| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing: Length-Scale Phenomena in Mechanical Response
|
| Presentation Title |
Quantitatively Describing Scan Strategies in Laser Powder Bed Fusion |
| Author(s) |
Kahraman G. Demir, Zhizhou Zhang, Grace Gu |
| On-Site Speaker (Planned) |
Kahraman G. Demir |
| Abstract Scope |
In laser powder bed fusion (LPBF), laser scan strategies are well known to be correlated with many properties; however, due to the lack of interpretable scan strategy descriptors and the consequential imposition of simple scan strategies, these correlations are not well understood and are difficult to investigate. This work proposes a methodology for an intuitive quantitative descriptor of scan strategies that has the potential to provide more insight into process-property correlations. Furthermore, a neural network is trained to predict post-print residual stress distributions using only the descriptor, demonstrating the correlative capacity of the descriptor. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Machine Learning, Modeling and Simulation |