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Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials
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DOI：10.1002/adma.202504075.png)
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Abstract
En 中文
Grayscale digital light processing (g-DLP) is gaining recognition for its capability to create material property gradients within a single resin system, enabling programmable mechanical responses, enhanced shape accuracy, and improved toughness. However, research on the mechanical robustness of g-DLP is constrained by the limited range of tailorable properties in photocurable resins and insufficient exploration of structural optimization for complex geometries. This study presents a synergistic g-DLP strategy that integrates the synthesis of dynamic bond-controlled polyurethane acrylate (PUA) with a machine learning-based multi-objective optimization, enabling mechanically robust 3D-printed gradient materials. A PUA-based resin system is developed that expands the achievable elastic modulus from 8.3 MPa to 1.2 GPa, while maintaining superior damping performance, making it suitable for diverse applications. Furthermore, a multi-objective Bayesian optimization framework is constructed to efficiently identify optimal gradient structures, reducing strain concentrations and controlling effective stiffness. This approach is applicable to various 3D and arbitrary geometries, achieving a significant strain concentration reduction of up to 83% and demonstrating delayed crack initiation. By combining the developed material with this optimization framework, a versatile platform is established for creating mechanically robust g-DLP printed components, applicable in areas ranging from biomimetic artificial cartilage to automotive energy-absorbing structures.
Keywords:
3D printing
dynamic bond
gradient structure
grayscale digital light processing
machine learning
multi-objective optimization
polyurethane acrylate
Journal
IF:
26.8
Papers:
3.4W
Citations:
46.0W
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