| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Symposium
|
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Correlating Layer-wise Laser Powder Bed Fusion Process Data |
| Author(s) |
Srikar Rairao, Caleb Campbell, Brett Brady, Kevin Shay, Kevin Lamb, Bradley Jared |
| On-Site Speaker (Planned) |
Srikar Rairao |
| Abstract Scope |
Optimizing the process of layer-wise laser powder bed fusion (L-PBF) is crucial for enhancing part quality. Our research focuses on the integration of image and computed tomography (CT) data to develop efficient datasets as inputs for models that can predict and analyze defects across various layers of fabricated parts. Utilizing a Farsoon FS271M L-PBF machine with low-cost cameras, our team seeks to correlate data streams to improve real-time monitoring and post-process validation of stainless steel. This project aims to refine defect detection through advanced image analysis techniques and also focuses on using neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to ensure part reliability. The goal is to create a predictive model that enhances the detection of defects, thereby reducing manufacturing time and costs while maintaining high-quality standards in additive manufacturing processes. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |