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Precursor Damage Inception Quantification

S. PATRA, S. BANERJEE

Abstract


Quantification of precursor of damage initiation in composite materials is extremely challenging using an on-board Structural health Monitoring (SHM) system. Here we present the recent advancement in online ultrasonic sensing methodology for precursor damage quantification. Present SHM techniques are incapable of detecting the damage stage at sub wave length scale. In this paper, first we have studied a simple but novel off-board approach for precursor damage state quantification using high frequency ultrasonic image correlation technique. Further proper sensing methodology is established from the information extracted from proposed off-board technique. To correlate the on-board SHM technique with the off-board understanding, high frequency piezoelectric sensors were mounted on the specimen and lamb wave interaction with stiffness reduction is studies to excerpt information of damage incubation. Here, we investigated damage state of Carbonfiber- reinforced polymer (CFRP) composite. Precursor damage states are generally in the form of matrix cracking, deboning and fiber breakage in composites. Here in this work we performed tensile-tensile fatigue testing on ASTM standard specimen at an interval of 10000 cycles until cracks / delamination are developed. At each interval of loading, volumetric ultrasonic scans are performed on the gage area of the specimen using Scanning Acoustic Microscope (SAM). The stiffness degradation is calculated at each pixel points of the selected gage area using nonlocal elastoplasticity theory. We used our novel observable parameter called ‘’Damage Entropy” (DE) which is a measure of material damage, are calculated, and plotted with fatigue loading cycle until visible damage incubates. Damage incubation in the specimen was clearly observed from the Z-scan images from SAM. Advance signal processing technique and sensor selection is used for this process. This study has the potential to correlate the DE from SAM and Damage index (DI) from online Sensor Data for online precursor damage quantification.

doi: 10.12783/SHM2015/322


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