| 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 |
Deep Learning Based Prediction of Thermal History During the Laser-based Powder Bed Fusion (PBF-LB) Additive Manufacturing Process |
| Author(s) |
Philipp Schuessler, Volker Schulze, Stefan Dietrich |
| On-Site Speaker (Planned) |
Philipp Schuessler |
| Abstract Scope |
Understanding the thermal history during the laser-based powder bed fusion (PBF-LB) additive manufacturing process is crucial for minimizing failed printjobs and optimizing part properties. Finite Element Method (FEM) simulations are commonly used but pose challenges for materials with phase transformations due to high computational costs (requirements for temporal and spatial accuracy). In this study, we propose a novel approach utilizing deep learning, specifically the Recurrent Neural Network (RNN) Long Short-Term Memory (LSTM) models, to predict thermal histories based on validated FEM simulations for quench and tempering steel AISI 4140. Our sequential model leverages the thermal history data from multiple simulations as input for training, offering a computationally efficient solution. By accurately predicting thermal histories, our method facilitates the optimization of printing parameters and material properties, thereby enhancing the reliability and performance of additive manufacturing processes for AISI 4140 steel components. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |