Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Trabalho de conclusão de curso |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFSCAR |
Texto Completo: | https://repositorio.ufscar.br/handle/ufscar/17800 |
Resumo: | Surface integrity is a critical concept in the field of mechanical engineering, which refers to the quality and properties of a material's surface after it has undergone a manufacturing process. Therefore, controlling the quality of the material surface is essential since it can significantly affect its performance in aspects such as fatigue strength, wear resistance, corrosion resistance, and mechanical properties. The focus of this study was to develop and train an artificial neural network to predict the surface roughness of hardened steel after cylindrical turning, based on its hardness and machining parameters. Additionally, the behavior of the neural network was studied for different iterations and datasets to optimize the results. Experimental data previously obtained by the research group for O1 tool steel were used to validate the neural network. Finally, it was possible to develop an artificial neural network based on data from various hardened steels found in the literature, resulting in an estimated surface roughness of the O1 tool steel with an approximate error of 20%. Furthermore, it was determined that the best combination of data volume for training and validating the neural network is between 45-60%. |
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Santos, Juan Wesley dosAntonialli, Armando Ítalo Settehttp://lattes.cnpq.br/4367459395417045http://lattes.cnpq.br/29769896552031282023-04-18T14:32:09Z2023-04-18T14:32:09Z2023-04-05SANTOS, Juan Wesley dos. Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial. 2023. Trabalho de Conclusão de Curso (Graduação em Engenharia Mecânica) – Universidade Federal de São Carlos, São Carlos, 2023. Disponível em: https://repositorio.ufscar.br/handle/ufscar/17800.https://repositorio.ufscar.br/handle/ufscar/17800Surface integrity is a critical concept in the field of mechanical engineering, which refers to the quality and properties of a material's surface after it has undergone a manufacturing process. Therefore, controlling the quality of the material surface is essential since it can significantly affect its performance in aspects such as fatigue strength, wear resistance, corrosion resistance, and mechanical properties. The focus of this study was to develop and train an artificial neural network to predict the surface roughness of hardened steel after cylindrical turning, based on its hardness and machining parameters. Additionally, the behavior of the neural network was studied for different iterations and datasets to optimize the results. Experimental data previously obtained by the research group for O1 tool steel were used to validate the neural network. Finally, it was possible to develop an artificial neural network based on data from various hardened steels found in the literature, resulting in an estimated surface roughness of the O1 tool steel with an approximate error of 20%. Furthermore, it was determined that the best combination of data volume for training and validating the neural network is between 45-60%.Integridade superficial é um conceito crítico no campo da engenharia mecânica, que se refere à qualidade e propriedades da superfície de um material após ter passado por um processo de fabricação. Portanto, o controle da qualidade da superfície do material é imprescindível, pois essa pode afetar de forma significativa o desempenho do mesmo, em aspectos como: resistência à fadiga, resistência ao desgaste, resistência à corrosão e propriedades mecânicas. O foco do presente trabalho foi desenvolver e treinar uma rede neural artificial para que essa seja capaz de prever a rugosidade superficial de um aço endurecido após torneamento cilíndrico, a partir de sua dureza e parâmetros de usinagem. Além disso, estudou-se o comportamento da rede neural para diferentes iterações e conjuntos de dados com o objetivo de otimizar o resultado. Para a validação da rede neural, foram usados dados experimentais obtidos anteriormente pelo grupo de pesquisa, para o Aço ferramenta O1. Por fim, foi possível desenvolver uma rede neural artificial, baseada em dados de vários aços endurecidos, encontrados na literatura, o que resultou em uma estimativa para rugosidade superficial do aço ferramenta O1, com erro aproximado de 20% e ainda foi possível determinar que a melhor combinação entre o volume de dados para treinamento e validação da rede neural está entre 45-60%.Não recebi financiamentoporUniversidade Federal de São CarlosCâmpus São CarlosEngenharia Mecânica - EMecUFSCarAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessTorneamentoSimulaçãoRugosidadeParâmetros de usinagemMachine learningENGENHARIAS::ENGENHARIA MECANICAPrevisão da rugosidade superficial de aços endurecidos através de rede neural artificialPrediction of surface roughness of hardened steels through artificial neural networkinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bachelorThesisreponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8810https://repositorio.ufscar.br/bitstream/ufscar/17800/2/license_rdff337d95da1fce0a22c77480e5e9a7aecMD52ORIGINALTCC_Juan_Wesley_Revisado.pdfTCC_Juan_Wesley_Revisado.pdfapplication/pdf1390096https://repositorio.ufscar.br/bitstream/ufscar/17800/1/TCC_Juan_Wesley_Revisado.pdfa5e57e35b4d65a8ad7b0715142a83b42MD51TEXTTCC_Juan_Wesley_Revisado.pdf.txtTCC_Juan_Wesley_Revisado.pdf.txtExtracted