About this Abstract |
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: |