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Surrogate Model Development for Digital Experiments in Welding
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Deep convolutional neural network for weld defect classification in radiographic images.

Dayana Palma-Ramírez1, Bárbara D Ross-Veitía2, Pablo Font-Ariosa3

  • 1Postgraduate Program Doctorate in Applied Computer Engineering School of Computer Engineering. University of Valparaiso. Valparaiso, Chile.

Heliyon
|May 10, 2024
PubMed
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A new deep learning model accurately detects weld defects like cracks and pores in radiographic images. This advanced CNN technology enhances construction safety and quality control, even with low-resolution images.

Area of Science:

  • Engineering
  • Computer Science
  • Materials Science

Background:

  • Weld quality is paramount for structural integrity and safety in construction.
  • Early detection of weld defects is essential for preventing structural failures.
  • Machine vision and deep learning offer advanced solutions for automated weld inspection.

Purpose of the Study:

  • To develop and evaluate a novel Convolutional Neural Network (CNN) model for classifying weld defects in radiographic images.
  • To improve the accuracy and efficiency of weld quality control processes.
  • To assess the model's performance across diverse datasets, including low-quality images.

Main Methods:

  • Implementation of a CNN model leveraging the ResNet50 architecture.
  • Training and validation using stratified cross-validation, data augmentation, and regularization techniques.
Keywords:
CNNsClassificationRadiographic testingTransfer learningWeld defects

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  • Testing the model on three distinct datasets: RIAWELC, GDXray, and a private low-quality image dataset.

    • Main Results:

    • The CNN model achieved high classification accuracies: 98.75% on RIAWELC, 90.255% on GDXray, and 75.83% on the low-quality dataset.
    • Demonstrated robust performance across varied image qualities and dataset characteristics.
    • Effectively classified four defect types: crack, pore, non-penetration, and no defect.

    Conclusions:

    • The proposed ResNet50-based CNN model is a highly accurate and effective tool for weld defect detection.
    • The model significantly enhances the efficiency and reliability of quality control in the welding industry.
    • The approach shows promise for real-world applications, including inspections of low-resolution radiographic images.