Droplet volume prediction methods in electrohydrodynamic jet printing based on multi-source data fusion

Abstract

Electrohydrodynamic jet printing technology has garnered significant interest in additive manufacturing due to its advantages in higher resolution printing and greater viscosity material applicability. Nonetheless, the challenges associated with femtoliter-level droplets and the micrometer-scale printing space have rendered traditional optical observation methods for volume calculation impractical for integration into the printing systems. Obtaining the droplet volume accurately has become a formidable challenge in electrohydrodynamic printing. While existing research utilizes machine learning for the end-to-end prediction of process parameters to droplet volume, traditional prediction methods cannot achieve online prediction due to the complex spray states characteristic of electrohydrodynamic jet printing and pose integration difficulties within printing systems. This paper introduces a multi-source data fusion model suitable for electrohydrodynamic printing droplet volume prediction. The model employs the VGG network to extract features from Taylor cone images in the jetting state and synchronously fuse these with process parameter features. The fused features are then correlated with droplet volume labels through the MLP network for comprehensive model training. The performance of the proposed model has improved by 10% in prediction accuracy compared to single modal data. We integrated the prediction method into an electrohydrodynamic jet printing system and experimented with printing pixel-pit substrates. The results indicate that the prediction accuracy of the volume prediction system is over 92%. The printing efficiency has improved approximately 3 times compared to the traditional method, significantly enhancing overall performance.