A data-driven normal contact force model based on artificial neural network for complex contacting surfaces

摘要

Proper modelling of contact/impact phenomenon is critical to ensure reliable description of the overall dynamic behaviors of mechanical systems. The past few decades witnessed sub-stantial developments on contact/impact dynamics modelling, especially for the smooth contacting surfaces, like spheres or cylinders. Contrastingly, less attention has been paid to the urgent modelling demand for complex contacting bodies. By utilizing the data-driven modelling framework based on artificial neural network, this paper aims to provide a new and feasible scheme for the research of contact/impact process between complex contacting surfaces. Taking the contact/impact process between barrel and bourrelet as our research object, the indoor experiment rig is manufactured and displayed for the first time. Measurement results collected under different initial indentation velocities serve as the training datasets of the learning process for the data-driven normal contact force model. After that, the optimum hyper-parameters of the neural network, mainly including the performance index, activation function, structure of network, and learning algorithms, are tuned for the contact/impact process between barrel and bourrelet through trail-and-error method. Eventually, the neural-network-based normal contact force model can be established, of which the prediction performance for interaction modelling is further ana-lyzed and verified. Simulation results confirm that the proposed data-driven normal con-tact force model can achieve high accuracy and also present excellent generalization ability. Great agreements with the experimental results under the chosen network struc-ture demonstrate the effectiveness of data-driven interaction modelling methodology pre-sented for complex contacting geometries.