Computational analysis of Bayer colour filter arrays and demosaicking algorithms in digital photoelasticity

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.

Computational analysis of stress map variations by industrial light sources and load additions in digital photoelasticity

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.

Generalized adversarial networks for stress field recovering processes from photoelasticity images

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.

StressNet: A deep convolutional neural network for recovering the stress field from isochromatic images

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.

Thermal transient stepping: a powerful thermal-based approach for evaluating the stress field by using digital photoelasticity

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.

Fringe patterns recognition in digital photoelasticity images using texture features and multispectral wavelength analysis

Imaging the stress field is a complex process performed, and nowadays simulated, in digital photoelasticity. In these simulations we demonstrate that the Bayer and demosaicking effect must be considered because they introduce different errors.

Bayer and demosaicking effect for imaging the stress field in digital photoelasticity

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.

New computational hybrid three-wavelength-based approach of phase shifting for analyzing the stress field in photoelasticity studies

Analyzing fringe patterns in photoelasticity images is a common process for describing stress concentration zones. Notwithstanding, we show that an unique texture descriptor could be insufficient for describing the ROI texture in dynamic photoelasticity.

Relevance analysis for texture descriptors in studies of dynamic photoelasticity

En imagenes de fotoelasticidad, los patrones de franjas envuelven el campo de esfuerzos de un cuerpo birrefringente bajo cargas mecanicas. En estos casos, zonas con concentraciones de esfuerzos producen altas densidades de franjas, dificultando la recuperacion, y en ocasiones la perdida, de la informacion envuelta en la imagen. Este articulo evalua el crecimiento de tales regiones y su efecto en el campo de esfuerzos para un caso donde el cuerpo es sometido a cargas dinamicas. Aqui, una secuencia sintetica de imagenes de fotoelasticidad es generada partiendo del modelo analitico de la distribucion de esfuerzos en un disco bajo compresion diametral. El tamano de las regiones con perdida de informacion es estimado mediante la implementacion de transformadas de Fourier y filtros paso alto. Los resultados muestran que el incremento de la carga introduce aumentos en las regiones con perdida de informacion. En estos casos, los esfuerzos maximos recuperados a partir de las imagenes se alejan de los valores en el modelo analitico. Esto permite identificar regiones en las imagenes de fotoelasticidad, donde el campo de esfuerzos debe ser corregido

https://dialnet.unirioja.es/descarga/articulo/8084425.pdf

Hermes A. Fandiño Toro

Papers

Fringe patterns recognition in digital photoelasticity images using texture features and multispectral wavelength analysis

Bayer and demosaicking effect for imaging the stress field in digital photoelasticity

New computational hybrid three-wavelength-based approach of phase shifting for analyzing the stress field in photoelasticity studies

Relevance analysis for texture descriptors in studies of dynamic photoelasticity

Análisis de resolución en imágenes de fotoelasticidad: caso carga dinámica