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

Abstract

Abstract 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.