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Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network.

Ran Li1, Manshu Dong2, Hongming Gao1

  • 1State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, West Straight Street 92, Harbin 150001, China.

Materials (Basel, Switzerland)
|April 3, 2021
PubMed
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This study developed an artificial neural network to predict weld bead geometry, accounting for changing welding speeds. The model accurately predicts bead width and reinforcement, improving quality detection in gas metal arc welding.

Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Robotics

Background:

  • Weld bead geometry is critical for industrial design and quality control.
  • Predicting bead geometry in dynamic welding processes is challenging due to complex parameter interactions.
  • Existing models struggle to account for real-time variations in welding parameters.

Purpose of the Study:

  • To develop an artificial neural network (ANN) model for predicting weld bead geometry.
  • To investigate the impact of spatially varying welding speed on bead geometry.
  • To enhance quality detection in gas metal arc welding (GMAW) through accurate geometry prediction.

Main Methods:

  • Utilized a welding robot for gas metal arc welding (GMAW) with stochastically varied welding speeds.
Keywords:
artificial neural networkbead geometryprediction modelwelding parameter

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  • Employed transient response tests to determine the spatial influence of welding speed on bead geometry.
  • Developed an ANN model with spatial welding speed sequences as input and bead width/reinforcement as outputs.
  • Measured bead geometry using a structured laser light sensor and polynomial fitting.

    • Main Results:

    • Demonstrated that changing welding speed has a significant spatial influence on bead geometry (10 mm backward to 22 mm forward).
    • The developed ANN model (33-6-2 structure) achieved high prediction accuracy.
    • Achieved 99% accuracy on the training dataset and 96% accuracy on the test dataset.

    Conclusions:

    • The ANN model effectively predicts weld bead geometry under dynamic welding conditions.
    • Accurate prediction of bead width and reinforcement is feasible despite changing welding speeds.
    • This approach offers a robust solution for real-time quality assessment in automated welding.