Data-Driven autonomous printing process optimization and real-time abnormality identification in aerosol jet-deposited droplet morphology

Abstract

Aerosol Jet Printing (AJP) is a digital direct ink writing technology, which excels in maskless patterning and fine conductive line deposition. However, its potential in droplet-based printing remains largely unexplored, which presents a unique opportunity to pioneer advances in sectors that require precise droplet control. In this research, a novel data-driven approach integrating representative deep learning and machine learning technologies is developed to optimise droplet deposition in AJP. In the proposed method, a stepwise machine learning approach is applied to refine and model droplet morphology in AJP, ensuring systematic process optimisation before deposition. A convolutional neural network (CNN) model is then deployed for real-time process monitoring based on droplet morphology, which facilitates the detection of droplet anomalies during printing. In the subsequent experiments, the autonomous optimisation of process variables and abnormality identification achieved accuracies of 96.1% and 95.5%, respectively, highlighting its potential for droplet deposition optimisation in the AJP process.

Keywords

• Aerosol jet printing
• autonomous printing process optimization
• abnormality identification
• deep learning
• machine learning