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Machine Learning of Part Variability for Predictive Maintenance

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-20-P-0617
Agency Tracking Number: AFX20A-TCSO1-7023
Amount: $25,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF20A-TCSO1
Solicitation Number: 20.A
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-03-06
Award End Date (Contract End Date): 2020-06-04
Small Business Information
1039 Parkway Drive
Spring Hill, TN 37174-1111
United States
DUNS: 028566698
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Kurt Nichol
 (931) 486-0081
Business Contact
 Amanda Farmer
Title: CEO, PE
Phone: (931) 486-0000
Research Institution
 University of Notre Dame
 Dr. Aleksandar Jemcov
University of Notre Dame
Notre Dame, IN 46556
United States

 (574) 631-5000
 Nonprofit College or University

Extensive testing at substantial cost is a major part of military propulsion development programs. These programs involve numerous component and engine tests aimed at producing safe and reliable engines. Despite these efforts, engines still experience unexpected failures. Geometric variability of parts within design tolerances is a well-known source of uncertainty in overall reliability. USAF sustainment activities increasingly include geometric measurements of parts to quantify variations, but these data are generally ignored in the test process. In the proposed STTR program, the overall technical objective it to use the geometry and response data from test of one set of parts to describe the response behavior of a second population of parts of known, or formulated geometry. The University of Notre Dame will extend methods for parameterizing geometry and computation of response sensitivities for utilization of measured geometry data by the test community. APEX Turbine Testing Technologies will integrate these methods in GageMap, a commercially developed FEA post-processing product, together with response measurements, to describe the response for other geometries. This allows the USAF to identify geometric features that can produce higher responses. This can assist in focusing inspections, adjustment of tolerances, or perhaps culling parts as needed for fleet management.

* Information listed above is at the time of submission. *

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