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. |
