In-situ Droplet Monitoring of Inkjet 3D Printing Process using Image Analysis and Machine Learning Models

Abstract

Additive manufacturing (AM) has yielded major innovations in the electronics, biomedical and energy domains. One of the AM techniques which has witnessed widespread use is the inkjet 3D printing (IJP). The IJP process fabricates parts by depositing colloidal liquid droplets on substrates. Despite its advantages, variations in input process parameters and fluid properties can have a profound impact on the print quality. This paper aims to address this issue by presenting a novel vision-based approach for in-situ monitoring of droplet formation. Further, a machine learning model was used to study the relationship between droplet attributes and droplet modes. A drop watcher camera was used to capture a sequence of videos obtained from different combinations of voltage and frequency. Custom source code was developed using python libraries to capture variations in droplet attributes (droplet size, velocity, aspect ratio, and presence of satellites) and their impact on the droplet modes (normal, satellite, and no-droplet) using computer vision. A backpropagation neural network mode (BPNN) was applied, with the droplet features as inputs, to classify output droplet modes. The BPNN classified droplet modes with 90% (high) accuracy. This research forms the basis for future development of digital twin model of inkjet 3D printing towards predictive analysis and process optimization.