Previsão das propriedades mecânicas de vergalhões de aço utilizados na construção civil

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: Murta, Raphaella Hermont Fonseca
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/65228
Resumo: The mechanical properties of steel depend strongly on its chemical composition and the parameters used during submitted thermomechanical processing. Understanding how each variable affects such properties is indispensable for obtaining high-quality steel products at a lower cost. However, the large number of variables involved in the manufacturing process makes this task difficult. It is possible to use statistical tools combined with predictive modeling to identify the most relevant parameters and to obtain a mathematical model that can adequately describe the mechanical properties of the rebar from the selected input-output pairs. In the present work, information about the chemical composition and the variables of thermomechanical processing were collected at steel industry and used to predict the mechanical properties of steel rebar submitted to heat treatment using the methods of estimation: Linear Regression Analysis(LRA), Minimal Learning Machine (MLM), Artificial Neural Networks (ANN), Support-Vector Machine (SVM) and Least-Squares Support-Vector Machines (LSSVM). The determination coefficient was calculated between the observed and predicted values for mechanical properties: Yield Strength (YS), Ultimate Tensile Strength (UTS), UTS/YS ratio, and Percent Elongation (PE). The results estimated by the algorithms were promising, indicating that MLM and LSSVM can be useful in evaluating and choosing the most adequate parameters to be used during the steel production process.