PSI - Issue 79

Qinghui Huang et al. / Procedia Structural Integrity 79 (2026) 291–297

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prevent overfitting, dropout regularization (with rates of 0.001 and 0.1) and L2 regularization (with a strength of 0.001) are applied to the hidden layers. The training process incorporates early stopping techniques while visualizing loss values on the validation set. The model is trained to predict ∆ , and the accumulated plastic strain is updated recursively via , +1 = , + ∆ , , starting from an initial value of zero. The forward propagation is defined by Equation (1), and model loss function is quantified by the formula in Equation (2). ( α ) = ∑ ( ℎ ) = 1 (1) Where is the output of the -th input neuron, ( ℎ ) is the weight between the input and hidden layers, and ( ) is the weighted input of the -th hidden neuron (before activation). The ReLU function given by ( )= (0, ) . = 1 ∑ � Δ , − Δ , �� 2 = 1 (2) Where Loss is the mean squared error, is the number of samples, Δ , is the true incremental value, and Δ� , is the predicted incremental value. 3. Results and discussion 3.1 Analysis and prediction of accumulated plastic strain growth Fig. 2 presents the accumulated plastic strain contours for the first fifty cycles under different strain amplitudes ( εₐ ) at R = -1. The red regions indicate areas with higher plastic strain accumulation. Under different strain amplitudes, high-strain hotspots persist at specific grain boundaries, indicating microstructural control over fatigue nucleation sites, which is consistent with CPFEM-based fatigue initiation studies. The figures show that the locations of extreme accumulated plastic strain values are independent of the strain amplitude but are intrinsically related to the grain microstructure itself. Fig. 3 presents the actual accumulated plastic strain evolution curves (from CPFEM simulations) and neural network predictions for LPBF GH4169 under va rying strain amplitudes (0.15%, 0.195% and 0.25%) and - 1 load ratio conditions.

Figure 2 Accumulated plastic strain contour map based on CPFEM.

Figure 3 INN predicted accumulated plastic strain (Pac or ε p,a) curves at different strain amplitudes.