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相关概念视频

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....
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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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基于声信号的缺陷识别用于使用波段时间频率图的定向能量沉积弧.

Hui Zhang1, Qianru Wu1, Wenlai Tang1

  • 1Jiangsu Key Laboratory of 3D Printing Equipment & Manufacturing, School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China.

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|July 13, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用波段时间频率图的声信号方法,用于检测定向能量沉积弧 (DED-arc) 制造中的缺陷. 该方法实现了高精度,改善了缺陷识别时间.

关键词:
声学信号 声学信号 声学信号 声学信号卷积神经网络是一种卷积神经网络.缺陷识别 缺陷识别 缺陷识别电缆弧增材制造 电缆弧增材制造

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科学领域:

  • 增材制造 增材制造 增材制造
  • 材料科学 材料科学 材料科学
  • 声学信号处理 声学信号处理

背景情况:

  • 定向能量沉积弧 (DED-arc) 提供高沉积率和低成本.
  • 在DED-arc制造过程中,可能会出现不连续性和孔隙等缺陷.
  • 有效的缺陷识别对于增材制造中的质量控制至关重要.

研究的目的:

  • 提出一种基于声信号的新方法,用于在DED-arc增材制造中识别缺陷.
  • 用波纹时间频率图来进行缺陷分析.
  • 评估卷积神经网络 (CNN) 模型用于缺陷检测的性能.

主要方法:

  • 在DED-arc制造过程中获取现场声信号.
  • 将1D声信号转换为2D时间频率图,使用连续波形变换.
  • 在生成的图表上进行CNN模型 (AlexNet,ResNet-18,VGG-16,MobileNetV3) 的培训,验证和测试.

主要成果:

  • 提出的方法有效地发现了DED-arc制造过程中的缺陷.
  • 移动NetV3以98.31%的准确率达到最高,其次是ResNet-18以97.92%的准确率.
  • 在时间和频率领域观察到正常和异常声信号之间能量分布的显著差异.

结论:

  • 基于声信号的方法与波段时间频率图是DED-arc中缺陷识别的可行方法.
  • 该研究证明了CNN模型在增材制造中的实时质量评估方面的潜力.
  • 拟议的技术提前缺陷识别时间,使质量控制干预更快.