|About this Abstract
||2023 TMS Annual Meeting & Exhibition
||Quantifying Microstructure Heterogeneity for Qualification of Additively Manufactured Materials
||Correlative Modeling of Laser Powder Bed Fusion Surface Characteristics to Internal Defects
||Sean Dobson, Ashely Paz y Puente
|On-Site Speaker (Planned)
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.
||Additive Manufacturing, Characterization, Machine Learning