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Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials.

Jisoo Nam1, Boxin Chen1,2, Miso Kim1

  • 1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.

Advanced Materials (Deerfield Beach, Fla.)
|July 16, 2025
PubMed
Summary

This study introduces a new 3D printing method using dynamic bond-controlled resins and machine learning to create mechanically robust gradient materials. The approach enhances material properties and structural design for improved performance in various applications.

Keywords:
3D printingdynamic bondgradient structuregrayscale digital light processingmachine learningmulti‐objective optimizationpolyurethane acrylate

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Area of Science:

  • Materials Science
  • Additive Manufacturing
  • Polymer Chemistry

Background:

  • Grayscale digital light processing (g-DLP) enables material property gradients for advanced 3D printing.
  • Current limitations include restricted material property ranges and insufficient structural optimization for complex designs.
  • Mechanical robustness of g-DLP printed parts remains a key challenge.

Purpose of the Study:

  • To develop a synergistic g-DLP strategy for mechanically robust gradient materials.
  • To integrate dynamic bond-controlled resins with machine learning-based multi-objective optimization.
  • To overcome limitations in tailorable properties and structural design for g-DLP.

Main Methods:

  • Synthesis of dynamic bond-controlled polyurethane acrylate (PUA) resin system.
  • Development of a multi-objective Bayesian optimization framework for structural design.
  • Application of the synergistic strategy to 3D and arbitrary geometries.

Main Results:

  • Achieved a wide range of elastic modulus (8.3 MPa to 1.2 GPa) with superior damping performance.
  • Reduced strain concentrations by up to 83% in gradient structures.
  • Demonstrated delayed crack initiation and enhanced mechanical robustness.

Conclusions:

  • A versatile platform for creating mechanically robust g-DLP printed components is established.
  • The developed PUA resin and optimization framework enable tailored material properties and structural integrity.
  • Potential applications include biomimetic artificial cartilage and automotive energy-absorbing structures.