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Related Experiment Video

Updated: Sep 20, 2025

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Weld Feature Extraction Based on Semantic Segmentation Network.

Bin Wang¹, Fengshun Li¹, Rongjian Lu¹

¹College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)

|June 10, 2022

Summary

This summary is machine-generated.

This study introduces a novel lightweight semantic segmentation network for accurate laser welding seam tracking. The developed model achieves a 96% success rate, enhancing industrial robot automation efficiency.

Keywords:

deep learning laser welding seam tracking semantic segmentation

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Area of Science:

Robotics and Automation
Computer Vision
Materials Science

Background:

Industrial production relies heavily on laser welding, necessitating automation for efficiency.
Accurate weld joint positioning is crucial for welding seam tracking systems.
Complex interference in laser welding images poses challenges for traditional tracking methods.

Purpose of the Study:

To design and develop a robust weld tracking module for industrial robot laser welding.
To achieve high-accuracy, real-time weld seam tracking in challenging image conditions.
To improve the efficiency and success rate of automated laser welding processes.

Main Methods:

A lightweight semantic segmentation network with an encoder-decoder architecture was designed.
A channel attention mechanism was introduced to enhance segmentation performance.
A dataset of 737 high-resolution (1920x1200) laser welding images was created and utilized.
The proposed network was compared against established models like ERF-Net, SegNet, and DFA-Net.

Main Results:

The developed network demonstrated a 96% success rate in weld segmentation.
The model achieved faster segmentation speeds compared to ERF-Net, SegNet, and DFA-Net.
The proposed network exhibited higher segmentation accuracy than the compared models.
Remarkable segmentation results were observed, indicating effective weld tracking.

Conclusions:

The lightweight semantic segmentation network offers a promising solution for accurate laser welding seam tracking.
The integration of a channel attention mechanism significantly improves segmentation performance.
The developed system enhances the potential for efficient and reliable automated laser welding.