An artificial intelligence-based axle surface electrophoretic coating control method

Abstract

This invention belongs to the field of intelligent control technology, and particularly relates to an artificial intelligence-based control method for electrophoretic coating of axle surfaces. This method acquires images of the axle surface after electrophoresis using machine vision, extracts color features using an improved convolutional neural network, and generates a color uniformity index. Combined with time-series data of process parameters, large-scale sample annotation and amplification are achieved through semi-supervised clustering and parameter calibration transformation to establish the correlation between process parameters and the color uniformity index. Key process parameters are then screened using mutual information entropy and correlation analysis. After training the uniformity index prediction model, a genetic algorithm is used to optimize the key process parameters, constrained by the control range of the key parameters, to obtain the optimal parameter combination corresponding to the target color quality. This invention achieves intelligent and optimized control of the electrophoretic coating process on axle surfaces.

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