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 …
Artificial intelligence in predicting mechanical properties of composite materials
The determination of mechanical properties plays a crucial role in utilizing composite
materials across multiple engineering disciplines. Recently, there has been substantial …
materials across multiple engineering disciplines. Recently, there has been substantial …
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
NN Vlassis, WC Sun - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
We introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-
tuned properties. Denoising diffusion probabilistic models are generative models that use …
tuned properties. Denoising diffusion probabilistic models are generative models that use …
Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method
Industrial big data technology has become one of the important driving forces to intelligent
manufacturing in the steel industry. In this study, the characteristics of data in steel …
manufacturing in the steel industry. In this study, the characteristics of data in steel …
[HTML][HTML] Characterization of porous membranes using artificial neural networks
Y Zhao, P Altschuh, J Santoki, L Griem, G Tosato… - Acta Materialia, 2023 - Elsevier
Porous membranes have been utilized intensively in a wide range of fields due to their
special characteristics and a rigorous characterization of their microstructures is crucial for …
special characteristics and a rigorous characterization of their microstructures is crucial for …
Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders
Abstract The field of Integrated Computational Materials Engineering (ICME) combines a
broad range of methods to study materials' responses over a spectrum of length scales. A …
broad range of methods to study materials' responses over a spectrum of length scales. A …
Harnessing structural stochasticity in the computational discovery and design of microstructures
This paper presents a deep generative model-based design methodology for tailoring the
structural stochasticity of microstructures. Although numerous methods have been …
structural stochasticity of microstructures. Although numerous methods have been …
Efficient design of harmonic structure using an integrated hetero-deformation induced hardening model and machine learning algorithm
Harmonic structured material (HSM) of coarse-grained core and fine-grained shell
microstructure in SS304L was designed using a three-dimensional numerical model …
microstructure in SS304L was designed using a three-dimensional numerical model …
Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks
This paper investigates the structure–property relations of thin-walled lattices, characterized
by their cross-sections and heights, under dynamic longitudinal compression. These …
by their cross-sections and heights, under dynamic longitudinal compression. These …
Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling
Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of
materials with the aid of integrated computational materials engineering (ICME) approaches …
materials with the aid of integrated computational materials engineering (ICME) approaches …