Modeling of EHD inkjet printing performance using soft computing-based approaches

Nature-inspired heuristic and/or metaheuristic algorithms have been used for solving complex real-world problems in recent years. Electrohydrodynamic (EHD) inkjet printing is a microadditive manufacturing process in which high-resolution jets of polarizable functional materials were deposited on the defined spot of a substrate at the appointed time. The quality of the printed features is derived by the complex physics of the system. Parameter modeling of this process was carried out by using regression analysis, a feed-forward neural network trained with backpropagation (BPNN) and a neural network trained with a genetic algorithm (GA-NN) separately. This study emphasizes the droplet diameter prediction of an EHD inkjet printing system and explores the applicability of the soft computing-based methods for this new emerging technology. Soft computing-based approaches have been developed for the first time in this area to model the EHD inkjet process. Five hundred data were produced through the conventional regression analysis to train the neural network-based models. Output droplet diameter was predicted for different combinations of input parameters such as standoff height (SH), applied voltage (AV) and ink flow rate (FR) using the above three approaches, and their performances were analyzed through some randomly created real experimental test cases. All three models gave good prediction accuracy with less than 10% error in the prediction of the droplet diameter. Furthermore, it had been observed that the performance of GA-NN surpasses both the regression- and BPNN-based approaches in most of the test cases. It achieved quite satisfactory average absolute percentage deviation value of 2.51% between the target and predicted output using GA-NN model, which also showed an improvement over the regression or BPNN model.