SAFEFORMING- Sistema inteligente de prevenção de defeitos em componentes estampados a frio
Description
O objetivo do SAFEFORMING é o desenvolvimento de um sistema inteligente de prevenção de defeitos em componentes estampados a frio”, sendo liderado pela empresa TOOLPRESSE, Peças Metálicas por Prensagem, Lda. e com copromotores TECNISATA – Indústria Metalomecânica, S.A., Instituto Superior Técnico e Universidade de Coimbra, co-financiado pelo Fundo Europeu de Desenvolvimento Regional (FEDER), através do programa Portugal-2020 (PT2020), no âmbito do Sistema de Incentivos à Investigação e Desenvolvimento Tecnológico (SI I&DT), e pelo Programa Operacional Competitividade e Internacionalização (POCI-01-0247-FEDER-017762).
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The purpose of the SAFEFORMING project is to investigate the factors that generate the defects in stamped components and to develop an intelligent methodology to predict and prevent the occurrence of cracks in the different stages of the production process. The proposed methodology is based on a thorough analysis of the factors that aggravate the occurrence of defects (material properties and batch-to-batch variability of these properties within the same standard specification, shape of the part in critical regions of defect occurrence and sequence of operations in the progressive tool) and on the use of a deformation simulation model, which provide data to an intelligent system able to estimate the probability of defect occurrence. Funded by Portugal-2020 (PT2020), in the scope of Sistema de Incentivos à Investigação e Desenvolvimento Tecnológico (SI I&DT), and by (POCI-01-0247-FEDER-017762).
Researchers
Funded by
FEDER - POCI-01-0247-FEDER-017762
Total budget
1 129 060,00 €
Local budget
262 348,00 €
Keywords
deep learning, metallurgical and mechanical characteristics of the materials, mathematical modeling
Start Date
2017-01-01
End Date
2020-09-30
Journal Articles
2020
(1 publication) - Marques, A. and Prates, P. and Oliveira, M. and Fernandes, V. and Ribeiro, B. , "Performance Comparison of Parametric and Non-Parametric Regression Models for Uncertainty Analysis of Sheet Metal Forming Processe", Metals, Special Issue Recent Advances and Applications of Machine Learning in Metal Forming Processes, vol. 10, pp. 457, 2020
2019
(1 publication)