DIC Experiment - Using EikoTwin DIC for Small Strain Measurement

Innovation in the Automotive Industry

Innovation is at the core of the automotive industry: autonomous vehicles, shared mobility, advanced equipment, etc. To reshape itself and maintain competitiveness, the automotive industry must innovate and develop new technologies. Therefore, these companies must conduct real simulations. The goal is to predict the behavior of their innovative components to forecast their current production and future use. This is why these simulations must accurately represent the geometric and mechanical behavior of the test parts. However, their validation also requires reproducing the boundary conditions that the test parts actually experience. In this article, we will focus specifically on the low-level mechanical strain behavior of the dashboard under compressive stress.

Small Strain Measurement Based on Digital Image Correlation

The global method-based digital image correlation addresses this issue. In fact, it allows for the measurement of displacement fields defined directly on the reference frame and simulation mesh nodes (Figure 1).

Experimental Setup

The purpose of the experiment being considered is to determine the overall mechanical response of the dashboard when the user presses one of the 13 points shown in red in Figure 1. In the experiment, we simulate the user's action by applying a 10N vertical compressive force. Such a load represents the user's forced contact. For this level of force, the total strain experienced by the component remains very small. Therefore, this is one of the challenges of the measurement techniques involved in the testing.

To measure through image correlation, spots are created on the part using a spray can. Applying texture to the part is necessary for the camera to track the surface. Thus, we can measure the experimental displacement field. Once the part is painted, it is placed in a rigid aluminum frame and positioned under the press head. The press head then applies a linear force of 0 to 10N at each of the 13 points selected for the study. The tracking of the surface displacement of the part is ensured by a pair of cameras that capture the experimental images. We ultimately obtain 60 images on each camera (Figure 2).

Based onEikoTwin DICImage Post-Processing

Then, the 13 sets of images are post-processed using EikoTwin DIC software. This allows for the measurement of the evolution of the surface displacement of the part at each loading point. One feature of this software developed by EikoSim is the use of prior knowledge of the part's geometry (provided by the finite element mesh) to calibrate the camera system. This method avoids the use of calibration targets and directly connects the measurement frame with the finite element model frame. Therefore, the displacement field will be directly represented at the nodes of the computational mesh. We can then visualize them directly on the images (Figure 3).

Small Displacement Measurement and Comparison with Simulation Results

Overall Displacement Field

With the help of digital image correlation, the displacements of each node in the mesh were measured. They are visible in Figure 4 below, applicable to loading point 8 with a 10N load.

The maximum displacement is located at the point of force application directly beneath the press head. Observed normal displacement gradients were found at the 13 test points. We also noted that for the selected instrumentation, the press head limited the total surface that could be measured within the field of view of a pair of cameras. As a reminder, to obtain three-dimensional displacement measurements, the texture must be observed simultaneously by at least two cameras. A possible improvement for future activities would be to add an additional camera on the opposite side of the press head. This would provide 360° results around the press head.

However, it should be noted that the information obtained through image correlation processing is much richer than that provided by strain gauges in the same configuration. As an example, throughout the test, CIN provided 6000 3D displacement measurements for each image. The purpose of the field measurements is to allow for the re-adjustment of the model across the entire area of interest. This is not the case for a limited set of point sensors.

Creation of Virtual Sensors

Point results can also be extracted from field measurements. In fact,EikoTwin DICallows for the placement of virtual displacement sensors on the mesh (Figure 5). This enables the rapid display of the displacement of points of interest within the part over time. The results are consistent with intuition. We observed a progressive collapse around the press head, while there was also a slight collapse at the edges of the part.

Comparison of Experimental and Simulation Fields

The main attraction remains the field comparison. This is accomplished by exporting results in hwascii format for quick comparison with the simulation results used by Faurecia in this study (Figure 6).

By comparing the experimental results with the simulation results, the predictive capability of the simulation for the considered experiment can be verified. We verified that the application of force generates displacements around the points of application in the correct areas. This validates that the constitutive equations used in the finite element model to describe the behavior of the dashboard allow for a very satisfactory estimation of the measured experimental field in terms of amplitude. The shape of the field, particularly near the press head, can be well predicted. On the other hand, larger deviations are shown at locations far from the press head (but still well below the measurement uncertainty).

The direct comparison of results conducted in the simulation reference frame brings a second potential point of improvement. In fact, if the point of force application is precisely transferred to the part through padding, the positioning of the part under the press head is done manually. This creates a discrepancy between the theoretical and actual points of force application. By processing the experimental field provided by CIN, the actual point of force application can be accurately identified. The simulation can then be updated to apply the force as closely as possible to the actual conditions of the field. Thus, the portion of uncertainty related to the application of boundary conditions in the calculations is excluded as a source of error when interpreting the comparison. This can be automated for all loading points of the tests.

A Brilliant Summary of Small Strain Measurement

In summary, this study will contribute to improving the simulation of innovative dashboards. Due to the adoption of unique imaging techniques,EikoTwin DICthe software will successfully highlight very small strains that are not visible to the naked eye. The correlation of digital images makes it possible to establish a rapid connection between test data and simulation data.