This research evaluates the efficiency of working fluids in direct adsorption solar collectors by incorporating magnetite (Fe3O4) nanoparticles. Samples with Fe3O4 concentrations ranging from 0% to 1% were evaluated under direct solar exposure conditions. It was determined that the nanofluids exhibit higher thermal efficiency than pure ethylene glycol, indicating that magnetite enhances solar radiation absorption. However, higher nanoparticle concentration was observed to decrease the Specific Absorption Rate (SAR), likely due to lower radiation penetration. These results suggest that SAR could be a useful selection criterion for formulating nanofluids in DASC collector applications.
Hyperthermia is a cancer treatment that utilizes magnetic nanofluidic and magnetic induction. The experimental evaluation techniques used to validate heat transfer and changes in thermal diffusivity include the use of phantom models to simulate human tissue and infrared thermography. The present study applies phantom models with magnetic inserts, which pose challenges in determining temperature distribution due to their location and depth, affect thermal contrast. Processing thermograms is crucial for identifying areas of interest, which enables the development of new hyperthermia evaluation techniques.
A microscope design was developed using 3D printing and liquid crystals to achieve variable polarization configurations and semi-automatic processes without the need for mechanical movement of polarizers and retarders. The device includes a polariscope composed of electronically controlled liquid crystals and a compliant mechanism for precise micrometric movements. Stepper motors and software control these movements, and the process of acquiring images requires connecting a sensor to a Raspberry Pi. The microscope's lens and extension tube assembly magnify the image 600 times. The study compares two polarization techniques: Stokes and ellipsometric polarization. Additionally, it analyzes crystals and meiotic spindles of maturing porcine oocytes. The ellipsometric technique is more effective in detecting low retards, as indicated by the results. This prototype has the potential to reduce the cost of in vitro fertilization in assisted reproduction laboratories, with a focus on animals of high genetic value.
Active thermography is a non-destructive testing (NDT) technique with great potential for evaluating the integrity, thickness, homogeneity, and structure of thermal barrier coatings (TBCs), which are widely used in the hot gas path of thermoelectric power plants. In this study, active thermography was used as a non-destructive analysis technique to measure the thickness of the YSZ ceramic layer in thermal barrier coatings subjected to a chemical removal process as a function of exposure time to the agent. The TBC samples analyzed consisted of ZrO2-7 wt% Y2O3 atmospheric plasma sprayed with NiCoCrAlY bond coat in nickel-based superalloy. The ablation process consisted of complete immersion of the sample in an aqueous solution of ammonium bifluoride (0 min, 90 min, 240 min, 330 and 420 min), maintaining a temperature of about 60 °C to about 68 °C and subjecting it to an ultrasonic bath. Thermographic evaluation was performed at room temperature using two 600 W flash lamps, one 500 W halogen lamp, and a long-wave infrared camera with an acquisition rate of 30 Hz. Samples were taken for microstructural analysis and thickness measurement using SEM microscopy and eddy currents. Thermographic information was processed using thermal signal reconstruction algorithms and correlated with microstructural characteristics and thickness as a function of immersion time. The results show differences in both the heating and cooling curves of the samples, which can be correlated to differences in the thickness of the coatings. In this study, active thermography was used as a non-destructive analytical technique to measure the thickness of the YSZ ceramic layer in thermal barrier coatings subjected to chemical removal. ZrO2-7 wt% Y2O3 coatings were immersed in an aqueous solution of ammonium bifluoride. Thermographic information was acquired using a 30 Hz infrared camera and two 600 W flash lamps. The results show differences in their heating and cooling curves, which allow them to be correlated with the coating thickness measured by SEM microscopy and eddy currents.
Active infrared thermography (IRT) has been used to detect delamination on yttria-stabilized zirconia thermal barrier coatings (YSZ-TBC) using various heat sources. Thermal shock tests were performed to induce delamination on 1x1 inch flat samples. Each thermal cycle consisted of heating the samples in a muffle furnace, holding at 1020°C for 5 minutes, and quenching on water at approximately 20°C. Two samples were used, the first was brought to failure (184 cycles) and the second was almost intact to the naked eye (100 cycles). In addition to these samples, a discarded gas turbine blade was cut, and a delaminated fragment was used for comparison. IRT was performed using two 600 W flash lamps, one 500 W halogen lamp, and a long-wave infrared camera with a frame rate of 30 Hz. The acquired signal was first processed by the Thermal Signal Reconstruction (TSR) method using a 4th order polynomial interpolation and its first and second time derivatives. The thermographic data were processed using the Principal Component Analysis (PCA) algorithm. PCA was applied to both raw and pre-processed data. The results were compared using signal-to-noise ratio (SNR) to measure the quality of the algorithms used to detect delaminated areas. The comparisons showed that PCA results are improved when applied to the TSR sequences. However, the selection of the principal component with the highest SNR depends on the heating condition (flash or long pulse) and on the samples analyzed.
Photoelasticity is a non-destructive optical testing technique that focuses on stress analysis. Traditional methods of demodulating stress fields are limited by various conditions, such as the image acquisition set, material properties, load values, light sources and isoclinics. As an alternative, deep convolutional neural networks (DCNNs) have been used to recover stress fields in automated and predictive methods. In this study, different DCNNs architectures are trained by means of two datasets, each one with 45000 images. First dataset has images with four polarization states (0°, 45°, 90° and 135°). Second dataset has images with 3-channel, each one corresponding to a Stokes parameter (s0, s1, s2). The quality of predicted images is evaluated with quality metrics such as MSE, SSIM, and PSNR. MSE and Adam are used as loss function and optimizer, respectively. Results show that on average, the use of DCNNs with images with four polarization states achieve better quality metrics than DCNNs with Stokes images. These results indicate that it is possible to obtain real-time stress fields using different representations of polarized images in deep networks and opens new opportunities for representing polarized images in deep learning models and extending its applications of stress analysis.
