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基于卷积神经网络算法的GMAW过程的缺陷检测.

Haichao Li1, Yixuan Ma2, Mingrui Duan1

  • 1State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin, 150001, China.

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概括

本研究介绍了一个卷积神经网络 (CNN) 算法,用于预测气体金属弧中的接质量. 美国有线电视新闻网 (CNN) 模型在使用化池图像检测透,石坑和渣等缺陷时,达到95%以上的准确性.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 制造业 工程 制造工程
  • 人工智能的人工智能

背景情况:

  • 预测接质量对于气体金属弧 (GMAW) 工艺至关重要.
  • 现有的方法可能会与弧光干扰作斗争,影响化池图像清晰度.

研究的目的:

  • 使用卷积神经网络 (CNN) 开发一个强大的接缺陷检测算法.
  • 通过分析化池图像,准确预测接透,坑形成和渣渣缺陷.
  • 为了在各种接场景中提高算法的稳定性.

主要方法:

  • 开发了一种传感系统和图像处理算法,以捕获清晰的化池图像,克服弧光干扰.
  • 利用CNN模型训练和测试化池图像的数据集.
  • 优化了CNN参数 (内核大小,批量大小,学习率) 以提高预测准确度.

主要成果:

  • 在接缺陷方面实现了超过95%的预测准确度.
  • 成功地将化池的视觉特征与特定缺陷相关联,例如烧透 (黑洞),表面孔隙 (圆形空隙) 和融合孔.
  • 证明了该模型在火山口缺乏腔时识别过度透的能力.

结论:

  • 开发的CNN算法有效地预测接质量,并检测GMAW中的缺陷.
  • 该模型对化池特征的分析为缺陷形成机制提供了洞察力.
  • 该算法显示了增强的稳定性和现实世界的接应用的潜力.