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自动视觉检查用于CBN插件中精确的缺陷检测和分类.

Li Zeng1, Feng Wan2, Baiyun Zhang3

  • 1School of Mechanical and Electrical Engineering, Zhejiang Industry Polytechnic College, Shaoxing 312000, China.

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概括

一个自动化的机器视觉系统准确地检测和分类立方化 (CBN) 插件的表面缺陷. 这提高了精密制造的质量控制,准确度超过90%.

关键词:
这是一个CBN插入.深度学习是一种深度学习.发现缺陷检测检测缺陷检测视觉检查技术是指视觉检查技术.

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 制造业 制造技术 制造技术
  • 计算机视觉和图像处理

背景情况:

  • 立方化 (CBN) 插件由于其特殊的硬度,在精密制造中至关重要.
  • 在CBN插件上的表面缺陷可以显著降低产品的完整性和性能.
  • 现有的缺陷检测方法可能缺乏自动化生产线所需的速度和准确性.

研究的目的:

  • 开发和验证用于检测和分类CBN插件表面缺陷的自动化机器视觉系统.
  • 评估CBN插件检查的各种缺陷检测算法的性能.
  • 创建一个强大而高效的系统,以加强高速制造业的质量控制.

主要方法:

  • 集成一个光学支架,高分辨率的工业摄像头,精确的照明,以及一个先进的开发板来获取图像.
  • 应用数字图像处理技术用于缺陷识别和分类.
  • 对多个缺陷检测算法的比较分析,考虑参数调整和数据集多样性.

主要成果:

  • 开发的系统可以实现多种缺陷类型的检测准确度超过90%.
  • 该系统显示牙表面识别效率为每秒三.
  • 工具的前面和侧面切割表面在每个中都被有效地捕获和分析.

结论:

  • 拟议的机器视觉系统为CBN插件的自动表面缺陷检测提供了一个可扩展和可靠的解决方案.
  • 这项技术显著改善了自动化,高速精密制造环境中的质量控制.
  • 该系统的高精度和效率为加强生产线监控和缺陷管理铺平了道路.