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Updated: May 10, 2025

Surrogate Model Development for Digital Experiments in Welding
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Resistance Welding Quality Through Artificial Intelligence Techniques.

Luis Alonso Domínguez-Molina1, Edgar Rivas-Araiza1, Juan Carlos Jauregui-Correa1

  • 1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, México.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a non-destructive computer vision method for assessing resistance spot welding (RSW) quality. Visible images proved more effective than thermal images for classifying weld quality and predicting mechanical strength.

Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Computer Vision

Background:

  • Resistance spot welding (RSW) quality assessment is crucial in manufacturing.
  • Non-destructive evaluation (NDE) methods are increasingly important for maintaining material integrity.
  • Existing methods may alter joint properties, necessitating alternative approaches.

Purpose of the Study:

  • To develop a cost-effective, non-destructive quality evaluation methodology for RSW using computer vision.
  • To correlate welding process parameters with visual and mechanical quality outcomes.
  • To assess the performance of machine learning models trained on visual and thermal weld data.

Main Methods:

  • A manual RSW machine was used to collect process parameters (current, time, pressure) and visual/thermal images.
Keywords:
convolutional neural networkelectrode forceresistance spot weldingthermal imagesvisible imageswelding currentwelding time

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  • Six machine learning models were trained on image data to classify weld quality.
  • Model performance was evaluated using cross-validation, correlating image data with mechanical properties (pull force, weld diameter).

    • Main Results:

    • Welding time and electrode angle significantly influence joint mechanical strength.
    • Computer vision models utilizing visible images outperformed those using thermal images.
    • The proposed methodology effectively links process parameters to weld quality via visual data.

    Conclusions:

    • A non-destructive, computer vision-based approach offers a viable solution for RSW quality assessment.
    • Visible image analysis is a promising technique for predicting weld quality and mechanical properties.
    • This methodology can enhance manufacturing quality control in RSW applications.