Computational models of structures are widely used to inform decisions about design, maintenance and operational life of engineering infrastructure, including airplanes. Confidence in the predictions from models is provided via validation processes that assess the extent to which predictions represent the real world, where the real world is often characterised by measurements made in experiments of varying sophistication dependent on the importance of the decision that the predictions will inform. There has been steady progress in developing validation processes that compare fields of predictions and measurements in a quantitative manner using the uncertainty in measurements as a basis for assessing the importance of differences between the fields of data. In this case study, three recent advances in a validation process, which was evaluated in an inter-laboratory study 5 years ago, are implemented using a ground-test on a fuselage at the aircraft manufacturer’s site for the first time. The results show that the advances successfully address the issues raised by the inter-laboratory study, that the enhanced validation process can be implemented in an industrial environment on a complex structure, and that the model was an excellent representation of the measurements made using digital image correlation.

The increased use of optical measurement techniques in industrial environments has the potential to increase knowledge and creates an opportunity for a more comprehensive validation of computational predictions. In this paper, a quantitative validation methodology is applied to a 1 m × 1 m panel from an aircraft fuselage subject to compression and torsion, in order to evaluate the predicted response of the panel. A test matrix with four loading cases, namely pre-buckling and post-buckling compression with and without torsion, was used to demonstrate the capabilities of the validation methodology on the industrial component. The out-of-plane displacement fields were analysed with the aid of image decomposition and a validation process was successfully performed using a quantitative metric. The feature vectors, obtained through image decomposition, representing the surface curvature of the physical and virtual specimens were analysed to assess the similarity of the component’s overall curvature. Then, the feature vectors representing measured and predicted displacements for the four loading cases were used to analyse the deformed shapes and conduct a validation process for the simulation outcomes. The predictions of the deformation of the fuselage panel were found to have a high probability of representing the measured data.

© CCM 2020 - 18th European Conference on Composite Materials. All rights reserved. Model validation is a major step in achieving computational models with good predictive capabilities. It is normal practice to validate simulation models by comparing their numerical results to experimental data. A critical issue when performing a validation procedure with information-rich data fields is the identification of effective techniques for data compression to allow the application of statistical measures to the comparison of predictions and measurements. Recently, image decomposition techniques have successfully been applied in a laboratory environment to condense data and extract features of surface deformation maps obtained with the aid of optical measurement techniques and finite element analysis. In this work, the integration of orthogonal decomposition with a validation metrics is explored and a new metric introduced. For the purpose of illustration, a case study of a composite car bonnet liner subject to impact loading has been used. Displacement fields from the entire surface of the bonnet liner were captured at equal time increments for 0.1s following the impact and then decomposed while a parallel process was applied to predictions from a finite element model. The validation metric was calculated from the resultant feature vectors and used to evaluate the quality of the predictions. It is anticipated that the outcomes of this investigation will support the development of a robust validation methodology for industrial applications.

A quick method to directly determine the true stress-strain curve over a large strain range is proposed. The true stress-strain curve includes the post-necking strain, is directly measured via a special multi-camera digital image correlation (DIC) system. A tension test with the standard dog-bone shape specimens is used in the system. The cross-section area of the specimen throughout the test is measured by the DIC, as well as the entire field true strain. The true stress is calculated based on the measured cross-section area and the load. A dual-phase steel with a minimum 780 MPa tensile strength (DP780), as a typical advanced high-strength steel (AHSS), is used in the test to demonstrate the method and to validate the result. The true stress-strain curve including the post-necking data of DP780 is presented. The test result is validated with the theoretical calculation as well as the experimental method. Furthermore, the measured true stress-strain curve is approximated by the power law equation to identify the material constants. The methodology, experimental test, and the evaluated result are described in detail.

Imaging systems for measuring surface displacement and strain fields such as stereoscopic Digital Image Correlation (DIC) are increasingly used in industry to validate model simulations. Recently, CEN has published a guideline for validation that is based on image decomposition to compare predicted and measured data fields. The CEN guideline was evaluated in an inter-laboratory study that demonstrated its usefulness in laboratory environments. This paper addresses the incorporation of the CEN methodology into an industrial environment and reports progress of the H2020 Clean Sky 2 project MOTIVATE. First, while DIC is a well-established technique, the estimation of its measurement uncertainty in an industrial environment is still being discussed, as the current approach to rely on the calibration uncertainty is insufficient. Second, in view of the push towards virtual testing it is important to harvest existing data in the course of the V&V activities before requesting a dedicated validation experiment, specifically at higher levels of the test pyramid. Finally, it is of uttermost importance to ensure compatibility and comparability of the simulation and measurement data so as to optimize the test matrix for maximum reliability and credibility of the simulations and a quantification of the model quality.

