Evaluation of residual and thermal stresses using temporal analysis of color in photoelasticity images was applied to three discs with residual stresses in different zones. The stress field generated by a compressive load is deformed under residual stress presence. 3D color trajectories for interest pixels show behavior differences between locations with and without residual stress. Finally, k-means analysis for three experiments shows the presence of residual stresses and relates their temporal behavior with a high stress level zone.
Digital photoelasticity is used for evaluating the stress in loaded bodies. However, when dynamic analyses are needed, the motions of optical elements are an experimental challenge. This new computational hybrid approach calculates the stress field by extract the phase steps from RGB color channels of a photoelastic color image. Our approach integrated the load stepping strategy with a computational hybrid phase algorithm, hence only bright field images are required. Although, our method has a lower performance than phase shifting methods evaluated, the principal advantage of this hybrid strategy is that only a color- image is required to analyze stress field, avoided capture multiple images for analyzing phase maps.
To simplify the implementation of photoelasticity studies, the recently introduced Thermal Transient Stepping (TTS) method produces a stress field, from images with fringe displacements induced by temperature. These images are acquired without using mechanically-induced load variations, nor rotating optical devices. However, TTS produces stress fields with unwrapping errors, due to the lack of a strategy to select adequately the fringe displacements. We addressed this limitation by evaluating different thermal stimulations, and their effects in the performance of TTS. This allows us to achieve stress fields with higher fringe orders.
Fatigue tests impose experimental conditions that are difficult to achieve, and require using highly specialized equipment. We propose an inexpensive and easy to implement approach, to evaluate fatigue caused by cyclic loading. The approach includes a custom-built assembly and a modified jigsaw. The later impacts a circular plate of epoxy resin repeatedly, which produces their cyclic loading. After evaluating the plate with different loading frequencies, we found a direct relation between photoelasticity fringes and temperature at the plate geometry, despite experiencing some difficulties when obtaining photoelasticity data due to high loading frequencies and stress saturation of the samples. This approach also provides an integration tool between photoelasticity and infrared thermography for experimental stress analysis related to fatigue.
A thermal approach for measuring the stress field was developed by using digital photoelasticity. The approach relies on applying a thermal stimulation to the examined model, in conjunction with a computational hybrid algorithm of load stepping, to determine the isochromatic phase value from only three experimental images. The proposal was validated by using a PMMA disk under compressive load and exposed to thermal variations. This experiment was conducted in reflection photoelasticity where a face of the disk was used to observe the fringe patterns, and the back face to capture thermal variations. The results obtained in synthetic and experimental images, indicate that the approach is effective, easy to reproduce, and could enhance the capabilities of existing approaches to analyze stress fields.
In digital photoelasticity, fringe pattern analysis is crucial because the photoelastic fringes provide information about direction and magnitudes of the principal stresses at the surface of the inspected object. These fringes exhibit visual properties that depend on the applied load, their spatial location in the inspected object geometry, and the illumination source. Traditional methods for fringe analysis in photoelasticity have limited performance when dealing with noisy or not well contrasted fringes, or if the spatial resolution of the fringes is lost. This work presents an approach for analyzing fringe patterns in photoelasticity images using texture information, in conjunction with machine learning techniques. Stress fields are simulated in multiple spectral bands for two models. Then, different regions of interest in these models are characterized with well-known texture descriptors. Furthermore, feature ranking and five classification schemes are used to describe the texture variations that occur in the models when they undergo diametral compression in the different spectral bands considered. The results show that texture descriptors are suitable tools for describing the stress information provided by photoelastic fringe patterns. Also, it is possible to use machine learning techniques to learn, recognize, and predict the behavior of models subjected to mechanical load in photoelasticity experiments.
In digital photoelasticity, evaluating the stress map is often affected in regions with critical values. This phenomenon is associated to color degradation effect and high fringe densities. It is a consequence of different experimental conditions, such as: type of birefringent material, relative spectral content of light source, relative spectral response of camera sensor, polarization optical elements, load application, etc. In this study field, the main goal accounts for evaluating the stress values, as better as possible, from photoelasticity images. Which turns the view towards the process that allow to acquire photoelasticity images with more complete information. This makes necessary to analyze the possible effects that each element could introduce into the photoelasticity image generation. This paper presents a computational analysis on the effect that different industrial light sources introduces for recovering the stress maps. Hence, four common industrial light sources are considered for generating the photoelasticity images. In this case, results reveal that there are light sources which represent stronger limitations for evaluating the stress, and that Such effect varies with the load increments. This approach is useful for predicting the possible effect that a light source selection could introduce into the stress evaluation process.
In digital photoelasticity images, regions with high fringe densities represent a limitation for unwrapping the phase in specific zones of the stress map. In this work, we recognize such regions by varying the light source wavelength from visible to far infrared, in a simulated experiment based on a circular polariscope observing a birefringent disk under diametral compression. The recognition process involves evaluating the relevance of texture descriptors applied to data sets extracted from regions of interest of the synthetic images, in the visible electromagnetic spectrum and different sub-bands of the infrared. Our results show that extending photoelasticity assemblies to the far infrared, the stress fields could be resolved in regions with high fringe concentrations. Moreover, we show that texture descriptors could overcome limitations associated to the identification of high-stress values in regions in which the fringes are concentrated in the visible spectrum, but not in the infrared.
Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection.
It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those
flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest
(ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology
for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform
heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in
the image.
In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is
robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian
scale analysis and local edge detection. In this methodology local correlation between image and Gaussian
window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well
modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using
multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size.
Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide
a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the
other dedicate algorithms proposed in the state of art.
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