Fusing Macro and Micro Material Characteristics to Enhance Fatigue Life Prediction Accuracy for Spiral Bevel Gears

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$69,795.00
Award Year:
2010
Program:
SBIR
Phase:
Phase I
Contract:
W911W6-11-C-0024
Award Id:
97984
Agency Tracking Number:
A103-169-0272
Solicitation Year:
n/a
Solicitation Topic Code:
ARMY 10-169
Solicitation Number:
n/a
Small Business Information
200 Canal View Blvd, Rochester, NY, 14623
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
073955507
Principal Investigator:
Avinash Sarlashkar
Director, Technology
(585) 424-1990
avinash.sarlashkar@impact-tek.com
Business Contact:
Mark Redding
President
(585) 424-1990
mark.redding@impact-tek.com
Research Institute:
n/a
Abstract
Given the criticality of gears in the main and tail-rotor drivetrains for rotorcrafts, there is a need for robust life prediction tool that will provide improved design guidance, provide current health assessment as well as provide prognostics capabilities to support CBM initiatives at the Army. Typically, drivetrain for the main rotor uses a combination of spur gears and spiral bevel gears. The tail-rotor drivetrain rotor typically uses pairs of spiral bevel gears in both IGB and TRGB. In terms of the complexity of the geometry as well as the loading, the spiral bevel gears are most challenging. This complexity translates into the complexity of associated 3-D stress fields and further into fatigue life analysis. Developing robust prediction models for fatigue life is critical to minimizing the total cost of ownership for the existing platforms (H-60s) as well as the rotorcraft currently in the development (CH-53Ks). Impact Technologies proposes to demonstrate an advanced fatigue life prediction environment that will consider both the macro- and micro-material aspects of typical gear materials. These material models will fuse the influences of macro-material characteristics such as hardness with micro-material characteristics such as residual stresses to develop a user-friendly yet accurate fatigue life prediction environment.

* information listed above is at the time of submission.

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