| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Powder Materials Processing and Fundamental Understanding
|
| Presentation Title |
Understanding Powder Morphology and Its Effect on Flowability in Additive Manufacturing through Machine Learning Techniques |
| Author(s) |
Srujana Rao Yarasi, Anthony Rollett, Elizabeth Holm |
| On-Site Speaker (Planned) |
Srujana Rao Yarasi |
| Abstract Scope |
Metal powders are extensively used in additive manufacturing processes which necessitates standardized characterization methods for powder properties. 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 AM processes. Flowability is measured through rheological experiments conducted with the FT4 rheometer and the Granudrum, as well as the Hall Flowmeter. 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 particles. Powder property metrics are correlated to their performance metrics for several powder systems to characterize their performance in AM processes. This analysis approach is intended to be agnostic to the type of AM process and can be adapted to various powder forming techniques. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Powder Materials, Characterization |