Title

Probabilistic Approach to Predict Contact Fatigue of Straight Toothed Net-Shape Forged Bevel Gears

Date of Award

6-2021

Degree Name

Master of Science in Engineering

Department

Mechanical and Aerospace Engineering

First Advisor

Dr. Jinseok Kim

Second Advisor

Dr. Claudia Fajardo

Third Advisor

Dr. Daniel Kujawski

Fourth Advisor

Dr. Carlos Wink

Keywords

Gear, contact fatigue, life prediction, probabilistic, modeling

Access Setting

Masters Thesis-Abstract Only

Restricted to Campus until

6-15-2031

Abstract

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.

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