About this Abstract |
| Meeting |
2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
|
| Symposium
|
2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
|
| Presentation Title |
Convolutional Neural Network Approach for Predicting Melt Pool Dimensions in Laser Powder Bed Fusion Process |
| Author(s) |
Oluwapelumi Oluwaseyi Adejumo, Nayan Pundhir, K Chandrashekhara, Cesar Ortiz Rios, Misak Heath |
| On-Site Speaker (Planned) |
Oluwapelumi Oluwaseyi Adejumo |
| Abstract Scope |
Laser powder bed fusion (LPBF) is an additive manufacturing process that uses a high-powered
laser to selectively melt metal powder and build parts layer by layer. In this study, a computational
fluid dynamics model that simulates the LPBF process was developed using Flow-3D software to
generate melt pool images. These images were used to train a convolutional neural network, a deep
learning model well-suited for image-based data, to predict the melt pool dimensions. The
performance of the model was evaluated using mean absolute error and root mean square error,
yielding values of 0.0275 and 0.0371, respectively. The model demonstrated predictive capabilities
in estimating accurate melt pool dimensions directly from the melt pool images, highlighting its
potential for data-driven monitoring and quality control in LPBF processes. |
| Proceedings Inclusion? |
Planned: Post-meeting proceedings |