[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126741-en":3,"doc-seo-126741-105":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},126741,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning model of acoustic signatures - Towards digitalised thermal spray manufacturing","Thermal spraying remains a skill-intensive surface manufacturing method across aerospace, energy, and biomedical sectors, often requiring multiple trial runs that reduce yield. Digitalising the process is difficult because the platform operates under harsh conditions such as UV exposure, very high plasma temperatures, dusty chemical environments, and limited booth accessibility. This work applies machine learning to acoustic emission spectra, converting the amplitude-time domain into a Fourier-transformed frequency-time representation to predict anomalies in real time. Experimental findings support industrial readiness by producing data for VGG transfer learning models that address CNN limitations and enable more sustainable digitalisation.","Machine learning model of acoustic signatures: Towards digitalised thermal spray manufacturing  \nV. Viswanathana, Alex McCloskeyb, Ruchir Mathura, Dinh T Nguyena, Nadimul Haque Faisalc, Anil Prathuruc, Iñigo Llavorib, Adrian Murphyd, Ashutosh Tiwarie, Allan Matthewsf, Anupam Agrawalg and Saurav Goela,h*  \na School of Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA, UK  \nb Mechanical and Industrial Manufacturing Department, Mondragon Unibertsitatea, Loramendi 4, 20500 Arrasate-Mondragon, Spain  \nc School of Engineering, Robert Gordon University, Garthdee Road, Aberdeen, AB10 7GJ, UK d Queen’s University, Belfast, BT95AH, UK  \ne The University of Sheffield, S1 3JD, UK  \nf The University of Manchester, Manchester, M13 9PL, UK  \ng Mays Business School, Texas A&M University, College Station, TX, USA  \nh Department of Mechanical Engineering, University of Petroleum and Energy Studies, Dehradun, 248007, India  \n*Corresponding author Tel.: +44 020 7815 7581, [Email address: Goels@lsbu.ac.uk](Email address: Goels@lsbu.ac.uk)  \nAbstract  \nThermal 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  \nthe 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) .  \nKeywords: Thermal spray; Acoustic; Digitalisation; Machine learning  \n1.0. 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 noncompliant properties, it often leads to resource-intensive rework.  \n1.1 Digitalisation of Thermal Spraying  \nDigitalised thermal spraying [5] is an important new area of research that could help in improving quality and reducing rework in this process. The first step towards digitising thermal spraying would be to collect the process data in a digital format. Extant research [6-8] provides insights into some of these aspects using computational fluid dynamics to reveal combustion models in relation to process parameters. However, the quest for a comprehensive in-situ sensor system for holistic coating quality monitoring remains a challenge. The need for diagnostics arises from the need for thermal spray processes to achieve coating homogeneity in terms of  \nthickness, microstructure, and mechanical properties. In general, diagnostics primarily revolve around in-flight particle velocity and temperature measurements. Measurement of individual particle parameters include diagnostics with DPV 2000TM and spray position trajectory sensor, AccurasprayTM, SpraywatchTM, Inflight Particle Pyrometer (IPP), particle image velocimetry (PIV) [9] . These sensors measure temperatures using IR emission emanating from molten particles and use time of flight to determine the particle velocities. Velocity can be precisely measured, but particle temperature readi","cbCailhkKsWRuUr1","https://ap.wps.com/l/cbCailhkKsWRuUr1","pdf",1334403,1,33,"English","en",105,"# Introduction\n## Digitalisation of Thermal Spraying\n## Using Acoustic wave spectra in high temperature manufacturing","[{\"question\":\"Why is digitalising thermal spraying challenging?\",\"answer\":\"Digitalisation requires instrumentation that can function under harsh conditions including UV exposure, high-plasma temperatures, dusty chemical environments, and spray booth inaccessibility.\"},{\"question\":\"How does the proposed machine learning approach use acoustic emission data?\",\"answer\":\"The method transforms acoustic signals from the amplitude-time domain into a Fourier-transformed frequency-time domain to enable real-time anomaly prediction.\"},{\"question\":\"What evidence supports industrial applicability in this work?\",\"answer\":\"The study generates useful datasets that can be used to train VGG-based transfer learning models, aiming to overcome traditional limitations of convolutional neural networks.\"}]","Machine learning model of acoustic signatures - 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