Predictive modeling of 3D reconstruction trajectories for potential programmable materials
IntroductionShape-morphing programmable materials, capable of dynamically adjusting their properties in response to external stimuli, hold significant potential in adaptive design and smart manufacturing. However, accurately predicting their 3D reconstruction trajectories remains a challenge due to...
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| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Materials |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2025.1558190/full |
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| author | Yechun Jin Jie Li Qi Yu Panghua Tian |
| author_facet | Yechun Jin Jie Li Qi Yu Panghua Tian |
| author_sort | Yechun Jin |
| collection | DOAJ |
| description | IntroductionShape-morphing programmable materials, capable of dynamically adjusting their properties in response to external stimuli, hold significant potential in adaptive design and smart manufacturing. However, accurately predicting their 3D reconstruction trajectories remains a challenge due to the complex interactions between material behavior and environmental factors.MethodsTo address this, we propose a computational framework, the Dynamic Morphology Engine (DME), designed to enhance predictive modeling of shape-morphing programmable materials by integrating advanced control mechanisms and optimization strategies. The DME framework consists of three key components: Stimulus Mapping, Property Optimization, and Structural Adaptation, enabling efficient trajectory prediction in dynamic environments. Additionally, we introduce the Stimulus-Informed Design Paradigm (SIDP), which leverages data-driven modeling to refine the interplay between external stimuli and material responses.Results and discussionExperimental results demonstrate that our approach improves robustness, scalability, and computational efficiency, offering a promising tool for modeling shape-morphing programmable materials in applications such as soft robotics, reconfigurable structures, and intelligent materials. |
| format | Article |
| id | doaj-art-a454f727c301459383c0c7ba75dd67a2 |
| institution | OA Journals |
| issn | 2296-8016 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj-art-a454f727c301459383c0c7ba75dd67a22025-08-20T02:13:14ZengFrontiers Media S.A.Frontiers in Materials2296-80162025-04-011210.3389/fmats.2025.15581901558190Predictive modeling of 3D reconstruction trajectories for potential programmable materialsYechun Jin0Jie Li1Qi Yu2Panghua Tian3College of Physics and Information Engineering, Cangzhou Normal University, Cangzhou, Hebei, ChinaCollege of Mechanical and Electrical Engineering, Cangzhou Normal University, Cangzhou, Hebei, ChinaCollege of Art, Shaanxi University of Technology, Hanzhong, Shaanxi, ChinaCollege of Art and Design, Shaanxi Institute of Technology, Xi’an, Shaanxi, ChinaIntroductionShape-morphing programmable materials, capable of dynamically adjusting their properties in response to external stimuli, hold significant potential in adaptive design and smart manufacturing. However, accurately predicting their 3D reconstruction trajectories remains a challenge due to the complex interactions between material behavior and environmental factors.MethodsTo address this, we propose a computational framework, the Dynamic Morphology Engine (DME), designed to enhance predictive modeling of shape-morphing programmable materials by integrating advanced control mechanisms and optimization strategies. The DME framework consists of three key components: Stimulus Mapping, Property Optimization, and Structural Adaptation, enabling efficient trajectory prediction in dynamic environments. Additionally, we introduce the Stimulus-Informed Design Paradigm (SIDP), which leverages data-driven modeling to refine the interplay between external stimuli and material responses.Results and discussionExperimental results demonstrate that our approach improves robustness, scalability, and computational efficiency, offering a promising tool for modeling shape-morphing programmable materials in applications such as soft robotics, reconfigurable structures, and intelligent materials.https://www.frontiersin.org/articles/10.3389/fmats.2025.1558190/fullprogrammable materialspredictive modelingdynamic morphology enginestimulus-informed design paradigm3D reconstruction |
| spellingShingle | Yechun Jin Jie Li Qi Yu Panghua Tian Predictive modeling of 3D reconstruction trajectories for potential programmable materials Frontiers in Materials programmable materials predictive modeling dynamic morphology engine stimulus-informed design paradigm 3D reconstruction |
| title | Predictive modeling of 3D reconstruction trajectories for potential programmable materials |
| title_full | Predictive modeling of 3D reconstruction trajectories for potential programmable materials |
| title_fullStr | Predictive modeling of 3D reconstruction trajectories for potential programmable materials |
| title_full_unstemmed | Predictive modeling of 3D reconstruction trajectories for potential programmable materials |
| title_short | Predictive modeling of 3D reconstruction trajectories for potential programmable materials |
| title_sort | predictive modeling of 3d reconstruction trajectories for potential programmable materials |
| topic | programmable materials predictive modeling dynamic morphology engine stimulus-informed design paradigm 3D reconstruction |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2025.1558190/full |
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