[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127834-en":3,"doc-seo-127834-105":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},127834,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Design of Active Brazing Fillers Based on Eutectic High Entropy Compositions by Machine Learning - Doctor of Philosophy Thesis","Brazing joins two materials by melting a filler metal that enables bonding while leaving the substrates largely unaffected. Active brazing requires metals that can react with ceramic, especially for metallic–non-metallic joints. Eutectic high entropy alloys are promising candidates because their structure can reduce segregation and thermal expansion, enabling high-temperature active components with still-usable melting behavior. A new machine learning framework using thermodynamic, electronic, and atomic-size features designs novel EHEAs, achieving >75% predictive accuracy for experimental compositions and successful vacuum brazing joints between Kovar and alumina via oxide formation.","Design of Active Brazing Fillers Based on Eutectic High Entropy Compositions by Machine Learning  \nDepartment of Materials Science and Engineering University Of Sheffield  \nA thesis submitted for the degree of Doctor of Philosophy  \nXavier Sanuy Morell  \nSeptember 2024  \ni  \nAbstract  \nBrazing, a technique for joining two materials, has long been used across various industries, from small-scale to large-scale operations. A key component of this technique is the filler metal, an alloy which is melted between the two components being joined, and acts to make the bond between them, with the materials themselves largely unaffected by the process. The filler usually has a tailored composition, since its interaction with substrate materials must be suitable for a successful union.  \nOne of the most difficult bonds to form is between metallic and non-metallic materials, and the properties and types of interaction on each side of the joint are very different. To achieve the bond, the brazing material must be active, meaning it must contain metals in its composition that can react with the ceramic.  \nTo widen the capabilities and improve the performance of such joints. New alloys are being sought, and new classes of materials, like eutectic high entropy alloys (EHEAs), attract a lot of interest as candidate fillers. Segregation and large thermal expansions are less likely in these alloys, due to their isothermal transformation and dual-phase structure. Furthermore, because of the character as a eutectic multi-component alloy, high-temperature active metals can be included in the composition, but a low melting point could still be expected.  \nEmpirical trial-and-error methods and thermodynamic modelling used previously have shown low efficiencies in designing new EHEAs, due to the need for extensive physical experiments, or limited in accuracy, because of the unavailability of appropriate assessed binary and ternary diagrams. As a consequence, a new machine learning methodology, based on thermodynamic, electronic and atomic size features, is here developed and used to design novel EHEAs that can be used as active brazing fillers.  \nWith the use of several predicting algorithms, that show predictive accuracy for novel experimental compositions of over 75 % for EHEAs, four new compositions based on active elements have been developed. Furthermore, vacuum brazing samples, of each alloy, have shown successful joints between Kovar and alumina (as an example metal-ceramic joint), by the formation of different oxides.  \nThis research highlights the capabilities of machine learning in alloy design and underscores the potential of eutectic high entropy alloys (HEAs) as active brazing materials for Kovaralumina.  \niii  \nAcknowledgements  \nI have been incredibly fortunate to receive immense support from numerous individuals during the journey of this project.  \nI wish to extend my utmost thanks to my supervisory team. Professor Russell Goodall, my first supervisor, has always been readily available and patient, especially with my English, while also providing wise advice and guidance. I am grateful to my second supervisor, Ed Pickering, for his valuable corrections, suggestions over the years, and assistance with Manchester facilities. My thanks also go to Phil Webb and Pat Rodgers, my industrial supervisors at VBC Group, for their practical advice, excellent networking opportunities in the industry, and guidance at conferences.  \nI gratefully acknowledge all the other PhD students, particularly the 2019 CDT cohort, and colleagues in the NoStraDAMUS research group, who have provided me with invaluable support. Special thanks to Sharon Brown, who does an incredible job managing the CDT and offered immense help at the start of my PhD, as well as Joan Kelly. I also extend my gratitude to the Engineering and Physical Sciences Research Council UK (EPSRC) and the Centre for Doctoral Training in Advanced Metallic Systems for their financial support.  \nMy","cbCailywP0SQFbz9","https://ap.wps.com/l/cbCailywP0SQFbz9","pdf",57291564,2,1,159,"English","en",105,"# Introduction\n## Background and Context\n## Aims and Objectives\n## Thesis Outline\n# Literature Review\n## High entropy alloys\n## Machine Learning","[{\"question\":\"Why are active brazing fillers needed for metal–ceramic joints?\",\"answer\":\"Metal–ceramic bonds require brazing fillers that contain active metals capable of reacting with the ceramic. This reactivity enables bonding despite the different interaction behaviors on each side of the joint.\"},{\"question\":\"What makes eutectic high entropy alloys attractive as active brazing filler candidates?\",\"answer\":\"Eutectic high entropy alloys are of interest due to reduced segregation risk and lower thermal expansion concerns, linked to their isothermal transformation and dual-phase structure. Their eutectic multi-component nature also allows inclusion of high-temperature active metals.\"},{\"question\":\"How does machine learning improve the design of new active EHEA compositions?\",\"answer\":\"The thesis introduces a machine learning methodology using thermodynamic, electronic, and atomic-size features to overcome the low efficiency of trial-and-error and the limited accuracy of prior thermodynamic modeling. Multiple algorithms are used to predict novel experimental compositions with over 75% accuracy, followed by vacuum brazing validation.\"}]","Design of Active Brazing Fillers Based on Eutectic High Entropy Compositions by Machine Learning - Doctor of Philosophy Thesis | PDF",1785942251,401,{"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},"design-of-active-brazing-fillers-based-on-eutectic-high-entropy-compositions-by-machine-learning-doctor-of-philosophy-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/design-of-active-brazing-fillers-based-on-eutectic-high-entropy-compositions-by-machine-learning-doctor-of-philosophy-thesis/127834/",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","2026-08-23","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},"Why are active brazing fillers needed for metal–ceramic joints?","Question",{"text":76,"@type":77},"Metal–ceramic bonds require brazing fillers that contain active metals capable of reacting with the ceramic. This reactivity enables bonding despite the different interaction behaviors on each side of the joint.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What makes eutectic high entropy alloys attractive as active brazing filler candidates?",{"text":81,"@type":77},"Eutectic high entropy alloys are of interest due to reduced segregation risk and lower thermal expansion concerns, linked to their isothermal transformation and dual-phase structure. Their eutectic multi-component nature also allows inclusion of high-temperature active metals.",{"name":83,"@type":74,"acceptedAnswer":84},"How does machine learning improve the design of new active EHEA compositions?",{"text":85,"@type":77},"The thesis introduces a machine learning methodology using thermodynamic, electronic, and atomic-size features to overcome the low efficiency of trial-and-error and the limited accuracy of prior thermodynamic modeling. 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