Inspection Correlation Study of Ultrasonic-Based In Situ Structural Health Monitoring Monthly Report for December 2014-January 2015

Abstract

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Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

“…Sample results for one specimen are provided in Figure 3 (Iglesias, 2015). In this case, the specimen failed after slightly more than 50,000 cycles.…”

“…The coupons used for crack growth are Al 7075-T6 dog-bone shaped, which have a nominal thickness of 1.6 mm (0.063 inch), ultimate tensile strength of 715 MPa (75 ksi), yield stress of 482 MPa (69.9 ksi), and modulus of elasticity of 71.7 GPa (10,400 ksi). Figure 1 (Iglesias, 2015) shows the dimensions of the fatigue test coupon, which was machined with a rivet size hole and a notch pre-crack. In order to accelerate crack growth, so that fatigue failure would occur in approximately 50,000 cycles, a notch cut was applied to the right of the hole in a direction perpendicular to the length of the specimen.…”

“…The hardware produces a hamming windowed pulse in one transducer, which in turn creates a surface wave that travels through the specimen, interacts with the boundary conditions (including damage), and is detected in another transducer. The transducers were configured in pitch-catch design such that three transducers, numbered 1-3, on one side of the hole act as actuators to generate the pulse and three transducers, numbered 4-6, on the other side of the hole act as sensors to detect the pulse; see Figure 2 (Iglesias, 2015). Reducing the raw sensor data to a damage index is necessary to establish a relationship with crack length.…”