Multi-Modal Vat Photopolymerization for Microscale Modulation of Scaffold Stiffness Assisted Via Machine Learning

25 Pages Posted: 16 May 2023

See all articles by Wisarut Kiratitanaporn

Wisarut Kiratitanaporn

affiliation not provided to SSRN

Jiaao Guan

affiliation not provided to SSRN

David Barnes Berry

affiliation not provided to SSRN

Alison Lao

affiliation not provided to SSRN

Shaochen Chen

University of California, San Diego (UCSD)

Abstract

The ability to precisely control a scaffold’s microstructure and geometry with light-based 3D printing has been widely demonstrated. However, the modulation of scaffold’s mechanical properties through prescribed printing parameters is still underexplored. This study demonstrates a novel 3D-printing workflow to create a complex, elastomeric scaffold with precision engineered stiffness control by utilizing machine learning. Various printing parameters including the exposure time, light intensity, printing infill, laser pump current, and printing speed were modulated to print poly (glycerol sebacate) acrylate (PGSA) scaffolds with mechanical properties ranging from 49.3 ± 3.3 kPa to 2.78 ± 0.3 MPa. This enables flexibility in spatial stiffness modulation in addition to high resolution scaffold fabrication. Then, a neural network-based machine learning method was trained and validated to optimize printing parameters to yield scaffolds with user-defined stiffness modulation for two different vat photopolymerization methods: a digital light processing (DLP)-based 3D printer was utilized to rapidly fabricate stiffness modulated scaffolds with features on the hundreds of microns scale and a two-photon polymerization (2PP) 3D printer was utilized to print fine structures on the micron scale. A novel 3D-printing workflow was designed to utilize both DLP-based and 2PP 3D printers to create multi-scale scaffolds with precision tuned stiffness control over both gross and fine geometric features. The described workflow can be used to fabricate scaffolds for a variety of tissue engineering applications, specifically for interfacial tissue engineering for which adjacent tissues possess heterogeneous mechanical properties (e.g. muscle-tendon).

Keywords: 3D printing, two-photon polymerization, digital-light-processing, machine learning, stiffness

Suggested Citation

Kiratitanaporn, Wisarut and Guan, Jiaao and Berry, David Barnes and Lao, Alison and Chen, Shaochen, Multi-Modal Vat Photopolymerization for Microscale Modulation of Scaffold Stiffness Assisted Via Machine Learning. Available at SSRN: https://ssrn.com/abstract=4450557 or http://dx.doi.org/10.2139/ssrn.4450557

Wisarut Kiratitanaporn (Contact Author)

affiliation not provided to SSRN ( email )

No Address Available

Jiaao Guan

affiliation not provided to SSRN ( email )

No Address Available

David Barnes Berry

affiliation not provided to SSRN ( email )

No Address Available

Alison Lao

affiliation not provided to SSRN ( email )

No Address Available

Shaochen Chen

University of California, San Diego (UCSD) ( email )

9500 Gilman Drive
La Jolla, CA 92093
United States

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