Defect Identification in GRID-LOCK(R) Joints

GRID-LOCK® structures;Optical nondestructive evaluation;Vibration-based damage detection;Finite element analysis;Artificial neural networks;Ultrasonic C-scan

Pandurangan, Pradeep ; Dr. Kara J. Peters, Committee Member,Dr. Fuh-Gwo Yuan, Committee Member,Dr. Mohammed N. Noori, Committee Member,Dr. Gregory D. Buckner, Committee Chair,Pandurangan, Pradeep ; Dr. Kara J. Peters ; Committee Member ; Dr. Fuh-Gwo Yuan ; Committee Member ; Dr. Mohammed N. Noori ; Committee Member ; Dr. Gregory D. Buckner ; Committee Chair

University:North Carolina State University

关键词: GRID-LOCK® structures;    Optical nondestructive evaluation;    Vibration-based damage detection;    Finite element analysis;    Artificial neural networks;    Ultrasonic C-scan;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/5017/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Bonded metallic GRID-LOCK® structures are being adopted for a variety of aerospace applications due to their structural efficiency and damage tolerance. The development of non-destructive evaluation (NDE) methods is necessary to identify bond defects that can lead to failures in these structures. However, this task is complicated by the lack of interior access and complex geometry of GRID-LOCK® components.In this dissertation, the feasibility of various NDE techniques for detecting the existence, location, and extent of bond defects in GRID-LOCK® joints is investigated. Experiments are conducted on customized test structures to compare the effectiveness of optical NDE, ultrasonic C-scans and vibration-based damage detection.Finite element analysis (FEA) is used to interpret experimental results and highlight the advantages of candidate methods. The qualitative effectiveness of optical NDE is further investigated using full-field surface slope measurements (shearography). Because accurate characterization of structural defects is critical to flight safety, a quantitative non-destructive evaluation (QNDE) method using artificial neural networks (ANNs) is developed. This method involves the use of radial basis function networks (RBFNs) trained and validated using FEA simulation data.The effectiveness of this QNDE approach is demonstrated using experimental data from a custom-built optical scanning system.