16 citations as recorded by crossref.

1. Influence of running-in operating conditions on the steady-state torque loss of ground spur gears C. Fernandes et al. 10.1007/s10010-021-00447-0
2. Using the Box–Behnken Response Surface Method to Study Parametric Influence to Improve the Efficiency of Helical Gears H. Nguyen & L. Duy 10.3390/machines9110264
3. Considerations concerning the power loss in a gear system A. Ilieş et al. 10.1088/1757-899X/1262/1/012007
4. Effect of redundant rollers in planetary gearbox A. Varadharajan et al. 10.1016/j.mechmachtheory.2024.105871
5. A comparison of analytical methods to predict the bulk temperature in polymer spur gears V. Roda-Casanova & C. Fernandes 10.1016/j.mechmachtheory.2022.104849
6. Load share model based gear loss factor prediction in high contact ratio spur gear drive R. Ravivarman & R. Prabhu Sekar 10.1177/13506501221107904
7. Energy efficiency tests in a full scale wind turbine gearbox C. Fernandes et al. 10.1016/j.triboint.2016.05.001
8. Power loss analysis in altered tooth-sum spur gearing H. Sachidananda et al. 10.1051/matecconf/201814401015
9. Coefficient of friction equation for gears based on a modified Hersey parameter C. Fernandes et al. 10.1016/j.triboint.2016.03.028
10. Power loss analysis in altered tooth-sum spur gearing H. Sachidananda et al. 10.1051/matecconf/201814401015
11. Determination of load dependent gear loss factor on asymmetric spur gear R. Sekar 10.1016/j.mechmachtheory.2019.02.011
12. Influence of Profile Correction Factor on Asymmetric Spur Gear Efficiency R. Prabhu Sekar 10.1007/s40032-022-00878-w
13. Finite element method model to predict bulk and flash temperatures on polymer gears C. Fernandes et al. 10.1016/j.triboint.2017.12.027
14. Real-time rolling bearing power loss in wind turbine gearbox modeling and prediction based on calculations and artificial neural network H. Fotso et al. 10.1016/j.triboint.2021.107171
15. Testing and modelling of a 2.5 MW wind turbine gearbox: Influence of lubricant formulation C. Fernandes et al. 10.1007/s10010-023-00716-0
16. Prediction of load-dependent power loss based on a machine learning approach in gear pairs with mixed elastohydrodynamic lubrication D. Im et al. 10.1016/j.triboint.2025.110597