Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design

A Sharma, T Mukhopadhyay, SM Rangappa… - … Methods in Engineering, 2022 - Springer
The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …

[HTML][HTML] Enhancing property prediction and process optimization in building materials through machine learning: A review

K Stergiou, C Ntakolia, P Varytis, E Koumoulos… - Computational Materials …, 2023 - Elsevier
Abstract Analysis and design, as the most critical components in material science, require a
highly rigorous approach to assure long-term success. Due to a recent increase in the …

[HTML][HTML] Stress field prediction in fiber-reinforced composite materials using a deep learning approach

A Bhaduri, A Gupta, L Graham-Brady - Composites Part B: Engineering, 2022 - Elsevier
Stress analysis is an important step in the design of material systems, and finite element
methods (FEM) are a standard approach of performing computational analysis of stresses in …

Buckling response of CNT based hybrid FG plates using finite element method and machine learning method

R Kumar, A Kumar, DR Kumar - Composite Structures, 2023 - Elsevier
In this study, a C 0 finite element model (FEM) based on modified third-order shear
deformation (MTSDT) theory in conjunction with a deep neural network (DNN), extreme …

[HTML][HTML] Multiscale modeling of viscoelastic behavior of unidirectional composite laminates and deployable structures

N An, Q Jia, H Jin, X Ma, J Zhou - Materials & Design, 2022 - Elsevier
Due to the inherent viscoelasticity of constituent matrix and the possibility of long-term
storage, space deployable structures made of composites are likely to exhibit relaxation in …

Machine learning-based accelerated property prediction of two-phase materials using microstructural descriptors and finite element analysis

E Ford, K Maneparambil, S Rajan… - Computational Materials …, 2021 - Elsevier
This study explores the use of supervised machine learning (ML) to predict the mechanical
properties of a family of two-phase materials using their microstructural images. Random two …

[HTML][HTML] Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data

GA Sengodan - Composites Part B: Engineering, 2021 - Elsevier
A novel method to predict the mechanical responses of arbitrary microstructures from the
deep learning of microstructures and their stress-strain response is presented in this work …

Development of machine learning methods for mechanical problems associated with fibre composite materials: A review

M Liu, H Li, H Zhou, H Zhang, G Huang - Composites Communications, 2024 - Elsevier
Fibre composite materials (FCMs) are widely used in the aerospace, military defence, and
engineering manufacturing industries due to their high strength and high modulus …

Artificial intelligence in predicting mechanical properties of composite materials

F Kibrete, T Trzepieciński, HS Gebremedhen… - Journal of Composites …, 2023 - mdpi.com
The determination of mechanical properties plays a crucial role in utilizing composite
materials across multiple engineering disciplines. Recently, there has been substantial …

[HTML][HTML] Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems

CE Okafor, S Iweriolor, OI Ani, S Ahmad, S Mehfuz… - Hybrid Advances, 2023 - Elsevier
Reinforced composite is a preferred choice of material for the design of industrial lightweight
structures. As of late, composite materials analysis and development utilizing machine …