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Droplet jetting velocity is one of the most important factors affecting the quality of piezoelectric ejection printing. Due to the nonlinear relationship between the two, predicting the droplet jetting velocity by conventional methods is very time-consuming and impractical. We propose a genetic algorithm (GA) combined with a back propagation neural network (BPNN) to predict the droplet jetting velocity. The network topology and the values of each parameter of the model are designed and validated to elucidate the relationship between the input parameters of the piezoelectric ejection system and the studied droplet ejection velocity. The GA-BP model was trained, tested, and tuned using a database consisting of data generated from finite element calculations, and the results showed an error of 3.34% between the predicted and finite element simulated droplet ejection velocities, which demonstrates the reliability and robustness of the method.

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