Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions
A thorough understanding of complex process-structure-property (PSP) relationships in
additive manufacturing (AM) has long been pursued due to its paramount importance in …
additive manufacturing (AM) has long been pursued due to its paramount importance in …
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 …
Distribution network expansion planning considering a distributed hydrogen-thermal storage system based on photovoltaic development of the Whole County of China
N Huang, X Zhao, Y Guo, G Cai, R Wang - Energy, 2023 - Elsevier
The large-scale access of a substantial proportion of the distributed photovoltaic (PV) power
sources has introduced considerable source-side randomness and volatility to the …
sources has introduced considerable source-side randomness and volatility to the …
[HTML][HTML] Deep learning-based phase prediction of high-entropy alloys: Optimization, generation, and explanation
Identifying phase information of high-entropy alloys (HEAs) can be helpful as it provides
useful information such as anticipated mechanical properties. Recently, machine learning …
useful information such as anticipated mechanical properties. Recently, machine learning …
Perspective: Machine learning in experimental solid mechanics
Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …
are rapidly proliferating into the discovery process due to significant advances in data …
Local–global decompositions for conditional microstructure generation
Conditional microstructure generation tools offer an important, inexpensive pathway to
constructing statistically diverse datasets for Integrated Computational Materials …
constructing statistically diverse datasets for Integrated Computational Materials …
Machine learning-based microstructure prediction during laser sintering of alumina
Predicting material's microstructure under new processing conditions is essential in
advanced manufacturing and materials science. This is because the material's …
advanced manufacturing and materials science. This is because the material's …
Virtual microstructure design for steels using generative adversarial networks
The prediction of macro‐scale materials properties from microstructures, and vice versa,
should be a key part in modeling quantitative microstructure‐physical property relationships …
should be a key part in modeling quantitative microstructure‐physical property relationships …
Generative deep learning as a tool for inverse design of high-entropy refractory alloys
Generative deep learning is powering a wave of new innovations in materials design. In this
article, we discuss the basic operating principles of these methods and their advantages …
article, we discuss the basic operating principles of these methods and their advantages …
Reconstructing microstructures from statistical descriptors using neural cellular automata
The problem of generating microstructures of complex materials in silico has been
approached from various directions including simulation, Markov, deep learning and …
approached from various directions including simulation, Markov, deep learning and …