[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119567-en":3,"doc-seo-119567-105":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},119567,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Adhesion Properties and Machine Learning Modeling of Multilayer Thermoplastic Composites","Advancement in multilayer thermoplastic composites requires adhesive materials capable of withstanding high temperatures and complex mechanical loading. This thesis develops and evaluates thermoplastic adhesive innovations combined with machine learning modeling to increase composite performance and reliability. It introduces an immiscible blend adhesive for improved barrier and mechanical behavior, examines carbon-fiber reinforcement to raise peel strength under thermal stress, and strengthens fiber–matrix interfaces via graphene nanoplatelet coating using chemical, morphological, and wettability characterization. Finally, an Advanced Multilayer Perceptron model predicts peel strength with validated feature analysis.","Adhesion Properties and Machine Learning Modeling of Multilayer Thermoplastic Composites  \nby Weiqing Fang  \nA thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Mechanical and Industrial Engineering University of Toronto  \n© Copyright by Weiqing Fang 2025  \nAdhesion Properties and Machine Learning Modeling of Multilayer Thermoplastic Composites  \nWeiqing Fang  \nDoctor of Philosophy  \nMechanical and Industrial Engineering  \nUniversity of Toronto  \n2025  \nAbstract  \nThe advancement of multilayer thermoplastic composites necessitates the development of robust adhesive materials that can withstand high temperatures and diverse mechanical stresses. This thesis presents a comprehensive approach to enhancing thermoplastic adhesives through material innovations and machine learning modeling, aiming to improve the performance and reliability of multilayer composites in demanding applications.  \nFirst in this study, an immiscible blend adhesive comprising Polyethylene of Raised Temperature, Polyamide 12 was developed. By optimizing the adhesive layer composition, the resulting trilayer composite demonstrated significantly enhanced barrier properties, and mechanical strength in Young’s modulus, creep resistance, and impact absorption, highlighting the blend's suitability for high-temperature, high-pressure applications. Secondly, the incorporation of carbon fibers into adhesive matrix was investigated to address weak adhesive properties at elevated temperatures. Utilizing a novel T-peel test under controlled conditions, CF reinforcement achieved remarkable increases in peel strength. The enhancement mechanisms were elucidated through macro-level improvements such as an expanded peel zone and elimination of crazing, and micro-level factors including stress transfer and energy dispersion into micro peel zones, thereby significantly  \nboosting the adhesive performance under thermal stress. Thirdly, the interface between carbon fibers and thermoplastic matrices was strengthened through nanostructure surface modification by graphene nanoplatelet coating. The coated carbon fibers exhibited an improvement in interfacial shear strength with polyethylene matrices, while a reduction with PA6 due to differing failure mechanisms. Comprehensive morphological, chemical, and wettability analyses, supported by machine learning-based image segmentation, X-ray photoelectron spectroscopy, and contact-angle measurements, provided a detailed understanding of the interfacial enhancements at the micro and nanoscale. Lastly, an Advanced Multilayer Perceptron Regressor model was developed to predict the peel strength of coextruded multilayer thermoplastic composites. This machine learning approach effectively captured the complex relationships between various input parameters and composite properties, despite being trained on a limited dataset. The model demonstrated robust predictive capabilities, validated through benchmark metrics and k-fold cross-validation. Additionally, feature importance analysis and dimensionality reduction facilitated a deeper insight into the key factors influencing adhesive strength, thereby enabling optimized design strategies for multilayer composite manufacturing.  \nThis thesis integrates material science innovations with advanced machine learning techniques to develop high-performance thermoplastic adhesives for multilayer composites. The synergistic enhancements in adhesive formulations, fiber interfaces, and predictive modeling contribute to the creation of composites with superior properties. These findings provide a solid foundation for future advancements in the design and optimization of thermoplastic composite materials for various industrial applications.  \nAcknowledgements  \nCompleting this PhD thesis has been a challenging and rewarding journey, and it would not have been possible without the support and guidance of many individuals. I am deeply grateful to all who have contribut","cbCaimrDC6MheiOw","https://ap.wps.com/l/cbCaimrDC6MheiOw","pdf",7537590,1,202,"English","en",105,"# Abstract\n# Adhesive Innovations for Multilayer Thermoplastic Composites\n## Immiscible Blend Adhesive Development\n## Carbon Fiber Reinforcement and T-Peel Testing\n## Graphene Nanoplatelet Surface Modification\n# Interface Characterization and Mechanism Elucidation\n## Image Segmentation via Machine Learning\n## XPS and Contact-Angle Measurements\n# Machine Learning Prediction of Peel Strength\n## Advanced Multilayer Perceptron Regressor\n## Validation and Feature Importance Analysis\n# Acknowledgements\n# Contents","[{\"question\":\"What adhesive material strategy is developed in the thesis to improve multilayer composite performance?\",\"answer\":\"The study develops an immiscible blend adhesive comprising Polyethylene of Raised Temperature and Polyamide 12. By optimizing the adhesive layer composition, the trilayer composite shows enhanced barrier properties and improved mechanical performance.\"},{\"question\":\"How does the thesis improve adhesive strength at elevated temperatures using carbon fibers?\",\"answer\":\"Carbon fibers are incorporated into the adhesive matrix, and a novel T-peel test under controlled conditions quantifies improvements in peel strength. Mechanisms include macro-level peel-zone expansion and micro-level stress transfer and energy dispersion into micro peel zones.\"},{\"question\":\"How is machine learning used to predict peel strength, and how is the model validated?\",\"answer\":\"An Advanced Multilayer Perceptron Regressor predicts the peel strength of coextruded multilayer thermoplastic composites. The model captures complex parameter-property relationships despite limited training data, and validation uses benchmark metrics and k-fold cross-validation, supported by feature importance and dimensionality reduction.\"}]","Adhesion Properties and Machine Learning Modeling of Multilayer Thermoplastic Composites | PDF",1785725011,509,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"adhesion-properties-and-machine-learning-modeling-of-multilayer-thermoplastic-composites","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/adhesion-properties-and-machine-learning-modeling-of-multilayer-thermoplastic-composites/119567/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What adhesive material strategy is developed in the thesis to improve multilayer composite performance?","Question",{"text":75,"@type":76},"The study develops an immiscible blend adhesive comprising Polyethylene of Raised Temperature and Polyamide 12. By optimizing the adhesive layer composition, the trilayer composite shows enhanced barrier properties and improved mechanical performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis improve adhesive strength at elevated temperatures using carbon fibers?",{"text":80,"@type":76},"Carbon fibers are incorporated into the adhesive matrix, and a novel T-peel test under controlled conditions quantifies improvements in peel strength. Mechanisms include macro-level peel-zone expansion and micro-level stress transfer and energy dispersion into micro peel zones.",{"name":82,"@type":73,"acceptedAnswer":83},"How is machine learning used to predict peel strength, and how is the model validated?",{"text":84,"@type":76},"An Advanced Multilayer Perceptron Regressor predicts the peel strength of coextruded multilayer thermoplastic composites. The model captures complex parameter-property relationships despite limited training data, and validation uses benchmark metrics and k-fold cross-validation, supported by feature importance and dimensionality reduction.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]