18 TH INTERNATIONAL CONFERENCE ON COMPOSITE MATERIALS DETECTION OF IMPACT LOCATIONS FOR A COMPOSITE WING BOX UNDER BENDING LOADS B. W. Jang 1 , Y. G. Lee 1 , J. H. Kim 1 , Y. Y. Kim 1 , C. G. Kim 1 *, C. Y. Park 2 1 School of Mechanical, Aerospace & Systems Engineering, Division of Aerospace Engineering, KAIST, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea, 2 Aeronautical Technology Directorate (7-2), Agency for Defense Development, Yuseong PO Box 35, Yuseong-gu, Daejeon, Republic of Korea * Corresponding author(cgkim@kaist.ac.kr) Keywords : fiber Bragg grating sensors, impact locations, composite wing box structure 1 Introduction mean square) database method. This method uses The use of composite materials in the aerospace RMS values between each sensor’s signals. The industries is gradually increasing thanks to their combinations of RMS values are unique in each excellent merits such as high specific strength and training point on the structure so that impact stiffness, excellent corrosion resistance. However, in locations can be identified. Moreover, impact order to increase their portions as primary materials, experiments under bending loads were performed higher reliability is required because their various for simulating flight conditions. Then, we verified and complex damage modes [1]. Moreover, their the applicability of suggested method. mechanical behaviors after damages are hard to be predicted. Due to these reasons, concepts of 2 Experiments structural health monitoring (SHM) have been studied to enhance reliability and safety. 2.1 Test Specimen and Sensor Installations Furthermore, structural data from built-in sensor The test specimen is a full scale composite wing box system are useful for efficient inspection and structure as shown in Figure 1. It has upper and maintenance of composite structures [2]. Above all, lower skins, three spars (front, intermediate, and impact monitoring including impact location rear), ribs and stringers. This wing box structure was detection and damage assessment is indispensable designed and manufactured by DACC Ltd. (Korea). because composite materials are susceptible to The test section is on the upper skin and the impact damages [3]. Moreover, most of impact dimension is 0.5 × 0.5 mm 2 with the grid size of 0.1 induced damages are hidden inside the laminates or m. occur on the opposite surface. Thus, detection of these damages without any information using conventional inspection methods is time and cost T est section consuming. Most of previous research for detection of impact locations used neural networks or triangulation method using impact wave speed [3-5]. However, these methods can sometimes induce significant errors because of unstable estimations of neural net’ input data and non-linearity of impact wave speed. 1,117 mm 2,340 mm Moreover, they require high quality impact signals so that the covering area is limited by sensitivity of used sensor types. Thus, for efficient impact Fig.1. Full scale UAV composite wing box. identifications, high probability of detections, large covering area and simple sensor system are required. For acquisition of impact signals, fiber Bragg In this study, impact identifications for a composite grating (FBG) sensors were used. Six FBG sensor wing box structure are suggested using RMS (root heads were multiplexed in one optical fiber line. As
depicted in Figure 2, each FBG sensor was attached energy was 2 J in order to prevent impact damage on the inner surface of test section. occurrences. Due to the curvature of wing skin, the impact test fixture was tilted as shown in Figure 3. FBG 6 FBG 1 FBG 5 FBG 2 FBG 4 FBG 3 0.5 Curved surface FBG 6 FBG 1 0.4 0.3 FBG 2 FBG 5 Y (m) Fig.3. Experimental set up. 0.2 0.1 FBG 3 FBG 4 3 Impact identifications 0.0 3.1 Construction of RMS database 0.0 0.1 0.2 0.3 0.4 0.5 X (m) Figure 4 shows the FBG sensor signals in case of Fig.2. Test section and sensor locations. impact on (2, 2) point. Using the acquired signals in the unloading case, the database was constructed for each grid point. In this database, there are twelve 2.2 High-speed FBG Interrogator values calculated from the FBG sensor signals. To acquire a FBG sensor signal at a sampling frequency of 100 kHz in multiplexing, a newly- 1.6 FBG 6 developed high-speed FBG interrogation system 1.4 (SFI-710, Fiberpro Inc., Korea) was adopted. Using FBG 5 this commercial FBG interrogation system, more 1.2 Wavelength shift (nm) applicable impact identification techniques could be 1.0 FBG 4 realized. 0.8 2.3 Low-velocity Impact Experiments FBG 3 0.6 In this study, in order to give the low-velocity 0.4 FBG 2 impact without dual impacts, an instrumented impact 0.2 test fixture was used. The low-velocity impacts were FBG 1 0.0 given on the grid points (32 points) in the test 0.00 0.01 0.02 0.03 0.04 section. To make loading conditions, up and down Time (sec) bending loads were applied to the wing box tip Fig.4. FBG sensor signals. positions. Thus, the loading conditions are unloading, up and down bending. Low-velocity impact tests Firstly, six RMS values between each sensor signal were performed in each loading case. The impact were calculated (R 12 , R 23 , R 34 , R 45 , R 56 and R 61 ).
PAPER TITLE These numbers between one and six mean the FBG the number and size of grids in the test section. Thus, sensor number. Secondly, six integration values of in order to detect the impact location more precisely, each sensor’ normalized signal were obtained (I 1 , I 2 , additional database are required on other regions in I 3 , I 4 , I 5 and I 6 ). For calculating of these integrations, the test section. The detected impact locations on the absolute values were used. Because the number of non-grid points were shown in Figure 6. grid points is thirty two, there are thirty two sets of RMS and integration values in the database. Unloading Once an impact signal is acquired, the RMS and 0.5 integration values are immediately calculated. Then, Point 1 by comparing these values and database, an impact 0.4 location could be detected. Figure 5 shows the flow Point 2 chart of this RMS database method. 0.3 Point 3 0.2 Data acquisitions 0.1 Baseline converting 0.0 0.0 0.1 0.2 0.3 0.4 0.5 Arrival time detection Up-bending 0.5 Subtracted data from arrival time Point 1 0.4 Point 2 0.3 RMS values Integration values Point 3 0.2 Comparison from 0.1 database 0.0 Location detection 0.0 0.1 0.2 0.3 0.4 0.5 Fig.5. Flow chart for impact identifications using Down-bending RMS database method. 0.5 Point 1 3.2 Results of Impact Localizations 0.4 In cases of unloading and bending loadings, impact Point 2 identifications were performed using RMS database 0.3 method. As a result, impact locations in all loading Point 3 cases were successfully detected. From this result, it 0.2 can be concluded that the suggested RMS database method has robustness to static loading conditions. 0.1 Then, in case of the non-grid points (no database locations), the grid points around each non-grid 0.0 point were detected, because the detected locations 0.0 0.1 0.2 0.3 0.4 0.5 are always on the grid points using this method. It Fig.6. Detected impact locations on the non-grid means that the detecting accuracy is determined by points in all loading cases. 3
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