Performance Comparison of Parametric and Non-Parametric Regression Models for Uncertainty Analysis of Sheet Metal Forming Processes

• Autor(a) principal: Marques, Armando E.
• Data de Publicação: 2020
• Outros Autores: Prates, Pedro A., Pereira, André F. G., Oliveira, Marta C., Fernandes, José V., Ribeiro, Bernardete M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/106437; https://doi.org/10.3390/met10040457
• topic: sheet metal forming; uncertainty analysis; metamodeling; machine learning

This work aims to compare the performance of various parametric and non-parametric metamodeling techniques when applied to sheet metal forming processes. For this, the U-Channel and the Square Cup forming processes were studied. In both cases, three steel grades were considered, and numerical simulations were performed, in order to establish a database for each combination of forming process and material. Each database was used to train and test the various metamodels, and their predictive performances were evaluated. The best performing metamodeling techniques were Gaussian processes, multi-layer perceptron, support vector machines, kernel ridge regression and polynomial chaos expansion.