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To ensure the structural integrity of large aerospace structures during operation, structural health monitoring is a major challenge. The monitoring can be performed by distributed strain measurements using strain gauges or fiber optical sensors. In this work, an advanced local residual classifier for strain-based damage detection and localization is introduced. The key principle is that a change in the relationship between a strain sensor and its neighbors indicates the presence of damage. After defining a sensor grid with sensor locations and their orientation, the relationship can be obtained from numerical simulations of the healthy structure. Here, local regression models are estimated between each master sensor and its neighboring sensors. Then, residuals of the predicted and measured strains are evaluated using a random forest classifier. The evaluation of the residuals has the advantage that the method is independent of the load level, as well as the fact that it is independent of certain environmental influences that are uniformly distributed over the entire structure. In addition to the numerical healthy strains, synthetically generated damage data are used for training the classifier. The synthetic data are obtained by statistical modifications of the healthy strains. This procedure avoids time-consuming and expensive damage simulations. The health monitoring approach is applied to a glass fiber reinforced polymer sandwich structure, imitating an aircraft spoiler, with a hole in the face layer considered as damage. The validation is performed by numerical finite element simulations as well as physical experiments under random loading conditions. The results demonstrate the high potential of the presented approach for strain-based structural health monitoring in composite sandwich structures. Version of record

Atmospheric corrosion of aluminum aircraft structures occurs due to a variety of reasons. A typical phenomenon leading to corrosion during aircraft operation is the deliquescence of salt contaminants due to changes in the ambient relative humidity (RH). Currently, the corrosion of aircraft is controlled through scheduled inspections. In contrast, the present contribution aims to continuously monitor atmospheric corrosion using the acoustic emission (AE) method, which could lead to a structural health monitoring application for aircraft. The AE method is frequently used for corrosion detection under immersion-like conditions or for corrosion where stress-induced cracking is involved. However, the applicability of the AE method to the detection of atmospheric corrosion in unloaded aluminum structures has not yet been demonstrated. To address this issue, the present investigation uses small droplets of a sodium chloride solution to induce atmospheric corrosion of uncladded aluminum alloy AA2024-T351. The operating conditions of an aircraft are simulated by controlled variations in the RH. The AE signals are measured while the corrosion site is visually observed through video recordings. A clear correlation between the formation and growth of pits, the AE and hydrogen bubble activity, and the RH is found. Thus, the findings demonstrate the applicability of the AE method to the monitoring of the atmospheric corrosion of aluminum aircraft structures using current measurement equipment. Numerous potential effects that can affect the measurable AE signals are discussed. Among these, bubble activity is considered to cause the most emissions. Osterreichische Forschungsforderungsgesellschaft 881095 Version of record

In recent years, structural health monitoring has been increasingly applied to composite sandwich structures, as typically used in aerospace applications. In addition, machine learning approaches are increasingly popular for damage detection, localization and size estimation, due to their great advantages in pattern recognition and anomaly detection. However, a major disadvantage of machine learning techniques is that these algorithms generally require large amounts of realistic data. In general, these data are expensive or even impossible to obtain within a feasible time. In order to overcome this hindrance, this work introduces a computationally inexpensive framework for physics-driven feature generation of strain data for the training of ML-based SHM methods using sub-structuring and the concept of reanalysis. First, the global FE model is subdivided into a monitored part, i.e., a smaller submodel, and a global model. Second, the stiffness matrix of the submodel is extracted from the finite element software. Then, static condensation is performed to further reduce the computational effort. Afterwards, selected eigenvectors are derived in terms of displacements of master nodes and the corresponding strains are calculated. Finally, a statistically varied linear combination between the different characteristic eigenvector load cases is performed based on the superposition principle. This procedure enables the efficient generation of a large number of different physics-driven determined strain solutions for a subsequent training of a ML algorithms. The proposed framework is evaluated by means of a damage detection approach, based on an artificial neuronal network classifier algorithm. The applied approach utilizes strain measurements from selected positions as physical quantity and is demonstrated using a composite sandwich structure imitating an aircraft spoiler. The key principle of the damage detection algorithm is based on the fact that a change in the relationship between sensors indicates the presence of damage. Additionally, to the numerical healthy strains resulting from the framework, synthetically generated damage data are used for training the neuronal network classifier. The synthetic data are obtained by statistical modifications of the healthy strains, to avoid time-consuming and expensive damage simulations. The feature generation framework and health monitoring approach are validated using experiments and numerical simulations of a glass fiber reinforced polymer sandwich structure with a hole considered as damage. The presented numerical and the experimental results clearly show the high potential for the efficient approach for damage detection in a sandwich structure. Version of record

