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250716s2025 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202504075
|2 doi
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|a pubmed25n1608.xml
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Nam, Jisoo
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials
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|c 2025
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 23.10.2025
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2025 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.
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|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
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|a Journal Article
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|a 3D printing
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4 |
|a dynamic bond
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4 |
|a gradient structure
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| 650 |
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4 |
|a grayscale digital light processing
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| 650 |
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4 |
|a machine learning
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| 650 |
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4 |
|a multi‐objective optimization
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| 650 |
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4 |
|a polyurethane acrylate
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| 700 |
1 |
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|a Chen, Boxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kim, Miso
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 37(2025), 42 vom: 29. Okt., Seite e2504075
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnas
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| 773 |
1 |
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|g volume:37
|g year:2025
|g number:42
|g day:29
|g month:10
|g pages:e2504075
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|u http://dx.doi.org/10.1002/adma.202504075
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