Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/42600
Title: Machine learning applications in sheet metal constitutive Modelling: a review
Author: Marques, Armando E.
Parreira, Tomás G.
Pereira, André F. G.
Ribeiro, Bernardete M.
Prates, Pedro A.
Keywords: Machine learning
Constitutive modelling
Parameter identification
Metamodeling
Data-driven learning
Sheet metal forming
Issue Date: 15-Oct-2024
Publisher: Elsevier
Abstract: The numerical simulation of sheet metal forming processes depends on the accuracy of the constitutive model used to represent the mechanical behaviour of the materials. The formulation of these constitutive models, as well as their calibration process, has been an ongoing subject of research. In recent years, there has been a special focus on the application of data-driven techniques, namely Machine Learning, to address some of the difficulties of constitutive modelling. This review explores different methodologies for the application of Machine Learning algorithms to sheet metal constitutive modelling. These methodologies include the use of machine learning algorithms in the identification of constitutive model parameters and the replacement of the constitutive model by a metamodel created by a machine learning algorithm. A discussion about the merits and limitations of the different methodologies is presented, as well as the identification of some possible gaps in the literature that represent opportunities for future research.
Peer review: yes
URI: http://hdl.handle.net/10773/42600
DOI: 10.1016/j.ijsolstr.2024.113024
ISSN: 0020-7683
Appears in Collections:TEMA - Artigos
DEM - Artigos

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