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The work develops methods that combine fringe projection profilometry with unsupervised learning to extract roughness metrics and cluster surface states from experimental imagery. It further proposes CONFORM, continuous normalizing flows tailored to curved-surface roughness measurement through surface parametrization and roughness-field generation. Experiments evaluate datasets, report clustering behavior and errors across test sets, and discuss limitations and future directions.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/from-corrosion-to-curvature-surface-characterization-using-machine-learning-thesis/290557/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/from-corrosion-to-curvature-surface-characterization-using-machine-learning-thesis/290557.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What machine-learning approach is used to characterize corrosion in Chapter 2?","Question",{"text":113,"@type":114},"Chapter 2 uses unsupervised machine learning with K-Means clustering to group surfaces based on roughness metrics derived from fringe projection profilometry.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does CONFORM (Chapter 3) support roughness measurement for curved surfaces?",{"text":118,"@type":114},"CONFORM uses continuous normalizing flows to model roughness fields, built from surface parametrization and evaluated on curved-surface datasets.",{"name":120,"@type":111,"acceptedAnswer":121},"What are the main experimental components and outcomes reported across the chapters?",{"text":122,"@type":114},"The work includes sample acquisition and preparation, visual inspection and parameter identification, experimental setup, and then results with clustering/roughness metrics and quantitative error summaries, followed by limitations and future work.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290557,1789642076,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":26,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","From corrosion to curvature: Surface characterization using machine learning  \nby  \nAnush Lakshman Sivaraman  \nA thesis submitted to the graduate faculty  \nin partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nMajor: Mechanical Engineering  \nAcademic Plan Committee:  \nAishwarya Pawar, Major Professor  \nBeiwen Li  \nYiliang Liao  \nEliot Winer  \nThe student author, whose presentation of the scholarship herein was approved by the academic plan 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  \nAmes, Iowa  \n2026  \nCopyright © Anush Lakshman Sivaraman, 2026 . All rights reserved.  \nii  \nDEDICATION  \nI would like to dedicate this thesis to my parents Siva and Geetha, sister Abhi, special friend Ruchi and my brothers Danush and Adam without whose support I would not have able to complete this work.  \niii  \nTABLE OF CONTENTS  \nPage  \nLIST OF TABLES .......................................... v  \n[LIST OF FIGURES .........................................](LIST OF FIGURES ......................................... vi)[ vi](LIST OF FIGURES ......................................... vi)  \n[ACKNOWLEDGMENTS ......................................](ACKNOWLEDGMENTS ...................................... vii)[ vii](ACKNOWLEDGMENTS ...................................... vii)  \n[ABSTRACT .............................................](ABSTRACT ............................................. viii)[ viii](ABSTRACT ............................................. viii)  \n[CHAPTER 1. INTRODUCTION .................................](CHAPTER 1. INTRODUCTION ................................. 1)[ 1](CHAPTER 1. INTRODUCTION ................................. 1)  \n[1.1 Motivation .........................................](1.1 Motivation ......................................... 1)[ 1](1.1 Motivation ......................................... 1)  \n[1.2 Background .........................................](1.2 Background ......................................... 2)[ 2](1.2 Background ......................................... 2)  \n1.2.1 Corrosion and Roughness ............................. 2  \n1.2.2 Roughness Measurement of Curved Surfaces ................... 4  \nReferences ............................................. 5  \nCHAPTER 2 . CORROSION CHARACTERIZATION OF ENGINE CONNECTING RODS USING FRINGE PROJECTION PROFILOMETRY AND UNSUPERVISED MACHINE LEARNING ............................................ 9  \n2.1 Methods ........................................... 10  \n2.1.1 Fringe Projection Profilometry .......................... 10  \n2.1.2 Determination of Roughness Metrics ....................... 13  \n2.1.3 K-Means Clustering ................................ 15  \n2.2 Experiments ......................................... 16  \n2.2.1 Sample Acquisition and Preparation ....................... 16  \n2.2.2 Visual Inspection and Parameter Identification ................. 17  \n2.2.3 Experimental Setup ................................ 19  \n2.3 Results and Discussion ................................... 21  \n2.3.1 Roughness Calculation and Clustering ...................... 21  \n2.3.2 Analysis of Clustering Results ........................... 22  \n2.3.3 Limitations ..................................... 26  \n2.4 Conclusion and Future Work ............................... 28  \nReferences ............................................. 29  \nCHAPTER 3 . CONFORM: CONTINUOUS NORMALIZING FLOWS FOR SURFACE ROUGHNESS MEASUREMENT OF CURVED SURFACES ..................... 32  \n3.1 Methodology ........................................ 33  \n3.1.1 Surface Parametrization .............................. 33  \n3.1.2 Roughness Field Generation ............................ 33  \niv  \n3.1.3 Continuous Normalizing Flows .......................... 35  \n3.2 Experiments ......................................... 40  \n3.2.1","cbCaic8HS4UB7yGO","https://ap.wps.com/l/cbCaic8HS4UB7yGO","pdf",22677127,"English","# LIST OF TABLES\n# LIST OF FIGURES\n# CHAPTER 1. INTRODUCTION\n## 1.1 Motivation\n## 1.2 Background\n### 1.2.1 Corrosion and Roughness\n### 1.2.2 Roughness Measurement of Curved Surfaces\n# CHAPTER 2 . CORROSION CHARACTERIZATION OF ENGINE CONNECTING RODS USING FRINGE PROJECTION PROFILOMETRY AND UNSUPERVISED MACHINE LEARNING\n## 2.1 Methods\n### 2.1.1 Fringe Projection Profilometry\n### 2.1.2 Determination of Roughness Metrics\n### 2.1.3 K-Means Clustering\n## 2.2 Experiments\n### 2.2.1 Sample Acquisition and Preparation\n### 2.2.2 Visual Inspection and Parameter Identification\n### 2.2.3 Experimental Setup\n## 2.3 Results and Discussion\n### 2.3.1 Roughness Calculation and Clustering\n### 2.3.2 Analysis of Clustering Results\n### 2.3.3 Limitations\n## 2.4 Conclusion and Future Work\n# CHAPTER 3 . CONFORM: CONTINUOUS NORMALIZING FLOWS FOR SURFACE ROUGHNESS MEASUREMENT OF CURVED SURFACES\n## 3.1 Methodology\n### 3.1.1 Surface Parametrization\n### 3.1.2 Roughness Field Generation\n### 3.1.3 Continuous Normalizing Flows\n## 3.2 Experiments\n### 3.2.1 Base Geometry Synthesis\n### 3.2.2 Dataset Samples\n## 3.3 Results and Discussion\n## 3.4 Conclusion and Future Work\n# CHAPTER 4 . CONCLUSION AND FUTURE WORK\n## 4.1 Summary and Discussion\n## 4.2 Future Directions","[{\"question\":\"What machine-learning approach is used to characterize corrosion in Chapter 2?\",\"answer\":\"Chapter 2 uses unsupervised machine learning with K-Means clustering to group surfaces based on roughness metrics derived from fringe projection profilometry.\"},{\"question\":\"How does CONFORM (Chapter 3) support roughness measurement for curved surfaces?\",\"answer\":\"CONFORM uses continuous normalizing flows to model roughness fields, built from surface parametrization and evaluated on curved-surface datasets.\"},{\"question\":\"What are the main experimental components and outcomes reported across the chapters?\",\"answer\":\"The work includes sample acquisition and preparation, visual inspection and parameter identification, experimental setup, and then results with clustering/roughness metrics and quantitative error summaries, followed by limitations and future work.\"}]","From corrosion to curvature: Surface characterization using machine learning - thesis | PDF",151]