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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Data Science and Analytics for Materials Imaging and Quantification
Presentation Title Understanding Powder Morphology and Its Effect on Flowability Through Machine Learning in Additive Manufacturing
Author(s) Srujana Rao Yarasi, Andrew Kitahara, Anthony Rollett, Elizabeth Holm
On-Site Speaker (Planned) Srujana Rao Yarasi
Abstract Scope The use of computer vision and machine learning tools in the additive manufacturing domain have enabled the quantitative investigation of qualitative factors like powder morphology, which affect the flowability in powder bed fusion processes. Flowability is measured through rheological experiments conducted with the FT4 rheometer and the Granudrum. Convolutional Neural Networks (CNN) are used to generate feature descriptors of the powder feedstock, from SEM images, that describe not just the particle size distribution but also the sphericity, surface defects, and other morphological features of the powder particles. These descriptors are then correlated to their respective flowability properties for numerous powder systems to evaluate powder performance in an AM powder bed fusion machine. This framework is intended to be a powder qualification system that can differentiate between powder systems and serve as a method to indicate the usability of recycled powder lots, for instance.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancements in EBSD Using Machine Learning
Computer Vision and Machine Learning for Microstructural Characterization and Analysis
Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys
Deep Neural Network Facilitated Complex Imaging of Phase Domains
Dictionary Indexing of EBSD Patterns Assisted by Convolutional Neural Network
High Dimensional Analysis of Abnormal Grain Growth under Dynamic Annealing Conditions
Improved EBSD Indexing through Non-Local Pattern Averaging
Materials Characterization in 3D Using High Energy X-ray Diffraction Microscopy: Irradiated and Deformed Materials
Microstructure Image Segmentation with Deep Learning: from Supervised to Unsupervised Methods
Quantitative EBSD Image Analysis and Prediction via Deep Learning
Quantitative X-ray Fluorescence Nanotomography
Resolving Pseudosymmetry in Tetragonal ZrO2 Using EBSD with a Modified Dictionary Indexing Approach
Understanding Powder Morphology and Its Effect on Flowability Through Machine Learning in Additive Manufacturing
Understanding the Keyhole Dynamics in Laser Processing Using Time-resolved X-ray Imaging Coupled With Computer Vision and Data Analytics

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