{"id":5418,"date":"2024-03-01T18:02:29","date_gmt":"2024-03-01T09:02:29","guid":{"rendered":"https:\/\/ktjcorp.co.jp\/?post_type=case-study&p=5418"},"modified":"2025-12-15T14:31:23","modified_gmt":"2025-12-15T05:31:23","slug":"rubber-tube-visual-inspection","status":"publish","type":"case-study","link":"https:\/\/ktjcorp.co.jp\/en\/case-study\/rubber-tube-visual-inspection\/","title":{"rendered":"Rubber Tube Visual Inspection"},"content":{"rendered":"

\u2699\ufe0f Case Study:
\nAI-Powered Online
\nVisual Inspection
\nof Rubber Tubes<\/span><\/h1>\n

 <\/p>\n

1. Challenges in Conventional Inspection<\/strong><\/h2>\n

Rubber tubes, used extensively in automotive components, medical devices, and other critical applications<\/strong>,
play a key role in ensuring the overall safety and performance of the products they are part of.<\/p>\n

However, traditional inspection methods face several challenges:<\/p>\n

1. Risk of Missing Defects<\/strong><\/h3>\n

Rubber tube production lines operate at high speeds, making it extremely difficult for human inspectors to continuously detect fine defects<\/strong> such as:<\/p>\n

    \n
  1. Ink streaks or discoloration<\/li>\n
  2. Bulges or depressions<\/li>\n
  3. Small holes or porosity<\/li>\n
  4. Surface scratches<\/li>\n<\/ol>\n

    2. Labor and Time Intensive<\/strong><\/h3>\n

    Accurately inspecting every single product requires a significant amount of time and effort<\/strong>.<\/p>\n

    To enhance quality assurance, we implemented AI-based inspection technology<\/strong>, fundamentally improving the inspection process.<\/p>\n

    \n

    2. AI-Enabled High-Precision Online Inspection<\/strong><\/h2>\n

    The AI-powered online inspection system ensures the quality of rubber tubes without halting the production line<\/strong>.<\/p>\n

    \n

    How the AI Inspection System Works<\/strong><\/h3>\n

    1. Real-Time Monitoring<\/strong><\/h4>\n

    The AI system continuously captures images of rubber tubes on the production line using high-performance cameras<\/strong>.
    It analyzes these images in real time<\/strong>, performing at a level equivalent to\u2014or even exceeding\u2014human inspectors maintaining hours of continuous attention.<\/p>\n

    2. Accurate Detection of Diverse Defects<\/strong><\/h4>\n

    The AI has been thoroughly trained with data sets containing both normal tubes and various types of defects<\/strong>, enabling rapid and highly accurate detection of:<\/p>\n

      \n
    1. \n

      Ink streaks or color irregularities<\/p>\n<\/li>\n

    2. \n

      Bulges or depressions<\/p>\n<\/li>\n

    3. \n

      Porosity or pinholes<\/p>\n<\/li>\n

    4. \n

      Surface scratches or foreign material<\/p>\n<\/li>\n<\/ol>\n

      3. Immediate Marking of Defect Locations<\/strong><\/h4>\n

      When a defect is detected, the AI system instantly identifies its location<\/strong> and communicates it to subsequent processes, e.g., by physically marking the defective tube.
      This allows production staff to quickly and reliably remove defective products<\/strong>, ensuring process efficiency and product quality.<\/p>\n

       <\/p>\n

      \"\"
      Rubber Tube Visual Inspection<\/figcaption><\/figure>\n

       <\/p>\n

       <\/p>\n

      \"\"
      Inspection Results<\/figcaption><\/figure>\n

      The introduction of AI technology has transformed rubber tube inspection from a time-consuming and labor-intensive task<\/strong> into a highly precise and efficient process<\/strong>.<\/p>\n

      Through this AI online inspection system, we continue to deliver products of the highest quality and reliability<\/strong>, providing customers with confidence and peace of mind.<\/p>\n

      For Inquiries<\/strong><\/h2>\n

      If you would like to explore the potential application of this AI inspection system to your products,
      or if you have specific technical requirements,
      please feel free to contact us.<\/p>\n","protected":false},"excerpt":{"rendered":"

      Rubber tubes, used extensively in automotive components,<\/p>\n","protected":false},"featured_media":3978,"template":"","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"enabled","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[33],"class_list":["post-5418","case-study","type-case-study","status-publish","has-post-thumbnail","hentry","category-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/ktjcorp.co.jp\/en\/wp-json\/wp\/v2\/case-study\/5418","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ktjcorp.co.jp\/en\/wp-json\/wp\/v2\/case-study"}],"about":[{"href":"https:\/\/ktjcorp.co.jp\/en\/wp-json\/wp\/v2\/types\/case-study"}],"version-history":[{"count":0,"href":"https:\/\/ktjcorp.co.jp\/en\/wp-json\/wp\/v2\/case-study\/5418\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ktjcorp.co.jp\/en\/wp-json\/wp\/v2\/media\/3978"}],"wp:attachment":[{"href":"https:\/\/ktjcorp.co.jp\/en\/wp-json\/wp\/v2\/media?parent=5418"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ktjcorp.co.jp\/en\/wp-json\/wp\/v2\/categories?post=5418"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}