{"id":90095,"date":"2025-03-03T09:12:44","date_gmt":"2025-03-03T01:12:44","guid":{"rendered":"https:\/\/solomon-3d.mesvr.tw\/?p=90095"},"modified":"2025-03-03T09:17:29","modified_gmt":"2025-03-03T01:17:29","slug":"connectivity-cable-classification-using-ar-ai","status":"publish","type":"post","link":"https:\/\/solomon-3d.mesvr.tw\/fr\/etudes-de-cas\/connectivity-cable-classification-using-ar-ai\/","title":{"rendered":"Classification des c\u00e2bles de connectivit\u00e9 \u00e0 l’aide de la r\u00e9alit\u00e9 augment\u00e9e et de l’IA"},"content":{"rendered":"
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<\/span>\"\"
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META-aivi\u00c9tude de cas<\/span><\/h2>\n\n\n\n
\u00c9lectronique<\/a><\/span><\/div>\n<\/div><\/div>\n\n\n\n
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Classification des c\u00e2bles de connectivit\u00e9 \u00e0 l’aide de la r\u00e9alit\u00e9 augment\u00e9e et de l’IA<\/h1><\/div><\/div>\n\n\n\n
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Client<\/h2>\n\n\n\n

Le client est un fabricant d\u2019\u00e9lectronique bas\u00e9 en Tha\u00eflande sp\u00e9cialis\u00e9 dans les c\u00e2bles de connectivit\u00e9.<\/p>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n

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Cas<\/h2>\n\n\n\n
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Am\u00e9lioration du processus d’inspection des c\u00e2bles<\/h3>\n\n\n\n

Le client recherchait une solution pour am\u00e9liorer la qualit\u00e9 de son processus d’inspection des c\u00e2bles USB, en garantissant la durabilit\u00e9, la fonctionnalit\u00e9 et la conformit\u00e9 aux normes de s\u00e9curit\u00e9. De plus, il devait proc\u00e9der \u00e0 une inspection compl\u00e8te des mat\u00e9riaux d’isolation externes et des connecteurs pour d\u00e9tecter d’\u00e9ventuelles fissures ou dommages. Une fois ce processus termin\u00e9, les c\u00e2bles seraient class\u00e9s et exp\u00e9di\u00e9s en fonction de leurs types respectifs.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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\"cable<\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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D\u00e9fi<\/h2>\n\n\n\n
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Optimisation du contr\u00f4le qualit\u00e9 et automatisation de la classification<\/h3>\n\n\n\n

Le recours \u00e0 l’inspection humaine seule pour les c\u00e2bles risque d’entra\u00eener des n\u00e9gligences dues \u00e0 la fatigue, ce qui incite \u00e0 mettre en \u0153uvre l’AOI traditionnelle<\/a> pour la d\u00e9tection des d\u00e9fauts. Cependant, cela se traduit souvent par des syst\u00e8mes trop sensibles avec des param\u00e8tres stricts, ce qui conduit \u00e0 l’identification erron\u00e9e de produits bons comme NG (no good). Cela exige des ajustements continus de la part des ing\u00e9nieurs et des inspections r\u00e9p\u00e9t\u00e9es de la part des op\u00e9rateurs.<\/p>\n\n\n\n

De plus, l’AOI, n\u00e9cessitant une plus grande quantit\u00e9 d’\u00e9chantillons, ne peut d\u00e9tecter que des d\u00e9fauts pr\u00e9d\u00e9finis, manquant souvent des d\u00e9tections (fuites) et entravant la capacit\u00e9 \u00e0 r\u00e9pondre rapidement aux demandes de production flexibles.<\/p>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n

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Solution<\/h2>\n\n\n\n
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AR + IA pour une reconnaissance et une classification rapides<\/h3>\n\n\n\n

Pour relever les d\u00e9fis de classification, de reconnaissance et de d\u00e9tection, META-aivi<\/a> int\u00e8gre l’intelligence artificielle et la r\u00e9alit\u00e9 augment\u00e9e. N\u00e9cessitant seulement 5 \u00e0 10 \u00e9chantillons d’images, l’IA int\u00e9gr\u00e9e cr\u00e9e rapidement un mod\u00e8le pour une identification et une classification rapides des c\u00e2bles en fonction du type (notamment\u00a0: Lightning, USB, RJ45 et HDMI).<\/p>\n\n\n\n

Associ\u00e9 \u00e0 des appareils intelligents, des lunettes AR, des tablettes ou des cam\u00e9ras portables, il permet une reconnaissance et une classification rapides de diff\u00e9rents c\u00e2bles. Le syst\u00e8me facilite le transfert direct des donn\u00e9es associ\u00e9es dans le syst\u00e8me ERP de l’entreprise, g\u00e9n\u00e9rant des enregistrements \u00e9lectroniques pour r\u00e9f\u00e9rence future et am\u00e9lioration des processus. Cela, \u00e0 son tour, am\u00e9liore les taux de rendement et r\u00e9duit la sortie de produits d\u00e9fectueux.<\/p>\n\n\n\n

<\/div>\n\n\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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R\u00e9sultat<\/h2>\n\n\n\n
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<\/path><\/svg>Inspection de c\u00e2bles plus pr\u00e9cise et plus efficace<\/span><\/div>
<\/path><\/svg>Risques minimis\u00e9s li\u00e9s aux produits d\u00e9fectueux<\/span><\/div>
<\/path><\/svg>Transfert de donn\u00e9es rationalis\u00e9 et am\u00e9lioration des processus<\/span><\/div><\/div>\n<\/div>\n<\/div>\n <\/div><\/div>","protected":false},"excerpt":{"rendered":"

META-aivi transforme l’inspection des c\u00e2bles avec l’IA, garantissant la pr\u00e9cision dans la reconnaissance, la classification et la d\u00e9tection des d\u00e9fauts pour une fabrication \u00e9lectronique efficace.<\/p>","protected":false},"author":36,"featured_media":105341,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_gspb_post_css":"#gspb_heading-id-gsbp-5f6009ef-2b72{font-size:60px;line-height:100px;text-align:center!important;color:var(--gs-colorone);margin-top:50px}#gspb_heading-id-gsbp-5f6009ef-2b72 .gsap-g-line{text-align:center!important}#gspb_heading-id-gsbp-5f6009ef-2b72 .gspb_heading_subtitle,#gspb_subheading-id-gsbp-5f6009ef-2b72{display:block;line-height:22px;margin-top:5px;font-size:30px;color:var(--wp--preset--color--white, #ffffff)}.gspb_text-id-gsbp-fa6ec577-bc74{font-size:20px;text-align:center!important;font-weight:500!important}.gspb_text-id-gsbp-fa6ec577-bc74 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