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 …
Rapid and flexible segmentation of electron microscopy data using few-shot machine learning
Automatic segmentation of key microstructural features in atomic-scale electron microscope
images is critical to improved understanding of structure–property relationships in many …
images is critical to improved understanding of structure–property relationships in many …
Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks
Microstructural evolution is a key aspect of understanding and exploiting the processing-
structure-property relationship of materials. Modeling microstructure evolution usually relies …
structure-property relationship of materials. Modeling microstructure evolution usually relies …
[HTML][HTML] Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships
We investigate the methods of microstructure representation for the purpose of predicting
processing condition from microstructure image data. A binary alloy (uranium–molybdenum) …
processing condition from microstructure image data. A binary alloy (uranium–molybdenum) …
Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network
Prediction of microstructure evolution during material processing is essential to control the
material properties. Simulation tools for microstructure evolution prediction based on …
material properties. Simulation tools for microstructure evolution prediction based on …
Preparation and property optimization of FeCrAl-based ODS alloy by machine learning combined with wedge-shaped hot-rolling
In this study, oxide dispersion strengthened (ODS) steel with a composition of Fe-12% Cr-
4.5% Al-2.0% W-0.3% Y 2 O 3 (wt%) was designed by machine learning (ML) and prepared …
4.5% Al-2.0% W-0.3% Y 2 O 3 (wt%) was designed by machine learning (ML) and prepared …
Swin–unet++: A nested swin transformer architecture for location identification and morphology segmentation of dimples on 2.25 cr1mo0. 25v fractured surface
P Liu, Y Song, M Chai, Z Han, Y Zhang - Materials, 2021 - mdpi.com
The precise identification of micro-features on 2.25 Cr1Mo0. 25V steel is of great
significance for understanding the mechanism of hydrogen embrittlement (HE) and …
significance for understanding the mechanism of hydrogen embrittlement (HE) and …
Adoption of image-driven machine learning for microstructure characterization and materials design: A perspective
The recent surge in the adoption of machine learning techniques for materials design,
discovery, and characterization has resulted in increased interest in and application of …
discovery, and characterization has resulted in increased interest in and application of …
Transfer learning enhanced nonlocal energy-informed neural network for quasi-static fracture in rock-like materials
XP Zhou, XL Yu - Computer Methods in Applied Mechanics and …, 2024 - Elsevier
Discontinuities, such as joints, faults and pores, are prevalent in rock formations, significantly
impacting the fracture behavior of rocks. This paper proposes an energy-driven machine …
impacting the fracture behavior of rocks. This paper proposes an energy-driven machine …
Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data
Prediction of the chemical composition and processing history from microstructure
morphology can help in material inverse design. In this work, we propose a fused-data deep …
morphology can help in material inverse design. In this work, we propose a fused-data deep …