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A significant factor and essential operation that ensures submersible pump impellers are inspected for quality to ensure top performance and reliability in different applications. The following paper discusses on how the techniques of computer vision and deep learning facilitate the ability to identify defects of the pump impellers in a quality control unit of the industry. The technical review, in this case, aims at providing a broad survey on a variety of deep and transfer learning approaches to quality control in manufacturing. The purpose of the study is to explore different approaches in identifying and classifying defects of impellers using an open dataset containing 7348 casting manufacturing top-view images. The goal is to explore and evaluate various recent implementations in computer vision and deep learning and arrive at the best approach used to detect defects. A comparative analysis of different novel techniques is also done as a part of this research, and the insights gained from this study can optimize industrial quality control techniques, aligning them with the industrial standards.

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Advancements in Vision-Based Deep Learning Techniques for Enhancing Quality Inspection in Submersible Pump Impellers

  • 会议论文

  • 发表日期：

    2025年

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  • Judeson Antony Kovilpillai,
  • K. C. Krishnachalitha,
  • Puneet Kumar Yadav,
  • K. Lalli,
  • S. Jayanthy,
  • Soumi Dhar

摘要

A significant factor and essential operation that ensures submersible pump impellers are inspected for quality to ensure top performance and reliability in different applications. The following paper discusses on how the techniques of computer vision and deep learning facilitate the ability to identify defects of the pump impellers in a quality control unit of the industry. The technical review, in this case, aims at providing a broad survey on a variety of deep and transfer learning approaches to quality control in manufacturing. The purpose of the study is to explore different approaches in identifying and classifying defects of impellers using an open dataset containing 7348 casting manufacturing top-view images. The goal is to explore and evaluate various recent implementations in computer vision and deep learning and arrive at the best approach used to detect defects. A comparative analysis of different novel techniques is also done as a part of this research, and the insights gained from this study can optimize industrial quality control techniques, aligning them with the industrial standards.