Probabilistic Approach to Predict Contact Fatigue of Straight Toothed Net-Shape Forged Bevel Gears
Date of Award
Master of Science in Engineering
Mechanical and Aerospace Engineering
Dr. Jinseok Kim
Dr. Claudia Fajardo
Dr. Daniel Kujawski
Dr. Carlos Wink
Gear, contact fatigue, life prediction, probabilistic, modeling
Masters Thesis-Abstract Only
Restricted to Campus until
In today's automotive manufacturing climate being first to market is critical to the overall project success. This is no different in the world of gearing, with an unrelenting market push for more power passing through smaller packages. These new customer requirements have pushed the time-tested analog straight-toothed design methodology to its limits, driving many companies to modernize their processes to incorporate an analysis-first design approach. However, the analytical design platform presents engineers with many new challenges that make successful prediction of the failure mechanism and corresponding time until failure difficult, even with the aid of analysis software. Pitting is one of the major failure modes observed in gears, but little research has been performed on net-forged straight-toothed bevel gears. Their rough surfaces, high contact pressures, and slow rotational velocities make all-inclusive models very difficult to develop.
The goal of this research was to develop an accurate macropitting failure prediction model of net-forged straight bevel gears operating in an automotive differential. A gear set was modeled using commercially available software and optimized, with a focus on maximizing the resistance to pitting. Physical experiments were conducted using the optimized gear set.
Gurd, Caleb, "Probabilistic Approach to Predict Contact Fatigue of Straight Toothed Net-Shape Forged Bevel Gears" (2021). Masters Theses. 5216.