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Meeting Materials Science & Technology 2020
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Identifying Crack Initiation Sites with CNNs
Author(s) Katelyn Jones, Elizabeth Holm, Anthony Rollett
On-Site Speaker (Planned) Katelyn Jones
Abstract Scope The application of machine learning techniques in materials science has allowed for a greater understanding of microstructure and more efficient analysis of large amounts of data. Convolutional neural networks (CNNs) have been used with images to make connections between microstructure, stress state, and fatigue life. This project uses CNNs on a combination of experimental and simulated image data to identify high stress points that can initiate cracks and cause fatigue failure. We focus on aerospace materials such as alloy 718, which will be studied because of its applicability for high temperature service and cyclic loading. These results will be used to create a model to locate the crack sites before they form and predict the causes of failure and life of future parts. The application of CNNs in this instance, simulations used, and identified causes of crack initiation will be presented.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerate TEM and Tomography Imaging by Deep-learning Enabled Compressive Sensing and Information Inpainting in High-dimensional Manifold
Assessment of the Ability of Laboratory Accelerated Corrosion Tests to Accurately Predict On-road Corrosion of 6xxx Al Alloys
Automated Optical Microscopy for Rapid Defect Screening
Computer Vision and Machine Learning for Microstructural Image Data
Developing Granular Dielectrics Based on Reconstructed Micro-CT Images
FAIR Digital Object Framework and High Throughput Experiment
Feature Characterization of Electron Backscatter Patterns from Rotating Lattice Single Crystals Using Machine Learning
Identifying Crack Initiation Sites with CNNs
Incorporating Materials Physics into Imaging Algorithms for Microscope Image Interpretation
Introductory Comments: Materials Informatics for Images and Multi-dimensional Datasets
Keyhole Porosity Threshold in Laser Melting Revealed by High-Speed X-ray Imaging
Microstructure Representation for Physically Meaningful Descriptors
Neural Networks and Community Driven Software for Scanning Transmission Electron Microscopy
Towards Smart Categorization of Growth Morphology by Machine Learning

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