Quantitative defect inspection in the curved composite structure using the modified probabilistic tomography algorithm and fusion of damage index

Ultrasonic imaging of delamination in thick CFRP laminates using an energy-compensation reverse time migration method

In ultrasonic reflection method, the precision of defect detection in thick carbon fiber reinforced plastics (CFRP) is compromised by acoustic energy attenuation. An energy-compensation reverse time migration (ECRTM) method is proposed to identify multiple defects accurately. Forward and backward wavefields are formed using the finite element method within an anisotropic acoustic model based on the Christoffel equation and Bond transformation. To enhance the imaging quality of CFRP laminates, a novel cross-correlation imaging condition is introduced to compensate for energy dissipation caused by geometric diffusion and variations of the far-field radiation intensity at the emitter with the propagation direction. Employing ultrasonic detection technology with a multi-element array, numerical and experimental research on defect imaging was conducted, considering delamination with various sizes and positions in a multidirectional CFRP laminate. In comparison to other ultrasonic imaging methods, the near-surface artifacts in RTM images are mitigated by the far-field radiation directivity factor, while the deep information is enhanced by the geometric diffusion compensation factor in the ECRTM images. As a result, the precise position of delamination in CFRP laminates is achievable, demonstrating superior imaging capabilities, especially for deep delamination.

Imaging of Fiber Waviness in Thick Composites with Unknown Material Properties Using Probability-Based Ultrasound Non-Reciprocity

Fiber waviness and voids may be produced in thick composites due to improper manufacturing conditions and consequently pose a risk of structural failure. A proof-of-concept solution for imaging fiber waviness in thick porous composites was proposed from both numerical and experimental studies, via calculating ultrasound non-reciprocity along different wave paths in a sensing network constructed by two phased array probes. Time-frequency analyses were conducted to reveal the cause of ultrasound non-reciprocity in wavy composites. Subsequently, the number of elements in the probes and excitation voltages was determined for fiber waviness imaging using the ultrasound non-reciprocity with a probability-based diagnostic algorithm. The fiber angle gradient was observed to cause ultrasound non-reciprocity and fiber waviness in the thick wavy composites were successfully imaged regardless of presence of voids. This study proposes a new feature for the ultrasonic imaging of fiber waviness and is expected to contribute to processing improvement in thick composites without prior knowledge of material anisotropy.

Through transmission ultrasonic inspection of fiber waviness for thickness-tapered composites using ultrasound non-reciprocity: Simulation and experiment

This study proposed the use of ultrasound non-reciprocity in periodic structures to inspect fiber waviness in thickness-tapered composites. Ultrasound propagation in plain and thickness-tapered composites with complex microstructure were precisely modelled using TexGen® and OnScale® simulation software. Ultrasound non-reciprocity and attenuation was comparatively calculated to inspect fiber waviness through both simulation and experiment. After comparison, energy of transmitted waves was found to be sensitive to the presence of fiber waviness in plain composites, however, thickness-dependent ultrasound attenuation introduces difficulties in determining the diagnosis baseline for thickness-tapered composites. On the other hand, fiber waviness introduces direction-dependent nonlinearity in the wavy region, which introduces a disparity between the two transmitted signals when the propagation direction is reversed. Ultrasound non-reciprocity, defined by the time-of-flight difference between the two transmitted signals, demonstrated its efficiency for fiber waviness inspection in both plain and thickness-tapered composites regardless of variations in the thickness.

Lamb Wave-Based Damage Localization and Quantification in Composites Using Probabilistic Imaging Algorithm and Statistical Method

Quantitatively and accurately monitoring the damage to composites is critical for estimating the remaining life of structures and determining whether maintenance is essential. This paper proposed an active sensing method for damage localization and quantification in composite plates. The probabilistic imaging algorithm and the statistical method were introduced to reduce the impact of composite anisotropy on the accuracy of damage detection. The matching pursuit decomposition (MPD) algorithm was utilized to extract the precise TOF for damage detection. The damage localization was realized by comprehensively evaluating the damage probability evaluation results of all sensing paths in the monitoring area. Meanwhile, the scattering source was recognized on the elliptical trajectory obtained through the TOF of each sensing path to estimate the damage size. Damage size was characterized by the Gaussian kernel probability density distribution of scattering sources. The algorithm was validated by through-thickness hole damages of various locations and sizes in composite plates. The experimental results demonstrated that the localization and quantification absolute error are within 11 mm and 2.2 mm, respectively, with a sensor spacing of 100 mm. The algorithm proposed in this paper can accurately locate and quantify damage in composite plate-like structures.

Imaging damage in plate waveguides using frequency-domain multiple signal classification (F-MUSIC)

Earlier, an ameliorated MUSIC (Am-MUSIC) algorithm is developed by the authors [1], aimed at expanding conventional MUSIC algorithm from linear array-facilitated nondestructive evaluation to in situ health monitoring with a sparse sensor network. Yet, Am-MUSIC leaves a twofold issue to be improved: i) the signal representation equation is constructed at each pixel across the inspection region, incurring high computational cost; and ii) the algorithm is applicable to monochromatic excitation only, ignoring signal features scattered out of the excitation frequency band which also carry information on structural integrity. With this motivation, a multiple-damage-scattered wavefield model is developed, with which the signal representation equation is constructed in the frequency domain, avoiding computationally expensive pixel-based calculation - referred to as frequency-domain MUSIC (F-MUSIC). F-MUSIC quantifies the orthogonal attributes between the signal subspace and noise subspace inherent in signal representation equation, and generates a full spatial spectrum of the inspected sample to visualize damage. Modeling in the frequency domain endows F-MUSIC with the capacity to fuse rich information scattered in a broad band and therefore enhance imaging precision. Both simulation and experiment are performed to validate F-MUSIC when used for imaging single and multiple sites of damage in an isotropic plate waveguide with a sparse sensor network. Results accentuate that effectiveness of F-MUSIC is not limited by the quantity of damage, and imaging precision is not downgraded due to the use of a highly sparse sensor network - a challenging task for conventional MUSIC algorithm to fulfil.

Real-Time Life-Cycle Monitoring of Composite Structures Using Piezoelectric-Fiber Hybrid Sensor Network

In this paper, an in situ piezoelectric-fiber hybrid sensor network was developed to monitor the life-cycle of carbon fiber-reinforced plastics (CFRPs), from the manufacturing phase to the life in service. The piezoelectric lead-zirconate titanate (PZT) sensors were inserted inside the composite structures during the manufacturing process to monitor important curing parameters, including the storage modulus of resin and the progress of the reaction (POR). The strain that is related to the storage modulus and the state of resin was measured by embedded fiber Bragg grating (FBG) sensors, and the gelation moment identified by the FBG sensors was very close to those determined by dynamic mechanical analysis (DMA) and POR. After curing, experiments were conducted on the fabricated CFRP specimen to investigate the damage identification capability of the embedded piezoelectric sensor network. Furthermore, a modified probability diagnostic imaging (PDI) algorithm with a dynamically adaptive shape factor and fusion frequency was proposed to indicate the damage location in the tested sample and to greatly improve the position precision. The experimental results demonstrated that the average relative distance error (RDE) of the modified PDI method was 68.48% and 46.97% lower than those of the conventional PDI method and the PDI method, respectively, with an averaged shape factor and fusion frequency, indicating the effectiveness and applicability of the proposed damage imaging method. It is obvious that the whole life-cycle of CFRPs can be effectively monitored by the piezoelectric-fiber hybrid sensor network.