Machine Learning‐Driven Grayscale Digital Light Processing for Mechanically Robust 3D‐Printed Gradient Materials
Year: 2025
Type: article
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.
Source: Advanced Materials
Authors: Jisoo Nam, Boxin Chen, Miso Kim
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $6,070
Awards: G104195973, G1042186330, G5646165541
OpenAlex ID: W4412487119