The fatigue behaviors of high-strength bearing steel were investigated with rotating bending fatigue loading with a frequency of 52.5 Hz. It was revealed that the high-strength steel tended to initiate at interior non-metallic inclusions in a very high-cycle fatigue regime. During fractography observation, it was also seen that the inclusion acting as a failure-originating site was seldom smaller than 10 μm. Moreover, prior austenite grains could also act as the originating source of failure when inclusion was absent. The crystal plasticity finite element method (CPFEM) was adopted to simulate the residual stress distribution around non-metallic inclusions of different sizes under different loading amplitudes. The accumulated plastic strain around the inclusion suggested that the existence of inclusion may reduce material strength and lead to more fatigue damage. The value of accumulated plastic strain around different inclusion sizes also resembled the crack nucleation or propagation of the materials. The simulation results also indicated that inclusions smaller than 5 μm had little influence on fatigue lifetimes, while inclusions larger than 10 μm had a significant influence on fatigue lifetimes.

By comparing two digital speckle images recorded before and after deformation, two-dimensional digital image correlation (DIC) method can accurately determine the in-plane displacement fields and strain fields. In a practical measurement, however, the variance of light source intensity, location and direction will cause the random uneven intensity change of the random speckle images and will lead to the obvious measurement error. Numerical simulation experiment is first carried out to analyse the influence of the recorded speckle images undergoing uneven light variation on DIC measurement accuracy. Then, a correction method for speckle images with uneven intensity change is proposed based on morphological Top-Hat transform. In addition, quantitative measurements of both in-plane rotation of a rigid body and three-point bending beam are investigated experimentally by DIC to verify the feasibility of the correction method. Experimental results show that the measurement accuracy of DIC is improved dramatically after the procedure of uneven light variation correction.

Robust gas sensing is fundamental to safety, environmental monitoring, and industrial control. However, the design of intelligent gas analysis algorithms remains constrained by a key instrumentation challenge: the lack of generalization across heterogeneous sensor arrays, gas compositions, and operating conditions. This study addresses this gap by introducing two deep learning models—graph-enhanced capsule network (GraphCapsNet) for mixture classification and graph-enhanced attention network (GraphANet) for concentration estimation—designed to operate directly on raw sensor data from structurally distinct platforms. GraphCapsNet integrates graph convolutional networks (GCNs) with dynamic routing mechanisms to extract key features from temporal data, while GraphANet combines GCNs with self-attention mechanisms to identify concentration-related features. Unlike prior approaches that rely on dataset-specific retraining or feature engineering, both models are deployed under fixed configurations across two divergent datasets, encompassing different sensor types, chamber sizes, and gas species. GraphCapsNet achieved over 98.00% accuracy in classification tasks, and GraphANet attained an ${R} {^{{2}}}$ score exceeding 0.96 across various gas components. These results highlight the models’ exceptional accuracy, stability, and scalability, offering a potential foundation for real-world deployment in variable sensing environments.

Infrared thermography techniques have proven to be very effective for assessing the fatigue limits of metallic materials with obvious temperature variations. But for some materials, it has been shown that the temperature variation is very limited, and the accuracy of infrared thermographic techniques is not verified. In this study, the fatigue properties of a high-strength steel (SAE52100) were evaluated with traditional fatigue-loading techniques and infrared thermographic methods. The traditional fatigue experiments were loaded at a frequency of 80 Hz with a stress ratio of R = −1, and the fatigue limit at the fatigue lifetime of N = 10⁷ cycles was about 800 MPa. Besides, three additional specimens were loaded with step-by-step increasing stress-loading amplitude, where the maximum temperature increments and temperature distribution were recorded via infrared thermographic techniques. The infrared detections revealed that the maximum value of the temperature increase was only about 1 °C. The fatigue limit was first evaluated based on the maximum temperature variation, then the prediction was refined based on fatigue intrinsic dissipation. The fatigue limits predicted with maximum temperature variation were shown to be 841 MPa, 772 MPa, and 787 MPa, respectively, while the fatigue limits predicted based on fatigue intrinsic dissipation were 793 MPa, 791 MPa, and 789 MPa. Finally, an FEM simulation of temperature variation during fatigue loading was implemented to verify the experimental results. This study provides a solid foundation for the applications of infrared thermography techniques for materials with lower energy dissipations.

With the rapid development of the wind energy industry, there is an increasing concern about operation safety and reliability of high-strength wind turbine bolts. The aim of this paper is to monitor the strain change around the cracks in wind turbine bolts by means of fiber Bragg grating (FBG) sensors for crack detection. Firstly, the strain distributions of wind turbine bolts’ cracks with different locations and angles in the service condition are simulated using finite element analysis (FEA). Then, three-point grating string FBG sensors were pasted on the surface of wind turbine bolts with fatigue cracks to monitor the strain changes around the cracks in real time. By analysing the monitored strain data elaborately, the location of the crack on the bolt surface was successfully detected by identifying the location of the maximum strain detected by FBG sensors. In addition, the strain distributions in the vicinity area of the crack at different angles (0°, 45° and 90°) were also monitored and analysed in depth. The different types of crack angles could be distinguished based on of different strain distribution of the vicinity of the crack tip at different angles. The experimental results show that the FBG sensing technology has a high degree of sensitivity and accuracy in crack detection of high-strength wind turbine bolts.