Machine learning based data driven inkjet printed electronics: jetting prediction for novel inks

Abstract

Abstract Machine learning (ML) as a predictive methodology can potentially reduce the configuration cost and workload of inkjet printing. Inkjet printing has many advantages for additive manufacturing and printed electronics including low cost, scalability, non-contact printing and on-demand customization. Inkjet generates droplets with a piezoelectric dispenser controlled through frequency, voltage pulse and timing parameters. A major challenge is the design of jettable inks and the rapid optimization of stable jetting conditions whilst preventing common problems (no ejection, perturbation, satellite drop, multiple drops, drop breaking, nozzle clogging). Material consuming trial and error experiments are replaced here with a ML based jetting window. A dataset of machine and material properties is created from literature and experimental data. After exploratory data analysis and feature identification, various (linear and non-linear) regression models are compared in detail. The models are trained on 80% of the data and root mean square error (RMSE) is calculated on 20% test data. Simple polynomial relationships between the input and output features yield coarse prediction. Instead, small ensembles of decision trees (DTs) (boosted DTs and random forests) have improved predictive power for drop velocity and radius with RMSE of 0.39 m s−1 and 2.21 µm respectively. The mean absolute percentage error is 3.87%. The models are validated with experimentally collected data for a novel ink where no data points with this ink were included in the training set. Additionally, several classification algorithms are utilized to categorize ink and printer parameters by jetting regime (‘single drop’, ‘multiple drops’, ‘no ejection’). Categorization and regression models are combined to improve overall model prediction. This article demonstrates that ML can be used to predict ink jetting behavior from 11 different ink and printing parameters. Different algorithms are analyzed and the optimal combination of algorithms is identified. It is shown that experimental and literature data can be combined and an initial dataset is created that other reserachers can build on in the future. ML enables efficient material and printing parameter selection speeding up the development of novel ink materials for printed electronics by eliminating jetting experiments that are money, time and material intensive.