Structural Health Monitoring (SHM) is believed to reduce uncertainties in the operation of mechanical structures, and thus, increase safety. Strain measurements are often used to conclude on the health state of a structure of interest. In combination with elastoresistive materials, Electrical Impedance Tomography (EIT) can be used to monitor larger areas of safety relevant structural components for strain changes that might indicate damage. The present research investigates the evaluation of spatial strain distributions by elastoresistive thin-film sensors using 2D Electrical Impedance Tomography. Therefore, spatially-limited strains and the subsequent conductivity changes in a thin-film surface sensor are numerically modelled and experimentally realized. Subsequently, the inverse reconstruction of both, simulated and experimental measurements by the Electrical Impedance Tomography are compared and analyzed with respect to the imaging of strain location, shape, extent and value. Linz Center of Mechatronics COMET-K2 Center Version of record

The ever-increasing demand for efficiency and cost improvements in lightweight structures with guaranteed safety and reliability is leading to the application of a damage-tolerant design philosophy. Here, accurate knowledge of structural health is critical to avoid catastrophic failures. This knowledge can be obtained by using advanced structural health monitoring (SHM) systems. For thin-walled lightweight structures, methods utilizing guided waves generated by piezoelectric transducers are well suited. The interaction between the guided waves and potential damages can be described by so-called wave damage interaction coefficients (WDICs). These WDICs are unique for each damage and depend solely on its characteristics for a given structure. Therefore, the comparison of known WDICs with estimated ones allows drawing conclusions about the current structural state. In this paper, a novel damage identification method for plate-like structures based on a database of such WDICs is presented. Selected damages are simulated numerically with finite elements to generate WDIC patterns. However, these simulations are computationally highly demanding, thus only a very limited number of damage scenarios can be simulated. This study proposes an innovative technique to substantially enhance the resulting WDIC database by using deep neural networks (DNNs). These DNNs enable smart interpolations and allow not only predicting WDICs for previously unseen damages at low computational costs but also the discovery of knowledge about the complex relationship between damage features and WDIC patterns. A comparison to other machine learning algorithms clearly shows the superior performance of the utilized DNNs for interpolating complex WDIC patterns. The proposed damage identification method is verified using advanced time-domain simulations of a large aluminum plate. A statistical analysis of correct identification rates in a common three-sensor setting is employed for assessing the general performance. It is demonstrated that carefully identified DNNs enable to accurately replicate and interpolate complex WDIC patterns. Furthermore, it is shown that these predicted WDICs allow identifying damage characteristics with high confidence. Version of record

Besides the design freedom offered by additive manufacturing, another asset lies within its potential to accelerate product development processes by rapid fabrication of functional prototypes. The premise to fully exploit this benefit for lightweight design is the accurate structural response prediction prior to part production. However, the peculiar material behavior, characterized by anisotropy, thickness dependency and scatter, still constitutes a major challenge. Hence, a modeling approach for finite element analysis that accounts for this inhomogeneous behavior is developed by example of laser-sintered short-fiber-reinforced polyamide 12. Orthotropic and thickness-dependent Young’s moduli and Poisson’s ratios were determined via quasi-static tensile tests. Thereof, material models were generated and implemented in a property mapping routine for finite element models. Additionally, a framework for stochastic finite element analysis was set up for the consideration of scatter in material properties. For validation, thin-walled parts on sub-component level were fabricated and tested in quasi-static three-point bending experiments. Elastic parameters showed considerable anisotropy, thickness dependency and scatter. A comparison of the predicted forces with experimentally evaluated reaction forces disclosed substantially improved accuracy when utilizing the novel inhomogeneous approach instead of conventional homogeneous approaches. Furthermore, the variability observed in the structural response of loaded parts could be reproduced by the stochastic simulations.

This paper investigates a strain-based structural health monitoring (SHM) method for damage detection and localization in composite sandwich structures. As case example, an idealized aircraft spoiler of a large civil aircraft is considered. Critical failure modes of such sandwich structures composed of fiber reinforced polymer (FRP) face layers and a honeycomb core are, e.g., face layer delamination and debonding from the core. The latter challenges todays non-destructive testing methods, and thus, shall be addressed in the present work. The presented research compromises an idealized spoiler model of the real spoiler structure in the scale 1:2. The model is composed of glass fiber reinforce polymer (GFRP) face layers and a honeycomb core. The damage identification is investigated based on static strain measurements along so-called zero-strain trajectories (ZST) at the loaded structure. A zero-strain direction exists for every plane strain state with major strain directions with opposite signs (tensile and compression). Connecting zero-strain direction vectors at various points of a structure yields a ZST. Strains along ZST are most sensitive to changes of the load path due to, e.g., damage. This work investigates the use of strain measurements along ZST for damage detection at the edge of the considered sandwich structure by means of numerical and experimental analysis. A real wind-load condition of a large civil aircraft spoiler is considered as load case. An in-depth numerical and experimental investigation is performed to calculate the ZST for the pristine structure. The experimental validation of the Finite element (FE) model is realized by deformation measurements via a digital image correlation system (DIC). Moreover, strain measurements via strain gauge (SG) rosettes were performed to evaluate the correct calculation of the zero-strain directions. Finally, the application of strain measurements along a ZST for debonding detection and localization is demonstrated and its potential and issues for real online monitoring is discussed. (VLID)6298884 Version of record

