Real-time welding defect classification using peak count analysis of current signals with statistical validation
Welding is a critical process in heavy industries such as construction, automotive, and oil and gas, where weld quality directly impacts structural performance and safety. Traditional non-destructive testing (NDT) methods, although effective, are often labour-intensive, costly, and reliant on operat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Institute of Physics
2025
|
| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/38117/1/Real-time%20welding%20defect%20classification%20using%20peak%20count%20analysis%20of%20current%20signals%20with%20statistical%20validation.pdf https://umpir.ump.edu.my/id/eprint/38117/ https://doi.org/10.1088/2631-8695/ae024d |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Welding is a critical process in heavy industries such as construction, automotive, and oil and gas, where weld quality directly impacts structural performance and safety. Traditional non-destructive testing (NDT) methods, although effective, are often labour-intensive, costly, and reliant on operator expertise. This study investigates an alternative approach using real-time monitoring of welding current signals to identify defects based on peak count variations. Under controlled laboratory conditions, welding current signals were captured and segmented into 1 mm intervals for detailed analysis. Statistical evaluation using Analysis of Variance (ANOVA) and Tukey’s post-hoc tests in R Studio revealed significant differences in peak distributions across various defect types. Good welds consistently exhibited 8-17 peaks per segment, while defects such as Lack of Penetration (LOP), Lack of Fusion (LOF), Burn-through, and Excess Weld displayed distinctive peak count deviations. These results confirm that peak count analysis is a statistically significant and reliable metric for real-time weld quality assessment. The findings lay the foundation for future development of intelligent welding systems capable of automated defect detection and adaptive process control. |
|---|