GMR-based eddy current probe for weld seam inspection and its non-scanning detection study. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Gao, Peng; Wang, Chao; Li, Yang; Wang, Libin; Cong, Zheng; Zhi, Ya
• Is Part Of: Nondestructive testing and evaluation. Volume 32:Issue 2(2017)
• Page Start: 133
• Page End: 151
• Publication Date: 2017-04-03
• Subjects: Defect detection -- weld defect -- giant magneto-resistive (GMR) sensor -- eddy current testing -- non-scanning testing; Non-destructive testing -- Periodicals; 620.112705
• DOI: 10.1080/10589759.2016.1149583
• Languages: English
• ISSNs: 1058-9759

Abstract

Abstract: Eddy current testing is one of the most important non-destructive testing methods for welding defects detection. This paper presents the use of a probe consisting of 4 giant magneto-resistive (GMR) sensors to detect weld defects. Information from four measuring points above and on both sides of the weld seam is collected at the same time. By setting the GMR sensors' sensing axes perpendicular to the direction of the excitation magnetic field, the information collected mainly reflects the change in the eddy current which is caused by defects. Digital demodulation technology is applied to extract the real part and imaginary part of the GMR sensors' output signals. The variables containing directional information of the magnetic field are introduced. Based on the data from the four GMR (4-GMR) sensors' output signals, four values, Ran, Mean, Var and k are selected as the feature quantities for defect recognition. Experiments are carried out on weld seams with and without defects, and the detection outputs are given in this paper. The 4-GMR probe is also employed to investigate non-scanning weld defect detection and the four feature quantities (Ran, Mean, Var and k ) are studied to evaluate weld quality. The non-scanning weld defect detection is presented. A support vector machine is used to classify and discriminate welds with and without defects. Experiments carried out show that through the method in this paper, the recognition rate is 92% for welds without defects and 90% for welds with defects, with an overall recognition rate of 90.9%, indicating that this method could effectively detect weld defects.