Machine learning applications in sheet metal constitutive Modelling: A review

• Published in: International journal of solids and structures Vol. 303; p. 113024
• Main Authors: Marques, Armando E., Parreira, Tomás G., Pereira, André F.G., Ribeiro, Bernardete M., Prates, Pedro A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.10.2024
• Subjects: Constitutive Modelling; Data-driven Learning; Machine Learning; Metamodeling; Parameter Identification; Sheet Metal Forming; Data-driven Learning; Parameter Identification; Constitutive Modelling; Sheet Metal Forming; Machine Learning; Metamodeling
• ISSN: 0020-7683
• DOI: 10.1016/j.ijsolstr.2024.113024

•Applications of Machine Learning to sheet metal constitutive modelling are reviewed.•Multiple different Machine Learning application methodologies are explored.•Neural Network based algorithms are the most prominent in literature. 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.