Quantitative Damage Study of Carbon Fiber Reinforced Polymeric Based on Electrical Impedance Tomography
Electrical impedance tomography(EIT)had pathological and ill posed characteristics during the inversion process,as well as difficulties in quantifying the detection results.Denoising convolutional neural networks(DnCNNs)and the construction of a probability of detection(POD)function for damage monitoring by electrical impedance tomography were used to achieve efficient and accurate visual inspection and quantitative evaluation of results for carbon fiber reinforced polymeric(CFRP).With the help of electrical impedance tomography reconstruction software(EIDORS)different shapes of damage reconstruction images were constructed by finite element division and solving the positive problem to simulate the time difference data.The noise mapping in the reconstructed result was then predicted by using a single residual unit in the denoising neural network,which effectively removes artifacts from the reconstructed image,resulting in an accurate and high quality detection image.In addition to this,carbon fiber reinforced polymeric damage was quantitatively analyzed based on statistical processing.The damage evolution process of carbon fiber reinforced polymeric was monitored by electrical impedance tomography(EIT),and the EIT data and acoustic emission events were collected in real time with the help of quasi-static indentation(QSI)test.Using the degree of damage and the amplitude of conductivity as defect parameters in the detection probability function,the POD curves were predicted by applying signal response method parameters.The results show that the above experimental methods and results can not only realize high-quality damage image reconstruction of carbon fiber reinforced polymeric,but also quantitatively evaluate the damage degree of carbon fiber reinforced polymeric,which are conducive to the application of composites with self-awareness in aerospace,transportation,energy and other fields.
万方数据
维普
