The traditional microscopic speckle interferometer has limited applications in engineering due to its small field of view. In this paper, we propose a large-field microscopic speckle interferometer which embeds two doublet lens groups in the improved Mach–Zehnder optical path structure to expand its field of view. At the same time, the new system can reduce the coherent noise of reflected light in the optical path. We use this new system to measure the dynamic displacement process of the entire surface of the microchips. The experimental results show that our improved measurement system can achieve large-field, real-time and high-precision dynamic measurement of micro-electromechanical systems (MEMS).

The fastener plays a critical role in ensuring the stability and safety of modern engineering structures. However, factors such as vibration, shock, temperature changes, and others can cause fasteners to become loose, thereby compromising the stability and safety of the overall structure. Currently, fastener inspection is primarily done manually, and there is no widely adopted automatic method for real-time detection of fastener looseness. We proposed a fastener looseness inspection method based on digital shearing speckle pattern interferometry (DSSPI) and convolutional neural network (CNN). The key distinction between our proposed method and traditional vision-based methods is that our approach can identify loose fasteners without relying on direct visual observation of the fasteners themselves. Our approach involved setting up a DSSPI system to capture shearing speckle patterns, and the CNN model automatically extracted features from these images and classified them to detect loose fasteners. Principle-validation experiments were conducted to verify the feasibility of the methodology. The trained model achieved a classification accuracy of 92.57%, with a resolution of 0.04 mm and a completion time of only 2.03 ms for a single judgment. That is quite accurate, more sensitive, and much faster than the three-dimensional vision-based method. Furthermore, our proposed method has strong robustness to ambient light, while the incorporation of Random Erasing technology enhances robustness to part occlusion or damage of the measured surface. Most notably, our method can detect loose fasteners before deformation, a capability that conventional vision-based methods lack. These findings demonstrate the tremendous potential of our method for real-time inspection of fastener looseness.

At present, most wavefront sensing methods analyze the wavefront aberration from light intensity images taken in dark environments. However, in general conditions, these methods are limited due to the interference of various external light sources. In recent years, deep learning has achieved great success in the field of computer vision, and it has been widely used in the research of image classification and data fitting. Here, we apply deep learning algorithms to the interferometric system to detect wavefront under general conditions. This method can accurately extract the wavefront phase distribution and analyze aberrations, and it is verified by experiments that this method not only has higher measurement accuracy and faster calculation speed but also has good performance in the noisy environments.

Traditional thermal stress measurement methods usually assume that the stress is generated only in one direction and cannot measure multidimensional stress components in real time. To realize the real-time and full-field measurement of multiple components of thermal stress, we improved the digital speckle shearing pattern interferometry method and predigested the measurement structure. Additionally, we were able to simplify the measurement process and measure the out-of-plane as well as the in-plane components of the stress tensor at the same time without the symmetric dual-beam illumination or phase-shifting devices. In this article, we demonstrated the possibility of measuring three component gradients ∂ u / ∂ x , ∂ v / ∂ x , and ∂ w / ∂ x , and carried out experiments proving that this method allows for measuring the multicomponent thermal stress measurement of the object under the premise of certain mechanical structure.