| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Powder Materials Processing and Fundamental Understanding
|
| Presentation Title |
Metal Powder Characterization Through the Experimental Method and Machine Learning Approach in Selective Laser Melting |
| Author(s) |
Jiahui Zhang, Manvinder Lalh, Yu Zou |
| On-Site Speaker (Planned) |
Jiahui Zhang |
| Abstract Scope |
In the selective laser melting process, the metal powder properties, e.g., powder packing behavior and flowability, have been reported to significantly affect the formation of anomalies inside the as-fabricated parts. In our recent studies, We investigate the effect of particle size distribution on powder flowability and apply a computer vision approach to evaluate powder flowability. Our results indicate: (1) Either a larger mean particle size or a narrower particle size distribution enhances the powder flowability; The multimodal distribution of PSD is beneficial for the powder bulk flow properties. (2) Feature-based image representation models are established to evaluate powder flowability based on scanning electron microscopy images. Our works provide an effective and efficient tool to evaluate and predict the powder flowability for AM. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Machine Learning, Powder Materials |