Simultaneous quantitative detection of thinning defects on the connection surface of heterogeneous metal plates
Abstract The significant difference in electromagnetic properties between aluminum and steel makes detecting corrosion thinning defects at their interface particularly challenging. In this study, we propose a method for the simultaneous detection and quantification of corrosion thinning defects at the interface of double-layered heterogeneous metal plates using pulsed eddy current technology. First, a finite element simulation model was developed to classify and identify feature signals through principal component analysis. Next, a novel approach based on characterization feature points (CFP) was introduced to detect and quantitatively evaluate corrosion thinning defects at the aluminum–steel interface. Finally, an experimental system was constructed, and comprehensive experimental studies were conducted. The results demonstrate that the proposed method can effectively distinguish and quantify corrosion thinning defects at aluminum–steel interfaces. Among the simultaneous quantification methods, the method based on CFP time exhibits a maximum relative measurement error of 33.54% and a minimum of 2.06%. In contrast, the method based on CFP amplitude demonstrates significantly lower errors, with a maximum relative measurement error of 5.95% and a minimum of 0.24%. This study presents a novel approach for the differentiation and simultaneous quantification of corrosion thinning defects in double-layer heterogeneous metal plates.
Authors
- Xiaochun Song (ORCID: https://orcid.org/0000-0002-2746-4580)
- Zhiyang Deng (ORCID: https://orcid.org/0000-0002-1299-6314)
- Jun Tu (ORCID: https://orcid.org/0000-0002-3731-4826)
- Yongqi Yin
- Tao Chen (ORCID: https://orcid.org/0000-0001-6588-9267)
- Cheng Lv (ORCID: https://orcid.org/0000-0002-0952-0323)
Institutions
- China Special Equipment Inspection and Research Institute (CN)
- Hubei University of Technology (CN)
- Hubei University (CN)
Publication Details
- Journal
- Measurement Science and Technology
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1088/1361-6501/adbf38
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00