[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125042-en":3,"doc-seo-125042-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125042,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Parametric Analysis of Critical Buckling in Composite Laminate Structures under Mechanical and Thermal Loads - A Finite Element and Machine Learning Approach","This research investigates the buckling strength of thin-walled composite structures containing holes of various shapes, coupled with different laminate configurations and composite materials. A parametric study optimizes and identifies the most suitable combinations of structural and material parameters to maintain resilience under both mechanical and thermal loading. Finite element modeling designs a C-section thin-walled composite structure, while key parameters—including spacing ratio, opening ratio, hole shape, fiber orientation, and laminate sequence—are systematically varied. Machine learning models trained on simulation data, using methods such as linear regression, lasso regression, decision trees, random forests, and gradient boosting, predict the optimal critical buckling load and show load variations consistent with finite element results.","9/25/24 , 9:11 AM Scopus-Print Document  \nDocuments  \nAhmed, O.S.a b , Ali, J.S. M. b , Aabid, A.a , Hrairi, M. b , Yatim, N. M. b  \nParametric Analysis of Critical Buckling in Composite Laminate Structures under Mechanical and Thermal Loads: A Finite Element and Machine Learning Approach  \n(2024) Materials, 17 (17), [art. no. 4367](art. no. 4367) , .  \nDOI: 10.3390/ma17174367  \na Department of Engineering Management, College of Engineering, Prince Sultan University, P.O. Box 66833, Riyadh, 11586, Saudi Arabia  \nb Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, Kuala Lumpur50728, Malaysia  \nAbstract  \nThis research focuses on investigating the buckling strength of thin-walled composite structures featuring various shapes of holes, laminates, and composite materials. A parametric study is conducted to optimize and identify the most suitable combination of material and structural parameters, ensuring the resilience of structure under both mechanical and thermal loads. Initially, a numerical approach employing the finite element method is used to design the C-section thin-walled composite structure. Later, various structural and material parameters like spacing ratio, opening ratio, hole shape, fiber orientation, and laminate sequence are systematically varied. Subsequently, simulation data from numerous cases are utilized to identify the best parameter combination using machine learning algorithms. Various ML techniques such as linear regression, lasso regression, decision tree, random forest, and gradient boosting are employed to assess their accuracy in comparison with finite element results. As a result, the simulation model showcases the variation in critical buckling load when altering the structural and material properties. Additionally, the machine learning models successfully predict the optimal critical buckling load under mechanical and thermal loading conditions. In summary, this paper delves into the study of the stability of C-section thin-walled composite structures with holes under mechanical and thermal loading conditions using finite element analysis and machine learning studies. © 2024 by the authors.  \nAuthor Keywords  \nbuckling analysis; C-section channel; composite laminates; FE analysis; machine learning  \nIndex Keywords  \nBuckling behavior, Buckling modes, Laminated composites, Local buckling, Thin walled structures; Buckling analysis, Csection channel, C-sections, Composite laminate, Composites structures, F. E. analysis, FE analysis, Machinelearning, Mechanical, Thin-walled composites; Buckling loads  \nReferences  \n Khan, T., Alshahrani, H. , Abd-Elaziem, W. , Umarfarooq, M.A. , Sebaey, T.A.  \nQuasi-Static Axial Crushing of Multi-Tubular Foam-Filled Carbon Fiber Reinforced Composite Structures  \n(2023) Polym. Compos , 44, pp. 7843-7854.  \n Ahmed, O.S. , Aabid, A. , Syed, J. , Ali, M. , Hrairi, M.  \nProgresses and Challenges of Composite Laminates in Thin-Walled Structures: A Systematic Review  \n(2023) ACS Omega , 8, pp. 30824-30837.  \n37663505  \n Junaedi, H. , Khan, T. , Sebaey, T.A.  \nCharacteristics of Carbon-Fiber-Reinforced Polymer Face Sheet and Glass-FiberReinforced Rigid Polyurethane Foam  \n(2023) Materials, 16.  \n37512375  \n Basha, M. , Wagih, A. , Khan, T. , Lubineau, G. , Sebaey, T.A.  \nOn the Benefit of Thin Plies on Flexural Response of CFRP Composites Aged at Elevated Temperature  \n(2023) Compos. Part AAppl. Sci. Manuf, 166, p. 107393.  \n[https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&src=s&stateKey=OFD_1840674480&eid=2-s2.0-85203618322&sort=&clickedL](https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&src=s&stateKey=OFD_1840674480&eid=2-s2.0-85203618322&sort=&clickedL)… 1/5  \n9/25/24 , 9:11 AM Scopus-Print Document  \n De Biagi, V. , Chiaia, B. , Marano, G.C. , Fiore, A. , Greco, R. , Sardone, L. , Cucuzza, R. , Lagaros, N. D.  \nSeries Solution of Beams with Variable Cross-Section  \n(2020) ","cbCaimVves3IJOBn","https://ap.wps.com/l/cbCaimVves3IJOBn","pdf",183541,1,5,"English","en",105,"# Abstract\n## Parametric Study Design\n## Finite Element Modeling\n## Machine Learning-Based Prediction\n## Evaluation Against Finite Element Results","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study analyzes how critical buckling strength changes for thin-walled composite C-section structures with holes under mechanical and thermal loads.\"},{\"question\":\"Which parameters are varied in the parametric analysis?\",\"answer\":\"Spacing ratio, opening ratio, hole shape, fiber orientation, and laminate sequence are systematically varied to find optimal combinations.\"},{\"question\":\"How are machine learning models used in the paper?\",\"answer\":\"Simulation data from many cases train machine learning models (e.g., linear regression, lasso, decision tree, random forest, gradient boosting) to predict the optimal critical buckling load under combined loading conditions.\"}]","Parametric Analysis of Critical Buckling in Composite Laminate Structures under Mechanical and Thermal Loads - 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