Digital twins for materials
Digital twins are emerging as powerful tools for supporting innovation as well as optimizing
the in-service performance of a broad range of complex physical machines, devices, and …
the in-service performance of a broad range of complex physical machines, devices, and …
Materials informatics for mechanical deformation: A review of applications and challenges
In the design and development of novel materials that have excellent mechanical properties,
classification and regression methods have been diversely used across mechanical …
classification and regression methods have been diversely used across mechanical …
Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
Data-driven methods are emerging as an important toolset in the studies of multiscale,
multiphysics, materials phenomena. More specifically, data mining and machine learning …
multiphysics, materials phenomena. More specifically, data mining and machine learning …
Microstructural materials design via deep adversarial learning methodology
Identifying the key microstructure representations is crucial for computational materials
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …
Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches
Data-driven methods are attracting growing attention in the field of materials science. In
particular, it is now becoming clear that machine learning approaches offer a unique avenue …
particular, it is now becoming clear that machine learning approaches offer a unique avenue …
Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning
We present an application of data analytics and supervised machine learning to allow
accurate predictions of the macroscopic stiffness and yield strength of a unidirectional …
accurate predictions of the macroscopic stiffness and yield strength of a unidirectional …
Nickel-based superalloy single crystals fabricated via electron beam melting
Additive manufacturing technologies have emerged as potentially disruptive processes
whose possible impacts range across supply chain logistics, prototy**, and novel …
whose possible impacts range across supply chain logistics, prototy**, and novel …
Quantitative representation of directional microstructures of single-crystal superalloys in cyclic crystal plasticity based on neural networks
H Weng, H Yuan - International Journal of Plasticity, 2023 - Elsevier
Nickel-based single-crystal alloys undergo microstructural degradation induced by thermal
exposure. The directional rafting of microstructures significantly affects the mechanical …
exposure. The directional rafting of microstructures significantly affects the mechanical …
[HTML][HTML] A deep learning convolutional neural network and multi-layer perceptron hybrid fusion model for predicting the mechanical properties of carbon fiber
Recently, deep learning methods have become one of the hottest topics in predicting
material properties, however, one bottleneck in current research is the simultaneous …
material properties, however, one bottleneck in current research is the simultaneous …
[HTML][HTML] Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data
GA Sengodan - Composites Part B: Engineering, 2021 - Elsevier
A novel method to predict the mechanical responses of arbitrary microstructures from the
deep learning of microstructures and their stress-strain response is presented in this work …
deep learning of microstructures and their stress-strain response is presented in this work …