Parameter identification of a non-associative elastoplastic constitutive model using ANN and multi-objective optimization-Reference-Cited by-同舟云学术

Parameter identification of a non-associative elastoplastic constitutive model using ANN and multi-objective optimization

Published:2009-03-04 Issue:2 Volume:2 Page:75-82
ISSN:1960-6206
Container-title:International Journal of Material Forming
language:en
Short-container-title:Int J Mater Form

Author:

Aguir H.,Chamekh A.,BelHadjSalah H.,Dogui A.,Hambli R.

Publisher

Springer Science and Business Media LLC

Subject

General Materials Science

Link

http://link.springer.com/content/pdf/10.1007/s12289-009-0392-1.pdf

Reference16 articles.

1. Cooreman S, Lecompte D, Sol H, Vantomme J, Debruyne D (2007) Elasto-plastic material parameter identification by inverse methods: Calculation of the sensitivity matrixInternational. Journal of Solids and Structures. 44:4329–4341

2. Ghouati O, Gelin JC (2001) A finite element-based identification method for complex metallic material behaviour. Computational Materials Science. 21:57–68

3. Kucharski S, Mróz Z (2007) Identification of yield stress and plastic hardening parameters from a spherical indentation testInternational. Journal of Mechanical Sciences. 49:1238–1250

4. Khalfallah A, BelHadjSalah H, Dogui A (2002) Anisotropic parameter identification using inhomogeneous tensile test. Eur. J. Mech. A: Solids. 21:927–942

5. Gahbiche A (2005) Experimental characterization of stamping sheets: application to the identification of the behaviour laws. PhD Thesis, ENIM, Tunisia

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP;Computational Materials Science;2024-09

2. Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification;Archive of Applied Mechanics;2024-07-12

3. Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA MAT_187_SAMP-1 Considering Failure with GISSMO;Materials;2022-01-15

4. Formability of aluminum 1050A at high temperatures: Numerical modeling and experimental validation;Latin American Journal of Solids and Structures;2021

5. Artificial Neural Networks-Based Material Parameter Identification for Numerical Simulations of Additively Manufactured Parts by Material Extrusion;Polymers;2020-12-10