Machine-Learning based root stress calculation model for gears with a progressive curved path of contact

The study aims to investigate the possibility of employing Machine-Learning models in the design of non-involute gears. Such a model would be useful for design calculations of non-standard gears, where there are no available guidelines. Multiple models for numerical prediction were tested, i.e. Linear Regression, Support Vector Machine, K-nearest neighbour, Neural Network, AdaBoost, and Random Forest. The aim is to create a surrogate model to the final element analysis simulations, from which the data for training was collected. The models were firstly validated with the N-fold cross-validation. Further validation was done with new simulations. The results from the simulations and the models were in good agreement. The best-performing ones were Random Forest and AdaBoost. Based on the validation results a Machine-Learning constructed model for calculation of root stress in gears with a progressive curved path of contact is proposed. The model can be used as an alternative to simulations for determining the root stress in real-time, and is able to calculate the stress for gears with different number of teeth, widths, modules, paths of contact, materials, and loads. Therefore, many combinations of gear geometries can be analysed and the most suitable can be chosen.