| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Towards a Generic Deposition Model in Wire-arc Directed Energy Deposition: A Deep Learning-based Wetted Area Prediction Model |
| Author(s) |
Magnus Glasder, Maicol Fabbri |
| On-Site Speaker (Planned) |
Magnus Glasder |
| Abstract Scope |
Wire-arc directed energy deposition poses significant challenges in accurately predicting the geometry of weld beads, particularly regarding the overlap and stacking of multiple beads. This is due to the complex interaction between electric arc and previously deposited layers. Existing methods are inadequate in capturing this relationship for arbitrary layer geometries. A novel approach is proposed, which separates the prediction task into wetted area and shape prediction. The wetted area is predicted using a deep learning model, while shape prediction is achieved through an energy minimization technique, which places no assumptions on bead geometry. The wetted area prediction is treated as a computer vision task. 3D surface scans of the workpiece, welding parameters, and torch positions are encoded into images. A pre-trained vision network is fine-tuned on these images to predict the wetted area. The presentation emphasizes the machine learning aspect of the approach and delves into data management and pre-processing. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |