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Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

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Published on: July 25, 2025

Sensor-Enabled Multi-Robot System for Automated Welding and In-Process Ultrasonic NDE.

Momchil Vasilev¹, Charles N MacLeod¹, Charalampos Loukas¹

¹Centre for Ultrasonic Engineering (CUE), Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK.

Sensors (Basel, Switzerland)

|August 10, 2021

Summary

This summary is machine-generated.

This study introduces an intelligent multi-robot system for automated welding and non-destructive evaluation (NDE). It enables real-time defect detection during welding, improving efficiency and reducing costs.

Keywords:

in-process NDE non-destructive evaluation robotic NDE robotic control robotic welding ultrasonic NDE ultrasound

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Area of Science:

Robotics and Automation
Materials Science and Engineering
Manufacturing Technology

Background:

Industry 4.0 drives demand for intelligent, sensor-enabled robotic systems in automated welding.
Increased production rates necessitate faster, robotic Non-Destructive Evaluation (NDE) to replace manual inspection.

Purpose of the Study:

To develop and deploy a novel multi-robot system for integrated automated welding and in-process NDE.
To enable real-time motion correction and on-the-fly inspection during welding processes.

Main Methods:

Development of a multi-robot system with full external positional control.
Integration of multi-sensory input for real-time data acquisition and analysis.
Demonstration of inspection at various stages: post-weld, inter-pass, and during live-arc deposition.

Main Results:

The system successfully performed automated welding and in-process NDE.
Defect detection capabilities were demonstrated on artificially induced defects.
Early defect detection was achieved, significantly reducing the delay between defect formation and discovery.

Conclusions:

The developed system offers early defect detection, enabling in-process weld repair.
This approach leads to higher production efficiency, reduced rework, and lower manufacturing costs.