[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128261-en":3,"doc-seo-128261-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128261,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Understanding the track integrity in wire arc additive manufacturing using mechanistic model and machine learning - Master of Science thesis","This thesis investigates track integrity in wire arc additive manufacturing by combining mechanistic modeling with machine learning to improve prediction of defects and process quality. A part-scale finite element framework is used to model thermal behavior under different deposition patterns, and a reduced order model is developed to capture key temperature-field characteristics efficiently. Data-driven machine learning approaches—including support vector machines, logistic regression, and multilayer perceptrons—are evaluated for humping prediction and performance analysis.","Understanding the track integrity in wire arc additive manufacturing using mechanistic model and machine learning  \nby  \nRakshith Reddy Sanvelly  \nA thesis submitted to the graduate faculty  \nin partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nMajor: Mechanical Engineering  \nProgram of Study Committee:  \nTuhin Mukherjee, Major Professor  \nAishwarya Rajiv Pawar  \nJakob D. Hamilton  \nThe student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this thesis. The Graduate College will ensure this thesis is globally accessible and will not permit alterations after a degree is conferred.  \nIowa State University Ames, Iowa  \n2025  \nCopyright © Rakshith Reddy Sanvelly, 2025. All rights reserved.  \nLIST OF CONTENTS  \nLIST OF TABLES ......................................................................................................................... iv  \nLIST OF FIGURES ........................................................................................................................ v  \nACKNOWLEDGEMENTS ........................................................................................................... xi  \nABSTRACT.................................................................................................................................. xii  \nCHAPTER 1. INTRODUCTION ................................................................................................... 1  \n1.1 Background and Motivation.................................................................................................. 1  \n1.2 Wire Arc Additive Manufacturing ........................................................................................ 3  \n1.3 Current Status ........................................................................................................................ 4  \n1.4 Deposited Track Integrity Issues ........................................................................................... 6  \n1.5 Modeling and Machine Learning .......................................................................................... 9  \n1.6 Goals and Objectives........................................................................................................... 12  \n1.7 Thesis Structure ................................................................................................................... 12  \nCHAPTER 2. LITERATURE STUDY ........................................................................................ 14  \n2.1 Wire Arc Additive Manufacturing ...................................................................................... 14  \n2.1.1 Process Principles and Variants.................................................................................... 14  \n2.1.2 Microstructure, Thermal Behavior, and Distortion Control ......................................... 15  \n2.1.3 Surface Finish, Mechanical Performance, and Advances ............................................ 20  \n2.1.4 Digital Tools and Industrial Applications .................................................................... 21  \n2.2 Numerical Methods ............................................................................................................. 22  \n2.3 Reduced Order Model ......................................................................................................... 29  \n2.4 Humping .............................................................................................................................. 35  \n2.5 Machine Learning ............................................................................................................... 40  \nCHAPTER 3. MODELLING APPROACH ................................................................................. 45  \n3.1 Introduction .........................................................................................................................","cbCairgtOY07lFMT","https://ap.wps.com/l/cbCairgtOY07lFMT","pdf",3678519,3,1,120,"English","en",105,"# List of Tables\n# List of Figures\n# Acknowledgements\n# Abstract\n# Chapter 1. Introduction\n## Background and Motivation\n## Wire Arc Additive Manufacturing\n## Current Status\n## Deposited Track Integrity Issues\n## Modeling and Machine Learning\n## Goals and Objectives\n## Thesis Structure\n# Chapter 2. Literature Study\n## Wire Arc Additive Manufacturing\n## Numerical Methods\n## Reduced Order Model\n## Humping\n## Machine Learning\n# Chapter 3. Modelling Approach\n## Part Scale Finite Element Model\n## Modelling of Temperature Field using Different Deposition Patterns\n## Reduced Order model\n# Chapter 4. Machine Learning Approach\n## Support Vector Machine (SVM)\n## Data-driven Machine Learning Framework\n## Logistic Regression and Multilayer Perceptron in WEKA\n# Chapter 5. Results and Discussion\n## Part-scale Finite Element Model\n## Modelling of Temperature Field using different deposition pattern\n## Reduced Order Modeling and Machine Learning for Humping Prediction\n# Chapter 6. Summary, Conclusions, and Future Work\n# References","[{\"question\":\"What problem does the thesis address in wire arc additive manufacturing?\",\"answer\":\"It focuses on deposited track integrity issues, particularly defects such as humping that affect part quality and reliability.\"},{\"question\":\"How is the thermal behavior modeled in this work?\",\"answer\":\"A part-scale finite element model is used to simulate the temperature field, including variations caused by different deposition patterns.\"},{\"question\":\"Which machine learning methods are evaluated for prediction?\",\"answer\":\"Support vector machines, logistic regression, and multilayer perceptrons in WEKA are assessed within a data-driven learning framework for humping prediction.\"}]","Understanding the track integrity in wire arc additive manufacturing using mechanistic model and machine learning - Master of Science thesis | PDF",1785946289,302,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"understanding-the-track-integrity-in-wire-arc-additive-manufacturing-using-mechanistic-model-and-machine-learning-master-of-science-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/understanding-the-track-integrity-in-wire-arc-additive-manufacturing-using-mechanistic-model-and-machine-learning-master-of-science-thesis/128261/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address in wire arc additive manufacturing?","Question",{"text":76,"@type":77},"It focuses on deposited track integrity issues, particularly defects such as humping that affect part quality and reliability.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the thermal behavior modeled in this work?",{"text":81,"@type":77},"A part-scale finite element model is used to simulate the temperature field, including variations caused by different deposition patterns.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning methods are evaluated for prediction?",{"text":85,"@type":77},"Support vector machines, logistic regression, and multilayer perceptrons in WEKA are assessed within a data-driven learning framework for humping prediction.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]