20.500.12556/RUL-138691 Machine learning based nominal 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. The aim is to create a decision-support model accompanying the Finite Element Method (FEM) simulations, from which the data for training was collected. Multiple models for numerical prediction were tested, i.e. linear regression, Support Vector Machine, K-nearest neighbour, neural network, AdaBoost, and random forest. The models were firstly validated with N-fold cross-validation. Further validation was done with new FEM 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 calculating nominal root stress in gears with a progressive curved path of contact is proposed. The model can be used as an alternative to FEM simulations for determining the nominal 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. machine learning nominal root stress gears Finite Element Method FEM random forest AdaBoost strojno učenje korenska napetost zobniki metoda končnih elementov true false true Angleški jezik Slovenski jezik Članek v reviji 2022-08-09 14:10:20 2022-08-09 14:10:25 2022-09-15 04:17:04 0000-00-00 00:00:00 2021 0 0 14 str. Vol. 165, art. 104430 Nov. 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85:621.833:519.61 0094-114X 10.1016/j.mechmachtheory.2021.104430 69206531 1-s2.0-S0094114X21001889-main.pdf 1-s2.0-S0094114X21001889-main.pdf 1 A82274E131FE8554C8C72A2B66A9A975 e664f6ce24d93e6968bd0f9c8ba45634abe109334bc0f38f9e26eb4c7d867e89 42fab0bb-17dc-11ed-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=159097 https://www.sciencedirect.com/science/article/pii/S0094114X21001889 1 f5ebc639-17db-11ed-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=159096 Fakulteta za strojništvo 0 0 0