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Meeting MS&T23: Materials Science & Technology
Symposium Leveraging Integrated Computational Materials Engineering for High-fidelity Physics-based and Machine Learning Models
Presentation Title Application of Machine Learning Framework in Predicting Creep Response of High Temperature Alloys
Author(s) Md Abir Hossain, Jacob T Pellicotte, Calvin M Stewart
On-Site Speaker (Planned) Md Abir Hossain
Abstract Scope This study explored the implementation of machine learning (ML) to build high throughput models that predict the creep response of Ni-enriched superalloy Inconel 718. For over a century, creep models were developed by human intuition and empirical knowledge leading to application-, condition-, and/or material-specific models. Machine learning has enabled a widespread improvement in the human ability to develop creep models and makes extrapolations to unprecedented timescales. In this study, stress-rupture data across four heats of materials at temperature range 500-750 C are gathered from NIMS. A randomized algorithm is employed to partition the data. A set of 20 different ML algorithms are trained with the training set and 5 best models are chosen. Based on the statistical rationale, the Wide Neural Network (WNN) is found to be the best ML algorithm. The ML framework provides a pathway to design next-generation alloys for elevated temperature applications.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Application of Machine Learning Framework in Predicting Creep Response of High Temperature Alloys
Hybrid Simulation Method Based on Molecular Dynamics and Machine Learning to Improve Property Prediction with Lower Computational Cost in Complex System
New Refractory High Entropy Alloys Discovery by Physics Discovery
Novel Convolutional-Recurrent Hybrid Neural Network for Predicting Fission Gas Release in UO2 Nuclear Fuel
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Simulating Macroscale Microstructures Using Advanced Programming and Numerical Methods

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