published},

tppubtype = {article}

}

@article{sanchez-gonzalez_local_2020,

title = {Local binary pattern features to detect anomalies in machined workpiece},

author = {Lidia Sánchez-González and Virginia Riego and Manuel Castejón-Limas and Laura Fernández-Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-030-61705-9_55},

year  = {2020},

date = {2020-01-01},

journal = {International Conference on Hybrid Artificial Intelligence Systems},

pages = {665–673},

abstract = {This paper proposes a vision-based system for evaluating the surface finish of machined workpieces by using Local Binary Pattern (LBP) vectors to represent image textures. The system detects wear on surfaces by analyzing the texture descriptors, as regular patterns correspond to unworn surfaces. Four classification techniques are tested, with the Random Forest algorithm achieving the highest accuracy of 86.0%, meeting the expert requirements for quality control.},

note = {Publisher: Springer International Publishing Cham},

keywords = {local binary pattern, Random Forest Classification, Surface Finish, Wear Detection},

pubstate = {published},

tppubtype = {article}

}

@article{castejon-limas_texture_2019,

title = {Texture descriptors for automatic estimation of workpiece quality in milling},

author = {Manuel Castejón-Limas and Lidia Sánchez-González and Javier Díez-González and Laura Fernández-Robles and Virginia Riego and Hilde Pérez},

url = {https://link.springer.com/chapter/10.1007/978-3-030-29859-3_62},

year  = {2019},

date = {2019-01-01},

journal = {Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, León, Spain, September 4–6, 2019, Proceedings 14},

pages = {734–744},

abstract = {This paper presents a method for automatically detecting workpiece wear in milling using texture descriptors. Images of the inner surface of the workpiece are captured with a boroscope and analyzed. Texture features are computed from the co-occurrence matrix, and feature vectors are classified using four methods: Decision Trees, K Neighbors, Naïve Bayes, and Multilayer Perceptron. Linear discriminant analysis reduces the feature set from six to two without sacrificing accuracy. The Decision Trees approach achieves a 91.8% hit rate, meeting industrial requirements.},

note = {Publisher: Springer International Publishing},

keywords = {Haralick Descriptors, Milling Machined Parts, Quality Estimation, Wear Detection},

pubstate = {published},

tppubtype = {article}

}