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Meeting 2019 TMS Annual Meeting & Exhibition
Symposium Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
Presentation Title Steel Inclusion Classification Using Computer Vision and Machine Learning
Author(s) Nan Gao, Mohammad Abdulsalam, Bryan Webler, Elizabeth Holm
On-Site Speaker (Planned) Nan Gao
Abstract Scope Inclusions form during liquid steel processing and affect subsequent material performance. However, it is hard to classify inclusion composition visually via scanning electron microscope (SEM) images. Energy dispersive x-ray spectroscopy (EDS) is therefore used for inclusion classification, but it adds to analysis time. In our work, we use computer vision and machine learning techniques to automatically classify steel inclusions in a large database of SEM images. We explore classifying these particles on the basis of image intensity distributions using different pre-processing and analysis methods. This approach is motivated by the difference in contrast in well-calibrated SEM images that arises from differences in chemical composition. The overall objective of this work is to automatically classify the different types of inclusion in steel directly from SEM images without the need for EDS.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

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Automated Sensitivity Analysis for High-throughput Ab Initio Calculations
Automatminer: An Automatic Materials Science Machine Learning Tool for Benchmarking and Prediction
Bayesian CALPHAD: From Uncertainty Quantification to Model Fusion
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Cloud-based Infrastructure for Big Data in the Materials Domain
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Comprehensive Quality Assurance of Additive Manufacturing Ti-6Al-4V by Learning from Prior Studies
Developing Fast-running Simulations Models for Manufacturing Using Deep Learning
Efficient Propagation of Uncertainty From CALPHAD to Multi-physics Phase Field Microstructure Simulations
Error Estimation for Stress Distributions and Macroscale Yield Prediction in Polycrystalline Alloys
Evaluation and Representation of Uncertainty in Thermodynamic Phase Diagrams
GB Property Localization: Inference and Uncertainty Quantification of GB Structure-property Models from Indirect Polycrystal Measurements
High-performance Computing in Artificial Neural Networks Atomistic Simulations
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M-10: Thermocouple Temperature Measurement and Thermal Modelling of Zircaloy-4 during Electron Beam Welding
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Software Tools, Crystal Descriptors, and Applications of Machine Learning Applied to Materials Design
Steel Inclusion Classification Using Computer Vision and Machine Learning
Towards Predictive Synthesis of Computer-designed Inorganic Materials
Uncertainty Quantification in Microstructural Reconstruction of Additively Manufactured Materials
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