Visualization method for stress-field evolution during rapid crack propagation using 3D printing and photoelastic testing techniques

Fringe pattern analysis to evaluate light sources and sensors in digital photoelasticity

When experimental photoelasticity images are acquired, the spectral interaction between the light source and the sensor used affect the visual information of the fringe patterns in the produced images. Such interaction can lead to fringe patterns with an overall high quality, but also can lead to images with indistinguishable fringes, and bad stress field reconstruction. We introduce a strategy to evaluate such interaction that relies on measuring the value of four handcrafted descriptors: contrast, an image descriptor that accounts simultaneously for blur and noise, a Fourier-based descriptor to measure image quality, and image entropy. The utility of the proposed strategy was validated by measuring the selected descriptors on computational photoelasticity images, and the fringe orders achieved when evaluating the stress field, from 240 spectral configurations: 24 light sources and 10 sensors. We found that high values of the selected descriptors can be related to spectral configurations that lead to better stress field reconstruction. Overall, the results show that the selected descriptors can be useful to identify bad and good spectral interactions, which could help to design better protocols for acquiring photoelasticity images.

In situ health monitoring of multiscale structures and its instantaneous verification using mechanoluminescence and dual machine learning

Extensive changes in the legal, commercial and technical requirements in engineering fields have necessitated automated real-time structural health monitoring (SHM) and instantaneous verification. An integrated system with mechanoluminescence (ML) and dual artificial intelligence (AI) modules with subsidiary finite element method (FEM) simulation is designed for in situ SHM and instantaneous verification. The ML module detects the exact position of a crack tip and evaluates the significance of existing cracks with a plastic stress-intensity factor (PSIF; K P ). ML fields and their corresponding K p M L values are referenced and verified using the FEM simulation and bidirectional generative adversarial network (GAN). Well-trained forward and backward GANs create fake FEM and ML images that appear authentic to observers; a convolutional neural network is used to postulate precise PSIFs from fake images. Finally, the reliability of the proposed system to satisfy existing commercial requirements is validated in terms of tension, compact tension, AI, and instrumentation.

An ultrafast time-resolution method based on picosecond pulsed laser for determining rock fracture toughness at multipoint during the crack propagation

An innovative ultrafast time-resolution method based on a picosecond pulsed laser was employed to investigate the mode-I crack propagation characteristics of fractured rock. Its time resolution is as fast as the degree of 45 picoseconds. Then, a series of three-point compressive loading tests with this method were conducted on tuff semi-circular bend (SCB) specimens. Based on this method, we found that the mode-I fracture process of the tuff specimens were composed of repeated crack initiation, arrest, and re-initiation. In addition, the experimental results showed that the fracture rates of the tuff specimens in the initial 10 μs were 636 m/s, 663.9 m/s, and 578 m/s. In comparison, the fracture rates of the specimens were 11.19 m/s, 19.23 m/s, 26.79 m/s during the whole fracture process. As a typical heterogeneous material with primary defects, rock has different fracture toughness at different locations. Therefore, we proposed a new method for determining rock fracture toughness at multipoint during the crack propagation. This new method emphasizes the effect of fracture toughness on crack propagation, which enables to determine the fracture toughness at multipoint and is closer to the original definition of fracture toughness.

PhotoelastNet: a deep convolutional neural network for evaluating the stress field by using a single color photoelasticity image

