Avaliação de propriedades mecânicas de aços via macroindentação instrumentada e inteligência artificial

• Ano de defesa: 2018
• Autor(a) principal: Freitas, Monique Alves Franco de Moraes
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Mecânica
• Palavras-chave em Português: Avaliação de propriedades mecânicas; Evaluation of mechanical properties; Métodos de aprendizado de máquina; Machine learning methods; RNA; ANN; Treinamento supervisionado; Supervised training; Macroindentação instrumentada; Instrumented macroindentation; Materiais - Propriedades mecânicas; Aprendizado do computador; Engenharia mecânica; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/21494; http://dx.doi.org/10.14393/ufu.te.2018.752

In this work, a methodology was developed to estimate the mechanical properties (Brinell hardness, ultimate tensile strength and yield strength) using instrumented macroindentation tests. This methodology was based on the training of artificial neural networks (ANNs) from experimental curves of spherical indentations. Different ANNs architectures were implemented and trained with three algorithms, namely: a) genetic algorithm (GA); b) hybrid algorithm between GA and Levenberg- Marquardt (LM) method with Bayesian regularization (BR), named GA-LMBR; and c) the GA-LMBR method, with the addition of a similarity verification step among GA individuals. The input patterns of the ANNs were obtained from the materials tests performed by Nicolosi (2015) through a portable macroindentator called PropInSitu 2. Among the results obtained, it was verified that the hybrid algorithm GA-LMBR provided the best results, i.e., it resulted in the smallest errors in the estimation of the mechanical properties. These results were achieved when the GA-LMBR method was applied to an ANN composed of a nine-variable input layer, a hidden layer with two neurons and an output layer with one neuron. In addition, certain values of GA performance parameters, such as the number of individuals of the initial population, stopping criterion, crossover rate, mutation rate, among others; and certain neuron activation functions were used. The activation functions employed in the hidden neurons and the output neuron were, respectively, sigmoid and linear. The methodology developed was efficient in determining the mechanical properties, since the best results provided small errors compared to the traditional methods. Considering 95 % confidence level, the errors for the Brinell hardness estimation were in the range of ±3 %, for the ultimate tensile strength estimation, in the range of ±6 % and for the yield strength estimation, in the range of ±8 %.