{"id":9045,"date":"2025-07-01T15:01:38","date_gmt":"2025-07-01T13:01:38","guid":{"rendered":"https:\/\/cim.eu\/casi-studio\/h-a-i-w-a\/"},"modified":"2026-01-12T11:51:02","modified_gmt":"2026-01-12T10:51:02","slug":"h-a-i-w-a","status":"publish","type":"case-study","link":"https:\/\/cim.eu\/en\/case-studies\/h-a-i-w-a\/","title":{"rendered":"H.A.I.W.A."},"content":{"rendered":"&#13;\n<p class=\"has-text-align-center\"><em>Funded by<\/em><\/p>&#13;\n\r\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\r\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">&#13;\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img decoding=\"async\" class=\"wp-image-25847\" style=\"width: 666px; height: auto;\" src=\"https:\/\/cim40.com\/wp-content\/uploads\/2025\/07\/thumbnail_image-2.png\" alt=\"\"\/><\/figure>&#13;\n<\/div>\r\n<\/div>\r\n","protected":false},"featured_media":9046,"template":"","annualita":[182],"area-di-intervento":[260],"area_tecnologica":[177],"settore":[185],"tecnologia-abilitante":[193],"class_list":["post-9045","case-study","type-case-study","status-publish","has-post-thumbnail","hentry","annualita-23-26","area-di-intervento-product","area_tecnologica-digital-factory","settore-automotive","tecnologia-abilitante-artificial-intelligence-computer-vision-deep-learning-synthetic-data-generation"],"acf":{"proponente":"Honda Italy","descrizione_breve":"Honda Artificial Intelligence for Wheels Assessment","logo_finanziatore":"","importo_finanziato":"","contesto":"Within the <strong>industrial production<\/strong> of the automotive sector, <strong>quality control<\/strong> of the wheels represents a critical phase to ensure safety, reliability, and performance. In the specific case of Honda, the quality control line for the wheels was not yet operational at the start of the project. This posed the challenge of developing an effective quality control system in the absence of a real dataset, typically derived from data acquired in-line.  ","soluzione":"To address this need, the project involved the development of a <strong>vision system<\/strong> based on <strong>artificial intelligence<\/strong> systems capable of performing advanced quality control on the wheels: specifically on the presence\/absence of bolts on the disc and the orientation of the disc and tire. In the absence of real data, a synthetic dataset was constructed by generating images and defect scenarios through simulations and digital models, thus automatically obtaining the labels of the components to be checked. AI algorithms based on deep learning were trained on this data to detect typical production anomalies and defects. The system was designed to integrate natively with the future production line, ensuring its operational readiness from the first start-up.   ","impatto":"The project demonstrated that the use of synthetic datasets can represent a concrete and scalable alternative for training AI models when a production line is not yet available. The solution allows Honda to implement a quality control system that is already mature and adaptable from the start of the plant's operation, <strong>reducing development times<\/strong> and <strong>optimizing performance<\/strong> from the beginning of production. Furthermore, the method can be reused for future lines or new models, <strong>reducing costs<\/strong> and <strong>increasing efficiency<\/strong> in production deployment.  ","supporto_cim":"CIM4.0 led the <strong>development of the AI system<\/strong>, with particular attention to the <strong>creation and management of the synthetic dataset<\/strong>, the <strong>design<\/strong> of the <strong>recognition algorithms<\/strong>, and the preparation of the <strong>PoC<\/strong> to be subsequently integrated in-line. The collaboration with Honda took place in constant dialogue to ensure consistency with the quality standards required by the automotive sector and maximum flexibility of the system once operational. ","gallery":"","video_di_progetto":"","partners":[3946],"tecnologia":"","competenza_in_campo":"Development of the AI system, dataset creation, design of recognition algorithms, preparation of the PoC "},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Casi Studio - H.A.I.W.A. - CIM<\/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:\/\/cim.eu\/en\/case-studies\/h-a-i-w-a\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Casi Studio - H.A.I.W.A. - CIM\" \/>\n<meta property=\"og:description\" content=\"&#013; Funded by &#013;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/cim.eu\/en\/case-studies\/h-a-i-w-a\/\" \/>\n<meta property=\"og:site_name\" content=\"CIM\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cim40\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-12T10:51:02+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/cim.eu\/wp-content\/uploads\/2025\/10\/immagini-schede-progetti-sito-web-7.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"500\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/cim.eu\\\/en\\\/case-studies\\\/h-a-i-w-a\\\/\",\"url\":\"https:\\\/\\\/cim.eu\\\/en\\\/case-studies\\\/h-a-i-w-a\\\/\",\"name\":\"Casi Studio - 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