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{riego_burr_2021,

title = {Burr detection and classification using rustico and image processing},

author = {Virginia Riego and Lidia Sánchez-González and Laura Fernández-Robles and Alexis Gutiérrez-Fernández and Nicola Strisciuglio},

url = {https://www.sciencedirect.com/science/article/pii/S1877750321001538},

year  = {2021},

date = {2021-01-01},

journal = {Journal of computational science},

volume = {56},

pages = {101485},

abstract = {This study focuses on classifying burrs in edge finishing of machined workpieces to reduce production costs and time. It identifies three types of burrs: knife-type (no imperfections), saw-type (small splinters), and burr-breakage (substantial deformation). The proposed method, RUSTICO, automatically classifies the edge of each piece, achieving a 91.2% F1-Score and successfully identifying the burr-breakage type.},

note = {Publisher: Elsevier},

keywords = {Burr Classification, Burrs in Workpiece, Milling Machined Parts, RUSTICO},

pubstate = {published},

tppubtype = {article}

}

@article{riego_del_castillo_burr_2020,

title = {Burr detection using image processing in milling workpieces},

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

url = {https://link.springer.com/chapter/10.1007/978-3-030-57802-2_72},

year  = {2020},

date = {2020-01-01},

journal = {International Workshop on Soft Computing Models in Industrial and Environmental Applications},

pages = {751–759},

abstract = {This paper proposes an automatic method to detect burrs and assess edge finishing quality in manufacturing processes using image processing and linear regression. The method isolates the slope of a calculated function to establish quality thresholds, successfully distinguishing between three types of burrs. The results validate its effectiveness in quality assessment.},

note = {Publisher: Springer International Publishing Cham},

keywords = {Burr Classification, Burrs in Workpiece, Milling Machined Parts, Quality Estimation},

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}

}