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For this experiment, the goal was to determine if a strain wave was traveling perpendicular to the crack propagation direction. This experiment was done in aluminum. The overall goal was to examine the travel of the crack in the material and determine if a neural network, which is trained on the strain waves detected by an acoustic emission system, could predict the location of the crack tip.

Data were collected from a crack propagation experiment on aluminum material, using a MTS-8100 system, as shown in Figure 5. The system was designed to be capable of acquiring the raw strain wave data at a sampling rate of 120 Hz. First, a 1500-cycle stress-controlled cyclic loading was performed. Next, a 50-cycle fatigue test was performed. After each test, the crack was stopped and the strain wave energy was measured. A signal was then generated to restart the crack propagation, and the experiment continued. This process was repeated for another 75 cycles. After these 75 cycles, the crack was stopped again to record the energy associated with the previous crack propagation. The energy was compared to the energy recorded during the initial 50 cycles to determine the actual crack extension. The data was labeled and saved to be used in an artificial neural network.

For the training set, the accuracy (or RMS error) was 83%, and for the test set, the accuracy was 94%. These results indicate that the network had learned the strain wave characteristics of the material in the 50 cycles. When the network was tested on the 75 cycles, it was successful in determining that there was no crack propagation.

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