Dados experimentais caracterização fisica, mecânica e simulação via aprendizado de máquina de ligas Mo-Nb-Ti-Cu

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Ortiz, Eder Lopes; Bortolozo, Ausdinir Danilo, 2026, "Dados experimentais caracterização fisica, mecânica e simulação via aprendizado de máquina de ligas Mo-Nb-Ti-Cu", https://doi.org/10.25824/redu/RPT6II, Repositório de Dados de Pesquisa da Unicamp , V1
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Description
Os dados aqui depositados correspondem aos dados experimentais e teóricos referentes à caracterização físico mecânica e simulação computacional da liga Ti-Nb-Mo-Cu(x) (com x = 0,0; 0,25; 0,5; 1,0 e 2,0), desenvolvidas no âmbito de pesquisas sobre ligas refratárias de média e alta entropia. O conjunto contempla resultados quantitativos e qualitativos obtidos para investigar a influência do teor de cobre na evolução microestrutural, na segregação de elementos, na estabilidade de fases e no desempenho mecânico da liga refratária base MoNbTi. O acervo reúne os dados brutos de difração de raios X (DRX), os dados de densificação obtidos conforme a norma ASTM B311, os dados de dureza medidos pelo método de micro dureza Vickers segundo a norma ASTM E384 e os dados de ensaios de compressão uniaxial quase estática conduzidos de acordo com a norma ASTM E9. Adicionalmente, engloba o banco de dados curado com cerca de 1600 entradas extraídas da literatura de ligas refratárias e multicomponentes, contendo descritores de Hume-Rothery, termodinâmicos e elásticos, bem como as saídas das predições de modelos de aprendizado de máquina (Random Forest, XGBoost e Gradient Boosting) e as análises da contribuição dos descritores por valores SHAP. Todos os arquivos estão organizados em formatos proprietários e abertos, incluindo planilhas, gráficos e dados textuais computacionais (2026-08-05)
Subject Engineering
Keyword Ligas de multicomponentes principais, Aprendizagem de máquina, Propriedades mecânicas, Mecanismos de endurecimento, Raios X - Difração
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dadossimulao.zip
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Dados de aprendizado de máquina
Origin-Dureza-Lambida-parâmetro.de.rede-Densificação.zip
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Published Aug 11, 2026
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MD5: f46dc1237ef421834249236252c1d2f9
Dados referentes a caracterização físico mecânica
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