Deep Learning-Based Prediction of Controllable Stress/Strain-Rate Loading and Design for Loading Conditions

Abstract

The graded density impactor (GDI) dynamic loading technique serves as a crucial method for achieving controllable stress/strain-rate loading, where the loading velocity and adaptability of GDI structural design critically govern the loading results. Numerical simulations of layer-wise modulation and shock wave transmission revealed a decoupling mechanism for stress and strain-rate parameters. Specifically, the loading velocity determines the overall magnitude, whereas variations in interlayer thickness modulate the specific strain-rate loading path. Building on this, a branched convolutional neural network (CNN)-bidirectional long short-term memory model (BLSTM) is developed to simultaneously predict stress/strain-rate curves achieving R² = 0.95 and loading velocity achieving R² = 0.99 while enabling GDI thickness design. This methodology resolves multi-physics coupling challenges in curve prediction and offers solutions for time-dependent issues in extreme conditions.