Besides the design freedom offered by additive manufacturing, another asset lies within its potential to accelerate product development processes by rapid fabrication of functional prototypes. The premise to fully exploit this benefit for lightweight design is the accurate structural response prediction prior to part production. However, the peculiar material behavior, characterized by anisotropy, thickness dependency and scatter, still constitutes a major challenge. Hence, a modeling approach for finite element analysis that accounts for this inhomogeneous behavior is developed by example of laser-sintered short-fiber-reinforced polyamide 12. Orthotropic and thickness-dependent Youngs moduli and Poissons ratios were determined via quasi-static tensile tests. Thereof, material models were generated and implemented in a property mapping routine for finite element models. Additionally, a framework for stochastic finite element analysis was set up for the consideration of scatter in material properties. For validation, thin-walled parts on sub-component level were fabricated and tested in quasi-static three-point bending experiments. Elastic parameters showed considerable anisotropy, thickness dependency and scatter. A comparison of the predicted forces with experimentally evaluated reaction forces disclosed substantially improved accuracy when utilizing the novel inhomogeneous approach instead of conventional homogeneous approaches. Furthermore, the variability observed in the structural response of loaded parts could be reproduced by the stochastic simulations. (VLID)6280022 Version of record

Additive manufacturing is progressively paving the way for optimised lightweight components that, due to their typically complex shape, would hardly be feasible with traditional production methods. However, the peculiar mechanical properties of additively manufactured materials limit the accuracy of structural analyses. In this research, a strategy for the implementation of thickness dependent anisotropy into finite element shell models is developed by example of laser sintered polyamide. The material behaviour was modelled by fitting parametric functions to experimental data. Subsequently, a routine was developed to map the adaptive material properties into a finite element model of a complex component. Numeric simulations with standard and mapped properties were compared and validated via experiments. Results show that the proposed approach is superior to the conventional method in predicting the structural response. The method is not only applicable to laser sintered polymers but relevant for all structures, where anisotropy and thickness must be considered. (VLID)6327458 Accepted version

This paper investigates the potential of a novel vibration-based thermal health monitoring method for continuous and on-board damage detection in fiber reinforced polymer sandwich structures, as typically used in aerospace applications. This novel structural health monitoring method uses the same principles, which are used for vibration-based thermography in combination with the concept of the local defect resonance, as a well known non-destructive testing method (NDT). The use of heavy shakers for applying strong excitation and infrared cameras for observing thermal responses are key hindrances for the application of vibration-based thermography in real-life structures. However, the present study circumvents these limitations by using piezoelectric wafer active sensors as excitation source, which can be permanently bonded on mechanical structures. Additionally, infrared cameras are replaced by surface temperature sensors for observing the thermal responses due to vibrations and damage. This makes continuous and on-board thermal health monitoring possible. The new method is experimentally validated in laboratory experiments by a sandwich structure with face layer debonding as damage scenario. The debonding is realized by introduction of an insert during the manufacturing process of the specimen. The surface temperature sensor results successfully show the temperature increase in the area of the debonding caused by a sinusoidal excitation of the sandwich structure with the PWAS at the first resonance frequency of the damage. This is validated by conventional infrared thermography. These findings demonstrate the potential of the proposed novel thermal health monitoring method for detecting, localizing and estimating sizes of face layer debonding in sandwich structures. (VLID)5697370 Version of record

An idealized 1:2 scale demonstrator and a numerical parameter optimization algorithm are proposed to closely reproduce the deformation shape and, thus, spatial strain directions of a real aerodynamically loaded civil aircraft spoiler using only four concentrated loads. Cost-efficient experimental studies on demonstrators of increasing complexity are required to transfer knowledge from coupons to full-scale structures and to build up confidence in novel structural health monitoring (SHM) technologies. Especially for testing novel sensor systems that depend on or are affected by mechanical strains, e.g., strain-based SHM methods, it is essential that the considered lab-scale structures reflect the strain states of the real structure at operational loading conditions. Finite element simulations with detailed models were performed for static strength analysis and for comparison to experimental measurements. The simulated and measured deformations and spatial strain directions of the idealized demonstrator correlated well with the numerical results of the real aircraft spoiler. Thus, using the developed idealized demonstrator, strain-based SHM systems can be tested under conditions that reflect operational aerodynamic pressure loads, while the test effort and costs are significantly reduced. Furthermore, the presented loading optimization algorithm can be easily adapted to mimic other pressure loads in plate-like structures to reproduce specific structural conditions. Version of record

