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先进的热成像处理和深度学习集成,用于增强碳纤维增强聚合物层材料的缺陷检测.

Renan Garcia Rosa1, Bruno Pereira Barella1, Iago Garcia Vargas1

  • 1Faculty of Computing, Federal University of Uberlandia, Uberlandia 38408-100, Brazil.

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概括

热图像预处理显著提高了碳纤维复合材料的缺陷检测. 这种方法提高了对非破坏性测试的细分精度,这对于航空航天和汽车等行业至关重要.

关键词:
用碳纤维增强的聚合物聚合物.深度学习是一种深度学习.非破坏性测试 (NDT) 是一种非破坏性测试.多项式的近似方法脉冲热谱是一种脉冲热谱.热图片预处理 热图片预处理

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科学领域:

  • 材料科学 材料科学 材料科学
  • 非破坏性测试 不破坏性测试
  • 图像处理 图像处理

背景情况:

  • 碳纤维增强聚合物 (CFRP) 层材在高性能工业中至关重要,因为它们的强度与重量比优越.
  • 在CFRP中检测缺陷是关键的,但具有挑战性,特别是在脉冲温度学中常见的低信号噪声比 (SNR) 条件下.
  • 由于噪音和信号变化,现有的细分方法往往无法达到高精度.

研究的目的:

  • 评估热图像预处理技术在改善CFRP层材缺陷细分方面的有效性.
  • 为了提高信号与噪声比 (SNR) 和温度数据中的缺陷可见性.
  • 评估U-Net架构在有或没有预处理的情况下对缺陷细分的性能.

主要方法:

  • 应用多项式近似和第一和第二顺序导数用于温度信号精细化.
  • 使用U-Net卷积神经网络架构进行图像细分.
  • 在应用预处理技术之前和之后对数据集进行细分性能比较.

主要成果:

  • 预处理显著提高了CFRP层材的缺陷细分精度.
  • 通过预处理实现了95%的IoU和99%的F1-Score.
  • 优于不包含预处理步骤的细分方法.

结论:

  • 热图像预处理对于提高CFRP非破坏性测试中的缺陷细分可靠性至关重要.
  • 开发的预处理方法大大提高了U-Net细分模型的性能.
  • 这项研究强调了优化图像处理的潜力,以提高关键行业的缺陷检测能力.