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相关概念视频

Ultrasonography01:17

Ultrasonography

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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超声波测试中近表面缺陷检测,使用域知识为基础的自我监督学习.

Minsu Jeon1, Minseok Choi2, Wonjae Choi3

  • 1School of Mechanical Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea; Department of Mechanical Engineering, Ajou University, Suwon 16499, Republic of Korea.

Ultrasonics
|November 29, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的AI驱动的超声波测试方法,用于检测没有标记数据的近表面缺陷. 该方法准确地识别出缺陷的存在和深度,优于现有技术.

关键词:
数据合成数据的合成.解除自动编码器的自动编码器诊断 诊断 诊断 诊断自主监督学习学习超声波测试 超声波测试 超声波测试

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科学领域:

  • 材料科学 材料科学 材料科学
  • 非破坏性测试是指非破坏性测试.
  • 人工智能的人工智能

背景情况:

  • 人工智能 (AI) 增强了超声波测试 (UT),但需要大量的标记数据,这带来了挑战.
  • 传统的UT在接近表面的缺陷检测方面扎,主要专注于更深层的缺陷.
  • 现有的UT人工智能方法由于数据稀缺和近表面缺陷挑战而面临局限性.

研究的目的:

  • 提出一种新的,数据效率高的AI方法,用于UT的近表面缺陷检测.
  • 开发一个自我监督的异常检测模型,将UT的领域知识纳入其中.
  • 能够实现准确的缺陷检测和深度测量,而不需要标记训练数据.

主要方法:

  • 通过将测量信号与后墙反射融合而生成合成有缺陷的UT信号,遵守超声波叠加原理.
  • 开发了一个去异常网络,以隔离UT信号中的微妙缺陷特征.
  • 使用三西格玛规则确定缺陷存在的剩余输出和深度通过飞行时间计算.

主要成果:

  • 拟议的方法在各种条件下成功检测到块的近表面缺陷.
  • 使用剩余输出的飞行时间分析实现了准确的缺陷深度确定.
  • 定性和定量比较显示出高于现有的近表面缺陷检测方法的性能.

结论:

  • 这种新型的自我监督,域知识集成的人工智能模型有效地检测UT的近表面缺陷,而无需标记数据.
  • 该方法在识别缺陷存在和深度方面表现出高度准确性,解决了传统UT的局限性.
  • 这种方法为增强的非破坏性评估提供了一个有希望的解决方案,特别是在挑战近表面缺陷时.