{"id":5697,"date":"2024-02-13T21:04:18","date_gmt":"2024-02-14T02:04:18","guid":{"rendered":"https:\/\/spraysensors.tecnar.com\/active-learning-driven-uncertainty-reduction-for-in-flight-particle-characteristics-of-atmospheric-plasma-spraying-of-silicon\/"},"modified":"2024-02-13T21:04:18","modified_gmt":"2024-02-14T02:04:18","slug":"active-learning-driven-uncertainty-reduction-for-in-flight-particle-characteristics-of-atmospheric-plasma-spraying-of-silicon","status":"publish","type":"post","link":"https:\/\/spraysensors.tecnar.com\/fr\/active-learning-driven-uncertainty-reduction-for-in-flight-particle-characteristics-of-atmospheric-plasma-spraying-of-silicon\/","title":{"rendered":"Active Learning-Driven Uncertainty Reduction For In-Flight Particle Characteristics of Atmospheric Plasma Spraying of Silicon"},"content":{"rendered":"<p>The first-of-its-kind use of the active learning (AL) framework in thermal spray is adapted to enhance the prediction accuracy of the in-flight particle characteristics. The successful AL framework implementation via Bayesian Optimisation is beneficial in, first, reducing the maximum uncertainty, which greatly improves the prediction accuracy and informativeness of the existing database. Second, it reduces local uncertainty around a contrived test point that offers the capability to find improvement in a limited search area, allowing an accurate prediction around a desired test point. The dataset for Machine Learning (ML) training consists of 26 atmospheric plasma spray (APS) parameters of silicon and a further six AL-guided spray runs carried out to reduce maximum uncertainty in the initial database. On average, a 52.9% improvement (error reduction) of RMSE and an R2 increase of 8.5% were reported on the predicted in-flight particle velocities and temperatures after the AL-driven optimisation. Furthermore, the contrived test point optimisation to predict the best possible characteristics in a limited search space resulted in a three-fold increase in prediction accuracy compared to the non-optimised prediction. The AL-driven optimisation proved to be greatly beneficial for resource-intensive thermal spraying, as the framework not only allowed an accurate prediction of the in-flight particle characteristics but also found expected improvement around a desired in-flight characteristic. Furthermore, the framework uses the Gaussian Process (GP) ML model as a surrogate that generalises a global solution without necessarily involving physical and underlying mechanisms, thus extending the framework to other thermal spraying methods.<\/p>\n<p><a class=\"btn-tec black\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0952197623016494\" target=\"_blank\" rel=\"noopener\">Link to publication<\/a><\/p>\n<p>Key words: Atmospheric plasma spray, Machine learning, Bayesian optimisation, Active learning, Thermal spray<\/p>\n<p><em><small>Originally published at Engineering Applications of Artificial Intelligence (Volume 128, February 2024, 107465)<br \/>\nBy Halar Memon, Eskil Gjerde, Alex Lynam, Amiya Chowdhury, Geert De Maere, Grazziela Figueredo, Tanvir Hussain<\/small><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The first-of-its-kind use of the active learning (AL) framework in thermal spray is adapted to enhance the prediction accuracy of [&#8230;]<\/p>\n<p><a class=\"btn btn-secondary understrap-read-more-link\" href=\"https:\/\/spraysensors.tecnar.com\/fr\/active-learning-driven-uncertainty-reduction-for-in-flight-particle-characteristics-of-atmospheric-plasma-spraying-of-silicon\/\">Lire la suite&#8230;<\/a><\/p>\n","protected":false},"author":22,"featured_media":5692,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[54],"tags":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Active Learning-Driven Uncertainty Reduction For In-Flight Particle Characteristics of Atmospheric Plasma Spraying of Silicon - Tecnar Thermal Spray<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/spraysensors.tecnar.com\/fr\/active-learning-driven-uncertainty-reduction-for-in-flight-particle-characteristics-of-atmospheric-plasma-spraying-of-silicon\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Active Learning-Driven Uncertainty Reduction For In-Flight Particle Characteristics of Atmospheric Plasma Spraying of Silicon - Tecnar Thermal Spray\" \/>\n<meta property=\"og:description\" content=\"The first-of-its-kind use of the active learning (AL) framework in thermal spray is adapted to enhance the prediction accuracy of [...]Lire la suite...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/spraysensors.tecnar.com\/fr\/active-learning-driven-uncertainty-reduction-for-in-flight-particle-characteristics-of-atmospheric-plasma-spraying-of-silicon\/\" \/>\n<meta property=\"og:site_name\" content=\"Tecnar Thermal Spray\" \/>\n<meta property=\"article:publisher\" 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