[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119815-en":3,"doc-seo-119815-105":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},119815,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Utilization of Machine Learning to Investigate Material State","Accurate prediction of material behavior under varying loading conditions is essential for developing reliable, safe components. Thermal and mechanical fatigue loading can progressively damage materials, increasing the risk of failure and safety hazards. This honors research reviews a machine learning approach that predicts material state without relying on the loading order. Unidirectional carbon fiber reinforced polymer (UD CFRP) specimens subjected to two opposite loading sequences are analyzed using a Naive Bayes classifier with PCA-based feature selection, achieving up to 80% accuracy.","Georgia Southern University  \nDigital Commons@Georgia Southern  \nHonors College Theses  \n5-2-2023  \nUtilization of Machine Learning to Investigate Material State  \nAna B. Abadie  \nGeorgia Southern University  \nFollow this and additional works at: [https://digitalcommons.georgiasouthern.edu/honors-theses](https://digitalcommons.georgiasouthern.edu/honors-theses)  \n Part of the Mechanical Engineering Commons, and the Other Materials Science and Engineering Commons  \nRecommended Citation  \nAbadie, Ana B., \"Utilization of Machine Learning to Investigate Material State\" (2023) . Honors College Theses. 876.  \n[https://digitalcommons.georgiasouthern.edu/honors-theses/876](https://digitalcommons.georgiasouthern.edu/honors-theses/876)  \nThis thesis (open access) is brought to you for free and open access by Digital Commons@Georgia Southern. It has been accepted for inclusion in Honors College Theses by an authorized administrator of Digital Commons@Georgia Southern. For more information, please [contact digitalcommons@georgiasouthern.edu](contact digitalcommons@georgiasouthern.edu).  \nUtilization of Machine Learning to Investigate Material State  \nAn Honors Thesis submitted in partial fulfillment of the requirements for Honors in  \nMechanical Engineering.  \nBy  \nAna Abadie  \nUnder the mentorship of Dr. Hossain Ahmed  \nABSTRACT  \nThe ability to predict material behavior that undergoes various loading conditions is critical to the development of reliable and safe components. Thermal and mechanical fatigue loading can cause significant damage to materials, leading to failure and potential safety hazards. Machine learning algorithms have emerged as a promising tool for improving accuracy and efficiency of predicting material behavior under such loading conditions. This research provides a comprehensive overview of a machine learning algorithm that is able to analyze and predict material state independently of the loading sequence. Unidirectional carbon fiber reinforced polymer (UD CFRP) composite which has undergone two different loading sequences is analyzed. On one hand, the pristine material sustains 40 cycles of thermal fatigue followed by a break where data is acquired and then the specimen undergoes 150k cycles of mechanical fatigue. The second load sequence consists of the same cycles but in a reversed manner. Data collected from a small section of the composite in each stage was used to train a Naive Bayes classifier. A feature selection process is carried out with the use of principal component analysis to identify the most relevant parameters for use. The results show that the Naive Bayes classifier can accurately predict the fatigue sequence of UD CFRP samples under thermal and mechanical loading conditions with an accuracy of up to 80%. The findings of this paper can have significant implications for the analysis of structures subjected to thermal and fatigue loading.  \nThesis Mentor: Dr. Hossain Ahmed  \nHonors Dean: Dr. Steven Engel  \nMay 2023  \nDepartment of Mechanical Engineering  \nHonors College  \nGeorgia Southern University  \nAcknowledgements  \nI would like to express my deepest gratitude to my thesis mentor Dr. Hossain Ahmed for his support, guidance, and encouragement throughout my research journey. Your insightful comments, constructive criticism and patience have greatly contributed to the success of this project. I’m also grateful to Georgia Southern University’s Mechanical Engineering Department for providing the necessary courses and resources for my research.  \nI want to thank my parents Francisco Abadie, Ana Lucia Molina and my grandmother Toty Abadie for their unwavering support, love and encouragement throughout my college career. Without them, this would not have been possible. You have all been my greatest motivation even from a long distance.  \nThank you all for the support and guidance which has made this journey an unforgettable experience.  \nTable of Contents  \n1. Introduction…………………………………………………………………….…4 ","cbCaipS72S9FcjiD","https://ap.wps.com/l/cbCaipS72S9FcjiD","pdf",667982,1,20,"English","en",105,"# Introduction\n## Background and Motivation\n## Principal Component Analysis\n## Naive Bayes\n# Experimental Methodology\n## Development of Experimental Data\n## Data Analysis with MATLAB\n# Results\n# Discussion\n# Conclusion\n# References","[{\"question\":\"What material and loading conditions are studied in the research?\",\"answer\":\"The study focuses on unidirectional carbon fiber reinforced polymer (UD CFRP) subjected to thermal fatigue followed by mechanical fatigue, and the same cycles in reversed order.\"},{\"question\":\"How is the machine learning model trained to predict material state?\",\"answer\":\"Data from a small section of the composite at each stage trains a Naive Bayes classifier, supported by a feature selection step using principal component analysis (PCA).\"},{\"question\":\"What accuracy does the approach achieve and what is its significance?\",\"answer\":\"The Naive Bayes classifier predicts the fatigue sequence under thermal and mechanical loading with accuracy up to 80%, supporting improved analysis of structures exposed to thermal and fatigue loading.\"}]","Utilization of Machine Learning to Investigate Material State | 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material and loading conditions are studied in the research?","Question",{"text":75,"@type":76},"The study focuses on unidirectional carbon fiber reinforced polymer (UD CFRP) subjected to thermal fatigue followed by mechanical fatigue, and the same cycles in reversed order.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the machine learning model trained to predict material state?",{"text":80,"@type":76},"Data from a small section of the composite at each stage trains a Naive Bayes classifier, supported by a feature selection step using principal component analysis (PCA).",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy does the approach achieve and what is its significance?",{"text":84,"@type":76},"The Naive Bayes classifier predicts the fatigue sequence under thermal and mechanical loading with accuracy up to 80%, supporting improved analysis of structures exposed to thermal and fatigue 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