Intelligent Optimization Design Framework for Alternating Current Pulse Modulation Electrohydrodynamic Printing Process Parameters

Abstract To achieve efficient size tuning of printed microstructures on insulating substrates, an integrated process parameter intelligent optimization design framework for alternating current pulse modulation electrohydrodynamic (AC‐EHD) printing is proposed for the first time. The framework is comprised of two stages: the construction of a prediction model and the acquisition of process parameters. The first stage employs the elk herd optimizer(EHO)‐artificial neural network(ANN) to establish a mapping relationship between printing process parameters and the size of deposited droplets. The analysis of the prediction performance of the EHO‐ANN model across various datasets reveals that the model exhibits commendable accuracy and robustness in predicting printed droplet size. In the second stage, the process parameters of AC‐EHD printing are intelligently determined by utilizing the error between the model output and the desired droplet size as the fitness value for EHO. By comparing three sets of experimental cases with varying droplet sizes, it is observed that the actual printed droplet sizes closely align with the desired values, thus validating the effectiveness of this framework. The framework proposed in this paper mitigates the time and material wastage caused by adjusting AC‐EHD printing process parameters on insulating substrates, thereby significantly enhancing the usability of the technology.