Researching the phenomenon of collision electrification between particles and flat materials based on machine learning

In this article, an experimental scheme for the real-time measurement of the collision charge between a particle and a plate material is presented. The experiment explored how six commonly utilized aircraft materials are charged at diverse rates by colliding with silica, silicon carbide (SiC), and ice crystal particles. Meanwhile, the influence of various factors on the impact law of the particle plate is investigated. Based on the experimental data, the relationship between triboelectricity and time is deduced. Through training the experimental data by machine learning, a stable model for predicting material collisions is acquired. Real-time measurement of plane material and particle collision friction electricity. A method for solving the triboelectric charging rate is presented. A stable electrification rate model is obtained by using machine learning.