Enhanced Force Sensing Method Using BP Neural Network and Sensitive Point Analysis for Forging Manipulator Clamping Mechanism in Operational Conditions

Given the challenging operational environment of forging machines, characterized by harsh conditions, dynamic load fluctuations, and multi-axis force coupling, traditional force measurement and sensing methods often fail to meet the high demands for precision and reliable load sensing. To address this challenge, this study introduces a novel force sensing methodology based on a Backpropagation (BP) neural network for the clamping mechanism of forging machines. First, a force sensing model is developed to describe the behavior of the clamping mechanism under forging conditions, elucidating the force transmission laws and identifying key hinge points for force sensing. Next, a BP neural network model is constructed to analyze and decouple the forging state, exploring the impact of hidden layer neuron nodes on decoupling effectiveness and determining the optimal configuration of these nodes. The effectiveness of the proposed methodology is demonstrated through the network’s ability to decouple both the direction and magnitude of internal forging forces. The results confirm that this approach enables accurate and reliable force sensing for the clamping mechanism, providing a robust foundation for force control in forging machines.