Multi-characteristic combination based reliability enhancement of optical bidirectional thermal wave radar imaging for GFRP laminates with subsurface defects

被引:6
作者
Gong, Jinlong [1 ,2 ,3 ]
Liu, Junyan [4 ,5 ]
Yu, Yanting [1 ,2 ,3 ]
Zheng, Yi [1 ,2 ,3 ]
机构
[1] Qilu Univ Technol, Shandong Acad Sci, Inst Oceanog Instrumentat, Qingdao 266100, Peoples R China
[2] Shandong Prov Key Lab Ocean Environm Monitoring T, Qingdao 266100, Peoples R China
[3] Natl Engn & Technol Res Ctr Marine Monitoring Equ, Qingdao 266100, Peoples R China
[4] State Key Lab Robot & Syst HIT, Harbin 150001, Peoples R China
[5] Harbin Inst Technol, Sch Mechatron Engn, Harbin 150001, Peoples R China
基金
中国国家自然科学基金;
关键词
Bidirectional chirp; Thermal wave radar imaging; Probability of detection; Multi-characteristic combination;
D O I
10.1016/j.ndteint.2021.102415
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
In this paper, the detection reliability of optical bidirectional thermal wave radar imaging (BTWRI) for glass fiber reinforced polymer (GFRP) laminates with subsurface defects using various imaging algorithms was quantitatively investigated. A set of GFRP laminates with artificial defects were prepared and inspected. Three frequently-used imaging algorithms (cross-correlation, CC; chirp lock-in, CLI; and Hilbert transform mean, HTM) were applied to construct characteristic images. An analysis for probability of detection (POD) was carried out based on the hit/miss data obtained by comparing the defect contrasts and noise thresholds of characteristic images. A multi-characteristic combination (MCC) method integrating the advantages of each algorithm was proposed. The reliability assessment of optical BTWRI for inspecting GFRP laminate defects was compared by the defect diameter-to-depth ratio (r(90/95)) at 90% POD with 95% confidence level and detection rates (DRs). The comparison results show that the MCC method exhibits enhanced reliability with smaller r(90/95) and higher DRs at a series of determination thresholds compared with CLI, HTM, and CC algorithms.
引用
收藏
页数:9
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