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Multimodal Three-Dimensional Printing for Micro-Modulation of Scaffold Stiffness Through Machine Learning.

Wisarut Kiratitanaporn1, Jiaao Guan2, David B Berry3

  • 1Department of Bioengineering, University of California San Diego, La Jolla, California, USA.

Tissue Engineering. Part A
|September 25, 2023
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Summary

This study introduces a new 3D printing method using machine learning to precisely control scaffold stiffness. This innovation allows for custom-designed elastomeric scaffolds for tissue engineering applications.

Keywords:
3D printingdigital light processingmachine learningstiffnesstwo-photon polymerization

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Area of Science:

  • Biomaterials Engineering
  • Tissue Engineering
  • Additive Manufacturing

Background:

  • Light-based 3D printing offers precise control over scaffold microstructure and geometry.
  • Modulating mechanical properties of 3D printed scaffolds via printing parameters remains underexplored.

Purpose of the Study:

  • To develop a novel 3D printing workflow for creating elastomeric scaffolds with engineered stiffness control using machine learning.
  • To enable spatial stiffness modulation in addition to high-resolution scaffold fabrication.

Main Methods:

  • Utilized machine learning to optimize printing parameters (exposure time, light intensity, infill, laser pump current, printing speed) for poly (glycerol sebacate) acrylate (PGSA) scaffolds.
  • Employed digital light processing (DLP) and two-photon polymerization (2PP) 3D printing methods to fabricate scaffolds with features ranging from submicron to hundreds of microns.
  • Developed and validated a neural network model for user-defined stiffness modulation.

Main Results:

  • Achieved a wide range of mechanical properties in PGSA scaffolds, from 49.3 ± 3.3 kPa to 2.8 ± 0.3 MPa.
  • Demonstrated successful spatial stiffness modulation in scaffolds fabricated using both DLP and 2PP techniques.
  • Created multiscale scaffolds with precise stiffness control over both gross and fine geometric features.

Conclusions:

  • The novel 3D printing workflow enables precision-engineered stiffness control in complex, elastomeric scaffolds.
  • This approach is suitable for various tissue engineering applications, particularly interfacial tissues with heterogeneous mechanical properties.
  • The integration of machine learning with multiscale 3D printing offers a powerful tool for advanced scaffold design.