Effect of hierarchical structure on the shape recovery properties and load-carrying capacity of 4D-printed kirigami-inspired honeycomb

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

Kirigami-inspired honeycomb structures demonstrate outstanding self-expanding capabilities and exceptional mechanical properties. To further enhance the shape recovery properties load-carrying capacity of kirigami-inspired honeycomb, hierarchical structure is introduced by importing porous into cell walls 4D-printed (Structure II), then an innovative I) designed for achieving effect. Additionally, three with different configurations III–V) are also comparing traditional Structure II. The finite element analysis experiments conducted to compare compression deformation behavior energy absorption I–V. It found that I exhibits significantly improved compared stiffness increased 50.43%, performance 65.00%. time has been shortened 27.27% it a better rate. III–V can performances IV best capacity, increase 76.25%. Thus, developed honeycombs have broad application prospects multifunctional applications in future.

Язык: Английский

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Unsupervised transfer learning for monitoring CFRP responses using discrete strains

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 110142 - 110142

Опубликована: Март 1, 2025

Язык: Английский

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From fabrication to prediction: unraveling the tensile strength of Al/SiN composites through machine learning

International Journal on Interactive Design and Manufacturing (IJIDeM), Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

Язык: Английский

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Extraction of kaolin and tribo informative analysis of the Al-kaolin composite through machine learning approaches

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 18, 2025

Язык: Английский

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Machine learning-assisted investigation on nonlinear vibration analysis of bio-inspired auxetic tubes

International Journal of Structural Integrity, Год журнала: 2025, Номер unknown

Опубликована: Апрель 20, 2025

Purpose Auxetic tubular structures with negative Poisson’s ratios have gained significant attention in biomedical applications, particularly vascular and esophageal stents, due to their potential reduce embolism risks. This study aims investigate the nonlinear vibration characteristics of such develop accurate predictive models using machine learning (ML) techniques. Design/methodology/approach The governing equations auxetic tubes are derived Hamilton’s principle von-Kármán’s assumptions, while Malek-Gibson relations determine effective mechanical properties. behavior polylactic acid (PLA) is experimentally analyzed through tensile testing digital image correlation (DIC) additional insights from scanning electron microscopy. solved via Ritz method, vibrational assessed direct displacement control approach. Predictive modeling performed six ML algorithms – CatBoost, decision tree, random forest, gradient boosting extreme (XGBoost) support vector regression (SVR) along an artificial neural network (ANN). Response surface methodology employed optimize effects edge supports, radius cell geometry on behavior. Findings results demonstrate a strong agreement between ML/ANN predictions analytical confirming reliability developed models. analysis reveals that variations significantly influence response structures. optimized configurations enhance structural performance, making these metastructures highly suitable for applications. Originality/value uniquely integrates modeling, experimental ML-based comprehensively assess metastructures. findings provide valuable design next-generation improving performance expanding

Язык: Английский

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Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding

Опубликована: Янв. 22, 2024

This study is a pioneering endeavor to investigate the capabilities of Large Language Models (LLMs) in addressing conceptual questions within domain mechanical engineering with focus on mechanics. Our examination involves manually crafted exam encompassing 126 multiple-choice questions, spanning various aspects mechanics courses, including Fluid Mechanics, Mechanical Vibration, Engineering Statics and Dynamics, Mechanics Materials, Theory Elasticity, Continuum Mechanics. Three LLMs, ChatGPT (GPT-3.5), (GPT-4), Claude (Claude-2.1), were subjected evaluation against faculties students with/without background. The findings reveal GPT-4’s superior performance over other two LLMs human cohorts answering across topics, except for signals potential future improvements GPT models handling symbolic calculations tensor analyses. performances all significantly improved explanations prompted prior direct responses, underscoring crucial role prompt engineering. Interestingly, GPT-3.5 demonstrates prompts covering broader domain, while GPT-4 excels focusing specific subjects. Finally, exhibits notable advancements mitigating input bias, as evidenced by guessing preferences humans. unveils substantial highly knowledgeable assistants both pedagogy scientific research.

Язык: Английский

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基于自适应性粗粒化建模的旋节分解三维膜结构形态设计和力学性能调控

Acta Mechanica Sinica, Год журнала: 2024, Номер 40(8)

Опубликована: Июнь 25, 2024

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Deep Learning-Based Semantic Segmentation for Morphological Fractography

Опубликована: Янв. 1, 2024

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Язык: Английский

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A Gan-Based Machine Learning Surrogate Model for the Stepwise Prediction of Full-Field Mechanical Behavior of Kirigami-Inspired Metamaterials

Опубликована: Янв. 1, 2024

Язык: Английский

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Kinematotropic linkage-based cellular metamaterials with bifurcation motion: Construction and analysis

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 279, С. 109487 - 109487

Опубликована: Окт. 1, 2024

Язык: Английский

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