NARX Neural Network Model for Describing the Flow Stress of Metallic Materials During High-Temperature Plastic Deformation

Accurate prediction of the behavior of alloys and metal matrix composites during high-temperature deformation requires strict consideration of the loading history. To address this problem, a hybrid rheological model for flow stress prediction has been developed, combining a phenomenological description of the yield stress with a recurrent neural network based on the NARX (Nonlinear AutoRegressive with eXogenous inputs) architecture. The memory effect is formed by expanding the input parameters with the response values from the previous step. The identification of the weight coefficients of the NARX neural network is implemented by training an equivalent multilayer perceptron. To improve the generalization ability of the model and eliminate its dependence on a fixed discretization step, the training dataset includes data obtained under non-monotonic changes in the strain rate over time and a variable time interval. The article justifies the structure of the model input parameters, excluding the accumulated strain from the input set due to its lack of informativeness during active softening processes. Verification of the hybrid model on the 7075/2.5% TiC composite in the temperature range of 300–500 °C demonstrated an average relative error of 1.5% when predicting modes that were not involved in the training. The predicted flow stress values fall within the experimental scatter interval of ±5% and accurately reproduce the local features of the flow stress curves. The proposed model and its identification technique provide correct consideration of the deformation history under the complex interaction of hardening and softening processes.