[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126726-en":3,"doc-seo-126726-105":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},126726,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning model of acoustic signatures - towards digitalised thermal spray manufacturing","Thermal spraying remains a skill-intensive manufacturing process in aerospace, energy, and biomedical sectors, with multiple trial runs that reduce yield. Digitalising thermal spraying is challenging due to harsh operating conditions such as UV exposure, high-plasma temperatures, dusty chemical environments, and limited spray-booth accessibility. The paper proposes a machine-learning approach using acoustic emission spectra and converts signals from the amplitude-time domain to a Fourier-transformed frequency-time domain to detect anomalies in real time. Experiments show industrial suitability by generating data for VGG transfer learning to address CNN limitations.","Machine learning model of acoustic signatures: towards digitalised thermal spray manufacturing.  \nVISWANATHAN, V., MCCLOSKEY, A., MATHUR, R., NGUYEN, D.T., FAISAL, N.H., PRATHURU, A., LLAVORI, I., MURPHY, A., TIWARI, A., MATTHEWS, A., AGRAWAL, A. and GOEL, S.  \n2024  \nThis document was downloaded from [https://openair.rgu.ac.uk](https://openair.rgu.ac.uk)  \nMechanical Systems and Signal Processing 208 (2024) 111030  \nContents lists available at ScienceDirect  \nMechanical Systems and Signal Processing  \njournal [homepage:](homepage: www.elsevier.com/locate/ymssp)[ www.elsevier.com/locate/ymssp](homepage: www.elsevier.com/locate/ymssp)  \n| Machine learning model of acoustic signatures: Towards digitalised thermal spray manufacturing\u003Cbr>V. Viswanathan a, Alex McCloskey b, Ruchir Mathura, Dinh T. Nguyen a, Nadimul Haque Faisal c, Anil Prathuruc, I˜nigo Llavorib, Adrian Murphy d, Ashutosh Tiwarie, Allan Matthewsf, Anupam Agrawal g, Saurav Goela, h, *\u003Cbr>a School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK\u003Cbr>b Mechanical and Industrial Manufacturing Department, Mondragon Unibertsitatea, Loramendi 4, 20500 Arrasate-Mondragon, Spain c School of Engineering, Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, UK\u003Cbr>d School of Mechanical and Aerospace Engineering, Queen’s University, Belfast BT95AH, UK e Department of Automatic Control and Systems Engineering, The University of Sheffield, S1 3JD, UK f Henry Royce Institute, The University of Manchester, Manchester M13 9PL, UK\u003Cbr>g Mays Business School, Texas A&M University, College Station, TX, USA\u003Cbr>h Department of Mechanical Engineering, University of Petroleum and Energy Studies, Dehradun 248007, India |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Thermal spray Acoustic Digitalisation Machine learning |  | Thermal spraying, an important industrial surface manufacturing process in sectors such as aerospace, energy and biomedical, remains a skill intensive process often involving multiple trial runs impacting the yield. The core research challenge in digitalisation of thermal spraying process lies in instrumenting the manufacturing platform as the process includes harsh conditions, including UV Rays, high-plasma temperature, dusty chemical environment, and spray booth inaccessibility. This paper introduces a novel application of machine learning to the acoustic emission spectra of thermal spraying. By transitioning from the amplitude-time domain to a Fourier-transformed frequency-time domain, it is possible to predict anomalies in real-time, a crucial step towards sustainable material and manufacturing digitalization. Our experimental results also indicate that this method is suitable for industrial applications by generating useful data that can be used to develop Visual Geometry Group (VGG) transfer learning models to overcome the traditional limitations of convoluted neural networks (CNN). |\n\n1. Introduction  \nThermal spraying dominates the surface manufacturing landscape as a special process for high technology sectors such as power generation, defense, bio medical and aerospace [1–4]. Despite its dominance, the process still relies heavily on strict manufacturing plans and work instructions to ensure quality and consistency. When post-coating analyses uncover non-compliant properties, it often leads to resource-intensive rework.  \n* Corresponding author at: School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK.  \n[E-mail address:](E-mail address: Goels@lsbu.ac.uk)[ Goels@lsbu.ac.uk](E-mail address: Goels@lsbu.ac.uk) (S. Goel).  \n[https://doi.org/10.1016/j.ymssp.2023.111030](https://doi.org/10.1016/j.ymssp.2023.111030)  \nReceived 17 August 2023; Received in revised form 7 November 2023; Accepted 11 December 2023 Available online 19 December 2023  \n0888-3270/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY licen","cbCaimWKITqb7e9P","https://ap.wps.com/l/cbCaimWKITqb7e9P","pdf",16293558,1,15,"English","en",105,"# Introduction\n## Digitalisation of thermal spraying\n## In-situ sensing and diagnostics with sensors\n# Machine learning approach based on acoustic emission","[{\"question\":\"为什么热喷涂过程很难实现数字化？\",\"answer\":\"数字化难点在于需要对制造平台进行仪器化采集，而喷涂环境包含UV、极高等离子温度、粉尘化学环境以及喷涂室难以触达等严苛条件。\"},{\"question\":\"本文如何利用声发射数据进行异常预测？\",\"answer\":\"将声发射信号从幅值-时间域转换到傅里叶变换后的频率-时间域，从而支持实时异常预测。\"},{\"question\":\"实验结果表明该方法如何用于工业场景？\",\"answer\":\"生成了可用于构建VGG迁移学习模型的数据，并用于缓解传统卷积神经网络在该任务中的局限性，从而具备工业应用潜力。\"}]","Machine learning model of acoustic signatures - towards digitalised thermal spray manufacturing | PDF",1785934434,38,{"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-model-of-acoustic-signatures-towards-digitalised-thermal-spray-manufacturing","",{"@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-model-of-acoustic-signatures-towards-digitalised-thermal-spray-manufacturing/126726/",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","2026-08-05",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},"为什么热喷涂过程很难实现数字化？","Question",{"text":75,"@type":76},"数字化难点在于需要对制造平台进行仪器化采集，而喷涂环境包含UV、极高等离子温度、粉尘化学环境以及喷涂室难以触达等严苛条件。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"本文如何利用声发射数据进行异常预测？",{"text":80,"@type":76},"将声发射信号从幅值-时间域转换到傅里叶变换后的频率-时间域，从而支持实时异常预测。",{"name":82,"@type":73,"acceptedAnswer":83},"实验结果表明该方法如何用于工业场景？",{"text":84,"@type":76},"生成了可用于构建VGG迁移学习模型的数据，并用于缓解传统卷积神经网络在该任务中的局限性，从而具备工业应用潜力。","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"]