Publications
2021
1.
Fernández-Robles, Laura; Sánchez-González, Lidia; Díez-González, Javier; Castejón-Limas, Manuel; Pérez, Hilde
Use of image processing to monitor tool wear in micro milling Artículo de revista
En: Neurocomputing, vol. 452, pp. 333–340, 2021, (Publisher: Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: digital image processing, Micro Milling, Tool Breakage, Tool wear
@article{fernandez-robles_use_2021,
title = {Use of image processing to monitor tool wear in micro milling},
author = {Laura Fernández-Robles and Lidia Sánchez-González and Javier Díez-González and Manuel Castejón-Limas and Hilde Pérez},
url = {https://www.sciencedirect.com/science/article/pii/S0925231220317501},
year = {2021},
date = {2021-01-01},
journal = {Neurocomputing},
volume = {452},
pages = {333–340},
abstract = {This paper presents a digital image processing method for monitoring tool wear in micro milling, a process where tools wear quickly due to the small size and complex geometries of the machined components. Direct measurement of tool wear is not feasible due to the size of the tools, so this method uses captured images of the tool to analyze wear progression. The wear is measured in terms of flank wear, crater wear, and tool radius reduction. Several approaches, including morphological operations, k-means clustering, and the Otsu Multilevel algorithm, were compared to determine the best method for analyzing images. The results show a 5% difference between predicted and actual worn areas, meeting industrial standards. This method can be applied in industrial environments and used in collaborative robots to enhance automation and decision-making processes.},
note = {Publisher: Elsevier},
keywords = {digital image processing, Micro Milling, Tool Breakage, Tool wear},
pubstate = {published},
tppubtype = {article}
}
This paper presents a digital image processing method for monitoring tool wear in micro milling, a process where tools wear quickly due to the small size and complex geometries of the machined components. Direct measurement of tool wear is not feasible due to the size of the tools, so this method uses captured images of the tool to analyze wear progression. The wear is measured in terms of flank wear, crater wear, and tool radius reduction. Several approaches, including morphological operations, k-means clustering, and the Otsu Multilevel algorithm, were compared to determine the best method for analyzing images. The results show a 5% difference between predicted and actual worn areas, meeting industrial standards. This method can be applied in industrial environments and used in collaborative robots to enhance automation and decision-making processes.
2013
2.
Alegre, Enrique; Biehl, Michael; Petkov, Nicolai; Sánchez-González, Lidia
Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ Artículo de revista
En: Computer methods and programs in biomedicine, vol. 111, no 3, pp. 525–536, 2013, (Publisher: Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: digital image processing, machine learning, Sperm Analysis, veterinary science
@article{alegre_assessment_2013,
title = {Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ},
author = {Enrique Alegre and Michael Biehl and Nicolai Petkov and Lidia Sánchez-González},
url = {https://www.sciencedirect.com/science/article/pii/S0169260713001478},
year = {2013},
date = {2013-01-01},
journal = {Computer methods and programs in biomedicine},
volume = {111},
number = {3},
pages = {525–536},
abstract = {This paper presents a digital image processing method to assess the acrosome state of boar spermatozoa heads. Using grayscale images labeled with fluorescent data, the sperm heads are segmented, and multiple inner contours are generated using a logarithmic distance function. Local texture features are computed for these contours, and classification performance is evaluated using Relevance Learning Vector Quantization, class conditional means, and KNN with cross-validation. The best results are achieved with gradient magnitude data, yielding a test error of only 1%, outperforming previous methods and demonstrating the potential for automated veterinary applications.},
note = {Publisher: Elsevier},
keywords = {digital image processing, machine learning, Sperm Analysis, veterinary science},
pubstate = {published},
tppubtype = {article}
}
This paper presents a digital image processing method to assess the acrosome state of boar spermatozoa heads. Using grayscale images labeled with fluorescent data, the sperm heads are segmented, and multiple inner contours are generated using a logarithmic distance function. Local texture features are computed for these contours, and classification performance is evaluated using Relevance Learning Vector Quantization, class conditional means, and KNN with cross-validation. The best results are achieved with gradient magnitude data, yielding a test error of only 1%, outperforming previous methods and demonstrating the potential for automated veterinary applications.