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In using acoustic emissions (AE) for mechanical diagnostics, one major problem is the discrimination of events due to different types of damage occurring during loading of composite materials. In the present work, a procedure for the investigation of local damage in composite materials based on the analysis of the signals of Acoustic Emission (AE)...
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In this paper, a very promising procedure to evaluate damage initiation and propagation of high speed drilled composite laminates base on Acoustic Emission (AE) and Finite Element (FE) techniques is proposed. First, the extent of delamination after high speed drilling was measured in unidirectional and woven composite specimens under different feed...
Citations
... The most informative selected features based on SFS are stacked in feature subset as follows: Z s = ( R R R R R R d R R , , , , , , , , , A p ). Vectors of the training data have been designated into categories based on the time moment of their acquisition during the injection molding cycle and unsupervised fuzzy C means clustering (FCM) for vectors of the damaged mold. FCM clustering was found to be an effective algorithm for clustering of acoustic emission vectors composed of multiple features [28] and [29]. ...
This paper presents measurements of acoustic emission (AE) signals during injection molding with resonant PZT sensors that were applied to the mold via waveguides. A polypropylene material was employed for injection molding of ISO specimens. Acoustic signals were measured during production cycles on a new mold and damaged one with cracks induced by laser surface heat treatment. The mold inserts integrity description by acquired AE signal together with the fractal algorithm using box counting method is presented. Implementation of AE signal analysis based on an idea of the box-counting method in a way to divide the measured AE signals to AE signal boxes is used. To improve the capability of clustering AE data during injection process cycle, AE burst descriptors are defined. To lower computational complexity and increase performance, the feature selection method was implemented. Neural network pattern recognition of AE signals feature subsets was used for evaluation of process steps and damage detection.
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In the present article, the low-cycle fatigue lifetime of carbon/epoxy laminated composites has been investigated. The sensitivity analysis has been also performed to study effects of the displacement amplitude and the loading frequency on the low-cycle fatigue lifetimes in composites. For such objective, displacement-control low-cycle fatigue testing has been done on the open-hole standard specimen. Fatigue tests included 4 different displacement amplitudes at 200 mm/min of the loading frequency and 4 different loading frequencies under 7 mm of the displacement amplitude. The sensitivity analysis was carried out by the MINITAB software, considering a linear function for fitting experimental data by the predicting model. Experimental results showed that by increasing the displacement amplitude, the low-cycle fatigue lifetime decreased, as expected. In addition, when the loading frequency enhanced, the low-cycle fatigue lifetime of composites decreased. Besides, the maximum stress had a reverse behavior, compared to the fatigue lifetime. The sensitivity analysis depicted that the displacement amplitude was sensitive on both the fatigue lifetime and the maximum stress. The loading frequency was sensitive on the maximum stress and was not sensitive on the fatigue lifetime.
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High ratio of strength to weight in carbon/epoxy composites causes to their applications in several structures, especially aerospace structures. In addition, to enhance the reliability in such structures, investigating damages in composites is essential. One way to detect defects in composites is to utilize the acoustic emission approach. Thus, the objective of the present research is to find failure mechanisms in open-hole laminate composite specimens with 〖[0_3/〖90〗_2/0_2]〗_s layup under cyclic loadings at different displacement amplitudes, using the acoustic emission. First, the standard specimen was examined and elastic waves due to failures in the specimen were detected by acoustic emission wide-band sensors. Two methods have been utilized to detect the failure percent, including Pocket wavelet transform and Fuzzy clustering approaches. Results from these methods were compared to micro-structure images by the scanning electron microscopy. Obtained results in this research indicated the appropriate efficiency of the acoustic emission approach to detect the type of failures and their percent in laminate composites.
