Physics-Informed Deep Neural Network Surrogate for Simulation-Based Prediction of Hybrid Press System Dynamics

Accurate prediction of hybrid press dynamics requires the integration of data-driven modeling with the governing principles of constrained multibody mechanics. This study proposes a physics-informed deep neural network (PIDNN) for predicting five engineering responses, including slider displacement, velocity, acceleration, actuator force, and required motor torque, from six operating inputs. The proposed framework integrates supervised learning with deterministic reconstruction of generalized mechanical states, incorporating kinematic consistency, dynamic equilibrium, constraint reaction estimation, frictional effects, and energy-rate conservation into a unified optimization objective. The model was developed and evaluated using 75 operating trajectories generated in MATLAB/Simscape, comprising 375,000 samples distributed across independent training, validation, and testing subsets. Experimental evaluation demonstrated that the proposed PIDNN achieved normalized root mean square errors (nRMSEs) of 0.118, 0.129, 0.154, 0.111, and 0.138 for the five predicted responses, respectively, yielding an overall mean nRMSE of 0.130 compared with 0.221 for the conventional data-driven artificial neural network, representing a 41.2% reduction in prediction error. Furthermore, the proposed model achieved a root mean square normalized dynamic residual of 0.018 and a mean energy imbalance of 0.91%, demonstrating improved consistency with the governing mechanical equations. Controlled ablation experiments confirmed the effectiveness of the combined physics-informed constraints in improving predictive accuracy and mechanical consistency. Computational evaluation demonstrated single-sample inference latency of 0.17–0.20 ms, indicating substantial computational advantages over conventional numerical integration. These findings demonstrate the effectiveness of the proposed PIDNN as an accurate, computationally efficient, and physically consistent surrogate model for simulation-based prediction and analysis of hybrid press system dynamics.