Machine learning-based prediction of specific energy consumption for cut-off grinding

Cut-off operation is widely used in the manufacturing industry and is highly energy-intensive. Prediction of specific energy consumption (SEC) using data-driven models is a promising means to understand, analyze and reduce energy consumption for cut-off grinding. The present article aims to put fort...

Descripción completa

Detalles Bibliográficos
Autores: Awan, Muhammad Rizwan, González Rojas, Hernán Alberto|||0000-0001-8911-0115, Hameed, Saqib, Riaz, Fahid, Hamid, Shahzaib, Hussain, Abrar
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/374179
Acceso en línea:https://hdl.handle.net/2117/374179
https://dx.doi.org/10.3390/s22197152
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Machine learning
Energy prediction
Data-driven modeling
Advanced manufacturing
Intelligent grinding
Intel·ligència artificial
Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria mecànica
id ES_a0ee3fdb16e1bcb53df7098e584780ae
oai_identifier_str oai:upcommons.upc.edu:2117/374179
network_acronym_str ES
network_name_str España
repository_id_str
spelling Machine learning-based prediction of specific energy consumption for cut-off grindingAwan, Muhammad RizwanGonzález Rojas, Hernán Alberto|||0000-0001-8911-0115Hameed, SaqibRiaz, FahidHamid, ShahzaibHussain, AbrarArtificial intelligenceMachine learningArtificial intelligenceMachine learningEnergy predictionData-driven modelingAdvanced manufacturingIntelligent grindingIntel·ligència artificialAprenentatge automàticÀrees temàtiques de la UPC::Enginyeria mecànicaCut-off operation is widely used in the manufacturing industry and is highly energy-intensive. Prediction of specific energy consumption (SEC) using data-driven models is a promising means to understand, analyze and reduce energy consumption for cut-off grinding. The present article aims to put forth a novel methodology to predict and validate the specific energy consumption for cut-off grinding of oxygen-free copper (OFC–C10100) using supervised machine learning techniques. State-of-the-art experimental setup was designed to perform the abrasive cutting of the material at various cutting conditions. First, energy consumption values were predicted on the bases of input process parameters of feed rate, cutting thickness, and cutting tool type using the three supervised learning techniques of Gaussian process regression, regression trees, and artificial neural network (ANN). Among the three algorithms, Gaussian process regression performance was found to be superior, with minimum errors during validation and testing. The predicted values of energy consumption were then exploited to evaluate the specific energy consumption (SEC), which turned out to be highly accurate, with a correlation coefficient of 0.98. The relationship of the predicted specific energy consumption (SEC) with material removal rate agrees well with the relationship depicted in physical models, which further validates the accuracy of the prediction models.Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20222022-09-2120222022-10-07journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/374179https://dx.doi.org/10.3390/s22197152reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3741792026-05-27T15:37:01Z
dc.title.none.fl_str_mv Machine learning-based prediction of specific energy consumption for cut-off grinding
title Machine learning-based prediction of specific energy consumption for cut-off grinding
spellingShingle Machine learning-based prediction of specific energy consumption for cut-off grinding
Awan, Muhammad Rizwan
Artificial intelligence
Machine learning
Artificial intelligence
Machine learning
Energy prediction
Data-driven modeling
Advanced manufacturing
Intelligent grinding
Intel·ligència artificial
Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria mecànica
title_short Machine learning-based prediction of specific energy consumption for cut-off grinding
title_full Machine learning-based prediction of specific energy consumption for cut-off grinding
title_fullStr Machine learning-based prediction of specific energy consumption for cut-off grinding
title_full_unstemmed Machine learning-based prediction of specific energy consumption for cut-off grinding
title_sort Machine learning-based prediction of specific energy consumption for cut-off grinding
dc.creator.none.fl_str_mv Awan, Muhammad Rizwan
González Rojas, Hernán Alberto|||0000-0001-8911-0115
Hameed, Saqib
Riaz, Fahid
Hamid, Shahzaib
Hussain, Abrar
author Awan, Muhammad Rizwan
author_facet Awan, Muhammad Rizwan
González Rojas, Hernán Alberto|||0000-0001-8911-0115
Hameed, Saqib
Riaz, Fahid
Hamid, Shahzaib
Hussain, Abrar
author_role author
author2 González Rojas, Hernán Alberto|||0000-0001-8911-0115
Hameed, Saqib
Riaz, Fahid
Hamid, Shahzaib
Hussain, Abrar
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Artificial intelligence
Machine learning
Artificial intelligence
Machine learning
Energy prediction
Data-driven modeling
Advanced manufacturing
Intelligent grinding
Intel·ligència artificial
Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria mecànica
topic Artificial intelligence
Machine learning
Artificial intelligence
Machine learning
Energy prediction
Data-driven modeling
Advanced manufacturing
Intelligent grinding
Intel·ligència artificial
Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria mecànica
description Cut-off operation is widely used in the manufacturing industry and is highly energy-intensive. Prediction of specific energy consumption (SEC) using data-driven models is a promising means to understand, analyze and reduce energy consumption for cut-off grinding. The present article aims to put forth a novel methodology to predict and validate the specific energy consumption for cut-off grinding of oxygen-free copper (OFC–C10100) using supervised machine learning techniques. State-of-the-art experimental setup was designed to perform the abrasive cutting of the material at various cutting conditions. First, energy consumption values were predicted on the bases of input process parameters of feed rate, cutting thickness, and cutting tool type using the three supervised learning techniques of Gaussian process regression, regression trees, and artificial neural network (ANN). Among the three algorithms, Gaussian process regression performance was found to be superior, with minimum errors during validation and testing. The predicted values of energy consumption were then exploited to evaluate the specific energy consumption (SEC), which turned out to be highly accurate, with a correlation coefficient of 0.98. The relationship of the predicted specific energy consumption (SEC) with material removal rate agrees well with the relationship depicted in physical models, which further validates the accuracy of the prediction models.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-09-21
2022
2022-10-07
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/374179
https://dx.doi.org/10.3390/s22197152
url https://hdl.handle.net/2117/374179
https://dx.doi.org/10.3390/s22197152
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869415085758218240
score 15,301603

Similares en LA Referencia