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Meeting MS&T21: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
Author(s) Michael Sprayberry, John Christopher Ledford, Michael M Kirka
On-Site Speaker (Planned) Michael Sprayberry
Abstract Scope Additive manufacturing using electron beam melting (EBM) has successfully reduced the manufacturing lead-time of complex geometric structures with materials that are nearly impossible to manufacture with conventional processing techniques. However, certification of the component quality can be challenging. Due to the continuous deposition of successive layers of material, components can be quantitatively and qualitatively examined without destructively testing the component. However, in-situ monitoring processes have been complicated due to the unique processing environment associated with EBM metal powder. This work describes a solution to one of the challenges of using Near-infrared (NIR) images as a component qualification process. Here, the correlation of in-process backscatter data with the NIR images increases predicting anomalies during the manufacturing process. Results are presented related to in-situ process monitoring and how this technique results in improved mechanical property prediction and reliability of the process.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Deep Generative Model for Parametric EBSD Pattern Simulation
Aluminum Alloy Design Using Physics Informed Machine Learning
De Novo Inverse Design of Nanoporous Materials by Machine Learning
Deep Learning and Uncertainty Quantification for Automated Experiments
Discovery of Novel Crystal Structures via Generative Adversarial Networks
Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy
Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Optimizing the Training of Convolutional Neural Networks for Image Segmentation
Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Refinements to the Production of Machine Learning Interatomic Potentials
Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning

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