Accurate and efficient prediction of contact parameters on rough surfaces is critical for the tribological design, condition monitoring, and failure analysis of line-contact components. Although physics-based models offer high prediction fidelity, their multi-field coupled iterative solvers are computationally expensive and complex, limiting practical engineering deployment. This work proposes a digital surrogate model based on the conditional U-Net (cU-Net) for predicting the spatial distributions of contact parameters, including contact temperature, pressure, and heat flux, on rough surfaces in line-contact friction. Real surface topographies are augmented via the Generative Patch Nearest-Neighbor (GPNN) model, and a wide-ranging training dataset is constructed through batch computation using a boundary lubrication physics-based model. The cU-Net incorporates an operating condition embedding mechanism to enable unified prediction across multiple operating conditions. Multi-scale spatial and channel attention modules are further integrated to enhance cross-scale feature extraction. A four-component progressive composite loss, assembled from established loss terms and tailored to the sparse extreme-value characteristics of rough surface contact parameters, is combined with Bayesian hyperparameter optimization and a three-stage progressive training strategy. On completely unseen rough surfaces the model achieves mean relative errors of 3.53%, 12.32%, and 13.66% for the temperature, pressure, and heat flux fields, respectively, with peak and top-5% errors that are lower still, and it is approximately 10⁵ times faster than the physics-based model on the same workstation. Out-of-distribution tests quantify the degradation of accuracy under extrapolated operating conditions. Retraining with datasets generated from the corresponding physics-based models is expected to extend the framework to other lubrication regimes.

A short commentary on the two research articles published in Friction in 2021 is presented. Both articles reported experimental results of applications of laser surface texturing technology to the raceway of rolling element bearings. After briefly reviewing the main findings of the articles, the arguable problems and distinctions between the two articles are pointed out.

Adhesive and corrosive wear at microscales are quantitatively distinguished in lifetime tests of resonant bulk-fabricated silicon microelectromechanical systems (MEMS). By analyzing the oscillation decay characteristics in different vapor environments, we find that wear is dominated by asperity adhesion during the initial stages of rubbing in dry N₂ or O₂/N₂ mixtures; in these situations the transient wear rate is inversely proportional to the wear depth. But in water or ethanol vapors, chemical reactions between the corrosive adsorbed layer and the silicon substrate limit the wear rate to a constant. These observations are consistent with atomic explanations. The differences between adhesive and corrosive wear explain the advantages offered by lubricating with alcohol vapors rather than using dry environments for tribo-MEMS devices. Compared to ethanol, the relatively poor anti-wear effect of water vapor is explained by aggressive and rapid tribo-reactions.