[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123071-en":3,"doc-seo-123071-105":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},123071,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Composite Material Design for Optimized Fracture Toughness Using Machine Learning","This paper investigates the optimization of 2D and 3D composite structures using machine learning techniques, targeting fracture toughness and crack propagation in the Double Cantilever Beam (DCB) test. By analyzing the coupling between microstructural arrangement and macroscopic composite properties, the study positions ML as a way to accelerate design optimization beyond traditional finite element analysis. Four cases are examined to evaluate crack growth and fracture toughness in both 2D and 3D composite models. Results show rapid and accurate exploration of large design spaces, with reliable mechanical-behavior prediction using limited training data, supporting broader composite design and optimization workflows.","Composite Material Design for Optimized Fracture Toughness Using Machine Learning  \nMohammad Naqizadeh Jahromi 1,*, Mohammad Ravandi 2  \n1Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, USA  \n2Aerostructures Innovation Research Hub (AIR Hub), Swinburne University of Technology, Melbourne 3122, VIC, Australia  \n*[Corresponding author: ](Corresponding author: mohammad.naqizadehjahromi@ucf.edu)[mohammad.naqizadehjahromi@ucf.edu](Corresponding author: mohammad.naqizadehjahromi@ucf.edu)  \nComposite Material Design for Optimized Fracture Toughness Using Machine Learning  \nAbstract:  \nThis paper investigates the optimization of 2D and 3D composite structures using machine learning (ML) techniques, focusing on fracture toughness and crack propagation in the Double Cantilever Beam (DCB) test. By exploring the intricate relationship between microstructural arrangements and macroscopic properties of composites, the study demonstrates the potential of ML as a powerful tool to expedite the design optimization process, offering notable advantages over traditional finite element analysis. The research encompasses four distinct cases, examining crack propagation and fracture toughness in both 2D and 3D composite models. Through the application of ML algorithms, the study showcases the capability for rapid and accurate exploration of vast design spaces in composite materials. The findings highlight the efficiency of ML in predicting mechanical behaviors with limited training data, paving the way for broader applications in composite design and optimization. This work contributes to advancing the understanding of ML's role in enhancing the efficiency of composite material design processes.  \nKeywords: Composite materials, Fracture toughness, Crack propagation, Machine learning, Design optimization, Material design  \n1. Introduction  \nIn modern mechanical engineering, the quest for favorable material qualities with flexible functionalities is paramount. The microstructural arrangement of materials has proven to be a significant consideration, playing a substantial role in defining the macro features of composites [1–3] . Composites, typically composed of two or more essentially distinct materials, exhibit thoroughly different large-scale features when contributing materials with various configurations are substituted [4] . The laminates of composites, formed by combining different fibers and matrices, significantly contribute to their behaviors under various loading conditions [5,6] . Traditional methods of composite manufacturing, constrained by the complexity of the gluing stage, are time-consuming. This limitation arises from the necessity to mount distinct plies on each other, with resin placed among prefabricated plies [7] . However, additive manufacturing, particularly 3D printing, has emerged as a promising tool, enabling the synthesis of composites with varying materials and features in 3D space, overcoming the hindrance of producing composites with diverse design complexities and numerous possible combinations [8] .  \nDue to their high throughput and diverse mechanical behaviors, composites find wide application in various industrial areas. Achieving an optimized model opens the door for enhanced applicability across industries. Gu et al. dedicated efforts to investigate 2D checkerboard composite design, identifying tougher and stronger configurations [9] . In another study, they explored biomimicry in a hierarchical 2D composite, aiming to eliminate inferior configurations in terms of toughness and strength. The high-performing microstructures resulting from their research were evaluated via additive manufacturing, showcasing potential configurations among the spatial possibilities [10] . Liu conducted a survey analyzing delamination growth in a laminated composite under compression, accompanied by buckling, using the finite element method (FEM) . The study also  \ncalculated the energy releas","cbCaihnASsnxht5k","https://ap.wps.com/l/cbCaihnASsnxht5k","pdf",5331623,1,36,"English","en",105,"# Introduction\n## Composite microstructure and macroscopic properties\n## Manufacturing approaches and design complexity\n## Applications and prior composite toughness research\n## Crack behavior modeling methods\n## Interlaminar fracture toughness and failure mechanisms","[{\"question\":\"What problem does the paper focus on?\",\"answer\":\"The paper focuses on optimizing composite material designs for improved fracture toughness and crack propagation behavior in the DCB test using machine learning.\"},{\"question\":\"How does the study connect microstructure to macroscopic composite properties?\",\"answer\":\"It examines how microstructural arrangements influence macroscopic mechanical properties, then uses machine learning to capture and exploit this relationship for optimization.\"},{\"question\":\"What advantages does machine learning provide over traditional finite element analysis?\",\"answer\":\"Machine learning enables faster and more accurate exploration of large composite design spaces and can predict mechanical behavior effectively even with limited training data.\"}]","Composite Material Design for Optimized Fracture Toughness Using Machine Learning | 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problem does the paper focus on?","Question",{"text":75,"@type":76},"The paper focuses on optimizing composite material designs for improved fracture toughness and crack propagation behavior in the DCB test using machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study connect microstructure to macroscopic composite properties?",{"text":80,"@type":76},"It examines how microstructural arrangements influence macroscopic mechanical properties, then uses machine learning to capture and exploit this relationship for optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"What advantages does machine learning provide over traditional finite element analysis?",{"text":84,"@type":76},"Machine learning enables faster and more accurate exploration of large composite design spaces and can predict mechanical behavior effectively even with limited training 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