This work aims to compare quantitatively different nondestructive testing (NDT) techniques and data fusion features for the evaluation of adhesive bonding quality. Adhesively bonded composite-epoxy single-lap joints have been investigated with advanced ultrasonic nondestructive testing and induction thermography. Bonded structures with artificial debonding defects in three different case studies have been investigated: debonding with release film inclusion, debonding with brass film-large, debonding with brass film-small. After completing preprocessing of the data for data fusion, the feature matrices, depending on the interface reflection peak-to-peak amplitude and the principal component analysis, have been extracted from ultrasonic and thermography inspection results, respectively. The obtained feature matrices have been used as the source in basic (average, difference, weighted average, Hadamard product) and statistical (Dempster–Shafer rule of combination) data fusion algorithms. The defect detection performances of advanced nondestructive testing techniques, in addition to data fusion algorithms have been evaluated quantitatively by receiver operating characteristics. In conclusion, it is shown that data fusion can increase the detectability of artificial debonding in single-lap joints.

In this paper, a 3-D modeling of a thermo inductive nondestructing testing (NDT) technique applied to carbon fiber reinforced polymer (CFRP) composite is presented. A multiscale approach is used to calculate the electromagnetic and thermal field distribution. The relevance of the technique is then discussed for different positions of flaws and the optimal frequency is estimated.

A fast crack profile reconstitution model in nondestructive testing is developed using an arrayed eddy current sensor. The inverse problem is based on an iterative solving of the direct problem using genetic algorithms. In the direct problem, assuming a current excitation, the incident field produced by all the coils of the arrayed sensor is obtained by the translation and superposition of the 2D axisymmetric finite element results obtained for one coil; the impedance variation of each coil, due to the crack, is obtained by the reciprocity principle involving the dyadic Green’s function. For the inverse problem, the surface of the crack is subdivided into rectangular cells, and the objective function is expressed only in terms of the depth of each cell. The evaluation of the dyadic Green’s function matrix is made independently of the iterative procedure, making the inversion very fast.

Characterization of anisotropic conductivity of thin carbon fiber/poly ether ether ketone (PEEK) composite was investigated by noncontact and nondestructive microwave probe pumping (MPP) technique. The microwave was pumped by a coaxial probe, and the pumped field intensity distribution was measured by a near-field scanning microwave microprobe (NSMM) and a thermography camera. From the measurement and simulation results, it was observed that intensity of the electromagnetic field was higher along the high conductive directions due to the larger eddy current along these directions. Additionally, electrical defect detection by pumping probe technique was investigated. It was observed that the field intensity drastically decreased around the electrical defect. We showed that through an anisotropic field distribution around the pumping probe, an electrical defect of a carbon fiber/PEEK composite can be detected by combination of MPP and NSMM techniques.

An improved method which can analyze the eddy current density in conductor materials using finite volume method is proposed on the basis of Maxwell equations andT-formulation. The algorithm is applied to solve 3D electromagnetic nondestructive evaluation (E’NDE) benchmark problems. The computing code is applied to study an Inconel 600 work piece with holes or cracks. The impedance change due to the presence of the crack is evaluated and compared with the experimental data of benchmark problems No. 1 and No. 2. The results show a good agreement between both calculated and measured data.

In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs.