Key Engineering Materials Vol. 1035

Abstract: Laser Shock Processing (LSP) is an excellent surface strengthening technology and an effective method to effectively solve this technical problem. The published literature only reports the LSP research on the blade tenon of large engine, and there is no LSP research on the turbine disk mortise of small engine (helicopter engine) with complex and narrow spatial structure. This paper was carried out Oblique LSP research on the complex structure of key parts of new helicopter engine, and the following research results were obtained: The residual stress of simulation and experiment were analyzed by the least-squares method, the flat-topped order of laser beam was corrected, and the pressure distribution model that better matches the shock wave induced by the flat-top laser beam was constructed; Due to the thin thickness and poor stiffness of the turbine disk mortise, the residual stress of the turbine disk mortise was compared between the LSP finite element simulation and the experiment by using the treatment method of equal laser power density and variable pulse width. The results show that the results of turbine disk mortise experiment were close to those of finite element simulation. The treatment method of equal power density and small pulse width can ensure the effect of strengthening the surface and reduce strain of turbine disk mortise.

Abstract: In this work, an experimental methodology is presented for investigating the kinetics of competing oxidation and metal-plating processes that occur on friction surfaces under variable load conditions. The aim of the study was to determine the critical parameters for the transition between the formation of dissipative secondary structures (DSS) and metal-plating films (MPFs), as well as to evaluate the contact electrical resistance (CER) as an indicator of the structural state of the surfaces. A universal tribometer with adjustable load (0.2–40 MPa) was used to test friction pairs of steel 45 and bearing steel Shkh15, employing a vaseline oil as an inert lubricant and CIATIM-201 grease with 7% copper powder as a metal-plating additive. A clear correlation was observed between the CER, the friction coefficient (μ) and the wear intensity (I) across four operating modes. The maximum CER values (up to 40 Ω·cm²) were recorded in the DSS formation regime, whereas the minimum values (below 1 Ω·cm²) corresponded to the metal-plating regime. The results demonstrate that the structural-energetic approach enables effective diagnosis of the tribological state and that the CER parameter serves as an informative criterion for distinguishing between friction regimes.

Abstract: The fatigue endurance of API 5L X42 pipeline steel was assessed through axial fatigue tests amongst Longitudinal (L), Diagonal (D) and Circumferential (C) directions. The S-N fatigue life curves were linearized by the stress-life relationship provided in the ASTM E-739 Std., and fatigue strength exponent was calculated by the linear regression method. Results showed an anisotropic behavior of fatigue properties, which is mainly controlled by the pearlite banding degree (A_i) and the ferritic grain orientation parameter (Ω₁₂). The interactions of the fatigue crack tip with the microstructure during the crack propagation stage have a significant effect on fatigue endurance. The C direction with the lowest values of banding degree (A_i) and grain orientation parameter (Ω₁₂) showed the strongest fatigue endurance behavior. Furthermore, fatigue strength exponents exhibited significant directional dependence, representing a reduction of up to 20% in fatigue lifetime depending on the evaluated direction.

Abstract: This study aims to evaluate the fatigue life of the frame assembly, including the chassis frame and subframe, of a medium dump truck. The combined method of finite element (FE) analysis and multi-body dynamic (MBD) simulation is employed to analyze the structural stress. Road roughness, as the vibration excitation source in the vehicle MBD model, is simulated according to random road profiles of ISO 8608:2016, considering the vehicle speed. The stress-time histories are calculated using quasi-static analysis when the frame structure is subjected to dynamic loads. The fatigue analysis model in the crack initiation period is developed based on the P(%)-S-N curve and linear damage rule. The results indicate that the frame assembly meets the required fatigue strength, achieving an estimated service life greater than 20 years under combined operating mode. These outcomes provide valuable insights for the design and durability assessment of the frame structure when vehicles are utilized under different operating conditions.

