Lamb Wave-based BVID Imaging for a Curved Composite Sandwich Panel

Composite sandwich structures, consisting of a low density core sandwiched between two laminated facesheets, have been widely used in various aerospace structures. A new Lamb wave-based imaging condition, which will be referred to as the inverse energy imaging criterion, is proposed in this paper to resolve the situations where the incident wave energy weakly penetrates into the damaged area on the top facesheet region. Current imaging conditions by analyzing wavefield reconstructed from laser Doppler vibrometer (LDV) scanning have been proven to be adequate for imaging damage in layered composite laminates. In this research, those current imaging conditions were found to be less effective in the composite foam structures for barely visible impact damage (BVID). A piezoelectric wafer was used to excite Lamb waves into the structure and a LDV was used to scan the potential damaged areas in the front facesheet of the panel. A few sites of BVID in a curved composite sandwich foam aileron were inspected using various wavefield analysis methods and the damage images were compared with C-scan images. The results show that the proposed imaging condition performs better when the incident waves have difficulty penetrating into the damaged region

Automated In-Process Cure Monitoring of Composite Laminates Using a Guided Wave-Based System with High-Temperature Piezoelectric Transducers

An in-process cure monitoring technique based on guided wave concept for carbon fiber reinforced polymer (CFRP) composites was developed. Key parameters including physical properties (viscosity and degree of cure) and state transitions (gelation and vitrification) during the cure cycle were clearly identified experimentally from the amplitude and group velocity of guided waves, validated via the semi-empirical cure process modeling software RAVEN. Using the newly developed cure monitoring system, an array of high-temperature piezoelectric transducers acting as an actuator and sensors were employed to excite and sense guided wave signals, in terms of voltage, through unidirectional composite panels fabricated from Hexcel IM7/8552 prepreg during cure in an oven. Average normalized peak voltage, which pertains to the wave amplitude, was selected as a metric to describe the guided waves phenomena throughout the entire cure cycle. During the transition from rubbery to glassy state, the group velocity of the guided waves was investigated for connection with degree of cure, Tg, and mechanical properties. This work demonstrated the feasibility of in-process cure monitoring and continued progress toward a closed-loop process control to maximize composite part quality and consistency

Damage Detection in Composite Structures Using Time-Reversal Migration Technique

The use of composites in aircraft manufacturing is growing dramatically. Ultrasonic guided waves (UGW) can propagate a long distance, and thus have been used in structural health monitoring (SHM) for large area composite structures like wings and fuselages. UGW can detect not only the damages on the surface of the structures, but also the flaws inside the structures. In this paper, a time-reversal migration (TRM) technique, which originates from the geophysical migration technique, is used to reconstruct the image of damage in composites. The TRM technique uses a linear-array transducer. Each time, one element is used as actuator and the rest of elements in the linear-array transducer are used as receiver. With the TRM scheme, a damage (or the secondary sources) in the composite specimen can be reconstructed. By sequentially switching the element in the linear-array transducer as the actuator, a set of images of the same damage in the composite are reconstructed. Through stacking all images together, a final image of the damage with higher accuracy and lower noise than each individual image is obtained. Figure 1 shows the stacked image from the time-reversal migration images of nine actuators. Experiment results confirm the promising of the TRM technique for damage detection in composite structures

Design of a magnetostrictive sensor for structural health monitoring of non-ferromagnetic plates

In this work, a magnetostrictive sensor (MsS) is designed and tested for monitoring damage in a non-ferromagnetic plate. Firstly, the mechanism of the MsS to generate and detect guided shear horizontal (SH) waves in a non-ferromagnetic plate is described. Both theoretical and experimental studies are conducted in order to prove that the sensor can generate the first non-dispersive shear horizontal wave mode (SH0) suitable for monitoring of the structural health. The sensor encompasses a nickel strip, a pair of permanent magnets, C-shaped cores wound by a figure-of-eight coil. The incident wave emitted from the MsS propagates in the plate and is reflected from the plate boundaries. Since the time of the arrival can be determined from the reflected wave signal through signal processing, the velocity of the wave can be extracted. Comparing the calculated velocity with the velocity predicted by the theory, the mode of the wave can be identified with a priori knowledge of plate velocity. To demonstrate the effectiveness of the proposed sensor for structural health monitoring, the location of the damage in an aluminum plate is examined. Finally, optimum design of the sensor is determined using ANSYS program yielding improved sensor performance. The effectiveness of the optimized magnetostrictive sensor is confirmed by experimental results

Detection and Quantification of Composite Manufacturing Defects Using Guided Waves

