Nano-delamination monitoring of BFRP nano-pipes of electrical potential change with ANNs

In this work, the electrical potential (EP) technique with an artificial neural networks (ANNs) for monitoring of nanostructures are used for the first time. This study employs an expert system to identify size and localize hidden nano-delamination (N. Del) inside layers of nano-pipe (N. P) manufactured from Basalt Fiber Reinforced Polymer (BFRP) laminate composite by using low-cost monitoring method of electrical potential (EP) technique with an artificial neural networks (ANNs), which are combined to decrease detection effort to discern N. Del location/size inside the N. P layers, with high accuracy, simple and low-cost. The dielectric properties of the N. P material are measured before and after N. Del introduced using arrays of electrical contacts and the variation in capacitance values, capacitance change and node potential distribution are analyzed. Using these changes in electrical potential due to N. Del, a finite element (FE) simulation model for N. Del location/size detection is generated by ANSYS and MATLAB, which are combined to simulate sensor characteristic, therefore, FE analyses are employed to make sets of data for the learning of the ANNs. The method is applied for the N. Del monitoring, to minimize the number of FE analysis in order to keep the cost and save the time of the assessment to a minimum. The FE results are in excellent agreement with an ANN and the experimental results available in the literature, thus validating the accuracy and reliability of the proposed technique.