The extraction of useful information and removal of redundant noise from data has become a major research topic in recent years. Data compression is necessary for all kinds of analysis, and the demand for efficient compression techniques has gained much attention. Digital image correlation is a camera-based measuring system, which has been widely applied in strain analysis because of the convenience of measuring displacement fields by simply selecting a region of interest. Currently, there is interest in applying such methods to engineering structures in dynamics. However, one of the major issues related to the integration of camera-based systems with dynamic measurement is the generation of huge amounts of data, typically extending to many thousands of data points, because of the requirements of high sampling rate, spatial resolution, and long duration of recording. In this paper a new algorithm is presented that addresses the need for efficiency in full-field data processing. By making use of the data itself and combining the concept of sparse representation with Gram-Schmidt orthogonalisation, the number of basis function used to represent the data can be reduced and a concise decomposition established. In both simulated and experimental cases, the compression ratios for data size and number of signals used in operational modal analysis are substantially diminished, thereby demonstrating the effectiveness of the proposed algorithm. A reduced number of new basis functions is determined for the representation of data under the condition that the reconstructed displacement map reproduces the raw measured data to within a chosen threshold on the coefficient of correlation. (C) 2018 Elsevier Ltd. All rights reserved.

Full-field optical techniques used for Non-Destructive Evaluation (NDE), such as Laser Shearography, Thermography or X-Ray, are gaining more and more importance in industry, as they allow the inspection of large component areas over a relatively short period of time. However, each of these techniques having varying sensitiv ity to different physical properties of the material. And so, for different materials to be inspected, different methods may be more suitable than others. As an interferometric technique, Laser Shearography is sensitive to changes of out-ofplane stiffness in the material, which cause differences in the surface deformation under load in the range of μm. In general, critical flaws cause a reduction in the stiffness of materials, which can easily be detected using Shearography. A description of the technique will be presented, focusing on the requirements for automated detection systems and show a measurement of a typical test sample. Using component samples with known defects, the detectability of the different types of flaws can be determined.

In material testing optical techniques take more and more over the role of classical mechanical length changing tools. Beside simple 1-dimensional measurement methods, full field techniques like digital image correlation (DIC), allow 2- or 3-dimensional characterisation of the materials and components. Especially for anisotropic materials the multidimensional information is important. E.g. up to now this is limited to 2D strain information on plane surfaces.Nowadays, the application of the DIC technique moves from academic to industrial fields. In this interest, one needs to prove the DIC technique on real components. The conventional 2- cameras 3D DIC inspection becomes then non-optimal since complex geometries imply hidden areas. The approach of using more than minimum required number of camera views overcome these limitations. An efficient way to overcome these limitations is to take advantage of the use of multi-cameras DIC system. This idea is already commonly used in other fields like photogrammetry.This publication describes the idea and principle of using the Cluster Approach for multi-cameras DIC systems. In order to illustrate the implications of this new technique, we present strain measurements in tensile testing and deformation measurements on complex geometrical structures.

Three Dimensional digital image correlation(3D-DIC) has been widely used by industry, especially for strain measurement. The traditional 3D-DIC system can accurately obtain the whole-field 3D deformation. However, the conventional 3D-DIC system can only acquire the displacement field on a single surface, thus lacking information in the depth direction. Therefore, the strain in the thickness direction cannot be measured. In recent years, multiple camera DIC (multi-camera DIC) systems have become a new research topic, which provides much more measurement possibility compared to the conventional 3D-DIC system. In this paper, a multi-camera DIC system used to measure the whole-field thickness strain is introduced in detail. Four cameras are used in the system. two of them are placed at the front side of the object, and the other two cameras are placed at the back side. Each pair of cameras constitutes a sub stereo-vision system and measures the whole field 3D deformation on one side of the object. A special calibration plate is used to calibrate the system, and the information from these two subsystems is linked by the calibration result. Whole-field thickness strain can be measured using the information obtained from both sides of the object. Additionally, the major and minor strain on the object surface are obtained simultaneously, and a whole-field quasi 3D strain history is acquired. The theory derivation for the system, experimental process, and application of determining the thinning strain limit based on the obtained whole-field thickness strain history are introduced in detail.

