Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review
SM Hashemi, S Parvizi… - International …, 2022 - journals.sagepub.com
In the current review, an exceptional view on the multi-scale integrated computational
modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials …
modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials …
Application of data-driven methods for laser powder bed fusion of Ni-based superalloys: A review
Ni-based superalloys, with their high mechanical integrity at high temperature, have become
critical engineering materials in modern advanced mechanical systems. However, this …
critical engineering materials in modern advanced mechanical systems. However, this …
Data-driven microstructure and microhardness design in additive manufacturing using a self-organizing map
To design microstructure and microhardness in the additive manufacturing (AM) of nickel
(Ni)-based superalloys, the present work develops a novel data-driven approach that …
(Ni)-based superalloys, the present work develops a novel data-driven approach that …
Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber
T Kojima, T Washio, S Hara, M Koishi - Scientific reports, 2020 - nature.com
Molecular dynamics (MD) simulation is used to analyze the mechanical properties of
polymerized and nanoscale filled rubber. Unfortunately, the computation time for a …
polymerized and nanoscale filled rubber. Unfortunately, the computation time for a …
[HTML][HTML] Stochastic 3D microstructure modeling of twinned polycrystals for investigating the mechanical behavior of γ-TiAl intermetallics
P Rieder, M Neumann, LM Fernandes, A Mulard… - Computational Materials …, 2024 - Elsevier
A stochastic 3D microstructure model for polycrystals is introduced which incorporates two
types of twin grains, namely neighboring and inclusion twins. They mimic the presence of …
types of twin grains, namely neighboring and inclusion twins. They mimic the presence of …
Eikonal-based models of random tessellations
B Figliuzzi - Image Analysis and Stereology, 2019 - ias-iss.org
In this article, we propose a novel, efficient method for computing a random tessellation from
its vectorial representation at each voxel of a discretized domain. This method is based upon …
its vectorial representation at each voxel of a discretized domain. This method is based upon …
Analysis on Microstructure–Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations
T Kojima, T Washio, S Hara, M Koishi, N Amino - Polymers, 2021 - mdpi.com
A better understanding of the microstructure–property relationship can be achieved by
sampling and analyzing a microstructure leading to a desired material property. During the …
sampling and analyzing a microstructure leading to a desired material property. During the …
Statistical approach to the Representative Volume Element Size of Random Composites
D Jeulin, S Forest - … Numerical Methods at the Mesoscopic Scale, 2024 - books.google.com
The usual theoretical prediction of effective properties of random materials concerns media
with an infinite extension, modeled by ergodic random structures. On the other hand, more …
with an infinite extension, modeled by ergodic random structures. On the other hand, more …