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dc.contributor.authorPinzón, César
dc.contributor.authorPlazaola, Carlos
dc.contributor.authorBanfield, Ilka
dc.contributor.authorFong, Amaly
dc.contributor.authorVega, Adán
dc.date.accessioned2017-08-23T17:00:01Z
dc.date.available2017-08-23T17:00:01Z
dc.date.issued2013-01
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/2864
dc.descriptionIn order to achieve automation of the plate forming process by line heating, it is necessary to know in advance the deformation to be obtained under specific heating conditions. Currently, different methods exist to predict deformation, but these are limited to specific applications and most of them depend on the computational capacity so that only simple structures can be analyzed. In this paper, a neural network model that can accurately predict distortions produced during the plate forming process by line heating, for a wide range of initial conditions including large structures, is presented. Results were compared with data existing in the literature showing excellent performance. Excellent results were obtained for those cases out of the range of the training data.en_US
dc.description.abstractIn order to achieve automation of the plate forming process by line heating, it is necessary to know in advance the deformation to be obtained under specific heating conditions. Currently, different methods exist to predict deformation, but these are limited to specific applications and most of them depend on the computational capacity so that only simple structures can be analyzed. In this paper, a neural network model that can accurately predict distortions produced during the plate forming process by line heating, for a wide range of initial conditions including large structures, is presented. Results were compared with data existing in the literature showing excellent performance. Excellent results were obtained for those cases out of the range of the training data.en_US
dc.language.isoenen_US
dc.subjectnetwork modelen_US
dc.subjectplate formingen_US
dc.subjectdistortion predictionen_US
dc.subjectline heatingen_US
dc.subjectback propagationen_US
dc.titleDevelopment of a neural network model to predict distortion during the metal forming process by line heatingen_US
dc.typeArticleen_US


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