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

© 2025 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 37(2025), 42 vom: 29. Okt., Seite e2504075
1. Verfasser: Nam, Jisoo (VerfasserIn)
Weitere Verfasser: Chen, Boxin, Kim, Miso
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2025
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article 3D printing dynamic bond gradient structure grayscale digital light processing machine learning multi‐objective optimization polyurethane acrylate
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520 |a 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 
650 4 |a Journal Article 
650 4 |a 3D printing 
650 4 |a dynamic bond 
650 4 |a gradient structure 
650 4 |a grayscale digital light processing 
650 4 |a machine learning 
650 4 |a multi‐objective optimization 
650 4 |a polyurethane acrylate 
700 1 |a Chen, Boxin  |e verfasserin  |4 aut 
700 1 |a Kim, Miso  |e verfasserin  |4 aut 
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