[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124644-en":3,"doc-seo-124644-105":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},124644,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles","Machine learning is proposed to accelerate aluminum alloy discovery and reduce the number of alloy grades in the 6XXX series at the T6 tempering state. The approach compiles features derived from chemical composition and tensile properties, then groups the alloys into clusters using a combined PCA and K-means algorithm. An explainable AI method connects cluster structures to metallurgical reasoning, helping interpret the underlying physics. The results narrow the search space to a few representative alloy groups and reduce the compositional space for practical development.","JOM, Vol. 75, No. 11, 2023  \n[https://doi.org/10.1007/s11837-023-06025-9](https://doi.org/10.1007/s11837-023-06025-9)[ ](https://doi.org/10.1007/s11837-023-06025-9)􀀂 2023 The Author(s)  \nAPPLICATIONS OF MACHINE LEARNING IN MATERIALS DEVELOPMENT AND ADDITIVE MANUFACTURING  \nClassification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles  \nTANU TIWARI ,1,2 SADEGH JALALIAN, 1,3 CHAMINI MENDIS, 1,4  \nand DMITRY ESKIN 1,5  \n1.—BCAST, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK. 2.—e-mail:  \n[Tanu.Tiwari@brunel.ac.uk. 3](Tanu.Tiwari@brunel.ac.uk. 3) .—e-mail: [Sadegh.Jalalian@brunel.ac.uk. 4](Sadegh.Jalalian@brunel.ac.uk. 4) .—e-mail: Chamini.  \n[Mendis@brunel.ac.uk. 5](Mendis@brunel.ac.uk. 5) .—e-mail: [Dmitry.Eskin@brunel.ac.uk](Dmitry.Eskin@brunel.ac.uk)  \nAluminum alloys are widely used in each sector of engineering because of their  \nlower density coupled with higher strength compared to many existing alloys  \nof other metals. Due to these unique characteristics, there is acceleration in  \ndemand and discovery of new aluminum alloys with targeted properties and  \ncompositions. Traditional methods of designing new materials with desired  \nproperties, like ‘domain specialists and trial-and-error ’ approaches, are  \nlaborious and costly. These techniques also lead to the expansion of alloy  \nsearch area. Also, high demand for recycling of aluminum alloys requires  \nfewer alloy groups. We suggest a machine learning design system to reduce  \nthe number of grades in the 6XXX series of aluminum alloys by collecting the  \nfeatures involving chemical composition and tensile properties at T6 tem  \npering state. This work demonstrates the efficiency of grouping the aluminum  \nalloys into a number of clusters by a combined PCA and K-means algorithm.  \nTo understand the physics inside the clusters we used an explainable artificial  \nintelligence algorithm and connected the findings with sound metallurgical  \nreasoning. Through machine learning we will narrow down the search space of  \n6XXX series aluminum alloys to few groups. This work offers a useful method  \nfor reducing compositional space of aluminum alloys.  \nINTRODUCTION  \nAluminum (Al) and its alloys provide the unique combination of properties, which makes them economical, versatile and attractive metallic materials for many uses–from highly ductile, soft wrapping foil to the most demanding structural applications.1 As pure aluminum is rather soft, the alloying elements are used to improve and control the properties in Al alloys. Most common additions are manganese, copper, silicon, zinc and magnesium. Up to 2 wt.% total amount of these elements can be typically present in an Al alloy, with some specialized alloys containing even large amounts of additives. Also, some minor alloying elements are added in the amounts \u003C 0.5% . These elements have a function of controlling some specific properties, e.g.,  \n(Received May 4, 2023; accepted July 18, 2023; published online August 22, 2023)  \nrecrystallisation or corrosion resistance. The Al alloys are classified as wrought, casting and rapidly solidified/ powder alloys, which are further subdivided as age- and work-hardenable alloys.1 These classes are further subdivided into various systems based on the selection of alloying elements.  \nAluminum alloy design, since the beginning of the twentieth century, has been essentially an iterative and empirical process, based on the lessons learned from experience and in-service use.2 A hill-climbing approach is taken in the traditional development and design of Al alloys.3 This traditional method of research and development is laborious, expensive and does not consider the full spectrum of potential properties. Testing of billions of combinations of alloys is not possible.4,5 Alloy development by mixing a combination of alloying elements and characterizing their structure and testing their properties is slow, costly and does not fully harness the data that ha","cbCaioqjfZrdRCJN","https://ap.wps.com/l/cbCaioqjfZrdRCJN","pdf",1714640,1,12,"English","en",105,"# Applications of Machine Learning in Materials Development and Additive Manufacturing\n## Classification of T6 Tempered 6XXX Series Aluminum Alloys\n## Introduction: Aluminum alloy design and recycling challenges\n## Proposed machine-learning design system and clustering workflow","[{\"question\":\"What problem does the study address in 6XXX aluminum alloy development?\",\"answer\":\"It targets the laborious and costly traditional process for designing and searching aluminum alloys with targeted properties, and the need to reduce alloy grades during development and recycling.\"},{\"question\":\"Which inputs and methods are used to classify T6 tempered 6XXX aluminum alloys?\",\"answer\":\"The method collects features from chemical composition and tensile properties at the T6 tempering state, then performs clustering using a combined PCA and K-means algorithm.\"},{\"question\":\"How does the study interpret the clustered results?\",\"answer\":\"It uses an explainable AI algorithm to understand the physics within clusters and links those findings to established metallurgical reasoning.\"}]","Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles | 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problem does the study address in 6XXX aluminum alloy development?","Question",{"text":75,"@type":76},"It targets the laborious and costly traditional process for designing and searching aluminum alloys with targeted properties, and the need to reduce alloy grades during development and recycling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which inputs and methods are used to classify T6 tempered 6XXX aluminum alloys?",{"text":80,"@type":76},"The method collects features from chemical composition and tensile properties at the T6 tempering state, then performs clustering using a combined PCA and K-means algorithm.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study interpret the clustered results?",{"text":84,"@type":76},"It uses an explainable AI algorithm to understand the physics within clusters and links those findings to established metallurgical 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