[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120745-en":3,"doc-seo-120745-105":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":20,"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},120745,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Optimization of stabilized annealing of Al-Mg alloys utilizing machine learning algorithms","The corrosion behavior of Al-Mg alloys depends on physical parameters in the alloy preparation process, while experiments are complex, time-consuming, and limited in data availability. This work identifies corrosion-mechanism drivers—magnesium content, deformation, annealing temperature, and annealing time—as key factors for corrosion resistance. Using existing experimental data, a machine learning framework is developed to support smart manufacturing and reliably predict NAMLT values. With further data acquisition, the approach can guide efficient and intelligent production process adjustment for machining.","The University of Manchester Research  \nOptimization of stabilized annealing of Al-Mg alloys utilizing machine learning algorithms  \nDOI:  \n10.1016/j.mtcomm.2023.106177  \nDocument Version  \nAccepted author manuscript  \nLink to publication record in Manchester Research Explorer  \nCitation for published version (APA):  \nXue, D. , Wei, W. , Shi, W. , Zhou, X. R. , Qi, J. T. , Wen, S. P. , Wu, X. L. , Gao, K. Y. , Xiong, X. Y. , Huang, H. , & Nie, Z. R. (2023) . Optimization of stabilized annealing of Al-Mg alloys utilizing machine learning algorithms. Materials Today Communications, 35,[106177] . [https://doi.org/10.1016/j.mtcomm.2023.106177](https://doi.org/10.1016/j.mtcomm.2023.106177)  \nPublished in:  \nMaterials Today Communications  \nCiting this paper  \nPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscript or Proof version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the Research Explorer are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTakedown policy  \nIf you believe that this document breaches copyright please refer to the University of Manchester’s Takedown Procedures [[http://man.ac.uk/04Y6Bo](http://man.ac.uk/04Y6Bo)] or contact [uml.scholarlycommunications@manchester.ac.uk](uml.scholarlycommunications@manchester.ac.uk) providing  \nrelevant details, so we can investigate your claim.  \nDownload date:18 . Jul. 2023  \nJournal Pre-proof  \nOptimization of stabilized annealing of Al-Mg alloys utilizing machine learning algorithms  \nD. Xue, W. Wei, W. Shi, X.R. Zhou, J. T. Qi, S.P. Wen, X.L. Wu, K.Y. Gao, X.Y. Xiong, H. Huang, Z.R. Nie  \nPII: S2352-4928(23)00868-1  \nDOI: [https://doi.org/10.1016/j.mtcomm.2023.106177](https://doi.org/10.1016/j.mtcomm.2023.106177)  \n[Reference: MTCOMM106177](Reference: MTCOMM106177)  \nTo appear in: Materials Today Communications  \nReceived date: 31 March 2023  \nRevised date: 26 April 2023  \nAccepted date: 9 May 2023  \nPlease cite this article as: D. Xue, W. Wei, W. Shi, X.R. Zhou, J.T. Qi, S.P.  \nWen, X.L. Wu, K.Y. Gao, X.Y. Xiong, H. Huang and Z.R. Nie, Optimization of stabilized annealing of Al-Mg alloys utilizing machine learning algorithms, Materials Today Communications, (2023)  \ndoi:[https://doi.org/10.1016/j.mtcomm.2023.106177](https://doi.org/10.1016/j.mtcomm.2023.106177)  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2023 Published by Elsevier.  \nOptimization of stabilized annealing of Al-Mg alloys  \nutilizing machine learning algorithms  \nD. Xue a, W. Wei a *, W. Shi b, X.R. Zhou c, J.T. Qi d, S.P. Wen a, X.L. Wu a, K.Y. Gao a, X.Y.  \nXiong a, H. Huang a**, Z.R. Nie a  \na Key Laboratory of Advanced Functional Materials, Education Ministry of China, Beijing University of Technology, Beijing 100124, China  \nb Institute of Corrosion Science and Technology, Guangzhou 510530, China c School of Materials, The University of Manchester, Manchester, M13 9PL, UK  \nAbstract  \nThe corrosion properties of the alloy are influenced by the physical parameters involved in the preparation process. Experiments to explore the preparation process of Al-Mg alloys are very complex and time-consuming, and the amou","cbCaisRwt3MmAFku","https://ap.wps.com/l/cbCaisRwt3MmAFku","pdf",1155828,1,15,"English","en",105,"# Abstract\n# Introduction\n## Background on Al-Mg alloys and β phase\n# Corrosion mechanism factors\n## Magnesium content, deformation, annealing parameters\n# Machine learning framework\n## Smart manufacturing and prediction of NAMLT values\n# Results and expected applications","[{\"question\":\"Which preparation parameters most affect the corrosion resistance of Al-Mg alloys in this study?\",\"answer\":\"The study identifies magnesium content, deformation, annealing temperature, and annealing time as important factors affecting corrosion resistance.\"},{\"question\":\"Why is a machine learning approach proposed for optimizing stabilized annealing?\",\"answer\":\"Experiments are complex, time-consuming, and limited by scarce data, so a machine learning framework is used to extract patterns from existing experimental results.\"},{\"question\":\"What capability does the proposed machine learning framework demonstrate?\",\"answer\":\"It can reliably predict the NAMLT values of the alloy based on constructed models from existing experimental data.\"}]","Optimization of stabilized annealing of Al-Mg alloys utilizing machine learning algorithms | 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preparation parameters most affect the corrosion resistance of Al-Mg alloys in this study?","Question",{"text":75,"@type":76},"The study identifies magnesium content, deformation, annealing temperature, and annealing time as important factors affecting corrosion resistance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is a machine learning approach proposed for optimizing stabilized annealing?",{"text":80,"@type":76},"Experiments are complex, time-consuming, and limited by scarce data, so a machine learning framework is used to extract patterns from existing experimental results.",{"name":82,"@type":73,"acceptedAnswer":83},"What capability does the proposed machine learning framework demonstrate?",{"text":84,"@type":76},"It can reliably predict the NAMLT values of the alloy based on constructed models from existing experimental 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