Abstract Understanding the three-dimensional evolution of internal damage within contact zones during wear is critical for failure analysis, yet it remains experimentally challenging, particularly at elevated temperatures. In this study, a dedicated in-situ wear testing system compatible with computed tomography (CT) and capable of operating at up to 380 °C was developed to address this gap. Rotational wear tests were performed on Ti-6Al-4V alloy at 25 °C, 200 °C, and 350 °C, and a "wear–pause–scan" strategy was employed to capture the progressive evolution of the contact interface. The results reveal a temperature-dependent evolution of the contact state and a corresponding shift in the dominant wear mechanism, underscoring the close coupling between interfacial structural evolution and macroscopic friction behavior. This work demonstrates the feasibility of in-situ, three-dimensional observation of the wear process from ambient to elevated temperatures for the first time, providing a versatile experimental platform that can be extended to a wide range of materials and contact configurations.
Abstract In rough tribological interfaces, the real contact area and local contact-pressure distribution are essential descriptors of asperity-scale load transfer and provide input fields for analyses of friction, wear, contact fatigue, and thermal or electrical contact resistance. Repeated high-resolution reconstruction of these fields from surface topography remains computationally demanding. This study proposes a physics-constrained two-stage deep-learning framework that separates latent pressure regression from contact-support prediction and combines the two outputs through hard gating. Hard gating enforces non-negative pressure and exactly zero pressure outside predicted support, while a Focal root-mean-square error (RMSE) loss emphasizes sparse high-pressure pixels. Separate load-specific models were trained at 30, 60, and 90 N. A controlled critic-on/off ablation at 256×256 and 30 N showed comparable reconstruction accuracy with and without adversarial training, indicating that the dominant performance gain arises from the two-stage formulation and hard gating rather than from the adversarial component itself. On synthetic RMD-generated rough-surface datasets, the model achieved a mean Dice score of 0.9909 and a mean pixel-wise accuracy of 0.9587; at 30 N, RU2 was 0.9995 and the p99.9 relative error was 0.0049. Batch-1 end-to-end inference required 9.0 ms at resolution on an NVIDIA RTX 3090 GPU. The predictions preserved prescribed load, contact-area trends, and dependence on RMS slope. An auxiliary test on axisymmetric oblate spheroidal profiles assessed smooth circular single-patch reconstruction, with the numerical fields checked against independent full-profile contact theory. On measured sandpaper-finished steel topographies without fine-tuning, mean Dice and coefficient of determination were 0.940 and 0.970 against FFT-BEM numerical references. Within the prescribed-load, frictionless linear-elastic regime, the framework provides a physics-constrained surrogate for rapid reconstruction of FFT-BEM-consistent contact-support and pressure fields.
Abstract Research on electric vehicles (EVs) has gained increasing attention due to the global transition toward cleaner and more sustainable transportation. However, ensuring the efficiency, reliability and long-term durability of EV drivetrains remains a major challenge. In EV bearings, inverter-induced shaft voltage and bearing currents can trigger electrical discharge across the lubricating film when the local electric field exceeds the dielectric strength of the grease. These discharge events can result in surface damage and lubricant degradation, leading to premature bearing failure and affecting the motor reliability. This review critically discusses the mechanisms of electrical discharge damage in EV bearings and reviews recent mitigation strategies including motor and inverter design optimisation, bearing materials and surface engineering, and specialised lubricant formulation. This review further discusses the development of bio-based greases for EV bearing applications, including formulation requirements, bio-based oil selection, thickener system design and additive strategies. The key challenge is to balance conductivity, dielectric stability, tribofilm formation, and grease durability under coupled mechanical, thermal, and electrical stresses. Overall, this review provides a framework for developing high-performance bio-based EV bearing greases that can mitigate electrical discharge damage while supporting sustainable lubrication technologies.
Abstract High-velocity particle impact on solid surfaces is a fundamental issue in contact mechanics, tribology, and impact dynamics, prevalent in cold spraying, shot peening, and erosive wear. In these processes, particle rebound is governed by coupled normal contact and tangential friction associated with impact velocity, surface morphology, and impact angle. However, a systematic understanding of how surface roughness and impact angle regulate particle rebound and energy dissipation remains lacking, as these coupled effects induce heterogeneous stress localization and multiple dissipation mechanisms. Here, molecular dynamics simulations are used to investigate the rebound behavior of particles impacting rough surfaces. A semi-analytical model is developed for the normal restitution coefficient under head-on impact, revealing an inverse scaling with dimensionless velocity. Furthermore, the effect of surface roughness on coefficient of restitution is velocity dependent: it reduces restitution at low velocities by enhancing local plastic dissipation, but slightly increases restitution at high velocities by suppressing overall particle deformation. Under oblique impact, the deformation mode transitions from normal compression to plowing wear with increasing impact angle. These findings deepen the understanding of energy dissipation and particle deformation modes during impact and provide guidance for surface pretreatment and process optimization in surface modification technologies.
Abstract In pursuit of decarbonization, ammonia has attracted extensive attention as a zero-carbon fuel for marine propulsion. However, unburned ammonia and associated byproducts will alter the lubrication environment of the cylinder liner–piston ring (CL-PR) friction pair and induce abnormal damage, while the underlying mechanisms remain unclear. Hence, reciprocating friction tests were conducted under simulated service environments in ammonia marine engines using lubricating oils with varying ammonia contamination forms and concentrations. Based on the integrated evaluation of friction behavior, wear characteristics, lubrication condition, vibration response and mechanical properties, the results indicate that ammonia contamination universally degrades tribological performance, with ammonia solution exerting a more detrimental effect than ammonia gas, and the severity increasing with ammonia concentration. The introduction of ammonia gas primarily weakens oil film integrity and additive anti-wear performance, thereby intensifying mechanical wear. In contrast, the introduction of ammonia solution deteriorates the anti-wear effectiveness of additives, promotes localized starvation and interfacial corrosion, and induces the formation of extensive corrosion products. These effects collectively result in surface softening, stress concentration, and aggravated spalling, leading to a drop of over 40% in elastic modulus and hardness, with an increase of up to 2.7-fold in wear rate compared to fresh oil, ultimately driving the interfacial damage toward a corrosive-wear coupling effect associated with low-strength corrosion layers. These findings reveal the influence of ammonia contamination on the evolution of CL-PR tribological performance, providing a theoretical basis for optimizing lubricant formulations and improving anti-wear design of tribological components in marine ammonia engines.
