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Related Experiment Video

Updated: Jun 11, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

Penetration State Recognition during Laser Welding Process Control Based on Two-Stage Temporal Convolutional

Zhihui Liu^1,2, Shuai Ji^2,3, Chunhui Ma⁴

¹Joint SDU-NTU Centre for Artificial Intelligence Research, School of Software, Shandong University, Jinan 250101, China.

Materials (Basel, Switzerland)

|September 28, 2024

Summary

This study introduces a new deep learning method for laser welding control, achieving 98.96% accuracy in recognizing welding penetration. The approach uses a temporal convolutional network for precise, real-time quality control.

Keywords:

image processing laser welding weld penetration monitoring weld process control

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Last Updated: Jun 11, 2025

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Area of Science:

Welding quality control
Deep learning applications
Robotic automation

Background:

Vision-based laser penetration control is crucial for welding quality.
Deep learning models face challenges with parameter complexity and long-term information for penetration identification.

Purpose of the Study:

To develop an effective deep learning method for online laser welding process control.
To improve penetration recognition accuracy and speed in laser welding.

Main Methods:

A coaxial vision welding monitoring system was developed.
A lightweight segmentation model with channel pruning extracted molten pool and keyhole features.
A temporal convolutional network with an attention mechanism was used for penetration recognition.

Main Results:

The proposed method achieved 98.96% accuracy in penetration identification.
The average inference speed was 20.4 ms, enabling real-time control.
Experiments demonstrated effective recognition from long image sequences and closed-loop control.

Conclusions:

The developed two-stage temporal convolutional network method accurately identifies laser welding penetration.
The system enables effective online process control, power adjustment, and stable welding quality.