Prediction of rolling force based on finite element data enhancement and physical information neural network

Hybrid modelling of hot-rolling force prediction based on PSO-optimised physical parameters and SSA-BP neural network

Accurate rolling force prediction is critical for hot-rolling control, yet conventional models are oversimplified and data-driven approaches lack interpretability. Hybrid methods, despite their potential, remain constrained by the accuracy of key physical parameters. To address this, the present study proposes a doub...

Numerical analysis and neural-network modeling for predicting the thermo-viscoplastic behavior of Cu/AA2030 panel during the hot rolling process

This study presents a three-dimensional thermo-mechanical finite element (3D-FEM) investigation of the hot rolling process for Cu/AA2030/Cu laminated composite panels, coupled with an artificial neural network (ANN) surrogate model for rapid prediction of process responses. The numerical model was developed to evaluate...

Application of artificial neural networks for the prediction of traction performance parameters

Deep Neural Networks for Inverse Identification of Soil Parameters

A hybrid ANN–FEM framework for high accuracy slope stability prediction

Purpose. To develop a hybrid methodology that integrates Artificial Neural Networks (ANN) with Finite Element Method (FEM) simulations for the rapid and accurate prediction of slope stability. Methodology. A dataset of 1,000 FEM simulations was generated by systematically varying seven key input parameters: slope geo...

Identification of Combined Isotropic–Kinematic Hardening Parameters from Reverse Bending Tests Using Recurrent Neural Networks

Accurate identification of constitutive parameters is essential for reliable finite element simulations of sheet metal forming processes. Conventional inverse identification techniques, such as Finite Element Model Updating (FEMU), generally require iterative optimization procedures involving numerous finite element si...