Pinion-rack contacts in rack railway systems run under high stress in open environments, where wear limits reliability. Reciprocating ball-on-flat tests on a 20CrMnMoA/42CrMo steel pair benchmarked four interfacial strategies: unlubricated (dry) sliding, an OEM grease (L-WGR000-17), an open-gear grease (CEPLATTYN 300), and a magnetron-sputtered diamond-like carbon (DLC)-like carbon coating. The coefficient of friction (COF) and infrared contact-zone temperature were recorded in situ, and wear/tribochemistry was examined by 3D profilometry, SEM/energy-dispersive spectrometer (EDS), and X-ray photoelectron spectroscopy (XPS). The as-deposited coating was further characterized by Raman spectroscopy, cross-sectional SEM/EDS, and apparent surface microhardness testing. Steady-state COF ranked as unlubricated (0.478) approximate to CEPLATTYN 300 (0.453) > DLC (0.163) > L-WGR000-17 (0.120). The unlubricated condition produced severe adhesive tearing and third-body ploughing with strong tribo-oxidation. CEPLATTYN 300 reduced wear but formed undulated scars with oxygen-rich, patchy deposits, indicating an unstable boundary/deposit layer. L-WGR000-17 delivered ultra-low wear but occasional localized micropitting, consistent with incipient surface fatigue that may be promoted by transient local boundary-film breakdown. The DLC-like carbon coating achieved grease-free low wear with smoother scars and carbon-rich interfacial coverage. Overall, under the present laboratory benchmark conditions, friction, temperature rise, and wear are strongly influenced by the continuity and stability of the interfacial isolation layer, providing a controlled basis for comparing protection strategies for exposed, highly loaded steel contacts.
Growing environmental concerns and stringent sustainability mandates have accelerated demand for high-performance biolubricants as viable alternatives to conventional petroleum-based lubricants. This study synthesized Jatropha trimethylolpropane ester (JTMPE) via esterification and transesterification processes, subsequently enhanced with reduced graphene oxide/molybdenum disulfide (rGO/MoS2) hybrid nanoparticles at concentrations of 0.1-1.0 wt%. Successful JTMPE synthesis was confirmed by Fourier transform infrared spectroscopy (FTIR), while Raman, X-ray diffractometry (XRD), and transmission electron microscopy (TEM) analyses verified the formation of well-integrated rGO/MoS2 heterostructures and uniform MoS2 deposition on rGO layers. Rheological evaluation demonstrated Newtonian behavior at operational shear rates, with the 1.0 wt% formulation enhancing dynamic viscosity by 18.2% at 40 degrees C and 38.1% at 100 degrees C, alongside a 16.0% improvement in viscosity index. Four-ball tribological testing revealed that the 0.6 wt% formulation achieved optimal performance, yielding 27.9% friction reduction and 21.9% wear reduction relative to base JTMPE, along with a lambda ratio transition from boundary to mixed lubrication. Field emission scanning electron microscopy (FE-SEM) and energy-dispersive X-ray spectroscopy (EDAX) analyses confirmed the formation of chemically enriched tribofilms containing carbon, molybdenum, and sulfur at the worn interface. An artificial neural network model employing Bayesian regularization with a 16-neuron hidden layer achieved exceptional predictive accuracy (coefficient of regression (R) = 0.99964, and mean squared error (MSE) = 4.09 & times; 10(-4)), outperforming the Levenberg-Marquardt algorithm in generalization. These findings establish rGO/MoS2-enhanced JTMPE as a high-performance, environmentally responsible lubricant platform for demanding industrial applications.
Rolling contact fatigue tests are mostly done on small-scale bearings. For large slewing bearings, very few public results are available. The article analyzes laboratory rolling contact fatigue test results of 32 large slewing bearings. Test results of the Large Bearing Laboratory at Fraunhofer IWES and test results from the literature are combined for the analyses. Most bearings in the analysis are original-scale bearings that have been designed as pitch bearings for wind turbines, and some of the bearings have been designed to replicate such bearings. Based on an analysis of the test results, a corrective factor of 0.55 is proposed to adjust ISO 16281 calculations for large slewing bearings. The results can be generalized to other slewing bearings operating under similar conditions, both in wind turbines and in other applications.
Metal matrix self-lubricating composites have emerged as key materials in sliding electrical contact applications due to their integrated lubrication and electrical conductivity. In this study, Cu-(CF)(n) composite and pure Cu were prepared by powder metallurgy. The effects of electric currents on the tribological properties of the Cu-(CF)(n) composite and pure Cu were investigated. The results show that the (CF)(n) particles are uniformly distributed and show no significant decomposition in the Cu-(CF)(n) composite. As the electric current increases from 1 A to 15 A, both pure Cu and the Cu-(CF)(n) composite show a declining friction coefficient alongside increased wear. Nevertheless, the composite maintains a lower and more stable friction coefficient and exhibits less wear on both itself and its counter ball compared to pure Cu. With increasing current, pure Cu shows rising contact resistance and electrical noise, whereas the Cu-(CF)(n) composite exhibits the opposite trend. The worn surface of the Cu-(CF)(n) composite is smooth and flat, showing no evidence of arcing. The wear debris primarily consists of thin flakes, characteristic of delamination wear. The improved sliding electrical contact performance stems from the (CF)(n)-induced lubrication, which enhances the real contact area, effectively suppressing arc erosion and oxidation. The incorporation of (CF)(n) substantially improves the tribological properties of the Cu-(CF)(n) composite under electric currents.
As manufacturing-related surface deviations considerably influence film thickness and pressure distributions in elastohydrodynamic lubrication (EHL) contacts, their incorporation into numerical modeling is crucial. Deviated EHL simulations, however, are computationally demanding and complex. Against this background, this study aims to enable real-time prediction modeling of film thickness as well as pressure parameters in EHL line contacts with underlying surface waviness. To this end, prediction models were developed by means of machine learning approaches, namely artificial neural networks (ANNs) and Gaussian process regression (GPR). Following Latin hypercube sampling, more than 1800 transient EHL simulations, accounting for non-Newtonian lubricant behavior and double-sided deterministic waviness, served as a database for prediction modeling. After hyperparameter optimization, ANN and GPR models revealed comparably high prediction accuracies with adjusted coefficients of determination greater than 0.998 and 0.975 with respect to film thickness and maximum pressure parameters, respectively. The results obtained contribute to the fast and precise consideration of surface waviness in EHL contacts in further research and early phases of product development.