Abstract: This study investigates the optimization of input parameters for artificial neural networks (ANNs) to enhance the accuracy of fatigue life prediction under multiaxial loading conditions. Three models were developed using input features derived from the Fatemi–Socie and Ito et al. approaches, as well as a combined set of parameters. The dataset, consisting of 226 experimental results for 10 different materials and various loading paths, was extended to 427 data points through interpolation and normalized using min–max scaling. SHAP (SHapley Additive exPlanations) analysis was applied to assess the contribution of each parameter to fatigue life prediction. The results identified the shear strain range on the maximum shear plane, the normal strain range on the maximum tensile plane, and the non-proportionality parameter as the most influential features, along with Coffin–Manson equation coefficients characterizing material fatigue behavior. The material non-proportional hardening parameter, treated as a constant, was not found to be significant, indicating the potential need for its functional adaptation based on strain amplitude. Retraining the ANN using only the most relevant parameters maintained prediction accuracy, emphasizing their critical role.

Abstract: Reliable fretting fatigue prediction requires rigorous evaluation of analytical methods under realistic loading conditions. This study builds upon previous research on the fretting fatigue behavior of 42CrMo4+QT steel by incorporating new experimental data from square cross-section specimens tested under axial loading with various pad geometries. The application of a non-zero tensile mean bulk load promoted localized crack initiation near the specimen edges, leading to more asymmetric crack growth in the majority of cases unlike the more symmetric behavior observed under fully reversed loading (R = –1). Finite element analysis (FEA), along with the Dang Van and Papuga QCP methods, was employed to evaluate whether this behavior could be accurately modeled. In addition, a linear-elastic fracture mechanics approach was used to model and explain these observations. Furthermore, fretting fatigue tests on 34CrNiMo6+QT steel revealed that tribological effects governed crack initiation, in contrast to the stress-driven failure observed in 42CrMo4+QT. These findings enhance understanding of fretting fatigue mechanisms and improve predictive modeling approaches.

Abstract: Accurately modeling the fatigue life and strength of additively manufactured (AM) components ensures their reliability and performance in critical applications. However, this task is hindered by the complexities of AM processes, including material defects, anisotropy, residual stress, and surface roughness. This review explores how integrating surrogate modeling, transfer learning, and Bayesian inference can address these challenges and elevate predictive capabilities to new levels of accuracy and robustness. Surrogate modeling offers computationally efficient approximations of the intricate relationships between AM process parameters and fatigue behavior, enabling rapid exploration and optimization of design spaces. Transfer learning facilitates the adaptation of knowledge across different machines and process conditions, improving predictions even in low-data scenarios. Bayesian inference adds a layer of reliability by incorporating uncertainty quantification and prior knowledge into the modeling process. Together, these advanced methodologies present a transformative opportunity to improve the quality, efficiency, and robustness of fatigue life predictions for AM components, setting the stage for their broader adoption in high-performance applications

Abstract: This study presents a detailed analysis of the catastrophic failure of a Pelton turbine bucket, revealing a complex mechanism involving multiple interacting factors. Through a root cause analysis (RCA), the primary crack was identified to have originated in a high-stress concentration zone, exacerbated by pre-existing discontinuities. The turbine runner had accumulated approximately 90,000 service hours, suggesting a low-stress, high-cycle fatigue as the initial damage mechanism. However, the rapid crack propagation was driven by an abrupt shift in the fatigue regime, transitioning to high-stress, low-cycle fatigue induced by severe impact loads during counter-jet entry. This phenomenon led to the fracture of the bucket segment. This work emphasizes the importance of considering the synergistic interaction of accumulated fatigue, pre-existing discontinuities, and changes in the loading regime in the design and maintenance of Pelton turbines, to prevent premature failures and ensure the structural integrity of these critical components.

Abstract: This paper introduces a novel fatigue failure criterion that leverages the evolution of residual stresses under cyclic loading to more accurately predict fatigue life in advanced materials. Traditional fatigue models often overlook the dynamic nature of residual stresses, which can significantly influence crack initiation and propagation. The proposed criterion incorporates a combination of experimentation and mathematical modeling to capture the complex interplay between cyclic loading, material microstructure, and fatigue damage. The criterion's effectiveness is validated through a series of fatigue tests on representative materials, demonstrating its superior predictive capability compared to conventional methods. This research offers a new paradigm for fatigue analysis, enabling more reliable design and performance assessment of critical components in various engineering applications.