This article shows an experimental validation of the volume conservation assumption (zero plastic volume change assumption) for aluminum alloy (AA6000) sheet metal. A series of tensile tests were conducted. During the tensile tests, an optimized digital image correlation setup was used to simultaneously measure three principal strain components. The experimental results show that, at locations outside the necking band, AA6000 strictly follows the zero plastic volume change assumption throughout the duration of the test. Inside the necking band, AA6000 follows the zero plastic volume change assumption in the elastic range and early plastic range. However, before failure, a visible volume strain increase can be found inside the necking band, which shows that, in the deep plastic zone, AA6000 does not always follow the volume conservation assumption. The experiment plan, measurement setup optimization, experimental results and data analysis are shown in detail.

Conventional 3D image correlation systems use two cameras to capture images of test objects. Here, only those object regions can be measured, which are detected by both cameras simultaneously. The expansion of a 3D image correlation system with additional cameras allows applications that were previously difficult or impossible to be measured. The multi camera cluster approach calibrates all cameras automatically in one common coordinate system and gives measurement data on every surface point that is at least seen by two cameras. In particular, the new cluster based approach allows to measure simultaneously the front and back side of tensile test specimens. In addition to the strain components on the front and back the 4 camera system measures for the first time the full field dilution and thus the strain in the thickness direction with high precision. The multi-camera approach allows for non planar objects the measurement around corners or even a full 360 measurement of the entire object with one measurement system. The presented cluster approach is an integrated solution that requires no additional time consuming post processing like stitching of data clouds..

The 3D DIC method is a well-established full-field measurement technique in experimental mechanics which is used for the characterization of e.g. displacements, vibrations and strains. The results are often employed in order to determine material properties, to validate numerical models and many more.

A new material's forming limit diagram (FLD) must be obtained before it can be used for stampings. Compared to conventional methods, the ISO method with a digital image correlation system provides an efficient way to conduct these tests. However the ISO method is position dependent, so the evaluation procedure for aluminum alloy will be seriously affected by the multiple necking strain distribution, resulting in much error. In this paper, a modified ISO method, which can be used for the evaluation of the aluminum alloy FLD, is proposed, and errors resulting from multiple necking were eliminated through detection of the major necking area. An FLD evaluation software was also programmed. An FLD0 punch test was conducted to verify the new modified ISO method. Through comparison of the results with the accepted value, the FLD0 value evaluated from the modified method was determined to be much more accurate than the standard method.

Optical full field techniques, like digital image correlation (DIC) become more and more standard tools for the determination of material parameters. Especially for anisotropic materials the multidimensional information is important. For example, up to now this is limited to 2D strain information. The use of the innovative cluster approach for DIC and a multi camera setup in a standard tensile test allows the access to the third dimension and measure not just the strain on one surface but on both surfaces and in thickness direction.With the cluster approach the points to be evaluated are not defined by an image of one camera, like in conventional DIC systems, but on the object. In this case every object point seen by two or more cameras can be measured, independently of the arrangement of the cameras. In a front back side arrangement e.g. during a tensile test, both sides of the sample are measured simultaneously. As all cameras are calibrated in a common coordinate system the thickness of sample and any changes are measured directly. In this way the strain in thickness direction is measureable directly.As an example we present the measurement on a simple aluminium sample up to failure. The difference in the strain in direction of thickness and on the surface indicates anisotropic material properties caused by the process of manufacturing.

A composite bonnet liner subject to a high-velocity (70 m/s), low-energy (<300 J) impact by a 50-mm-diameter projectile has been investigated using computational simulation and by experiment. High-speed digital image correlation was employed to generate maps of displacement fields over the 1-m(2) bonnet at 0.2 ms increments for 0.1 s, that is, 500 datasets, and the results have been compared to those predicted by finite element analysis. Image decomposition was utilised to reduce the dimensionality of both datasets by representing them using adaptive geometric moment descriptors; these descriptors were used to perform quantitative comparisons of the datasets and to test the validity of the model based on all the available data. The model was found to be a good representation of the physical experiment during the first half of the impact event but a less good representation in the remainder of the test, probably because damping effects were not adequately incorporated into the simulation. The methodologies for data comparison and evaluation of model validity proposed and demonstrated in this study represent a significant advance in procedures for ensuring model fidelity and for creating model credibility in the simulation of dynamic engineering events.