| 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 |
Multi-bead and Multilayer Printing Geometric Defect Identification Using Single Bead Trained Models |
| Author(s) |
Nowrin Akter Surovi, Gim Song Soh |
| On-Site Speaker (Planned) |
Nowrin Akter Surovi |
| Abstract Scope |
In Wire Arc Additive Manufacturing (WAAM), a geometric defect is a defect that creates voids in the final printed part due to incomplete fusion between two non-uniform overlapping bead segments. Such a defect poses the onset of a severe problem during multi-bead prints. In our earlier work, a methodology has been developed to construct machine learning (ML)-based models to identify geometrically defective bead segments using acoustic signals. In this paper, we investigate the performance of these single-bead segments trained defect detection model scalability for identifying voids during multi-bead prints. A comparative study of the performance of a variety of ML models is explored based on Inconel 718 material block printing. The results show that the single bead segments-based defect identification model can identify defective and non-defective segments in multi-bead printing effectively. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |