Neural network for prediction of holes diameters and surface roughness in drilling process
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.2316/P.2011.717-057 http://hdl.handle.net/11449/226354 |
Resumo: | Several systems are currently tested in order to obtain a feasible and safe method for automation and control of drilling process. This work aims to predict the final diameters and surface roughness of titanium (Ti-6A1-4V) and aluminum (2024 T3) alloy during the machining process in a drilling machine. Acoustic emission, vibration, electrical motor power and force signals were acquired by a commercial data acquisition system. These signals were digitally processed through known statistics methods and were used as input data for an artificial neural network (newff), which estimates the current diameter and surface roughness. After this procedure other neural network (newfftd) was used for predicting the next hole diameter and surface roughness based on the output information from the first neural network. The neural network newff, the mathematical logical method that interprets the signals acquired, was used for estimating the actual hole diameter and surface roughness. The neural network newfftd is the most straightforward dynamic network, which consists of a feed-forward network with a tapped delay line at the input. The neural network newfftd predicts the next hole diameter and the surface roughness one step forward. The results from the neural networks were compared with the actual diameters and surface roughness taken from the worpiece, and showed a good accuracy and a sophisticated method for monitoring and controlling the drilling process, especially the one step drilling process. |
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Neural network for prediction of holes diameters and surface roughness in drilling processAcoustic emissionDrilling processElectric powerFeed-forward networkNeural networkOne step drillingTime delay neural networkSeveral systems are currently tested in order to obtain a feasible and safe method for automation and control of drilling process. This work aims to predict the final diameters and surface roughness of titanium (Ti-6A1-4V) and aluminum (2024 T3) alloy during the machining process in a drilling machine. Acoustic emission, vibration, electrical motor power and force signals were acquired by a commercial data acquisition system. These signals were digitally processed through known statistics methods and were used as input data for an artificial neural network (newff), which estimates the current diameter and surface roughness. After this procedure other neural network (newfftd) was used for predicting the next hole diameter and surface roughness based on the output information from the first neural network. The neural network newff, the mathematical logical method that interprets the signals acquired, was used for estimating the actual hole diameter and surface roughness. The neural network newfftd is the most straightforward dynamic network, which consists of a feed-forward network with a tapped delay line at the input. The neural network newfftd predicts the next hole diameter and the surface roughness one step forward. The results from the neural networks were compared with the actual diameters and surface roughness taken from the worpiece, and showed a good accuracy and a sophisticated method for monitoring and controlling the drilling process, especially the one step drilling process.Univ. Estadual Paulista - UNESP - Bauru Campus School of Engineering - FEB Electrica Engineering Departments, Av. Luiz Ed. Carrijo Coube, 14-01, Bauru - SPUniv. Estadual Paulista - UNESP - Bauru Campus School of Engineering - FEB Mechanical Engineering Departments, Av. Luiz Ed. Carrijo Coube, 14-01, Bauru - SPUniv. Estadual Paulista - UNESP - Bauru Campus School of Engineering - FEB Electrica Engineering Departments, Av. Luiz Ed. Carrijo Coube, 14-01, Bauru - SPUniv. Estadual Paulista - UNESP - Bauru Campus School of Engineering - FEB Mechanical Engineering Departments, Av. Luiz Ed. Carrijo Coube, 14-01, Bauru - SPUniversidade Estadual Paulista (UNESP)Contrucci, Joào G. [UNESP]Cruz, Carlos E.D. [UNESP]Aguiar, Paulo R. [UNESP]Bianchi, Eduardo C. [UNESP]Ulson, José A.C. [UNESP]2022-04-28T22:37:25Z2022-04-28T22:37:25Z2011-06-13info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject122-128http://dx.doi.org/10.2316/P.2011.717-057Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011, p. 122-128.http://hdl.handle.net/11449/22635410.2316/P.2011.717-0572-s2.0-79958131934Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011info:eu-repo/semantics/openAccess2022-04-28T22:37:25Zoai:repositorio.unesp.br:11449/226354Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T22:37:25Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Neural network for prediction of holes diameters and surface roughness in drilling process |
title |
Neural network for prediction of holes diameters and surface roughness in drilling process |
spellingShingle |
Neural network for prediction of holes diameters and surface roughness in drilling process Contrucci, Joào G. [UNESP] Acoustic emission Drilling process Electric power Feed-forward network Neural network One step drilling Time delay neural network |
title_short |
Neural network for prediction of holes diameters and surface roughness in drilling process |
title_full |
Neural network for prediction of holes diameters and surface roughness in drilling process |
title_fullStr |
Neural network for prediction of holes diameters and surface roughness in drilling process |
title_full_unstemmed |
Neural network for prediction of holes diameters and surface roughness in drilling process |
title_sort |
Neural network for prediction of holes diameters and surface roughness in drilling process |
author |
Contrucci, Joào G. [UNESP] |
author_facet |
Contrucci, Joào G. [UNESP] Cruz, Carlos E.D. [UNESP] Aguiar, Paulo R. [UNESP] Bianchi, Eduardo C. [UNESP] Ulson, José A.C. [UNESP] |
author_role |
author |
author2 |
Cruz, Carlos E.D. [UNESP] Aguiar, Paulo R. [UNESP] Bianchi, Eduardo C. [UNESP] Ulson, José A.C. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Contrucci, Joào G. [UNESP] Cruz, Carlos E.D. [UNESP] Aguiar, Paulo R. [UNESP] Bianchi, Eduardo C. [UNESP] Ulson, José A.C. [UNESP] |
dc.subject.por.fl_str_mv |
Acoustic emission Drilling process Electric power Feed-forward network Neural network One step drilling Time delay neural network |
topic |
Acoustic emission Drilling process Electric power Feed-forward network Neural network One step drilling Time delay neural network |
description |
Several systems are currently tested in order to obtain a feasible and safe method for automation and control of drilling process. This work aims to predict the final diameters and surface roughness of titanium (Ti-6A1-4V) and aluminum (2024 T3) alloy during the machining process in a drilling machine. Acoustic emission, vibration, electrical motor power and force signals were acquired by a commercial data acquisition system. These signals were digitally processed through known statistics methods and were used as input data for an artificial neural network (newff), which estimates the current diameter and surface roughness. After this procedure other neural network (newfftd) was used for predicting the next hole diameter and surface roughness based on the output information from the first neural network. The neural network newff, the mathematical logical method that interprets the signals acquired, was used for estimating the actual hole diameter and surface roughness. The neural network newfftd is the most straightforward dynamic network, which consists of a feed-forward network with a tapped delay line at the input. The neural network newfftd predicts the next hole diameter and the surface roughness one step forward. The results from the neural networks were compared with the actual diameters and surface roughness taken from the worpiece, and showed a good accuracy and a sophisticated method for monitoring and controlling the drilling process, especially the one step drilling process. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-06-13 2022-04-28T22:37:25Z 2022-04-28T22:37:25Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.2316/P.2011.717-057 Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011, p. 122-128. http://hdl.handle.net/11449/226354 10.2316/P.2011.717-057 2-s2.0-79958131934 |
url |
http://dx.doi.org/10.2316/P.2011.717-057 http://hdl.handle.net/11449/226354 |
identifier_str_mv |
Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011, p. 122-128. 10.2316/P.2011.717-057 2-s2.0-79958131934 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA 2011 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
122-128 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965019548418048 |