Digital photoelasticity is a non-contact inspection technique, that requires new strategies to unwrap the stresses map based on color images. Therefore, this paper presents a new three-wavelength chromatic-corrected hybrid phase-shift method for single-camera digital photoelasticity applications. For the experiments, the intensities of isochromatic images are simulated considering a birefringent sample compressed and a circular polariscope configured to produce a bright field image that avoids the effects caused by isoclinics. To increase the range of the fringe order, three LEDs with peaks of close wavelengths were used. Additionality, for each LED a RGB color image is simulated. The red channel of each image is used to generate a new synthetic chromatic-corrected image (CCI) thereby: the red channel of λ3 is the red channel of the new CCI, and the green and blue channels of the CCI use the respective red channels of λ2 and λ1; additionally, the wavelengths must satisfy the following condition (λ3 > λ2 < λ1). An inverted image of the CCI is computed. Thus, with the CCI and its inverted image, six images are stored, and with these images and some trigonometric relations proposed by Ekman and Nurse a wrapped phase map is extracted. Finally, an unwrapping algorithm is applied to reconstruct the stresses map. The results show that the method improves the detected maximum order and reduces stress map distortions compared to similar color phase shifting approaches. Furthermore, since the algorithm requires only a camera and a circular polariscope setup, it can be implemented in dynamic experimental applications.
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.
This paper used digital photoelasticity to evaluate the temporal variations of the stress field in an epoxy-metal embedded actuator. Stress variations were generated with magnetic induction heating-cooling cycles and they were analyzed with frames of a color digital video acquired with a circular polariscope. A phase wavelength stepping algorithm and an unwrapped standard algorithm were applied to obtained unwrapped map. The modification of the fields of bi-material stresses opens the opportunity to generate photoelastic actuators, in consequence they can be used as phase modulators. In consequence, digital photoelasticity is an excellent technique to characterize this effect in bimaterials.
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.
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.
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.
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.
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.
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.
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.
A method is proposed to automatically evaluate the focal planes of spherical particles. This method compares the correlation coefficients of multiple reconstructed planes relative to a reference plane. The particles are located where a minimum correlation is found, and reconstructions are made using an angular spectrum propagator. The Hough transform is employed to segment the hologram, thereby enabling the detection of circular shapes, such as Airy patterns, and the edges of the particles themselves. The autofocus is improved by creating a correlation matrix using an iterative process, which reduces the computational cost of the particle display processes in their respective focal planes. A theoretical model was studied to estimate the longitudinal and transverse magnifications of the focused particles caused by the influence of aberrations in the reconstruction of digital holograms due to the spherical reference wave used. Experimentally, laser light was used to illuminate 5-μm latex particles, which was recorded by a CCD camera with a 9.9-μm pixel size. The reconstructions measured an average particle radius of 71.3±16.3 μm in the average focal plane, which was estimated to be 60.65±0.22 mm from the hologram where the magnifications were considered.
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.
3D reconstruction of small objects is used in applications of surface analysis, forensic analysis and tissue reconstruction
in medicine. In this paper, we propose a strategy for the 3D reconstruction of small objects and the identification of some
superficial defects. We applied a technique of projection of structured light patterns, specifically sinusoidal fringes and
an algorithm of phase unwrapping. A CMOS camera was used to capture images and a DLP digital light projector for
synchronous projection of the sinusoidal pattern onto the objects. We implemented a technique based on a 2D flat pattern
as calibration process, so the intrinsic and extrinsic parameters of the camera and the DLP were defined. Experimental tests
were performed in samples of artificial teeth, coal particles, welding defects and surfaces tested with Vickers indentation.
Areas less than 5cm were studied. The objects were reconstructed in 3D with densities of about one million points per
sample. In addition, the steps of 3D description, identification of primitive, training and classification were implemented
to recognize defects, such as: holes, cracks, roughness textures and bumps. We found that pattern recognition strategies
are useful, when quality supervision of surfaces has enough quantities of points to evaluate the defective region, because
the identification of defects in small objects is a demanding activity of the visual inspection.
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.
A strategy for to compress color images in digital holograms of gray tones was developed. The procedure codifies the
information of each channel of the RGB model in a system of fringes, it is a gray image denominated "hologram". The
fringes in their intensity of gray tone carry the signal of the channel, in this manner the amplitude information for each
channel of color image is stored. The angles of fringes define how the information of each channel will be
packaged.
The sum of the different gray fringes images is the hologram, it is the "object" for a digital holographic system. The
RGB channels are high intensity peaks of information in the hologram's Fourier space, and when the peaks are filtered
each channel can be extracted.
Parameters such as: space frequency, visibility, direction and quality of the fringes affect the quality of the reconstructed
image. However, the propose methodology allow a radius 3:1 for the compression of color image, too with this
process is possible the compression of different spectrum in a one color image.
For the first time, transmission digital holography microscopy is applied to observe coal palynofacies, which are organic
fossil microcomponents contained in the coal grains. The recorded holograms were produced by using microscope lenses
with 20x and 40x of lateral magnification respectively, and He-Ne laser of wavelength 594.5 nm. The results show that
reflection digital holography microscopy is required for observing relative opaque particles, because the phase recovery
is strong diminished by light transmission in those cases. On the other hand, the phase distribution is related to the relief
of the particles and the variations of their refraction index. Therefore, a priori information should be necessary to
properly relate the phase information to physical features of the particles. Numerical unwrapping procedures are also
crucial. Procedures with special requirements can be needed for analysing fast varying phase distributions. However,
digital holography microscopy becomes a high performance tool for 3D modelling of fossil particles if the above
requirements are enough fulfilled.
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