| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
|
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Predicting Print Morphology in Aerosol Jet Printing Using Deep Learning-based cGANs |
| Author(s) |
Shihab Shakur, Akash Deep, Srikanthan Ramesh |
| On-Site Speaker (Planned) |
Shihab Shakur |
| Abstract Scope |
Aerosol jet printing (AJP), a micro-scale additive manufacturing technique, is predominantly utilized for producing flexible electronic devices. In AJP, the morphology of the printed features contain rich information about the process phenomena. Early prediction of these morphological signatures can provide opportunities for process monitoring and mitigating suboptimal process conditions, thereby minimizing defect occurrence. Consequently, a scalable and efficient data-driven model is essential to effectively address this issue. This research presents the development of a deep learning-based conditional generative adversarial network (cGAN) model to predict and emulate the morphological signatures in AJP. The model generates images of print morphologies conditioned on the process parameters. It allows for early prediction of future morphological signatures for an in-process part, based on process parameters. The efficacy of the model is demonstrated in a case study on AJP of conductive circuits using a poly(3,4-ehtylenedioxythiophene) (PEDOT) and polystyrene sulfonate (PSS) ink (PEDOT:PSS) on flexible substrates. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |