6 papers · 1 filter
Bridging Phase-Field Model and Deep Learning for Predicting 2D and 3D Microstructure Evolution in Ternary Alloys
Owais Ahmad, Aravind K, Naveen Kumar +3
We develop a hybrid framework that integrates a phase-field model (PFM) with an attention-enhanced deep learning (DL) architecture to study ternary spinodal dealloying, a sophistic…
Multiscale Modeling of Abnormal Grain Growth: Role of Solute Segregation and Grain Boundary Character
Albert Linda, Rajdip Mukherjee, Somnath Bhowmick
Abnormal grain growth (AGG) influences the properties of polycrystalline materials; however, the underlying mechanisms, particularly the role of solute segregation at the grain bou…
Deep Learning-Driven Prediction of Microstructure Evolution via Latent Space Interpolation
Sachin Gaikwad, Thejas Kasilingam, Owais Ahmad +2
Phase-field models accurately simulate microstructure evolution, but their dependence on solving complex differential equations makes them computationally expensive. This work achi…
Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study
Owais Ahmad, Vishal Panwar, Kaushik Das +2
The generative adversarial network (GAN) is one of the most widely used deep generative models for synthesizing high-quality images with the same statistics as the training set. Fi…
Effect of Cr Segregation on Grain Growth in Nanocrystalline α-Fe Alloy: A Multiscale Modelling Approach
Sandip Guin, Albert Linda, Yu-Chieh Lo +2
We present a multiscale modelling framework that integrates density functional theory (DFT) with a phase-field model (PFM) to explore the intricate dynamics of grain growth in nano…
Interplay between thermal and compositional gradients decides the microstructure during thermomigration: a phase-field study
Sandip Guin, Soumya Bandyopadhyay, Saswata Bhattacharyya +1
The presence of thermal gradients in alloys often leads to non-uniformity in concentration profiles, which can induce the thermomigration of microstructural features such as precip…