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Figure 8 illustrates the architecture of the network. Since there were three inputs of varying length, the number of neurons in the hidden layer was determined, as well as the number of neurons in the output layer. Because of the limited amount of time available for the experiment, a large number of neurons in each layer was not feasible. The goal of the network was to predict the values of the crack length increment (i.e., the crack growth rate) over time. Therefore, the network was designed to predict the incremental crack growth from the effect of the plastic zone.
Figure 9 illustrates the segmentation of the area of interest in the loadplot. The loadplot is shown in Figure 9(a) through Figure 9(b). For the purpose of the crack positioning experiment, only the area of interest was segmented. The areas immediately ahead of the crack and behind the crack were also excluded in order to avoid plastic zone deformation as well as to eliminate any effects of self-excited crack motion, such as damage waves. The outputs of the segmented data were connected to the supposed inputs of the neural network, which were the elapsed time and the three acoustic emission signals. This network arrangement allowed the crack length to be predicted as the crack grew. The network used for this experiment was a feed forward network (neuron from input layer to hidden layer to neuron in the output layer).
Figure 10(a) through Figure 10(b) show the inputs and outputs of the neural network architecture, respectively. Four types of neurons were used for the inputs (elapsed time, 3 acoustic emission sensors, 2 acoustic emission sensors, and one input). Inputs to the network were thresholded to the range of 1 to 10. The outputs from the network were then multiplied by the scaling input for each neuron and then passed into an activation function. The activation function was selected to be the sign function, which outputs the sign of the supplied argument. The outputs of all neurons were added to get the final output of the network. The score output was then converted into the form of incremental crack growth. An average crack growth rate was then calculated for each possible input group by averaging all of the resulting outputs. d2c66b5586
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