[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121869-en":3,"doc-seo-121869-105":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},121869,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning for Shape Memory Alloy Property Optimization - Doctor of Philosophy Dissertation","Shape memory alloys face deployment barriers from low efficiency and functional instability driven by transformation thermal hysteresis and wide temperature ranges during martensitic phase change. Prior ternary and quaternary alloying of NiTi-based systems reduced hysteresis and transformation range, yet reported alloys did not sustain a narrow hysteresis under applied stress. An AI-enabled materials discovery workflow was used to identify SMA chemistries and thermo-mechanical processing steps achieving narrow hysteresis under stress. A NiTi-based alloy, Ni32Ti47Cu21 (at.%), was predicted and experimentally confirmed as the narrowest so far, showing excellent cyclic stability and actuation strain. A new high-importance descriptor improved machine learning and enabled experimentally validated extrapolative prediction for quaternary alloys, supporting extension to other SMA functions.","MACHINE LEARNING FOR SHAPE MEMORY ALLOY PROPERTY  \nOPTIMIZATION  \nA Dissertation  \nby  \nWILLIAM FRANKLIN TREHERN  \nSubmitted to the Graduate and Professional School of Texas A&M University  \nin partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nChair of Committee, Committee Members,  \nHead of Department,  \nIbrahim Karaman Raymundo Arróyave Alaa Elwany Dimitris Lagoudas Ibrahim Karaman  \nDecember 2022  \nMajor Subject: Materials Science and Engineering  \nCopyright 2022 William Trehern  \nABSTRACT  \nOne of the obstacles to the deployment of shape memory alloys (SMAs) in solidstate actuation is the low efficiency and functional instability due to the transformation thermal hysteresis and large temperature ranges during martensitic phase transformation. Numerous studies have been conducted in an effort to minimize the thermal hysteresis and transformation temperature range of SMAs through ternary and quaternary alloying of known binary alloy systems, such as NiTi, and considerable success has been achieved. However, and crucially, the alloys discovered so far have failed to maintain a narrow hysteresis under applied stress. In the present study, an AI-enabled materials discovery framework was successfully used to identify both SMA chemistries and the associated thermo-mechanical processing steps that result in narrow transformation hysteresis and transformation range under an applied stress. The major elements of the proposed workflow are described in detail and its materials-agnostic character makes it widely applicable to other alloy discovery challenges. Following this framework, a large, high-quality SMA dataset is developed for use in data-enabled alloy design. The dataset is then used to train machine learning models and candidate alloy predictions are selected based on expected improvement. Using this framework, and without relying on subsequent experimental exploratory analysis, an SMA composition, i.e. Ni32Ti47Cu21 (at. %), was predicted and confirmed to have the narrowest thermal hysteresis and  \ntransformation range under stress achieved thus far for a NiTi-based SMA. Furthermore, the alloy was shown to exhibit excellent cyclic stability and actuation strain. Additionally, a new descriptor is created with high feature importance and is used in training new machine learning models which are experimentally validated for extrapolative prediction accuracy with quaternary alloy predictions in a fractionalfactorial experimental design. The methodology and the dataset introduced here can be extended to design novel SMAs with other target functions.  \nDEDICATION  \nI dedicate this dissertation to my daughter, Diana Jane Trehern. You will learn many exciting things about the world in your lifetime, and you will have many questions. Some of those questions may not have answers yet. Keep asking those questions-they will have the most interesting answers. The world is a magnificent place full of adventure and many paths to choose from, be the trailblazer that finds the way. When the day grows dark and the paths become harder to find, you will always have the moon. Remember that today, tomorrow, forever, I’ll follow your trail. Just call my name.  \nACKNOWLEDGEMENTS  \nI would like to thank my committee chair, Dr. Ibrahim Karaman, and my committee members, Dr. Raymundo Arróyave, Dr. Alaa Elwany, and Dr. Dimitris Lagoudas, for their expert guidance and advice. I would also like to thank Dr. Othmane Benafan for the mentorship and support. A special thanks to Dr. Ibrahim Karaman for being a great advisor and mentor.  \nI extend thanks to my fellow researchers, Nathan Hite, Tejas Umale, Abhinav Srivastava, Cafer Acemi, Eli Norris, and Hande Ozcan, for our extensive collaborative efforts and friendships.  \nFinally, thanks to my wife Samantha Trehern for the encouragement and support throughout the entirety of my college career.  \nCONTRIBUTORS AND FUNDING SOURCES  \nContributors  \nThis work was supervised Professo","cbCaiaM4GrZsQCOs","https://ap.wps.com/l/cbCaiaM4GrZsQCOs","pdf",5999297,1,125,"English","en",105,"# Abstract\n## AI-enabled materials discovery workflow\n## Data set and machine learning model training\n## Predicted Ni32Ti47Cu21 alloy results\n## New descriptor and extrapolative quaternary predictions\n## Extension to other SMA target functions","[{\"question\":\"What main challenge limits the deployment of shape memory alloys?\",\"answer\":\"Transformation thermal hysteresis and large temperature ranges during martensitic phase transformation reduce efficiency and functional stability.\"},{\"question\":\"How does the study use AI to improve SMA performance under applied stress?\",\"answer\":\"An AI-enabled materials discovery framework searches for both SMA chemistries and thermo-mechanical processing steps that produce narrow transformation hysteresis and transformation range under stress.\"},{\"question\":\"Which alloy composition was predicted and confirmed, and what properties were reported?\",\"answer\":\"Ni32Ti47Cu21 (at.%) was predicted and confirmed to achieve the narrowest thermal hysteresis and transformation range under stress for a NiTi-based SMA, along with excellent cyclic stability and actuation strain.\"}]","Machine Learning for Shape Memory Alloy Property Optimization - Doctor of Philosophy Dissertation | PDF",1785807345,315,{"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},"machine-learning-for-shape-memory-alloy-property-optimization-doctor-of-philosophy-dissertation","",{"@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/machine-learning-for-shape-memory-alloy-property-optimization-doctor-of-philosophy-dissertation/121869/",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","2026-08-04",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 main challenge limits the deployment of shape memory alloys?","Question",{"text":75,"@type":76},"Transformation thermal hysteresis and large temperature ranges during martensitic phase transformation reduce efficiency and functional stability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study use AI to improve SMA performance under applied stress?",{"text":80,"@type":76},"An AI-enabled materials discovery framework searches for both SMA chemistries and thermo-mechanical processing steps that produce narrow transformation hysteresis and transformation range under stress.",{"name":82,"@type":73,"acceptedAnswer":83},"Which alloy composition was predicted and confirmed, and what properties were reported?",{"text":84,"@type":76},"Ni32Ti47Cu21 (at.%) was predicted and confirmed to achieve the narrowest thermal hysteresis and transformation range under stress for a NiTi-based SMA, along with excellent cyclic stability and actuation strain.","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"]