{"id":9049,"date":"2025-07-01T14:41:55","date_gmt":"2025-07-01T12:41:55","guid":{"rendered":"https:\/\/cim.eu\/casi-studio\/tires-4-0\/"},"modified":"2026-01-12T11:51:03","modified_gmt":"2026-01-12T10:51:03","slug":"tires-4-0","status":"publish","type":"case-study","link":"https:\/\/cim.eu\/en\/case-studies\/tires-4-0\/","title":{"rendered":"Tires 4.0"},"content":{"rendered":"&#13;\n<p class=\"has-text-align-center\"><em>Funded by the European Union \u2013 Next Generation EU<\/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-large is-resized\"><img decoding=\"async\" class=\"wp-image-22683\" style=\"width: 225px; height: auto;\" src=\"https:\/\/cim40.imgix.net\/wp-content\/uploads\/2024\/11\/IT-Finanziato-dallUnione-europea_PANTONE-1-1024x228.jpg\" alt=\"\"\/><\/figure>&#13;\n<\/div>\r\n\r\n\r\n\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-22685\" style=\"width: 201px; height: auto;\" src=\"https:\/\/cim40.imgix.net\/wp-content\/uploads\/2024\/11\/logo-ministero.png-2.webp\" alt=\"\"\/><\/figure>&#13;\n<\/div>\r\n<\/div>\r\n&#13;\n<p>\u00a0<\/p>&#13;\n","protected":false},"featured_media":9051,"template":"","annualita":[182],"area-di-intervento":[260],"area_tecnologica":[177],"settore":[185],"tecnologia-abilitante":[199],"class_list":["post-9049","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-edge-computing-sensor-technology"],"acf":{"proponente":"Michelin Italiana S.A.M.I.","descrizione_breve":"AI and process sensorization","logo_finanziatore":"","importo_finanziato":"400.000","contesto":"The production of Michelin tires is a complex process that requires a rigorous selection of materials, constant monitoring of production phases, and effective storage management to ensure high quality standards. Currently, significant challenges arise related to the need to reduce diagnostic times, minimize the use of solvents, and optimize warehouse inventory management. ","soluzione":"<p class=\"paragraph\" style=\"margin: 0cm; vertical-align: baseline;\">The project adopted an approach in line with Industry 4.0 principles, focusing on increasing sensor technology and automation through the use of advanced technologies and artificial intelligence algorithms. Activities focused on four main technological areas. The first is <b>quality control of talc application<\/b>, implemented through an artificial vision system based on Edge AI, capable of detecting anomalies in talc distribution and preventing production defects. The second area involved <b>solvent tracking<\/b>, with the integration of ATEX sensors for continuous monitoring of octane consumption, to improve environmental sustainability and energy efficiency. The third area involved <b>predictive diagnostics<\/b>, utilizing acoustic analysis of machinery to predict potential failures and thus reduce downtime. Finally, a <b>semi-automatic inventory management system<\/b> was developed, based on the use of drones assisted by machine learning algorithms, allowing for more precise and rapid detection of warehouse status.     <\/p>","impatto":"<p class=\"paragraph\" style=\"margin: 0cm; vertical-align: baseline;\">The introduction of these technologies has the potential to generate significant benefits both operationally and economically. The adoption of artificial intelligence and computer vision has contributed to a <strong>reduction in waste<\/strong> and an <strong>improvement in product quality<\/strong>. Solvent consumption has been reduced by 2%, with an estimated <strong>economic saving<\/strong> of 181,000 euros per year. Additionally, <strong>production efficiency<\/strong> has increased, along with <strong>operational safety<\/strong>, thanks to the integration of automated and intelligent systems. Inventory <strong>management<\/strong> has also been optimized, with greater precision and reduced risk for operators. The project has also promoted the <strong>enhancement of internal digital skills<\/strong> and contributed to a <strong>reduction in environmental emissions<\/strong>, improving the overall sustainability of the process.     <\/p>","supporto_cim":"CIM4.0 supported Michelin throughout the entire project journey, from the requirements analysis phase to the prototyping and in-line testing of the developed solutions. During the twelve months of activity, support included the definition of technological solutions and benchmarking with the most advanced available in the sector. Proofs of concept were designed and implemented to assess the technical feasibility of the proposals, followed by the operational implementation of the solutions, which also included the development of AI algorithms and integration with existing systems. Finally, industrial validation was conducted, demonstrating the scalability of the technologies and contributing to the definition of the adoption roadmap at the European level for Michelin plants.   \n\n<strong>Other partners involved:<\/strong>\n<ul>\n \t<li><strong>Reply<\/strong> \u2013 AI solutions development and prototyping<\/li>\n<\/ul>","gallery":"","video_di_progetto":"","partners":[3712],"tecnologia":"","competenza_in_campo":"Requirements analysis, prototyping and in-line testing, definition of technological solutions, PoC for technical feasibility assessment of proposals, operational implementation of solutions, algorithm development, integration with existing systems, industrial validation, definition of adoption roadmap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Casi Studio - Tires 4.0 - 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\/tires-4-0\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Casi Studio - Tires 4.0 - CIM\" \/>\n<meta property=\"og:description\" content=\"&#013; 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