| 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 |
A Surrogate Model for Capturing the Relationship between Physics-based Process Model Parameters and Interpass Temperature History in Laser Powder Bed Fusion Parts |
| Author(s) |
Shawn Hinnebusch, Alaa Olleak, Praveen Vulimiri, Florian Dugast, Albert To |
| On-Site Speaker (Planned) |
Shawn Hinnebusch |
| Abstract Scope |
Layerwise thermal process simulations are critical for predicting heat buildup in Laser Powder Bed Fusion (LPBF) parts. However, calibrating and validating process simulation models, such as absorptivity and convection coefficients, through optimization methods typically requires hundreds to thousands of simulations. Each simulation can take hours to run, resulting in optimization processes lasting weeks. This study proposes a surrogate model to accurately capture the relationship between simulated temperature history and the model parameters. This surrogate model, constructed via polynomial fitting in a low-dimensional model space obtained through Principal Component Analysis (PCA), enables rapid calibration of model parameters while providing details about the most important calibration parameters. Demonstrations show the surrogate model effectively calibrates multiple geometries with less than 3.3% mean absolute percentage error compared to infrared camera experiments. This approach offers a fast and accurate method for calibrating layerwise process models, facilitating precise temperature field predictions for large LPBF parts. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |