The main objective of this study is to predict the joint strength and frequency ranges for failure modes on single-lap joints of glass fiber/epoxy specimens under tensile loading, without causing much damage to the lap joint specimen. To design structural components using composite materials, a deep understanding about the material behavior and its failure modes are necessary. To create a better understanding of the adhesive failure, unstable growth and failure process monitoring during mechanical loading is important. Parametric analysis and multistage approach appears to be an efficient tool to correlate the mechanical behavior of composite single-lap joints namely bonded joint, riveted joint, and hybrid joint with acoustic response. The dominant failure modes and their characteristic frequency ranges are assigned to the different acoustic emission (AE) signal levels on the basis of AE waveform in the time domain using fast Fourier transform (FFT) analysis. FFT analysis is performed for the peak frequency ranges and the results are interpreted with respect to the amplitude and duration hit activity for each failure mode. The results obtained from AE parametric analysis are compared with FFT analysis results to find the peak frequency ranges for each failure mode. Scanning electron microscopy was used to categorize the defects in the post-test specimen, and AE data were correlated to the damage events.
A series of 18 tensile coupons were monitored with an acoustic emission (AE) system, while loading them up to failure. AE signals emitted due to different failure modes in tensile coupons were recorded. Amplitude, duration, energy, counts, etc., are the effective parameters to classify the different failure modes in composites, viz., matrix crazing, fiber cut, and delamination, with several subcategories such as matrix splitting, fiber/matrix debonding, fiber pullout, etc. Back propagation neural network was generated to predict the failure load of tensile specimens. Three different networks were developed with the amplitude distribution data of AE collected up to 30%, 40%, and 50% of the failure loads, respectively. Amplitude frequencies of 12 specimens in the training set and the corresponding failure loads were used to train the network. Only amplitude frequencies of six remaining specimens were given as input to get the output failure load from the trained network. The results of three independent networks were compared, and we found that the network trained with more data was having better prediction performance.
True autonomous system for off road vehicle are most important requirement for the future 2 military and rescue operations. At any point the Autonomous All Terrain Vehicle AATV will get 3 instruction to reach the destination location defined by the geographical co ordinates [1]. AATV 4 should estimate a path and move towards its destination using GPS However the Finding GPS signal 5 consistently anywhere in the off road is difficult due to huge attenuation. As a result estimating right 6 path is difficult. Hence the autonomous vehicle may go around an long loop or travel huge distance 7 to reach its destination. Existing INS technologies has some limitation which is also improved in 8 this research work. The wedyork delivers a combined improvement in system performance by various 9 technology, Algorithm, Hardware, software and Methodology. We present this paper with more of 10 experimental aspects.
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