Rolling bearing simulations are often too computationally expensive for early design decisions, because many simulations are required in a large design of experiments. Therefore, the aim of this systematic literature review is to provide an overview of how machine learning (ML) is used to integrate engineering knowledge in advance when simulations are the primary data source for supervised learning. In the 11 included studies, ML is mainly applied as regression models trained on simulation data to replace repeated solver calls. The applications can be classified into three domains—contact mechanics, lubrication, and dynamics—mostly linked to their domain specific outputs. In most cases, ML models replace the simulation once the model is trained and validated, followed by optimization, which is often performed on the surrogate using evolutionary algorithms. Surrogates have the potential to enable design-space exploration, sensitivity analysis, and uncertainty propagation, but this capability is not yet fully exploited in current practice. The purpose of this review article is to provide a summary of methodological building blocks and practical guidelines to assist researchers and engineers in selecting appropriate ML workflows for simulation-based analysis of rolling bearings in the areas of tribology, dynamics, service life, load capacity, and system-level investigations.
Pfister et al. (Fri,) studied this question.