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Summary
This summary is machine-generated.

This study presents a method to accurately measure joint temperature during arc welding by correcting for reflected heat. This improved temperature data helps distinguish between quality and faulty welded joints.

Keywords:
TIGclusteringhardnessreflected temperaturethermographywelding

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Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Thermophysics

Background:

  • Accurate on-line joint temperature assessment is crucial for controlling material properties during welding.
  • Arc welding generates significant heat, causing surface reflections that interfere with infrared temperature measurements.
  • Existing methods struggle to compensate for reflected thermal radiation from the welding arc and torch.

Purpose of the Study:

  • To develop and evaluate a method for increasing the credibility of temperature measurements during arc welding.
  • To reduce the influence of reflected thermal radiation on infrared temperature data of welded joints.
  • To enable precise temperature characterization for quality assessment of welded joints.

Main Methods:

  • Utilized an infrared camera for surface temperature observation of the solidifying weld.
  • Employed finite element method (FEM) simulations, calibrated to fusion zone geometry, to model heat source properties.
  • Developed a temperature measurement credibility increase method by comparing infrared data with FEM simulations.
  • Applied a selected reflected temperature correction map to the infrared data.

Main Results:

  • Achieved a temperature representation with less than 10 °C difference from the FEM baseline after correction.
  • Enabled precise temperature value acquisition for welded joints.
  • Successfully clustered welded joints in a 3D feature space (temperature, hardness, linear energy).

Conclusions:

  • The proposed method effectively corrects for reflected heat, providing accurate on-line joint temperature measurements.
  • Precise temperature data, combined with hardness and linear energy, allows for reliable classification of welded joints.
  • K-means clustering successfully distinguishes between acceptable and faulty welded joints based on mechanical properties.