Electrohydrodynamic printing process monitoring by microscopic image identification

Area of Science:

• Biomaterials Engineering
• Nanotechnology
• Process Control

Background:

• Electrohydrodynamic printing (EHDP) enables precise fabrication of micro-/nano-fiber scaffolds from biopolymer solutions.
• The influence of EHDP jet characteristics and process parameters on fiber patterns requires further investigation.
• Establishing a relationship between Taylor cone stability and scaffold fabrication quality is crucial.

Purpose of the Study:

• To investigate the relationship between Taylor cone characteristics and EHDP scaffold fabrication stability.
• To analyze the impact of key process parameters on EHDP cone shapes and jet features.
• To develop a method for real-time monitoring and control of EHDP for improved scaffold quality.

Main Methods:

• Digital microscopic imaging was employed to monitor Taylor cones during EHDP.
• Image processing algorithms extracted features like centroid and jet diameter from cone images.
• A convolutional neural network (CNN) was developed to classify EHDP cone modes.

Main Results:

• EHDP cone modes and extracted features (centroid, jet diameter) are significantly influenced by nozzle-substrate distance, applied voltage, and stage speed.
• The study successfully correlated process parameters with observable Taylor cone characteristics.
• A CNN model demonstrated effective classification of EHDP cone modes.

Conclusions:

• Taylor cone stability is a critical indicator for successful EHDP scaffold fabrication.
• Process parameter optimization based on real-time cone monitoring can enhance printing quality.
• The developed methods provide a foundation for a control algorithm to regulate EHDP parameters for effective scaffold fabrication.