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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Presentation Title Investigating Crystallographic Texture Control Using Laser Powder-bed Fusion Additive Manufacturing
Author(s) Joseph Pauza, Anthony Rollett
On-Site Speaker (Planned) Joseph Pauza
Abstract Scope Additive manufacturing of parts fabricated from structural materials occurs on a much finer scale than many other popular manufacturing techniques. The ability to make processing decisions at this scale offers new and promising paths for microstructure control and design. A major component of microstructure in structural materials is crystallographic texture. Parts produced by laser powder bed fusion additive manufacturing have been observed to develop a range of textures during fabrication. The strength of these textures is variable and dependent on a variety of processing factors. The driving physics of these textures is well understood and can be used to inform the processing decisions made during fabrication. We present a study of the modification of laser powder-bed fusion processing parameter to tune crystallographic texture within Inconel 718 parts. Experimental testing is undertaken to understand the mechanical response of the texture control and its impact on overall part performance.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Physics-informed AI Assistant for Atomic Layer Deposition
Accelerating the Discovery of New DP-steel Using Machine Learning-based Multiscale Materials Simulations
AI-driven Discovery of Novel High Entropy Semiconductor Alloys
Artificial Intelligence for Material and Process Design
Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
Enabling Process Optimization Using High-throughput Machine Learning-based Image Analysis
High-fidelity Accelerated Design of High-performance Electrochemical Systems
Investigating Crystallographic Texture Control Using Laser Powder-bed Fusion Additive Manufacturing
Learning Through Domain Knowledge: A Hierarchical Machine Learning Approach Towards the Prediction of Thermoplastic Polyurethane Properties
Machine Learning Prediction of Glass Properties Informed by Synthetic Data
MeltNet: Predicting alloy melting temperature by machine learning
Multi-information Source Batch Bayesian Optimization of Alloys
NEW - Polymer Property Prediction and Design through Multi-task Learning
Realistic 3D Microstructure Generation via Generative Adversarial Networks
Statistics-based Microstructural Digital Image Correlation Method for Estimating Ex-situ Strain from Dissimilar Micrographs
Text and Data Mining for Materials Synthesis

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