@article{cobo2017automatic,
title = {Automatic classification of metal allows from their LIBS spectra and its robustness against spectrometer decalibration},
author = {A.Cobo and A.Picón and M.Martinez and C.L.Saratxaga and Z.Amondarain and J.M.Lopez-Higuera},
url = {https://computervision.tecnalia.com/wp-content/uploads/2017/02/poster_emslibs2017.pdf},
year = {2017},
date = {2017-01-01},
keywords = {machine learning},
pubstate = {published},
tppubtype = {article}
}
@article{picon2012statistical,
title = {A statistical recommendation model of mobile services based on contextual evidences},
author = {Artzai Picón and Sergio Rodríguez-Vaamonde and Javier Jaén and Jose Antonio Mocholi and David García and Alejandro Cadenas},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2012_AStatisticalRecommendationModel.pdf},
year = {2012},
date = {2012-01-01},
journal = {Expert Systems with Applications},
volume = {39},
number = {1},
pages = {647--653},
publisher = {Elsevier},
keywords = {machine learning},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{rodriguez2012modified,
title = {Modified fuzzy c-means applied to a Bragg grating-based spectral imager for material clustering},
author = {Aida Rodríguez and Juan Luis Nieves and Eva Valero and Estíbaliz Garrote and Javier Hernández-Andrés and Javier Romero},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2012-estibaliz-Garrote-Aida-etal-ElectronicImaging2012.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {Image Processing: Machine Vision Applications V},
volume = {8300},
pages = {83000J},
organization = {International Society for Optics and Photonics},
keywords = {machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Olabarria2008b,
title = {Surface inspection of hot rolled seamless tubes},
author = {Gutierrez Olabarria, Jose Angel and Lago Martinez, Alberto and Cruz-Lopez Claret, Antonio and Ayani, Alex},
editor = {Stahl Eisen},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/Artículo-MPT-HoTubEye.pdf},
year = {2008},
date = {2008-05-15},
journal = {MPT International},
volume = { 5/2008},
abstract = {During hot rolling of laminated seamless tubes, surface marks and other defects may occur sporadically due to wear of the roll stands.
This article describes a new automatic inspection technology for surface defects developed for a seamless tube manufacturer in Spain. The automated inspection system is installed at the exit of the
push bench, just where the defects originate. Under harsh environmental conditions due to dirt, vapour and high temperatures, the system must be capable of inspecting the whole surface of the tubes
in real time. An intelligent classification software tool based on a support vector machine is used to recognize the previously learned defects.},
keywords = {computer vision, machine learning, surface quality},
pubstate = {published},
tppubtype = {article}
}
During hot rolling of laminated seamless tubes, surface marks and other defects may occur sporadically due to wear of the roll stands.
This article describes a new automatic inspection technology for surface defects developed for a seamless tube manufacturer in Spain. The automated inspection system is installed at the exit of the
push bench, just where the defects originate. Under harsh environmental conditions due to dirt, vapour and high temperatures, the system must be capable of inspecting the whole surface of the tubes
in real time. An intelligent classification software tool based on a support vector machine is used to recognize the previously learned defects.
@conference{Olabarria2008,
title = {Surface Inspection of Hot Laminated Seamless Tubes},
author = {Gutierrez Olabarria, Jose Angel and Lago Martinez, Alberto and Cruz-Lopez Claret, Antonio and Ayani, Alex},
year = {2008},
date = {2008-02-27},
booktitle = {International Surface Inspection Summit ISIS 2008. Ámsterdam, 27-28/02/2008},
abstract = {This paper describes an application for hot steel tubes surface defects automatic inspection that has been developed for TR SA an important seamless tubes manufacturer company.
At the Hot rolling process when the rolling stands that presses the tubes in order to reduce their diameter are damaged, they sporadically cause surface marks and defects that were not detected at this stage, and this raise the risk of several costly “value added” operations could be performed before detection. Additionally, other metallurgical defects could be present, caused by previous production stages.
The inspection conditions are specially critical due to tube temperature, about 1200ºC, and to tube speed: about 5,5 m/s at the inspection stage. Tube diameters are from 142 mm to 200 mm.
The automated inspection system is installed at the exit of the push bench, just when the defects are originated. This is an especially hard point due to dirt, vapour and temperature conditions, and the system must be capable of inspecting the whole surface of the tubes in real time for defects detection, monitoring and localization of these defects.
The system uses a specially designed illumination, optical filtering and three linear cameras located in three sensorized protective enclosures to inspect the surface, acquiring a complete image of great quality and uniformly illuminated.
An intelligent classification software tool based on Support Vector Machine is used to recognize the previously learned defects.
},
keywords = {computer vision, machine learning, surface quality},
pubstate = {published},
tppubtype = {conference}
}
This paper describes an application for hot steel tubes surface defects automatic inspection that has been developed for TR SA an important seamless tubes manufacturer company.
At the Hot rolling process when the rolling stands that presses the tubes in order to reduce their diameter are damaged, they sporadically cause surface marks and defects that were not detected at this stage, and this raise the risk of several costly “value added” operations could be performed before detection. Additionally, other metallurgical defects could be present, caused by previous production stages.
The inspection conditions are specially critical due to tube temperature, about 1200ºC, and to tube speed: about 5,5 m/s at the inspection stage. Tube diameters are from 142 mm to 200 mm.
The automated inspection system is installed at the exit of the push bench, just when the defects are originated. This is an especially hard point due to dirt, vapour and temperature conditions, and the system must be capable of inspecting the whole surface of the tubes in real time for defects detection, monitoring and localization of these defects.
The system uses a specially designed illumination, optical filtering and three linear cameras located in three sensorized protective enclosures to inspect the surface, acquiring a complete image of great quality and uniformly illuminated.
An intelligent classification software tool based on Support Vector Machine is used to recognize the previously learned defects.
Este sitio web utiliza cookies para que usted tenga la mejor experiencia de usuario. Si continúa navegando está dando su consentimiento para la aceptación de las mencionadas cookies y la aceptación de nuestra política de cookies, pinche el enlace para mayor información.plugin cookies
