Multi-Modal Vat Photopolymerization for Microscale Modulation of Scaffold Stiffness Assisted Via Machine Learning
25 Pages Posted: 16 May 2023
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
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