| About this Abstract |
| Meeting |
Materials Science & Technology 2019
|
| Symposium
|
Additive Manufacturing: Effective Production, Characterization, and Recycling of Powder Materials
|
| Presentation Title |
Understanding Powder Morphology and Its Effect on Flowability through Computer Vision and Machine Learning In Additive Manufacturing |
| Author(s) |
Srujana Rao Yarasi, Andrew R Kitahara, Anthony D Rollett, Elizabeth A 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 affects the flowability in powder bed fusion processes. Flowability is measured through rheological experiments conducted with the FT4 rheometer and the GranuDrum. The use of Convolutional Neural Networks (CNN) to generate hypercolumn descriptors is proposed as part of a framework to generate powder fingerprints that describe characteristics of the powder feedstock such as particle size distribution, sphericity, surface defects, and other morphological features. These descriptors are then correlated to their respective flowability properties for numerous powder systems to evaluate powder performance. This framework is intended as a powder qualification system to differentiate powder systems and serve as a method to indicate the usability of recycled powder lots. |
| Proceedings Inclusion? |
Planned: At-meeting proceedings |