[HTML][HTML] A review of artificial neural networks in the constitutive modeling of composite materials

X Liu, S Tian, F Tao, W Yu - Composites Part B: Engineering, 2021 - Elsevier
Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …

A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials

D Bishara, Y Xie, WK Liu, S Li - Archives of computational methods in …, 2023 - Springer
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …

[HTML][HTML] An FE–DMN method for the multiscale analysis of short fiber reinforced plastic components

S Gajek, M Schneider, T Böhlke - Computer Methods in Applied Mechanics …, 2021 - Elsevier
In this work, we propose a fully coupled multiscale strategy for components made from short
fiber reinforced composites, where each Gauss point of the macroscopic finite element …

Microstructure-guided deep material network for rapid nonlinear material modeling and uncertainty quantification

T Huang, Z Liu, CT Wu, W Chen - Computer Methods in Applied Mechanics …, 2022 - Elsevier
Modeling nonlinear materials with arbitrary microstructures and loading paths is crucial in
structural analyses with heterogeneous materials with uncertainty. However, it is …

Rapid inverse calibration of a multiscale model for the viscoplastic and creep behavior of short fiber-reinforced thermoplastics based on Deep Material Networks

AP Dey, F Welschinger, M Schneider, S Gajek… - International Journal of …, 2023 - Elsevier
In this work, we propose to use deep material networks (DMNs) as a surrogate model for full-
field computational homogenization to inversely identify material parameters of constitutive …

LS-DYNA machine learning–based multiscale method for nonlinear modeling of short fiber–reinforced composites

H Wei, CT Wu, W Hu, TH Su, H Oura… - Journal of …, 2023 - ascelibrary.org
Short fiber–reinforced composites (SFRCs) are high-performance engineering materials for
lightweight structural applications in the automotive and electronics industries. Typically …

[HTML][HTML] A probabilistic virtual process chain to quantify process-induced uncertainties in Sheet Molding Compounds

N Meyer, S Gajek, J Görthofer, A Hrymak… - Composites Part B …, 2023 - Elsevier
The manufacturing process of Sheet Molding Compound (SMC) influences the properties of
a component in a non-deterministic fashion. To predict this influence on the mechanical …

Neural networks for constitutive modeling: From universal function approximators to advanced models and the integration of physics

J Dornheim, L Morand, HJ Nallani, D Helm - Archives of Computational …, 2024 - Springer
Analyzing and modeling the constitutive behavior of materials is a core area in materials
sciences and a prerequisite for conducting numerical simulations in which the material …

[HTML][HTML] A space-time upscaling technique for modeling high-cycle fatigue-damage of short-fiber reinforced composites

N Magino, J Köbler, H Andrä, F Welschinger… - … Science and Technology, 2022 - Elsevier
Characterizing short-fiber reinforced polymers under high-cycle fatigue loading is a tedious
experimental task. To reduce the necessary experiments to a minimum, we introduce a …

Training deep material networks to reproduce creep loading of short fiber-reinforced thermoplastics with an inelastically-informed strategy

AP Dey, F Welschinger, M Schneider, S Gajek… - Archive of Applied …, 2022 - Springer
Deep material networks (DMNs) are a recent multiscale technology which enable running
concurrent multiscale simulations on industrial scale with the help of powerful surrogate …