{"id":8333,"date":"2026-06-25T14:54:59","date_gmt":"2026-06-25T18:54:59","guid":{"rendered":"https:\/\/spraysensors.tecnar.com\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\/"},"modified":"2026-06-25T14:54:59","modified_gmt":"2026-06-25T18:54:59","slug":"prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks","status":"publish","type":"post","link":"https:\/\/spraysensors.tecnar.com\/fr\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\/","title":{"rendered":"Prediction Of Control Parameters Corresponding To In-Flight Particles In Atmospheric Plasma Spray Employing Convolutional Neural Networks"},"content":{"rendered":"<p>Optimization of control parameters for plasma spraying process is of great importance in thermal spray technology development. Engineers may limit themselves to local optimal solution by considering countable potential design solutions when selecting the plasma spray parameters in practice. This work proposes one decision support model by employing convolutional neural network (CNN) to explore the suitability of preliminary design. The approach aims to help engineers select global optimal solution with short time and low labor cost by invoking the models&#8217; capability of extracting potential features of in-flight particle characteristics. Simulation results make it possible to analyze new spraying process and train the designed model under the condition of insufficient experience and data. Firstly, the distributions of particle status obtained from simulation results act as the input and the control parameters are the output. Secondly, the projections between the in-flight particles and the control parameters are built implicitly and analyzed through CNN models. Thirdly, we validate the statistical information of particle state distributions through visualizing the feature maps and filters. Finally, the trained CNN models are verified by the fitted Gaussian distributions with basically consistent results. By building projections of in-flight particles and control parameters, new entrants and apprentices are capable of deducing the control parameters with the help of the pre-trained CNN model, thus cutting down the threshold for new practitioners.<\/p>\n<p><a class=\"btn-tec black\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0257897220305314\" target=\"_blank\" rel=\"noopener\">Link to publication<\/a><\/p>\n<p><em><small>Originally published at\u00a0Surface and Coatings Technology\u00a0(Volume 394, 25 July 2020, 125862)<br \/>\nBy Jinwei Zhu, Xinzhi Wang, Luyao Kou, Lili Zheng, and Hui Zhang<\/small><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Optimization of control parameters for plasma spraying process is of great importance in thermal spray technology development. Engineers may limit [&#8230;]<\/p>\n<p><a class=\"btn btn-secondary understrap-read-more-link\" href=\"https:\/\/spraysensors.tecnar.com\/fr\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\/\">Read More&#8230;<\/a><\/p>\n","protected":false},"author":22,"featured_media":8331,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[55],"tags":[],"class_list":["post-8333","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dpv-evolution-fr"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Prediction Of Control Parameters Corresponding To In-Flight Particles In Atmospheric Plasma Spray Employing Convolutional Neural Networks - 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\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Prediction Of Control Parameters Corresponding To In-Flight Particles In Atmospheric Plasma Spray Employing Convolutional Neural Networks - Tecnar Thermal Spray\" \/>\n<meta property=\"og:description\" content=\"Optimization of control parameters for plasma spraying process is of great importance in thermal spray technology development. Engineers may limit [...]Read More...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/spraysensors.tecnar.com\/fr\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\/\" \/>\n<meta property=\"og:site_name\" content=\"Tecnar Thermal Spray\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/tecnarautomation\" \/>\n<meta property=\"article:published_time\" content=\"2026-06-25T18:54:59+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/spraysensors.tecnar.com\/wp-content\/uploads\/2026\/06\/Website-library-image-9.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"500\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Flavia Fu\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Flavia Fu\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/spraysensors.tecnar.com\\\/fr\\\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/spraysensors.tecnar.com\\\/fr\\\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\\\/\"},\"author\":{\"name\":\"Flavia Fu\",\"@id\":\"https:\\\/\\\/spray-sensors.shany.ca\\\/#\\\/schema\\\/person\\\/34d54d027447e60a3c51e3378480de0f\"},\"headline\":\"Prediction Of Control Parameters Corresponding To In-Flight Particles In Atmospheric Plasma Spray Employing Convolutional Neural Networks\",\"datePublished\":\"2026-06-25T18:54:59+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/spraysensors.tecnar.com\\\/fr\\\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\\\/\"},\"wordCount\":261,\"publisher\":{\"@id\":\"https:\\\/\\\/spray-sensors.shany.ca\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/spraysensors.tecnar.com\\\/fr\\\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/spraysensors.tecnar.com\\\/wp-content\\\/uploads\\\/2026\\\/06\\\/Website-library-image-9.jpg\",\"articleSection\":[\"DPV\"],\"inLanguage\":\"fr-FR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/spraysensors.tecnar.com\\\/fr\\\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\\\/\",\"url\":\"https:\\\/\\\/spraysensors.tecnar.com\\\/fr\\\/prediction-of-control-parameters-corresponding-to-in-flight-particles-in-atmospheric-plasma-spray-employing-convolutional-neural-networks\\\/\",\"name\":\"Prediction Of Control Parameters Corresponding To In-Flight Particles In Atmospheric Plasma Spray Employing Convolutional Neural Networks - 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