Prisms: An integrated, open-source framework for accelerating predictive structural materials science
Abstract The Center for Predictive Integrated Structural Materials Science (PRISMS Center)
is creating a unique framework for accelerated predictive materials science and rapid …
is creating a unique framework for accelerated predictive materials science and rapid …
An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems
Materials design aims to identify the material features that provide optimal properties for
various engineering applications, such as aerospace, automotive, and naval. One of the …
various engineering applications, such as aerospace, automotive, and naval. One of the …
Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation
Microstructure sensitive design has a critical impact on the performance of engineering
materials. The safety and performance requirements of critical components, as well as the …
materials. The safety and performance requirements of critical components, as well as the …
Machine learning reinforced microstructure-sensitive prediction of material property closures
This study addresses a machine learning (ML)-reinforced strategy to build both linear and
non-linear property closures for metallic materials. A property closure is a closed space of …
non-linear property closures for metallic materials. A property closure is a closed space of …
Deep reinforcement learning methods for structure-guided processing path optimization
A major goal of materials design is to find material structures with desired properties and in a
second step to find a processing path to reach one of these structures. In this paper, we …
second step to find a processing path to reach one of these structures. In this paper, we …
Database development and exploration of process–microstructure relationships using variational autoencoders
S Sundar, V Sundararaghavan - Materials Today Communications, 2020 - Elsevier
The paper demonstrates graphical representation of a large database containing process–
microstructure relationships using an unsupervised machine learning algorithm. Correlating …
microstructure relationships using an unsupervised machine learning algorithm. Correlating …
Application of Gaussian process autoregressive models for capturing the time evolution of microstructure statistics from phase-field simulations for sintering of …
While phase-field models have been demonstrated to be highly versatile in performing
physics-based simulations of a large variety of materials phenomena involving …
physics-based simulations of a large variety of materials phenomena involving …
Stochastic design optimization of microstructures with utilization of a linear solver
Microstructure design can have a substantial effect on the performance of critical
components in numerous aerospace applications. However, the stochastic nature of metallic …
components in numerous aerospace applications. However, the stochastic nature of metallic …
Design of β-Titanium microstructures for implant materials
Ş Çallıoğlu, P Acar - Materials Science and Engineering: C, 2020 - Elsevier
The present work addresses the design of β-Titanium alloy, TNTZ, microstructure to be used
in biomedical applications as implant materials. The TNTZ alloy has recently started to …
in biomedical applications as implant materials. The TNTZ alloy has recently started to …
Stochastic design optimization of microstructural features using linear programming for robust design
P Acar, V Sundararaghavan - AIAA Journal, 2019 - arc.aiaa.org
Microstructure design can have a substantial effect on the performance of critical
components in numerous aerospace applications. However, the stochastic nature of metallic …
components in numerous aerospace applications. However, the stochastic nature of metallic …