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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title Application of Artificial Neural Networks to Low Cycle Fatigue and Creep Data Processing for Power Plant Materials
Author(s) Kuk-Cheol Kim, Jhin-Ik Suk, Byeong-Ook Kong
On-Site Speaker (Planned) Kuk-Cheol Kim
Abstract Scope The objective of this study is to obtain more reliable method for material data processing using artificial neural network. For low cycle fatigue, the design curve is necessary to ensure integrity against cyclic loading. To secure Coffin-Manson design curve in low cycle fatigue testing, tests are carried out under specific conditions such as temperature and stress ratio. However, to simulate various situations, fatigue design curve at arbitrary temperature and stress ratio conditions are also required, which have not been tested. To solve this problem, an artificial neural network technique has been proposed which can derive general fatigue design curve equation. For creep properties, survival analysis is best method which can utilize the ongoing test data. In this study, we applied artificial neural network technique to existing survival analysis method and to improve the reliability of analysis so that creep rupture curve can be used even when it is nonlinear.
Proceedings Inclusion? Definite: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D Nanoprinting: An Integrated Approach of Experiments, Computer-aided Design and Simulations
4D STEM Data Acquisition, Analytics and Functional Material Property Extraction
A Machine Learning Approach to Independent Component Analysis for Nuclear Magnetic Resonance Spectra
Adversarial Networks for Digital Microstructure Generation
Application of a Statistical Analysis Technique for Characterizing the Deformation Behavior of the Material under Dynamic Impact Loading
Application of Artificial Neural Networks to Low Cycle Fatigue and Creep Data Processing for Power Plant Materials
Automated Defect Detection in Electron Microscopy with Machine Learning
Data Analytics for Correlative Multimodal Chemical and Functional Imaging
Data Science and the MGI
Deciphering the Atomic Origin of Glasses’ Properties by Machine Learning
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Digital Protocols for Statistical Quantification of Microstructure Features in Polycrystalline Nickel-based Superalloys
Human-in-the-loop Strategies for Dimensionality Reduction and Optimization in Materials Design
Model-based Reconstruction Algorithms for Time-of-Flight Neutron Tomography
Modeling and Simulation of Rare Events in Multidimensional Spaces
Multi-modal Data Fusion and 3D Reconstruction of Serial Sectioning Data
Neural Networks for Processing of Low Signal-to-noise Data in Scanning Probe Microscopy
P2-8: Evaluation for the Quality of Flake Graphite Cast Iron and Spheroidal Graphite Cast Iron by Tapping Test with Using Artificial Intelligence
Phase Field Regularization for Optimal Grain Reconstruction of Noisy Images
Python For Glass Genomics (PyGGi): A Machine Learning Package to Predict the Properties of Glasses
Recent Advances in 3D Reconstruction Based on Spherical Indexing of EBSD Data
Structure Prediction and Property-based Optimization of Molecular Crystals with GAtor
Workflows for Curation and Analysis of Microstructure-Aware Materials Data: Application to Aging of U-Nb Alloys

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