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Updated: Jun 23, 2026
07:58
Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
Published on: July 25, 2025
[Study on the arc spectral information for welding quality diagnosis]
Zhi-Yong Li1, Xiao-Yan Gu, Huan Li
1Welding Research Center, North University of China, Taiyuan 030051, China. lizhiyong@nuc.edu.cn
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|May 22, 2009
Summary
Spectral analysis of Gas Tungsten Arc (GTAW) and Gas Metal Arc (GMAW) welding arcs reveals distinct characteristics. Specific spectral zones enable real-time welding quality diagnosis for both processes.
Area of Science:
- Plasma physics principles applied to welding arc spectral analysis.
- Characterization of broadband radiation from infrared to ultraviolet frequencies.
- Analysis of line and continuous spectra in welding arcs.
Context:
- Welding arc spectra differ significantly between Gas Tungsten Arc (GTAW) and Gas Metal Arc (GMAW) due to varying metal densities.
- Gas Metal Arc (GMAW) exhibits higher radiation intensity and more metal line spectra compared to Gas Tungsten Arc (GTAW).
- Gas Tungsten Arc (GTAW) spectra are stable, reflecting disturbances via specific elemental spectral lines.
Purpose:
- To analyze spectral signals from GTAW and GMAW welding arcs using spectrometers.
- To differentiate spectral characteristics between GTAW and GMAW processes for quality diagnosis.
- To identify optimal spectral zones for reliable welding quality and disturbance detection.
Summary:
- Gas Metal Arc (GMAW) welding arc spectra fluctuate due to droplet transfer, making line-absent zones suitable for diagnosis.
- Gas Tungsten Arc (GTAW) welding quality is diagnosed using the 230-300 nm ultraviolet spectral zone.
- Gas Metal Arc (GMAW) welding quality is diagnosed using the 570-590 nm visible spectral zone.
- Integral intensity analysis in selected zones confirms reliable diagnosis of welding quality and disturbances.
Impact:
- Established a theoretical basis for welding quality diagnosis using spectral analysis.
- Demonstrated real-time welding quality diagnosis capability through spectral signal monitoring.
- Achieved good signal-to-noise ratios for accurate detection of welding quality and disturbances.
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