[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121001-en":3,"doc-seo-121001-105":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":20,"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},121001,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Experimental Analysis of Friction Stir Welding of Dissimilar Aluminium Alloys by Machine Learning","Current research investigates friction stir welding of two dissimilar aluminium alloys, AA5083 and AA6082. Tool rotational speed, tool tilt angle and weld speed are optimized using an L27 orthogonal design of experiments with tensile strength as the response. Machine learning models are then applied to anticipate joint strength, combining random forest regressor and artificial neural network approaches. Experimental readings are split for training and testing, while ANOVA-based variance analysis is compared with model predictions to evaluate differences.","Experimental Analysis of Friction Stir Welding of Dissimilar Aluminium Alloys by Machine Learning  \nAmbati Hemanth Sai Kumar, M.V.R. Durga Prasad, Yeole Shivraj Narayan*, Kode Jaya Prakash  \n1 Department of Mechanical Engineering,  \nVNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, 500090, INDIA  \n*Corresponding Author: [shivrajyeole@vnrvjiet.in](shivrajyeole@vnrvjiet.in)[ ](shivrajyeole@vnrvjiet.in)DOI: [https://doi.org/10.30880/ijie.2024.16.02.003](https://doi.org/10.30880/ijie.2024.16.02.003)  \n\n| Article Info | Abstract |\n| --- | --- |\n| Received: 21 September 2023\u003Cbr>Accepted: 15 October 2023\u003Cbr>Available online: 15 April 2024 | Current research investigates friction stir welding of two disparate aluminium alloys-AA5083 and AA6082. Rotational speed of tool, its tilt angle & weld speed are optimized using L27 orthogonal design of experiments with tensile strength as the response. To assess plausible |\n| Keywords | higher level machine learning approaches in anticipating friction stir welded AA5083 and AA6082 strength, random forest regressor and |\n| Friction stir welding, analysis of | artificial neural network algorithms are employed. These models are |\n| variance (ANOVA), ultimate tensile | used to investigate discrepancies between experimental and predicted |\n| strength, random forest regressor, | results. Of the available results, 21 readings are chosen to train the |\n| artificial neural network | model whereas remaining are utilized to test the model. Random forest regressor and artificial neural network techniques were formed using the data associated with the experiment. Moreover, results of the analysis of variances are compared to the machine learning predicted results to determine the variances. |\n\n1. Introduction  \nAlloys made from aluminium are recognized for their inherent and flexible properties, including corrosion prevention, formability, mechanical strength, low density, electrical conductivity, and low density. Due to these properties, it is highly sought after in a diverse variety of manufacturing sectors, construction of ships, packaging, automobiles, and architecture. Welding of aluminium alloys requires specialized expertise and experience as its demand is growing in many applications. Loss of strength is due to porosity, element loss, solidification, stress corrosion cracking’s and incompatibilities among the workpiece and filler alloy in welding with dissimilar metals. This is when aluminium alloys are welded [1] . These issues can be addressed best by solid-state welding of aluminium alloys [2] . Efficient manufacturing and low energy usage have risen to the top of the prioritized government programs aimed at fostering long-term growth. Industries strive to adopt solutions that utilize the fewest resources, optimize manufacturing methods, and produce improved materials in an attempt to achieve economic and environmental sustainability. Because of the need to use as few resources as possible, manufacturing designs are trending toward complicated structural joints and joints using both the same metal and distinct metals [3] . Joining two dissimilar materials results in a fusion of material properties from both materials, making the joined weldments suitable for military-based applications such as lighter weight tanks, military bridges, battle tanks, body armor ambulances, titanium lightweight howitzers, layer tanks, and so on. Aluminium alloys are primarily used in fusion-based welding processes. However, fusion-based welding creates significant challenges due to changes in alloy composition, thermal characteristics, and other metallurgical and mechanical properties. Furthermore, weld solidification complications such as fractures, undercut, porosity, and so on reduce weldment quality, resulting in the occurrence of very coarse grains and intermetallic compounds at  \nthe weld region and a drop in mechanical qualities. In addition to excellent mechanical properties, aluminiummagne","cbCaigzuqOpgNZtk","https://ap.wps.com/l/cbCaigzuqOpgNZtk","pdf",1435247,1,17,"English","en",105,"# Introduction\n## Challenges in dissimilar aluminium welding\n## Friction stir welding benefits\n## Machine learning in welding and quality prediction\n# Experimental Design and Optimization\n## L27 orthogonal design with tensile strength response\n# Machine Learning Models and Evaluation\n## Training/testing strategy for strength prediction\n## Random forest regressor and ANN comparison\n# Statistical Validation\n## ANOVA versus machine learning predicted variances","[{\"question\":\"Which aluminium alloys are investigated in the friction stir welding study?\",\"answer\":\"The study investigates two dissimilar aluminium alloys: AA5083 and AA6082. Their joint strength is analyzed under optimized welding parameters.\"},{\"question\":\"How are welding parameters optimized in the research?\",\"answer\":\"Tool rotational speed, tool tilt angle, and weld speed are optimized using an L27 orthogonal design of experiments. Tensile strength is used as the response variable.\"},{\"question\":\"What machine learning methods are used to predict joint strength?\",\"answer\":\"The research applies random forest regressor and artificial neural network algorithms to predict friction stir welded AA5083 and AA6082 strength. Experimental readings are split into training and testing sets for evaluation.\"}]","Experimental Analysis of Friction Stir Welding of Dissimilar Aluminium Alloys by Machine Learning | PDF",1785733258,43,{"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},"experimental-analysis-of-friction-stir-welding-of-dissimilar-aluminium-alloys-by-machine-learning","",{"@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/experimental-analysis-of-friction-stir-welding-of-dissimilar-aluminium-alloys-by-machine-learning/121001/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which aluminium alloys are investigated in the friction stir welding study?","Question",{"text":75,"@type":76},"The study investigates two dissimilar aluminium alloys: AA5083 and AA6082. Their joint strength is analyzed under optimized welding parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are welding parameters optimized in the research?",{"text":80,"@type":76},"Tool rotational speed, tool tilt angle, and weld speed are optimized using an L27 orthogonal design of experiments. Tensile strength is used as the response variable.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning methods are used to predict joint strength?",{"text":84,"@type":76},"The research applies random forest regressor and artificial neural network algorithms to predict friction stir welded AA5083 and AA6082 strength. 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