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Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA MAT_187_SAMP-1 Considering Failure with GISSMO
- Source :
- Materials, Vol 15, Iss 2, p 643 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.
- Subjects :
- parameter identification
machine learning
hyperparameter optimization
LS-DYNA
MAT_187_SAMP-1
GISSMO failure model
Technology
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Engineering (General). Civil engineering (General)
TA1-2040
Microscopy
QH201-278.5
Descriptive and experimental mechanics
QC120-168.85
Subjects
Details
- Language :
- English
- ISSN :
- 19961944
- Volume :
- 15
- Issue :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- Materials
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.2280746c9d124d9d84e48fe83b3dd942
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/ma15020643