| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing: Design, Materials, Manufacturing, Challenges and Applications
|
| Presentation Title |
Additive Manufacturing at (Sur)face Value: Correlating L-PBF Surface Features with Part Quality and Microstructure |
| Author(s) |
Ashley Elizabeth Paz y Puente |
| On-Site Speaker (Planned) |
Ashley Elizabeth Paz y Puente |
| Abstract Scope |
Ensuring part quality in laser powder bed fusion (L-PBF) additively manufactured samples remains a challenging and resource-intensive task, involving exhaustive defect searches or costly equipment. This research presents an alternative approach by focusing on surface characterization of L-PBF specimens and establishing correlations with key part quality metrics, such as relative density, maximum pore size, and microstructural characteristics. The primary technique utilized for surface characterization was confocal laser scanning microscopy, supplemented by metallography for internal defect analysis. The study reveals a significant correlation between internal defects and surface features, providing valuable insights for quality assessment. Building upon this, future work involves leveraging deep learning to streamline and automate the defect detection process. Additionally, the implementation of in situ monitoring is being explored, aiming to enhance real-time quality control during the L-PBF manufacturing process. This presentation will highlight recent results and demonstrate that we can potentially take additive manufacturing at (sur)face value. |