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Keywords = material consistency-informed constitutive neural network

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26 pages, 5724 KB  
Article
A Neural Network Constitutive Model and Automatic Stiffness Evaluation for Multiscale Finite Elements
by Aliki D. Mouratidou and Georgios E. Stavroulakis
Appl. Sci. 2025, 15(7), 3697; https://doi.org/10.3390/app15073697 - 27 Mar 2025
Cited by 1 | Viewed by 1188
Abstract
A neural network model for a constitutive law in nonlinear structures is proposed in this paper. The artificial neural network (ANN) model is constructed based on a data set of responses from representative volume elements, which was calculated by finite elements and using [...] Read more.
A neural network model for a constitutive law in nonlinear structures is proposed in this paper. The artificial neural network (ANN) model is constructed based on a data set of responses from representative volume elements, which was calculated by finite elements and using an open scientific software machine learning platform. The tangential stiffness matrix, which can be used within a multiscale finite element analysis, is calculated via the method of automatic differentiation. Two types of constitutive neural networks are proposed. The first approach involves training a residual model with three respective surrogate models for the stress components, ensuring less computing cost. The second approach considers a separate ANN for each stress component, ensuring a high rate of convergence. The numerical results are compared with the given data set as well as with the results obtained after applying a polynomial regression. The loss function, without and including the Sobolev metrics, is considered. In addition, a physics-informed constitutive neural model, which enforces hyperelasticity principles, is also analyzed. The choice of the hyperparameters is discussed. Full article
(This article belongs to the Special Issue Applications in Neural and Symbolic Artificial Intelligence)
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24 pages, 4038 KB  
Article
Application of Machine Learning to Bending Processes and Material Identification
by Daniel J. Cruz, Manuel R. Barbosa, Abel D. Santos, Sara S. Miranda and Rui L. Amaral
Metals 2021, 11(9), 1418; https://doi.org/10.3390/met11091418 - 7 Sep 2021
Cited by 26 | Viewed by 6548
Abstract
The increasing availability of data, which becomes a continually increasing trend in multiple fields of application, has given machine learning approaches a renewed interest in recent years. Accordingly, manufacturing processes and sheet metal forming follow such directions, having in mind the efficiency and [...] Read more.
The increasing availability of data, which becomes a continually increasing trend in multiple fields of application, has given machine learning approaches a renewed interest in recent years. Accordingly, manufacturing processes and sheet metal forming follow such directions, having in mind the efficiency and control of the many parameters involved, in processing and material characterization. In this article, two applications are considered to explore the capability of machine learning modeling through shallow artificial neural networks (ANN). One consists of developing an ANN to identify the constitutive model parameters of a material using the force–displacement curves obtained with a standard bending test. The second one concentrates on the springback problem in sheet metal press-brake air bending, with the objective of predicting the punch displacement required to attain a desired bending angle, including additional information of the springback angle. The required data for designing the ANN solutions are collected from numerical simulation using finite element methodology (FEM), which in turn was validated by experiments. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Metal Forming Processes)
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