A guided wave defect detection technique for cured carbon fiber reinforced polymer (CFRP) composites was investigated in this paper. This technique may be extended to perform in-process cure monitoring, defect detection and sizing, and ultimately as a closed-loop process control approach to maximize composite part quality and consistency. The predominant types of manufacturing defects associated with automated fiber placement/laminate layup followed by thermal cure include porosity, tow gaps, tow overlaps, through-the-thickness fiber waviness, and in-plane fiber waviness. The present study focused on detection methods for two classes of defects: (i) providing a metric to infer porosity formed due to variation in the matrix curing process, and (ii) imaging the overlap and gap of adjacent plies intentionally introduced during layup of the prepreg. In the first set of experiments, three 24-ply unidirectional epoxy composite panels were manufactured such that each subsequent panel had a higher degree of cure and different values of porosity by intentionally terminating the cure cycle prematurely. The average porosity was determined by acid digestion and qualitatively confirmed by ultrasonic Cscan and optical micrographs. These panels were also investigated by guided waves. It was demonstrated that the group velocity, propagating normal to the fiber direction, of the guided waves increased monotonically from the first to the third panel. Therefore, group velocity may be utilized as a metric for degree of cure and as a means to infer porosity. In the second set of experiments, a fully non-contact guided wave hybrid system composed of an air-coupled transducer and a laser Doppler vibrometer (LDV) was used for imaging gap and overlap defects in adjacent tows. By transforming the transient wave response in a region covering tow gap and tow overlap from the time-space domain to the frequency-wavenumber domain, the total wavefield was separated into the incident and backscatter waves. The gap and overlap region was imaged by using a denoised weighted zero-lag cross-correlation (DW-ZLCC) imaging condition

A Numerical and Experimental Study of Damage Growth in a Composite Laminate

The present study has three goals: (1) perform an experiment where a simple laminate damage process can be characterized in high detail; (2) evaluate the performance of existing commercially available laminate damage simulation tools by modeling the experiment; (3) observe and understand the underlying physics of damage in a composite honeycomb sandwich structure subjected to low-velocity impact. A quasi-static indentation experiment has been devised to provide detailed information about a simple mixed-mode damage growth process. The test specimens consist of an aluminum honeycomb core with a cross-ply laminate facesheet supported on a stiff uniform surface. When the sample is subjected to an indentation load, the honeycomb core provides support to the facesheet resulting in a gradual and stable damage growth process in the skin. This enables real time observation as a matrix crack forms, propagates through a ply, and then causes a delamination. Finite element analyses were conducted in ABAQUS/Explicit(TradeMark) 6.13 that used continuum and cohesive modeling techniques to simulate facesheet damage and a geometric and material nonlinear model to simulate core crushing. The high fidelity of the experimental data allows a detailed investigation and discussion of the accuracy of each numerical modeling approach

Detection of CFRP Composite Manufacturing Defects Using a Guided Wave Approach

NASA Langley Research Center is investigating a guided-wave based defect detection technique for as-fabricated carbon fiber reinforced polymer (CFRP) composites. This technique will be extended to perform in-process cure monitoring, defect detection and size determination, and ultimately a closed-loop process control to maximize composite part quality and consistency. The overall objective of this work is to determine the capability and limitations of the proposed defect detection technique, as well as the number and types of sensors needed to identify the size, type, and location of the predominant types of manufacturing defects associated with laminate layup and cure. This includes, porosity, gaps, overlaps, through-the-thickness fiber waviness, and in-plane fiber waviness. The present study focuses on detection of the porosity formed from variations in the matrix curing process, and on local overlaps intentionally introduced during layup of the prepreg. By terminating the cycle prematurely, three 24-ply unidirectional composite panels were manufactured such that each subsequent panel had a higher final degree of cure, and lower level of porosity. It was demonstrated that the group velocity, normal to the fiber direction, of a guided wave mode increased by 5.52 percent from the first panel to the second panel and 1.26 percent from the second panel to the third panel. Therefore, group velocity was utilized as a metric for degree of cure and porosity measurements. A fully non-contact guided wave hybrid system composed of an air-coupled transducer and a laser Doppler vibrometer (LDV) was used for the detection and size determination of an overlap By transforming the plate response from the time-space domain to the frequency-wavenumber domain, the total wavefield was then separated into the incident and backscatter waves. The overlap region was accurately imaged by using a zero-lag cross-correlation (ZLCC) imaging condition, implying the incident and backscattered waves are in phase over the overlap boundaries

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions

Structural Health Monitoring

Abstract In structural health monitoring, crack identification using scattered ultrasonic waves from a crack is one of the most active research areas. Crack size estimation is important for judging the severity of the damage. If measurements are frequently performed as the crack grows, then a better estimation of crack size may be possible by analyzing sensor signals for the same crack location with different sizes. The objective of this article is to explore the relationship between the sensor signal amplitude and crack size through experiments and simulation for estimating the size. Cracks are machined into an aluminum plate and measurements are carried out with ultrasound excitation using piezoelectric transducer arrays that alternate their role as actuators or sensors. Initially, a hole of 2.5 mm diameter is drilled in the plate, and it is gradually machined to a crack with a size up to 50 mm. Signal amplitude is measured from the sensor arrays. The migration technique is used to image the crack and to find the crack location. The maximum received signal amplitude is found to vary linearly with size from simulation and this agrees with measurements with crack size up to 30 mm. The deviation between the simulation and experiment increases as the crack grows