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This paper aims to design a novel test device and study the wear properties and the thermal mechanisms of roller pairs in dual-freedom sliding contacts.

On the transition process of lubrication regimes, experiments were conducted with various values of running speed and slip ratio obtained by two motorized spindles. Temperature and surviving time would be obtained of GCr15/GCr15 and DLC/GCr15 friction pairs. Micro photography was obtained with a PGI 3D stylus profiler and a confocal microscopy OLS4000-3D. An empirical mode decomposition method was used to eliminate measure errors.

Results showed that, even with little initial lubricant, rolling/sliding pairs still rotated for a certain time. With the synthetic actions of the dual-freedom sliding, the loss of lubrication and the tilt, interesting helical grooves appeared. Sliding speeds had remarkable effects on survive time, temperatures and surface topographies. In addition, the equilibrium values of the temperature and the surface roughness were obtained in sufficient oil supply. Extreme wear-out conditions were obtained with starved lubrication. Diamond-like carbon coatings showed better heat resistance and better wear resistance.

This work would be critical for the life design and the heat treatment of rolling bearings in the full flood lubrication and the starved lubrication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0164/

In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.

The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.

In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

Using the multifunctional friction and wear testing machine independently developed by the research group, the friction and wear tests of different friction conditions (contact pressure and sliding speed) are conducted on the brake materials of high-speed trains with the ambient humidity of 95% and the initial temperature of the disk of 200°C.

Friction and wear.

The test results show that changing the friction conditions has a significant effect on the braking performance of high-speed trains.

YES.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0171/