Quantifying the stress field induced into a piece when it is loaded is important for engineering areas since it allows the possibility to characterize mechanical behaviors and fails caused by stress. For this task, digital photoelasticity has been highlighted by its visual capability of representing the stress information through images with isochromatic fringe patterns. Unfortunately, demodulating such fringes remains a complicated process that, in some cases, depends on several acquisitions, e.g., pixel-by-pixel comparisons, dynamic conditions of load applications, inconsistence corrections, dependence of users, fringe unwrapping processes, etc. Under these drawbacks and taking advantage of the power results reported on deep learning, such as the fringe unwrapping process, this paper develops a deep convolutional neural network for recovering the stress field wrapped into color fringe patterns acquired through digital photoelasticity studies. Our model relies on an untrained convolutional neural network to accurately demodulate the stress maps by inputting only one single photoelasticity image. We demonstrate that the proposed method faithfully recovers the stress field of complex fringe distributions on simulated images with an averaged performance of 92.41% according to the SSIM metric. With this, experimental cases of a disk and ring under compression were evaluated, achieving an averaged performance of 85% in the SSIM metric. These results, on the one hand, are in concordance with new tendencies in the optic community to deal with complicated problems through machine-learning strategies; on the other hand, it creates a new perspective in digital photoelasticity toward demodulating the stress field for a wider quantity of fringe distributions by requiring one single acquisition.

Challenges and Accomplishments in Mechanical Testing Instrumented by In Situ Techniques: Infrared Thermography, Digital Image Correlation, and Acoustic Emission

A current trend in mechanical testing technologies is to equip researchers and industrial practitioners with the facilities for non-destructive characterisation of the deformation and fracture processes occurring on different scales. The synergistic effect of such a combination of destructive and non-destructive techniques both widens and deepens existing knowledge in the field of plasticity and fracture of materials and provides the feedback sought to develop new non-destructive testing approaches and in situ monitoring techniques with enhanced reliability, accuracy and a wider scope of applications. The macroscopic standardised mechanical testing is still dominant in the research laboratories and industrial sector worldwide. The present paper reviews multiple challenges commonly faced by experimentalists, aiming at enhancing the capability of conventional mechanical testing by a combination of contemporary infrared thermography (IRT), rapid video imaging (RVI) with non-contact strain mapping possibilities enabled by the digital image correlation (DIC) method, and the acoustic emission (AE) technique providing unbeatable temporal resolution of the stochastic defect dynamics under load. Practical recommendations to address these challenges are outlined. A versatile experimental setup uniting the unique competencies of all named techniques is described alone with the fascinating possibilities it offers for the comprehensive characterisation of damage accumulation during plastic deformation and fracture of materials. The developed toolbox comprising practical hardware and software solutions brings together measuring technologies, data, and processing in a single place. The proposed methodology focuses on the characterisation of the thermodynamics, kinematics and dynamics of the deformation and fracture processes occurring on different spatial and temporal scales. The capacity of the proposed combination is illustrated using preliminary results on the tensile and fatigue behaviour of the fcc Inconel-625 alloy used as a representative example. Dissipative processes occurring in this alloy are assessed through the complex interplay between the released heat, acoustic emission waves, and expended and stored elastic energy.

Birefringence- and Optical Distortion-Free Isotropic Polymer Lens Assisted by Photonic Microspheres

Development of low-cost and light polymer optical devices to substitute for inorganic materials is a major trend. Traditional molten processing methods are direct and have been extensively applied in optical product manufacturing. However, the inevitable intrinsic birefringence and optical distortion due to polymer molecular chain anisotropy limit their application in high-end optical devices. Here, we report a novel thermocompression strategy for isotropic polymer lens fabrication, in which a cross-linked photonic crystal (PC) consisting of closely stacked polymer microspheres is used as a precursor and then heated and pressed under the rubbery state. A polymethyl methacrylate microsphere-based PC is used as a demonstration, and the obtained isotropic lenses exhibit superior performance compared to the traditional counterpart, which are birefringence-free (Δn < 1 × 10^-5) and optical distortion-free and have excellent mechanical properties (hardness reaches 0.28 GPa), and the hidden mechanism is carefully studied. These properties enable the isotropic lens to be applied in precision optical components such as the lens of spectacles, microscope, telescope and endoscope, industrial camera, and astronaut helmet, and the proposed general method can extend to various polymers and provide new opportunities for the development of three-dimensional PCs.