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利用深度学习来检查手工工具组装中的缺陷.

Hong-Dar Lin1, Cheng-Kai Jheng1, Chou-Hsien Lin2

  • 1Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种自动化视觉检查系统,用于检测手工工具的组装缺陷. 该系统有效地识别了缺失,错位,外来或额外的零件,提高了产品质量和安全性.

关键词:
在R-CNN系列模型中.组装缺陷 组装缺陷 组装缺陷深度学习是一种深度学习.手工工具是指手工工具.视觉检查 视觉检查 视觉检查 视觉检查

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科学领域:

  • 制造业 工程 制造工程
  • 计算机视觉 计算机视觉
  • 质量控制 质量控制 质量控制

背景情况:

  • 在精密工业中,产品组装完整性至关重要,但手工监督会导致缺陷.
  • 常见的组装异常,如缺失,错位,外来或额外的部件,导致了大量的客户投诉,特别是在手工工具行业.
  • 目前的检查方法依赖于手工劳动和经验,缺乏全面的缺陷检测能力.

研究的目的:

  • 提出和评估用于检测手工工具制造中的组装缺陷的自动化视觉检查系统.
  • 通过提供一个系统的方法来识别常见的组装异常来解决手工检查的局限性.
  • 调查深度学习模型的性能,特别是R-CNN系列,用于自动化缺陷分类.

主要方法:

  • 从三个装配站收集了钥匙工艺的图像,记录了四个异常类别的28种缺陷类型.
  • 通过过噪音并使用圆形面罩提取感兴趣区域 (ROI) 进行预处理的图像.
  • 利用手动注释进行缺陷标记,并应用基于R-CNN的模型进行特征提取和分类,将其性能与其他物体检测模型进行比较.

主要成果:

  • 开发的自动化视觉检查系统在检测和分类组件缺陷方面表现出高效率.
  • 在每个站点中,表现最好的模型实现了平均92.64%的缺陷检测率 (1-β).
  • 该系统的平均误判率 (α) 为6.68%,平均正确分类率 (CR) 为88.03%.

结论:

  • 自动化视觉检查系统成功地解决了在手工工具组装中全面检测缺陷的需求.
  • 在每个装配站实施最适合的深度学习模型显著提高了缺陷检查的准确性和效率.
  • 这项技术提供了一个强大的解决方案,以减少客户投诉,提高整体产品质量和安全性.