Publications
2012
1.
Alegre, Enrique; Barreiro, Joaquín; Suárez-Castrillón, Alexci
A new improved Laws-based descriptor for surface roughness evaluation Artículo de revista
En: The International Journal of Advanced Manufacturing Technology, vol. 59, pp. 605–615, 2012, (Publisher: Springer-Verlag).
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, machining processes, surface roughness, texture analysis
@article{alegre_new_2012,
title = {A new improved Laws-based descriptor for surface roughness evaluation},
author = {Enrique Alegre and Joaquín Barreiro and Alexci Suárez-Castrillón},
url = {https://link.springer.com/article/10.1007/s00170-011-3507-z},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {59},
pages = {605–615},
abstract = {A new descriptor that allows to classify turned metallic parts based on their superficial roughness is proposed in this paper. The material used for the tests was AISI 6150 steel, regarded as one of the reference steels in the market. The proposed solution is based on a vision system that calculates the actual roughness by analysing texture on images of machined parts. A new developed R5SR5S kernel for quantifying roughness is based on the R5R5 mask presented by Laws. Results from computing standard deviation from images obtained with the proposed R5SR5S kernel allow us to classify the images with a hit rate of 95.87% using linear discriminant analysis and 97.30% using quadratic discriminant analysis. These results show that the proposed technique can be effectively used to evaluate roughness in machining processes.},
note = {Publisher: Springer-Verlag},
keywords = {Computer vision, machining processes, surface roughness, texture analysis},
pubstate = {published},
tppubtype = {article}
}
A new descriptor that allows to classify turned metallic parts based on their superficial roughness is proposed in this paper. The material used for the tests was AISI 6150 steel, regarded as one of the reference steels in the market. The proposed solution is based on a vision system that calculates the actual roughness by analysing texture on images of machined parts. A new developed R5SR5S kernel for quantifying roughness is based on the R5R5 mask presented by Laws. Results from computing standard deviation from images obtained with the proposed R5SR5S kernel allow us to classify the images with a hit rate of 95.87% using linear discriminant analysis and 97.30% using quadratic discriminant analysis. These results show that the proposed technique can be effectively used to evaluate roughness in machining processes.
2.
Morala-Argüello, Patricia; Barreiro, Joaquín; Alegre, Enrique; García-Ordás, María Teresa; Olivera, Óscar García-Olalla; González-Madruga, Daniel
Reliability of Monitoring Signals for Estimation of Surface Roughness in Metallic Turned Parts Artículo de revista
En: Advanced Materials Research, vol. 498, pp. 213–218, 2012, (Publisher: Trans Tech Publications Ltd).
Resumen | Enlaces | BibTeX | Etiquetas: Cutting Force Analysis, machining processes, Signal Monitoring, Surface Roughness Prediction
@article{morala-arguello_reliability_2012,
title = {Reliability of Monitoring Signals for Estimation of Surface Roughness in Metallic Turned Parts},
author = {Patricia Morala-Argüello and Joaquín Barreiro and Enrique Alegre and María Teresa García-Ordás and Óscar García-Olalla Olivera and Daniel González-Madruga},
url = {https://www.scientific.net/AMR.498.213},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {Advanced Materials Research},
volume = {498},
pages = {213–218},
abstract = {Modern machining processes aim to enhance productivity, reliability, and cost efficiency. In this context, signal monitoring systems play a crucial role in surface roughness inspection. This study evaluates different signal types, including cutting forces and vibrations, to predict surface roughness indirectly. The findings indicate that combining force measurements with cutting conditions yields the most accurate roughness predictions. The absolute error remained below 1.28 µm using the median as a descriptor and below 1.11 µm with root mean square (RMS), making these effective approaches for roughness evaluation.},
note = {Publisher: Trans Tech Publications Ltd},
keywords = {Cutting Force Analysis, machining processes, Signal Monitoring, Surface Roughness Prediction},
pubstate = {published},
tppubtype = {article}
}
Modern machining processes aim to enhance productivity, reliability, and cost efficiency. In this context, signal monitoring systems play a crucial role in surface roughness inspection. This study evaluates different signal types, including cutting forces and vibrations, to predict surface roughness indirectly. The findings indicate that combining force measurements with cutting conditions yields the most accurate roughness predictions. The absolute error remained below 1.28 µm using the median as a descriptor and below 1.11 µm with root mean square (RMS), making these effective approaches for roughness evaluation.