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Tuesday, June 9, 2009 - 4:00 PM
SSP2.5

Automated Fatigue Test Monitoring and Damage Evolution Tracking for Prognosis in Support of Condition Based Maintenance Decisions – Part I: Fatigue Tests

N. J. Goldfine, D. Grundy, D. Jablonski, Jentek Sensors, Inc., Waltham, MA; V. Zilberstein, JENTEK Sensors, Inc., Waltham, MA

As part of an ongoing U.S. Navy program to enable adaptive life management of dynamic rotorcraft components, the authors have enhanced previously developed mapping and tracking capability to apply it to continuous MWM-Array monitoring of fatigue-critical regions during fatigue tests.  This presentation describes the fatigue specimen design, MWM-Array sensor, in-situ scanning apparatus, and test procedure.   The tests were interrupted at preselected intervals, and acetate replicas were taken to document surface-connected cracks.  Examples of photomicrographs from the replicas will be presented and discussed, together with MWM-Array generated B-scans and C‑scans that are discussed in more detail in the companion presentation “Automated Fatigue Test Monitoring and Damage Evolution Tracking for Prognosis in Support of Condition Based Maintenance Decisions  – Part II: Prognosis”.

Summary: As part of an ongoing U.S. Navy program to enable adaptive life management of dynamic rotorcraft components, the authors have enhanced previously developed mapping and tracking capability to apply it to continuous MWM-Array monitoring of fatigue-critical regions during fatigue tests. This presentation describes the fatigue specimen design, MWM-Array sensor, in-situ scanning apparatus, and test procedure. The tests were interrupted at preselected intervals, and acetate replicas were taken to document surface-connected cracks. Examples of photomicrographs from the replicas will be presented and discussed, together with MWM-Array generated B-scans and C-scans that are discussed in more detail in the companion presentation “Automated Fatigue Test Monitoring and Damage Evolution Tracking for Prognosis in Support of Condition Based Maintenance Decisions – Part II: Prognosis”.