| 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 |
Toward Post-superficial Temperature Monitoring During Additive Manufacturing through Data-driven Inpainting |
| Author(s) |
Jiangce Chen, Mikhail Khrenov, Jiayi Jin, Sneha Prabha Narra, Chris McComb |
| On-Site Speaker (Planned) |
Jiangce Chen |
| Abstract Scope |
Understanding the temperature history of a built part during additive manufacturing (AM) is critical for studying the relationship between process parameters and product quality as temperature plays determinant role in melt pool dimensions, defect formation, and microstructure evolution. Unfortunately, the current thermal sensors used to monitor the AM process cannot provide a complete temperature distribution, which restricts the ability to study this relationship. In this paper, we propose a data-driven inpainting machine learning (ML) model that restores the temperature of the entire built part from incomplete temperature data captured by thermal sensors. We generate a dataset of temperature histories for parts with various geometries using a finite element model calibrated using experimental data. Our experiments demonstrate that the inpainting ML model accurately predicts both simulation and experimental data. This ML model has the potential to establish digital twins for AM-built parts, enabling efficient process optimization. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |