Scholarlink

<p>In modern industries, undetected material surface defects lead to increased scrap rates and costly rework, primarily due to the limitations of manual inspection done in a slow and inconsistent process with poor small-defect identification. This lack of high-speed inspection solutions for multistage quality control creates critical gaps in production efficiency and product reliability. Hence, the research introduces a Hybridized Convolutional Neural Network for Surface Quality Control model that integrates U-Net with a ResNet34 backbone for precise defect localization and EfficientNet-B4 for defect classification, enhanced by stroboscopic illuminant preprocessing to optimize defect visibility. The research is validated on the Metal Surface Defect Dataset containing 147,824 high-resolution images capturing eight critical industrial defect types. The research results provide 98.2% classification accuracy, 96.5% defect localization precision, minimizes false alarms, and 98.2% recall for incoming material inspection, preventing defective inputs for industrial quality inspection. By integrating these innovations, the research helps manufacturers with a unified, scalable quality inspection platform that reduces human inspection workload by 12% while operating at production line speeds of 20.6 frames/sec and achieves 83.2 fps. The research model delivers a production-ready quality inspection system, which leads to maintaining a significant leap forward in automated surface quality assurance for Industry 4.0 applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Using Convolutional Neural Networks for Material Surface Quality Inspection and Classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月27日

查看全文
  • HanLin Ke

摘要

In modern industries, undetected material surface defects lead to increased scrap rates and costly rework, primarily due to the limitations of manual inspection done in a slow and inconsistent process with poor small-defect identification. This lack of high-speed inspection solutions for multistage quality control creates critical gaps in production efficiency and product reliability. Hence, the research introduces a Hybridized Convolutional Neural Network for Surface Quality Control model that integrates U-Net with a ResNet34 backbone for precise defect localization and EfficientNet-B4 for defect classification, enhanced by stroboscopic illuminant preprocessing to optimize defect visibility. The research is validated on the Metal Surface Defect Dataset containing 147,824 high-resolution images capturing eight critical industrial defect types. The research results provide 98.2% classification accuracy, 96.5% defect localization precision, minimizes false alarms, and 98.2% recall for incoming material inspection, preventing defective inputs for industrial quality inspection. By integrating these innovations, the research helps manufacturers with a unified, scalable quality inspection platform that reduces human inspection workload by 12% while operating at production line speeds of 20.6 frames/sec and achieves 83.2 fps. The research model delivers a production-ready quality inspection system, which leads to maintaining a significant leap forward in automated surface quality assurance for Industry 4.0 applications.