[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 …

The effects of nano-additives on the mechanical, impact, vibration, and buckling/post-buckling properties of composites: A review

L Shan, CY Tan, X Shen, S Ramesh, MS Zarei… - Journal of Materials …, 2023 - Elsevier
This study presents a review of the effect of nano-additives in improving the mechanical
properties of composites. Nano-additives added to composites, also termed …

EMCS-SVR: hybrid efficient and accurate enhanced simulation approach coupled with adaptive SVR for structural reliability analysis

C Luo, B Keshtegar, SP Zhu, X Niu - Computer Methods in Applied …, 2022 - Elsevier
In structural reliability analysis, robust and efficient sampling methods that address low
failure probabilities are vital challenges. In this paper, a novel dynamical adaptive enhanced …

Application of DQHFEM for free and forced vibration, energy absorption, and post-buckling analysis of a hybrid nanocomposite viscoelastic rhombic plate assuming …

PH Wan, MSH Al-Furjan, R Kolahchi, L Shan - Mechanical Systems and …, 2023 - Elsevier
Vibrations and stability responses are two mechanical characteristics of engineering
materials that are highly important for designing new engineering structures. This numerical …

Analysis of the friction and wear of graphene reinforced aluminum metal matrix composites using machine learning models

MS Hasan, T Wong, PK Rohatgi, M Nosonovsky - Tribology International, 2022 - Elsevier
The effect of graphene on the material properties, friction, and wear of self-lubricating
aluminum-based metal matrix composites (MMC) was compared with the effect of graphite …

[HTML][HTML] Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques

E Champa-Bujaico, AM Díez-Pascual… - Composites Part B …, 2024 - Elsevier
Abstract Machine learning (ML) models provide fast and accurate predictions of material
properties at a low computational cost. Herein, the mechanical properties of multiscale poly …

Prediction of wear performance of ZK60/CeO2 composites using machine learning models

F Aydin, R Durgut, M Mustu, B Demir - Tribology International, 2023 - Elsevier
In this study, ZK60 magnesium matrix composites were produced with different content of
CeO 2 (0.25, 0.5 and 1 wt%) by hot pressing. The wear behaviour of the samples was …

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

P Jiao, ZL Wang, AH Alavi - Advanced Materials, 2024 - Wiley Online Library
Triboelectric nanogenerators offer an environmentally friendly approach to harvesting
energy from mechanical excitations. This capability has made them widely sought‐after as …

A generative approach to materials discovery, design, and optimization

D Menon, R Ranganathan - ACS omega, 2022 - ACS Publications
Despite its potential to transform society, materials research suffers from a major drawback:
its long research timeline. Recently, machine-learning techniques have emerged as a viable …

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 …