Rapidly and precisely fabricating solid microneedle by integrating vat photopolimerization and machine-learning (VP-ML)

Microneedles (μNs) have emerged as a transformative alternative to conventional hypodermic needles, offering notable benefits such as reduced hazardous waste, lower risk of injury, improved safety, and greater acceptance among individuals with needle phobia. In this study, we introduce an efficient...

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Bibliographic Details
Published in:Journal of manufacturing processes Vol. 141; pp. 181 - 192
Main Authors: Lestari, Dwi M., Chen, Pin-Chuan, Li, Jr-Shin, Shen, Wan-Yun
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.05.2025
Subjects:
3D printing
Machine learning
Microneedles
Vat photopolymerization
Vat photopolymerization
Microneedles
3D printing
Machine learning
ISSN:1526-6125
Online Access:Get full text
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Summary:Microneedles (μNs) have emerged as a transformative alternative to conventional hypodermic needles, offering notable benefits such as reduced hazardous waste, lower risk of injury, improved safety, and greater acceptance among individuals with needle phobia. In this study, we introduce an efficient fabrication method combining Vat Photopolymerization and Machine Learning (VP-ML) for μNs production. The method begins with creating a dataset, which is used to develop ML model. This model predicts the printing parameters needed for VP machine based on the desired dimensions. The VP machine works by selectively curing a liquid photopolymer resin layer by layer with a digital mask, enabling precise μNs fabrication. This cutting-edge approach surpasses traditional methods by eliminating the need for molds and complex multi-step processes, enabling faster, customizable fabrication. VP-ML significantly reduces material waste and production costs while yielding sharper needle tips. By integrating Bayesian regularization backpropagation algorithm with ten hidden layers (BR10), the process optimizes printing parameters to enhance production speed and ensure high-quality outcomes. Experimental results demonstrate that VP-ML effectively produces μNs with base diameters greater than 150 μm and heights exceeding 500 μm, achieving a Mean Absolute Percentage Error (MAPE) of less than 10 % among 32 cases demonstrated in this study. Additionally, penetration tests demonstrated that the μNs fabricated using VP-ML were able to penetrate up to 90 % of its total height. These results highlight VP-ML as a green-manufacturing and cost-effective solution for μNs fabrication, while maintaining remarkable improvements in precision, manufacturing efficiency, flexibility, and scalability in the field. •Vat Photopolimerization integrated Machine-Learning (VP-ML) for successfully fabricating μNs.•The ML model can create digital mask for Vat Photopolimerization with high flexibility.•This design-to-prototyping system can fabricate precise μNs within minutes.•The μNs is successfully demonstrated on the penetration test with depth up to 90 % of its total height.
ISSN:1526-6125
DOI:10.1016/j.jmapro.2025.02.042