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.

The wear performance of AlSi7Mg0.6 aluminum alloy, a casting aluminum alloy used in positioning devices for catenary systems of high-speed railways which fail frequently on lines where the speed of trains is higher than 300 m/s, is discussed in this study. It was estimated that sliding contact wear occurred and mainly contributed to the failure. To explore the competing mechanism for frictional wear failure, frictional experiments based on three groups of sliding distance (0.5 mm, 1.5 mm and 3.0 mm) and four groups of applied loads (20 N, 50 N, 100 N and 200 N) were implemented. Three-dimensional morphological observation results revealed that the wear volumes at a sliding distance of 0.5 mm were only about 1/10 of that at a sliding distance of 3.0 mm. It was also revealed that the wear volume based on a sliding distance of 3.0 mm and applied load of 20 N was still much larger than the wear volume under a sliding distance of 0.5 mm and applied load of 200 N. SEM observation of the microstructures revealed that abrasive wear was the dominant wear mechanism in dry sliding friction conditions. A simplified positioning device model was also established to study the influence of tension force on wear performance. The simulation results revealed that smaller tension force between the positioning support and positioning hook would lead to higher relative sliding distance and larger wear depth. Sliding contact friction should be avoided due to relatively large wear efficiency compared with rolling contact friction. Both experimental and simulation results suggested that proper tension force was preferred in assembling components which could ensure rolling contact friction rather than sliding contact friction.

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.

An exact analytical model based on three-dimensional (3D) thermo-elasticity theory is developed to investigate the bending behavior of fiber metal laminate (FML) plates under thermo-mechanical load. The temperature-dependent properties and the orthotropy of the component materials are considered in this model. The analytical model is based on the heat conduction theory and thermoelasticity theory, and the solutions are determined by employing the Fourier series expansion, the state space approach and the transfer matrix method. Comparison study shows that the FE results are generally in good agreement with the present analytical solutions, exhibiting relative errors of less than 2%, except in the regions near the upper and lower surfaces. The present solution is close to the experimental values for the laminated plate within the linear range, with errors less than 10%. The decoupling analysis indicates that the thermo-mechanical performance of FML plates no longer strictly adheres to the traditional superposition principle, with errors reaching 30.39%. A modified principle accounting for modulus degradation is introduced to address this discrepancy. Furthermore, parametric studies reveal that the temperature and the lamina number have significant effect on the stresses and displacements of the FML plate.

The fatigue behavior of a high-strength bearing steel tempered under three different temperatures was investigated with ultrasonic frequency and conventional frequency loading. Three kinds of specimens with various yield strengths exhibited obvious higher fatigue strengths under ultrasonic frequency loading. Then, a 2D crystal plasticity finite element method was adopted to simulate the local stress distribution under different applied loads and loading frequencies. Simulations showed that the maximum residual local stress was much smaller under ultrasonic frequency loading in contrast to that under conventional frequency at the same applied load. It was also revealed that the maximum local stress increases with the applied load under both loading frequencies. The accumulated plastic strain was adopted as a fatigue indicator parameter to characterize the frequency effect, which was several orders smaller than that obtained under conventional loading frequencies when the applied load was fixed. The increment of accumulated plastic strain and the load stress amplitude exhibited a linear relationship in the double logarithmic coordinate system, and an improved fatigue life prediction model was established.

Predicting the evolution of wear in metallic structural components is vital for accurately estimating the lifetime of engineering equipment. However, this remains a significant challenge due to the prohibitively large number of cycles required for traditional experiments or simulations. To address this, we established a data-driven approach to predict metal wear evolution during dynamic mechanical interactions. Our methodology involves two main steps: developing a high-fidelity finite element (FE) model to accurately simulate the wear, and then training a deep learning model that uses applied loads and historical wear data to predict future wear evolution. We selected contact wire clips in a high-speed railway system as a practical example, where the accuracy of our numerical model was successfully validated by experimental results in calculating wear distribution. The subsequent deep learning model demonstrated high accuracy ( R 2 > 0.95 ) in predicting future wear depth at distinct positions against ground truth data. This presented approach offers a wide range of applications for predicting the wear evolution of equipment in various engineering fields.