Optimization of Chrome-plating Craft Based on Orthogonal Test Design and Artificial Neural Network

A method combining orthogonal experimental design with artificial neural networks was proposed to optimize the chrome-plating craft parameter. The results show that the chrome-plating thickness and cathodic efficiency are influenced by the current density, galvanization time and galvanization temperature in turn; the best galvanization temperature was 45 ℃. The model between the galvanization technological parameters and the performance by artificial neural networks was established and the chrome-plating thickness and cathodic efficiency predicted by the model were closed to actual experimental results. The training precision was accurate, and the relative error between the predicted value and the experimental value was less than 1.2%. A comprehensive evaluation model was established to evaluate two indicators which were chrome-plating thickness and cathodic current efficiency. The model adjusted the weight value of two indicators respectively, calculated the chromium plating comprehensive performance value and got the optimal craft parameters.