A droplet state prediction method for inkjet printing under small sample conditions based on the two-stage TrAdaBoost.R2 algorithm

Inkjet printing is regarded as a new generation of green intelligent manufacturing technology. However, precise control of the state of droplets in inkjet printing is critical and costly. In this study, a voltage-driven signal-based ink droplet state prediction model suitable for a small sample data environment is proposed. Seven public ink datasets and self-collected experimental data are grouped together, and the decision tree, AdaBoost.R2, and two-stage TrAdaBoost.R2 algorithms are used, respectively. The two-stage TrAdaBoost.R2 model shows excellent prediction performance in the case of insufficient data by transferring knowledge of different inks. In particular, it achieves mean square errors of 0.00219 for droplet volume predictions and 0.0645 for velocity predictions. Similarly, the mean absolute percentage errors are 0.91% for droplet volume and 1.37% for velocity. Furthermore, the two-stage TrAdaBoost.R2 model demonstrates strong generalization ability and adaptability to maintain high prediction accuracy under different conditions. The results indicate that the two-stage TrAdaBoost.R2 effectively mitigates the prediction performance issues caused by data scarcity, paving the way for technological advancements in the field of inkjet printing.