Cited by Investigating Deep Learning-Based Stress Prediction in Particulate Polymer Composites Using Multiple Quality Measures

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

Guttikonda Manohar,

Venkateswara Reddy,

Vaishali Vaishali

и другие.

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

Опубликована: Март 17, 2025

Язык: Английский

Процитировано

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

V. S. S. Venkatesh, Guttikonda Manohar, Pandu R. Vundavilli

и другие.

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

Опубликована: Апрель 18, 2025

Язык: Английский

Процитировано

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

Fatemeh Ghasemi,

Arshia Salari,

Erfan Salari

и другие.

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

Процитировано

A generalizable framework of solution-guided machine learning with application to nanoindentation of free-standing thin films DOI

Ruijin Wang, Tianquan Ying, Chen Yang

и другие.

Thin-Walled Structures, Год журнала: 2024, Номер 200, С. 111984 - 111984

Опубликована: Май 7, 2024

Язык: Английский

Процитировано

Inverse design of programmable shape-morphing kirigami structures DOI

Xiaoyuan Ying,

Dilum Fernando, Marcelo A. Dias

и другие.

International Journal of Mechanical Sciences, Год журнала: 2024, Номер unknown, С. 109840 - 109840

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

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

Jixin Hou

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

Процитировано

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 DOI

Yujie Xiang, Jie Tian, Keke Tang

и другие.

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

Опубликована: Июнь 25, 2024

Процитировано

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

Yujie Xiang, Jixin Hou, Xianyan Chen

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

Deep Learning-Based Semantic Segmentation for Morphological Fractography DOI

Keke Tang, Peng Zhang,

Yindun Zhao

и другие.

Опубликована: Янв. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Язык: Английский

Процитировано

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

Yongquan Li,

Haojie Huang,

Yiwen Liu

и другие.

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

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано