ProgramMaster Logo
Conference Tools for 2021 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
Presentation Title Predicting Yield Stress of High Temperature Alloys via Computer Vision and Machine Learning
Author(s) Nan Gao, Zongrui Pei, Youhai Wen, Michael Gao, Elizabeth Holm
On-Site Speaker (Planned) Nan Gao
Abstract Scope Understanding the linkage between microstructure and properties is especially important to material design for high temperature performance. Generally, microstructures are characterized by visual inspection and metallographic measurements. Although morphology information can be captured and observed, the rich, multiscale microstructural feature data contained in a typical micrograph is rarely fully quantified or exploited. In this research, pre-trained convolutional deep neural networks (CNNs) are used to extract visual information from images, and machine learning methods are trained to make predictions of mechanical properties based on features that exist at a hierarchy of length scales. The temperature-dependent yield stress of steel alloys is predicted with good fidelity, and links to microstructural features that influence mechanical response are made. We find that computer vision and machine learning are promising tools for connecting microstructure to properties.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advanced Data SCiENce Toolkit for Non-data Scientists (ASCENDS) - A Case Study of the Oxidation Kinetics of NiCr-based Alloys
Coupling of Data Mining, Thermodynamics and Multi-objective Genetic Algorithms for the Design of High-temperature Alloys
Determining Solute Site Preference and Correlations to Antiphase Boundary Energy in Ni-based Superalloys
Domain and Uncertainty Quantification in Machine Learning Models of Alloy Properties
Domain Knowledge-informed, Process-mapping AI Graph for Designing Fe-based Alloys
Elastic Properties Machine-learning-based Descriptor for a Refractory High Entropy Alloy
Expanding Materials Selection via Transfer Learning for High-temperature Oxide Selection
Exploring the Compositional Space of High Entropy Alloys via Sequential Learning
Knowledge-driven Platform for Federated Multimodal Big Data Storage & Analytics
Machine Learning Augmented Predictive & Generative Models for Rupture Life in High Temperature Alloys
Optimal Design of High-temperature, Oxidation-resistant Complex Concentrated Alloys
Predicting Vibrational Entropy of FCC Solids Uniquely from Bond Chemistry Using Machine Learning
Predicting Yield Stress of High Temperature Alloys via Computer Vision and Machine Learning
Revealing Nanoscale Features Controlling Diffusion Within Multi-component Alloys through Machine Learning
Toward High Throughput Design and Development of Multi-principal Element Alloys for Corrosion and Oxidation Resistance (MPEAs)
Uncertainty Quantification for Thermo-mechanical Behavior of Aircraft Engine Materials in Elevated Temperatures
Uncertainty Reduction for Calculated Phase Equilibria

Questions about ProgramMaster? Contact programming@programmaster.org