| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Quantifying Microstructure Heterogeneity for Qualification of Additively Manufactured Materials
|
| Presentation Title |
Correlative Modeling of Laser Powder Bed Fusion Surface Characteristics to Internal Defects |
| Author(s) |
Sean Dobson, Ashely Paz y Puente |
| On-Site Speaker (Planned) |
Sean Dobson |
| Abstract Scope |
Laser powder bed fusion (L-PBF) additive manufacturing (AM) continues to find application in industries like medical and aerospace. As AM pushes into use for critical parts, reliable methods, such as in-process monitoring, will need to be devised to ensure part quality. Some in-process monitoring uses surface roughness; however, it is often only a metric for recoater health. This on-going work demonstrates the potential for such a method, by developing a correlative model of surface features to internal defect quantity and type, and even microstructural characteristics. Surface, porosity, and microstructure were characterized using high resolution 2-D and 3-D methods. Preliminary findings demonstrate a deep fundamental connection between internal and external defects. The final results of this endeavor will lay the foundation for the development of a novel in-process monitoring system employing deep learning. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Characterization, Machine Learning |