The onset of damage caused by the free-edge effect in plain-woven carbon fiber reinforced plastic (CFRP) specimens with an out-of-plane waviness under tension-tension fatigue loading is investigated. Numerical calculations show that interlaminar and intralaminar stresses close to the out-of-plane waviness are higher than the equivalent stresses at the surrounding edge regions. Using submodels, the influence of the chosen out-of-plane waviness can be better assessed. The free-edge effect of the considered specimens, which originates from stress gradients between plies of different orientation, is altered by the change in the stress field caused by the out-of-plane waviness. Large interlaminar stresses between plies of the same orientation are obtained, which contrasts with existing literature. In experimental fatigue testing it is found that cracks at the free edge appeared at the predicted locations, and after reaching crack saturation, in regions close to the out-of-plane waviness, interlaminar and intralaminar stresses lead to additional cracks along the whole free edges. The experimental tests are supported by a three dimensional image correlation system (3D-DIC), a thermal-imager and a digital photographic camera, which allows detailed examination of selected areas. Visual observation during fatigue testing and post-mortem inspection show good agreement between experimental data and numerical calculations in relation to the location of the damage initiation. As a result, out-of-plane waviness at free edges must be considered as an additional significant fatigue damage initiation location in laminate analysis. Version of record

Structural health monitoring (SHM) is the continuous on-board monitoring of a structure’s condition during operation by integrated systems of sensors. SHM is believed to have the potential to increase the safety of the structure while reducing its deadweight and downtime. Numerous SHM methods exist that allow the observation and assessment of different damages of different kinds of structures. Recently data fusion on different levels has been getting attention for joint damage evaluation by different SHM methods to achieve increased assessment accuracy and reliability. However, little attention is given to the question of which SHM methods are promising to combine. The current article addresses this issue by demonstrating the theoretical capabilities of a number of prominent SHM methods by comparing their fundamental physical models to the actual effects of damage on metal and composite structures. Furthermore, an overview of the state-of-the-art damage assessment concepts for different levels of SHM is given. As a result, dynamic SHM methods using ultrasonic waves and vibrations appear to be very powerful but suffer from their sensitivity to environmental influences. Combining such dynamic methods with static strain-based or conductivity-based methods and with additional sensors for environmental entities might yield a robust multi-sensor SHM approach. For demonstration, a potent system of sensors is defined and a possible joint data evaluation scheme for a multi-sensor SHM approach is presented.

Structural health monitoring (SHM) is the continuous on-board monitoring of a structures condition during operation by integrated systems of sensors. SHM is believed to have the potential to increase the safety of the structure while reducing its deadweight and downtime. Numerous SHM methods exist that allow the observation and assessment of different damages of different kinds of structures. Recently data fusion on different levels has been getting attention for joint damage evaluation by different SHM methods to achieve increased assessment accuracy and reliability. However, little attention is given to the question of which SHM methods are promising to combine. The current article addresses this issue by demonstrating the theoretical capabilities of a number of prominent SHM methods by comparing their fundamental physical models to the actual effects of damage on metal and composite structures. Furthermore, an overview of the state-of-the-art damage assessment concepts for different levels of SHM is given. As a result, dynamic SHM methods using ultrasonic waves and vibrations appear to be very powerful but suffer from their sensitivity to environmental influences. Combining such dynamic methods with static strain-based or conductivity-based methods and with additional sensors for environmental entities might yield a robust multi-sensor SHM approach. For demonstration, a potent system of sensors is defined and a possible joint data evaluation scheme for a multi-sensor SHM approach is presented. (VLID)5534458 Version of record

Adhesive bonds are particularly favored by lightweight structures due to their weight efficiency and insulating property regarding contact corrosion. However, it is challenging to interrogate directly over the bonding area due to its sandwiched structure. In this study, an inkjet-printed carbon nanotube (CNT) strain distribution sensing film is embedded at the interface between an adherend and the adhesive in a single-lap joint configuration to monitor the spatial strain distribution over the bonding area. The conductivity distribution reconstructed by an algorithm of electrical impedance tomography (EIT) results in similar trend to the correlated conductivity distribution numerically calculated from a three-dimensional (3D) finite element model. Moreover, the EIT result of a defect-embedded testing specimen shows a different conductivity distribution compared to the healthy specimen, indicating the existence of the debonding region. Refereed/Peer-reviewed Version of record