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Updated: Jun 8, 2026

High-resolution Patterning Using Two Modes of Electrohydrodynamic Jet: Drop on Demand and Near-field Electrospinning
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Intelligent Optimization Design Framework for Alternating Current Pulse Modulation Electrohydrodynamic Printing

Chang Liu1, Yiwen Feng1, Dazhi Wang1,2,3,4

  • 1Key Laboratory for Micro/Nano Technology and System of Liaoning Province, Dalian University of Technology, Dalian, 116024, China.

Small (Weinheim an Der Bergstrasse, Germany)
|January 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent framework for alternating current pulse modulation electrohydrodynamic (AC-EHD) printing to precisely control microstructure size on insulating substrates. The optimized process parameters significantly improve printing accuracy and reduce waste.

Keywords:
artificial neural networkelectrohydrodynamic printingelk herd optimizerinsulating substratesintelligent optimization design

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Area of Science:

  • Materials Science and Engineering
  • Additive Manufacturing
  • Microfabrication

Background:

  • Precise control over printed microstructure size is crucial for applications utilizing insulating substrates.
  • Traditional methods for adjusting alternating current pulse modulation electrohydrodynamic (AC-EHD) printing parameters are often time-consuming and wasteful.
  • Developing an intelligent optimization framework is necessary to enhance the usability and efficiency of AC-EHD printing.

Purpose of the Study:

  • To propose and validate an integrated intelligent optimization design framework for AC-EHD printing.
  • To achieve efficient and accurate size tuning of printed microstructures on insulating substrates.
  • To minimize time and material wastage during the parameter adjustment process.

Main Methods:

  • Developed a two-stage framework: prediction model construction and process parameter acquisition.
  • Employed the Elk Herd Optimizer (EHO) combined with an Artificial Neural Network (ANN) for predicting printed droplet size based on process parameters.
  • Utilized the EHO algorithm with prediction error as fitness to intelligently determine optimal AC-EHD printing parameters.

Main Results:

  • The EHO-ANN model demonstrated high accuracy and robustness in predicting printed droplet sizes across various datasets.
  • The intelligent optimization framework successfully aligned actual printed droplet sizes with desired values in experimental validation.
  • The proposed framework significantly reduced the trial-and-error associated with parameter tuning for AC-EHD printing on insulating substrates.

Conclusions:

  • The integrated intelligent optimization framework offers an effective solution for precise size control in AC-EHD printing.
  • This approach enhances the practical usability of AC-EHD printing technology, particularly on insulating materials.
  • The study successfully mitigates wastage and improves efficiency in microfabrication processes.