texttext/plain45542https://repositorio.ufscar.br/bitstream/ufscar/17800/3/TCC_Juan_Wesley_Revisado.pdf.txt0c22727bc4bbf3fb4aa0784468e96967MD53THUMBNAILTCC_Juan_Wesley_Revisado.pdf.jpgTCC_Juan_Wesley_Revisado.pdf.jpgIM Thumbnailimage/jpeg7443https://repositorio.ufscar.br/bitstream/ufscar/17800/4/TCC_Juan_Wesley_Revisado.pdf.jpg1f29ecc35ba62f9a74ebeaf165feaabdMD54ufscar/178002023-04-19 03:23:40.554oai:repositorio.ufscar.br:ufscar/17800Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-04-19T03:23:40Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.por.fl_str_mv |
Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial |
dc.title.alternative.eng.fl_str_mv |
Prediction of surface roughness of hardened steels through artificial neural network |
title |
Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial |
spellingShingle |
Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial Santos, Juan Wesley dos Torneamento Simulação Rugosidade Parâmetros de usinagem Machine learning ENGENHARIAS::ENGENHARIA MECANICA |
title_short |
Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial |
title_full |
Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial |
title_fullStr |
Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial |
title_full_unstemmed |
Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial |
title_sort |
Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial |
author |
Santos, Juan Wesley dos |
author_facet |
Santos, Juan Wesley dos |
author_role |
author |
dc.contributor.authorlattes.por.fl_str_mv |
http://lattes.cnpq.br/2976989655203128 |
dc.contributor.author.fl_str_mv |
Santos, Juan Wesley dos |
dc.contributor.advisor1.fl_str_mv |
Antonialli, Armando Ítalo Sette |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/4367459395417045 |
contributor_str_mv |
Antonialli, Armando Ítalo Sette |
dc.subject.por.fl_str_mv |
Torneamento Simulação Rugosidade Parâmetros de usinagem |
topic |
Torneamento Simulação Rugosidade Parâmetros de usinagem Machine learning ENGENHARIAS::ENGENHARIA MECANICA |
dc.subject.eng.fl_str_mv |
Machine learning |
dc.subject.cnpq.fl_str_mv |
ENGENHARIAS::ENGENHARIA MECANICA |
description |
Surface integrity is a critical concept in the field of mechanical engineering, which refers to the quality and properties of a material's surface after it has undergone a manufacturing process. Therefore, controlling the quality of the material surface is essential since it can significantly affect its performance in aspects such as fatigue strength, wear resistance, corrosion resistance, and mechanical properties. The focus of this study was to develop and train an artificial neural network to predict the surface roughness of hardened steel after cylindrical turning, based on its hardness and machining parameters. Additionally, the behavior of the neural network was studied for different iterations and datasets to optimize the results. Experimental data previously obtained by the research group for O1 tool steel were used to validate the neural network. Finally, it was possible to develop an artificial neural network based on data from various hardened steels found in the literature, resulting in an estimated surface roughness of the O1 tool steel with an approximate error of 20%. Furthermore, it was determined that the best combination of data volume for training and validating the neural network is between 45-60%. |
publishDate |
2023 |
dc.date.accessioned.fl_str_mv |
2023-04-18T14:32:09Z |
dc.date.available.fl_str_mv |
2023-04-18T14:32:09Z |
dc.date.issued.fl_str_mv |
2023-04-05 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
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bachelorThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
SANTOS, Juan Wesley dos. Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial. 2023. Trabalho de Conclusão de Curso (Graduação em Engenharia Mecânica) – Universidade Federal de São Carlos, São Carlos, 2023. Disponível em: https://repositorio.ufscar.br/handle/ufscar/17800. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufscar.br/handle/ufscar/17800 |
identifier_str_mv |
SANTOS, Juan Wesley dos. Previsão da rugosidade superficial de aços endurecidos através de rede neural artificial. 2023. Trabalho de Conclusão de Curso (Graduação em Engenharia Mecânica) – Universidade Federal de São Carlos, São Carlos, 2023. Disponível em: https://repositorio.ufscar.br/handle/ufscar/17800. |
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https://repositorio.ufscar.br/handle/ufscar/17800 |
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por |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
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openAccess |
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Universidade Federal de São Carlos Câmpus São Carlos Engenharia Mecânica - EMec |
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UFSCar |
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Universidade Federal de São Carlos Câmpus São Carlos Engenharia Mecânica - EMec |
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