[HTML][HTML] Scope of machine learning in materials research—A review
This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …
materials research across six key dimensions, redefining the field's boundaries. It explains …
Machine-learning-based predictions of polymer and postconsumer recycled polymer properties: a comprehensive review
There has been a tremendous increase in demand for virgin and postconsumer recycled
(PCR) polymers due to their wide range of chemical and physical characteristics. Despite …
(PCR) polymers due to their wide range of chemical and physical characteristics. Despite …
An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials
The purpose of this work is the development of a trained artificial neural network for
surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures …
surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures …
Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation
BramahHazela, J Hymavathi, TR Kumar… - Journal of …, 2022 - Wiley Online Library
In this paper, we represent a method for machine learning to predict the defect in foundry
operation. Foundry has become a driving tool to produce the part to another industry like …
operation. Foundry has become a driving tool to produce the part to another industry like …
Learning the stress-strain fields in digital composites using Fourier neural operator
Increased demands for high-performance materials have led to advanced composite
materials with complex hierarchical designs. However, designing a tailored material …
materials with complex hierarchical designs. However, designing a tailored material …
[HTML][HTML] Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network
Mechanical and thermal properties of materials are extremely important for various
engineering and scientific fields such as energy conversion and energy storage. However …
engineering and scientific fields such as energy conversion and energy storage. However …
Machine learning–assisted design of material properties
Designing functional materials requires a deep search through multidimensional spaces for
system parameters that yield desirable material properties. For cases where conventional …
system parameters that yield desirable material properties. For cases where conventional …
Estimation of fatigue life of welded structures incorporating importance analysis of influence factors: a data-driven approach
C Feng, M Su, L Xu, L Zhao, Y Han - Engineering Fracture Mechanics, 2023 - Elsevier
The fatigue life prediction of welded joints with different specifications under different
conditions was a challenging issue due to the quite complex influence. Specifically, the …
conditions was a challenging issue due to the quite complex influence. Specifically, the …
Application of machine learning in determining the mechanical properties of materials
Currently, the challenge in front of researchers is to discover new novel material with
superior properties as per the demand of the society with a vast range of applications. With …
superior properties as per the demand of the society with a vast range of applications. With …
Hybrid machine learning techniques and computational mechanics: Estimating the dynamic behavior of oxide precipitation hardened steel
A new generation of Oxide Dispersion Strengthened (ODS) alloys called Oxide Precipitation
Hardened (OPH) alloys, has recently been developed by the authors. The excellent …
Hardened (OPH) alloys, has recently been developed by the authors. The excellent …