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.
KEYWORDS: Fractal analysis, Solar concentrators, Photoelasticity, Digital photography, RGB color model, Optical components, Fringe analysis, Cameras, Phase shifts, Chemical elements
Identifying the state of stress around a concentrator is essential in a loaded structure. However, most studies are based on circular geometries, leaving aside complex ones such as fractals. In this paper, the effect of fractal concentrators are evaluated by means of digital photoelasticity by considering a circular disc of epoxy resin, a Canon color camera and a Baumer VCXU-50MP polarized camera. Additionally, a phase map was obtained with phase shifting, and phase wavelengths stepping algorithms. The digital photoelasticity executed detection of stress fields related to the fractal concentrator.
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.
Digital photoelasticity allows to evaluate the stress field in loaded bodies. There, load stepping method by Ekman and Nurse allowed to avoid inconsistencies and ambiguities. However, it did not become popular by needing six images from two polariscope configurations a three load steeps. This paper updates the conventional method by introducing a polarizer array camera into a circular polariscope. Hence, polarizations of 0° and 90° from a Baumer VCXU50MP camera conduced to bright, and dark field images, simultaneously.With this work, the stress field can be evaluated by using a single optical configuration into the load stepping method.
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.
For overcoming conventional photoelasticity limitations when evaluating the stress field in loaded bodies, this paper proposes a Generative Adversarial Network (GAN) while maintaining performance, gaining experimental stability, and shorting time response. Due to the absence of public photoelasticity data, a synthetic dataset was generated by using analytic stress maps and crops from them. In this case, more than 100000 pair of images relating fringe colors to their respective stress surfaces were used for learning to unwrap the stress information contained into the fringes. Main results of the model indicate its capability of recovering the stress field achieving an averaged performance of 0.93±0.18 according to the structural similarity index (SSIM). These results represent a great opportunity for exploring GAN models in real time stress evaluations.
For avoiding fails in loaded structures, adjust their geometry, removing material, or quantify residual stresses, photoelasticity studies often is limited by complex experiments, excessive computational procedures, expert supervision, narrow applications, and static focus. This paper proposes a pattern recognition-based strategy for evaluating the stress field from simplex dynamic experiments. Here, temporal color variations are processed to extract, select and classify stress magnitudes, isotropic points, and inconsistent information. This approach used synthetic photoelasticity videos from analytical stress models about disk and ring under diametric compression. Additional to improve limitations in conventional photoelasticity approaches, this strategy identifies isotropic and inconsistent points.
Extending photoelasticity studies to industrial applications is a complex process generally limited by the image acquisition assembly and the computational methods for demodulating the stress field wrapped into the color fringe patterns. In response to such drawbacks, this paper proposes an auto-encoder based on deep convolutional neural networks, called StressNet, to recover the stress map from one single isochromatic image. In this case, the public dataset of synthetic photoelasticity images `Isochromatic-art' was used for training and testing achieving an averaged performance of 0.95 +/- 0.04 according to the structural similarity index. With these results, the proposed network is capable of obtaining a continuous stress surface which represents a great opportunity toward developing real time stress evaluations.
KEYWORDS: Digital image correlation, Photoelasticity, RGB color model, Finite element methods, Digital photography, Cameras, Image processing, Reflection, Epoxies
Some engineering areas have the challenge to discover geometries with mechanical high performance against complex applications, which is a defiant design task. With this objective, recent works have demonstrated the powerful contributions that bioinspired experiments can offer a wide diversity of applications. In this sense, this paper analyzes differences in the mechanical response of a stress concentrator when comparing conventional rings and their respective bioinspired representation. Here, the bioinspired geometry comes from a cut of a transversal section of rice root due to its resistance to internal pressures. The mechanical analysis is carried out by hybrid integration of photoelasticity studies, digital image correlation, and finite element methods. In this case, results indicate that preserving the same quantity of material, bioinspired geometries are more sensitive to stress and strain than conventional rings.
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.
We proposed a Frenet-Serret descriptor to classify stress categories based on color dynamics of pixels stored in photoelasticity videos. A collection of image compression models of disc and ring with a monotonic incremental load was generated. For each pixel, a temporal curve was created using color changes each frame. A descriptor histogram with Frenet Serret parameters was used to train a neuronal network; it classified in four types of stress zones (concentrated, high, medium, low). With the proposed method, a dynamic differentiation was possible in the field of stress without considering traditional digital photoelastic procedures.
Evaluating the stress distribution in structures under temporal loads is being carry out by many of the engineering applications such as: impacts, cracks, bending, thermal-transient and other. In those cases, conventional photoelasticity techniques are more complex to evaluate the stress field because of their complicated and expensive experiments, quantity of computational procedures, and their time by time analysis. However, dynamic photoelasticity experiments produce temporal information, such as color variations, which could be analyzed, described, and classified in order to perform a whole stress field evaluation. In this paper, the one-dimensional local binary patterns (1D-LBP) are used to describe such color variations and use them to identify the stress values they belong. For different experimental configurations, this proposal achieved an accuracy of 98% when evaluating the stress field of cases with similar light sources than with a reference experiment, and 92% for experiments with other light conditions. These results make this descriptor able to determine categorical stress maps from a photoelasticity video itself, which significantly opens new opportunities to simplify the experimental and computational operations that limit the stress evaluation process in line with the dynamic experiment.
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.
Phase shifting techniques are often limited in digital photoelasticity by the quantity of acquisitions they require, and the process to perform them. This work simplifies such process by developing only a part of the acquisitions, and the rest are generated computationally. Our proposal was validated for a six-acquisition method by generating synthetic images from the analytical model of a disk under diametric compression. The results show that although our method uses less acquisitions, it is capable to recover the stress field with similar performance than conventional methods. This proposal could be useful for evaluating dynamic cases because the reduction of the exposure time expended during the acquisition stage.
Digital photoelasticity is based on image analysis techniques to describe the stress distribution in birefringent materials subjected to mechanical loads. However, optical assemblies for capturing the images, the steps to extract the information, and the ambiguities of the results limit the analysis in zones with stress concentrations. These zones contain stress values that could produce a failure, making important their identification. This paper identifies zones with stress concentration in a sequence of photoelasticity images, which was captured from a circular disc under diametral compression. The capturing process was developed assembling a plane polariscope around the disc, and a digital camera stored the temporal fringe colors generated during the load application. Stress concentration zones were identified modeling the temporal intensities captured by every pixel contained into the sequence. In this case, an Elman artificial recurrent neural network was trained to model the temporal intensities. Pixel positions near to the stress concentration zones trained different network parameters in comparison with pixel positions belonging to zones of lower stress concentration.
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