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 (<i>ANNs</i>) for monitoring of nanostructures are used for the first time. This study employs an expert system to identify size and localize hidden nano-delamination (<i>N.Del</i>) inside layers of nano-pipe (<i>N.P</i>) 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 (<i>ANNs</i>), which are combined to decrease detection effort to discern <i>N.Del</i> location/size inside the <i>N.P</i> layers, with high accuracy, simple and low-cost. The dielectric properties of the <i>N.P</i> material are measured before and after <i>N.Del</i> 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 <i>N.Del</i>, a finite element (FE) simulation model for <i>N.Del</i> 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 <i>ANNs</i>. The method is applied for the <i>N.Del</i> 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.