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This paper presents a comprehensive study on the application of computer vision, specifically Convolutional Neural Networks (CNNs), for quality control in automotive interior manufacturing using Injection Molding Machines (IMMs). Despite the high precision and productivity of IMMs, a small percentage of defects persist. Addressing this, the paper compares four popular CNN algorithms—ResNet-50, SegNet, FCN, and PSPNet—on their ability to detect and analyze defects in post-manufacturing. The study outlines the advantages of IMMs, the challenges in defect management, and the potential of CNNs to revolutionize quality control processes. Experimental results demonstrate the efficacy of these algorithms, paving the way for enhanced quality management and reduced defect rates in the automotive sector.

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Computer Vision Based Quality Control for Molding Injection Machines

  • 会议论文

  • 发表日期：

    2025年

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  • Ramón Moreno,
  • Oscar García,
  • Miguel Del Río Cristobal,
  • Revanth Shankar Muthuselvam,
  • José María Sanjuan,
  • Andrés Vallejo,
  • Ting Wang

摘要

This paper presents a comprehensive study on the application of computer vision, specifically Convolutional Neural Networks (CNNs), for quality control in automotive interior manufacturing using Injection Molding Machines (IMMs). Despite the high precision and productivity of IMMs, a small percentage of defects persist. Addressing this, the paper compares four popular CNN algorithms—ResNet-50, SegNet, FCN, and PSPNet—on their ability to detect and analyze defects in post-manufacturing. The study outlines the advantages of IMMs, the challenges in defect management, and the potential of CNNs to revolutionize quality control processes. Experimental results demonstrate the efficacy of these algorithms, paving the way for enhanced quality management and reduced defect rates in the automotive sector.