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基于深度学习的封故障检测:CNN的方法

S Arumai Shiney1, R Seetharaman2, V J Sharmila3

  • 1Department of Computer Science and Engineering, S.A. Engineering College, Chennai, India. arumaishiney@saec.ac.in.

Scientific reports
|February 8, 2025
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概括

本研究介绍了一种使用深度学习和卷积神经网络 (CNN) 的自动封装检查系统. 该系统准确地检测出错位或错误安装的密封件,提高制造质量控制和产品可靠性.

关键词:
在美国,CNN是CNN.深度学习是一种深度学习.气的气是一个气.气检查检查 气检查质量控制 质量控制 质量控制散热器 散热器 散热器

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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
  • 人工智能的人工智能

背景情况:

  • 气检查对于产品质量控制至关重要.
  • 手动检查方法耗时且容易出现错误.
  • 需要自动化解决方案来提高效率和准确性.

研究的目的:

  • 开发一种自动化系统,用于检测错位或不正确安装的密封件.
  • 为了利用深度学习,特别是卷积神经网络 (CNN),进行封检查.
  • 提高制造业质量控制的可靠性和效率.

主要方法:

  • 利用深度学习算法进行特征提取和分类.
  • 开发了一个CNN架构,包括卷积,批量规范化,ReLU和最大聚合层.
  • 在散热器图像上训练系统,以识别密封件安装缺陷.

主要成果:

  • 开发的基于CNN的系统在识别不对齐的具方面表现出了很高的准确性.
  • 该系统有效地自动检测错误安装的密封件.
  • 结果表明,在工业环境中具有强大的实际应用潜力.

结论:

  • 自动封装检查系统提供了可靠和高效的质量控制机制.
  • 实施可以显著减少产品缺陷,提高整体产品可靠性.
  • 深度学习方法为制造质量保证提供了强大的解决方案.