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Detection And Prediction Of Defects In Composite Materials Using Di-Electric Characterization And Neural Networks, Muthu Ram Prabhu Elenchezhian, Aishwarya Nandini, Vamsee Vadlamudi, Rassel Raihan, Kenneth Reifsnider
Detection And Prediction Of Defects In Composite Materials Using Di-Electric Characterization And Neural Networks, Muthu Ram Prabhu Elenchezhian, Aishwarya Nandini, Vamsee Vadlamudi, Rassel Raihan, Kenneth Reifsnider
Institute of Predictive Performance Methodologies (IPPM-UTARI)
The state of art non-destructive inspection techniques for composite materials detect the presence of defects in the composite material, but they do not identify what type of defect it is, and hence, further visual inspection of the details are needed. This visual classification is a costly and time-consuming process, and it’s difficult to distinguish all of the defects effectively. Broadband Dielectric Spectroscopy (BbDS), has been an established tool for dielectric material characterization in polymer industries for a long time. Dielectric spectra of heterogeneous materials are altered by constituent interfaces, with changes in morphological heterogeneity, electrical and structural interactions between particles, …