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Acoustic- and Parameters-Based Monitoring System for High Quality Arc Welding
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Acoustic- and Parameters-Based Monitoring System for High Quality Arc Welding

Xing-Meng-square.png
Name:
Xing Meng
University:
University of Birmingham
Research Title:
Acoustic- and Parameters-Based Monitoring System for High Quality Arc Welding
Abstract:
Arc welding is one of the main techniques used by the industry for joining metals. The arc welding process can be manual, semi-automatic or fully automatic. During the arc welding process, sometimes it is hard to obtain satisfactory weld quality because many unpredictable factors can occur. The quality of critical arc welds is normally evaluated using non-destructive testing after they have been completed using either radiographic or ultrasonic inspection techniques. Unfortunately, this means that if the weld is defective, the entire welding process needs to be repeated resulting in loss of time and additional costs which are highly undesirable.

 

Thus welding quality monitoring has attracted a lot of industrial interests. Welding quality monitoring is to ensure the integrity, reliability and usability of welded structures, in addition to welding technology, welding quality monitoring of welded structures is an important part of quality management.            

    

Acoustic emission (AE) is a dynamic non-destructive testing technique which is extensively used for structural integrity evaluation. AE has the potential to be employed as a tool to monitor the integrity of welding structures online, thus minimizing post-processing cost.                        

 

This project will investigate a novel approach for assessing online the quality of the arc welding process using advanced acoustic emission technology, arc parameter monitoring and appropriate signal processing. A multi-sensor monitoring device and processing system to be able to detect the defects in weld in real time. A through-arc sensor which used to collect welding voltage and current, acoustic emission sensor used to collect acoustic emission from weld will be adopt to gather multiple signals, then a system that can identify welding defects will be developed.

Publications: