Electrical Resistance Tomography (ERT) offers a non-destructive evaluation (NDE) technique that takes advantage of the inherent electrical properties in carbon fiber reinforced polymer (CFRP) composites for internal damage characterization. This paper investigates a method of optimum selection of sensing configurations for delamination detection in thick cross-ply laminates using ERT. Reduction in the number of sensing locations and measurements is necessary to minimize hardware and computational effort. The present work explores the use of an effective independence (EI) measure originally proposed for sensor location optimization in experimental vibration modal analysis. The EI measure is used for selecting the minimum set of resistance measurements among all possible combinations resulting from selecting sensing electrode pairs. Singular Value Decomposition (SVD) is applied to obtain a spectral representation of the resistance measurements in the laminate for subsequent EI based reduction to take place. The electrical potential field in a CFRP laminate is calculated using finite element analysis (FEA) applied on models for two different laminate layouts considering a set of specified delamination sizes and locations with two different sensing arrangements. The effectiveness of the EI measure in eliminating redundant electrode pairs is demonstrated by performing inverse identification of damage using the full set and the reduced set of resistance measurements. This investigation shows that the EI measure is effective for optimally selecting the electrode pairs needed for resistance measurements in ERT based damage detection.

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Internal damage in Carbon Fiber Reinforced Polymer (CFRP) composites modifies the internal electrical conductivity of the composite material. Electrical Resistance Tomography (ERT) is a non-destructive evaluation (NDE) technique that determines the extent of damage based on electrical conductivity changes. Implementation of ERT for damage identification in CFRP composites requires the optimal selection of the sensing sites for accurate results. This selection depends on the measuring scheme used. The present work uses an effective independence (EI) measure for selecting the minimum set of measurements for ERT damage identification using three measuring schemes: two-probe, four-probe and multi-probe. The electrical potential field in two CFRP laminate layups with 14 electrodes is calculated using finite element analyses (FEA) for a set of specified delamination damage cases. The measuring schemes consider the cases of 14 electrodes distributed on both sides and seven electrodes on only one side of the laminate for each layup. The effectiveness of EI reduction is demonstrated by comparing the inverse identification results of delamination cases for the full and the reduced sets using the measuring schemes and electrode sets. This work shows that the EI measure optimally reduces electrode and electrode combinations in ERT based damage identification for different measuring schemes.

Electrical resistance tomography (ERT) is a nondestructive evaluation technique that uses the internal conductivity variations of materials to assess structural integrity. Due to the low instrumentation required, the widespread use of ERT in the aerospace industry for monitoring the accumulation of damage in aircraft components can lead to significant reductions in inspections and maintenance costs. However, implementing the ERT method for mapping the damage state of structural components made of carbon fiber reinforced polymeric (CFRP) composites is challenging due to the inability of this method to distinguish between damage modes such as delamination and matrix cracking. This article explores the combined use of ERT and machine learning algorithms such as neural networks, random forests, k-nearest neighbors, and support vector machines to classify and characterize delamination and matrix cracking damage in CFRP laminates. Results show that the proposed classification algorithms can successfully estimate the damage severity of delaminated composites in the presence of matrix cracking. Similarly, the classification algorithms can characterize these independent damage modes with an accuracy of 95%. The algorithms showed robustness to predict the electrical resistance variations of damaged composites and characterize delamination and matrix cracking damage even when intrinsic noise was considered. Although neural networks characterized damage with the highest accuracy, these algorithms were also the most sensitive to noise. For applications where instrumentation noise cannot be completely removed from the ERT signals, the use of nearest neighbors is thus recommended.

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