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为提高镁合金微弧氧化陶瓷涂层性能,降低设备能耗,以镁合金微弧氧化涂层的耐腐蚀性作为评价标准,以电流密度、频率、占空比3个电参数和微弧氧化处理时间为优化对象,将神经网络和遗传算法结合来优化镁合金微弧氧化工艺中的电参数和处理时间,神经网络的学习样本采用均匀设计,达到布点均匀、减少实验次数的目的,得到的优化结果为:电流密度1.3A/dm2,频率700Hz,占空比20%,处理时间20min。
In order to improve the performance of micro-arc oxidation ceramic coating of magnesium alloy and reduce the energy consumption of the equipment, the corrosion resistance of micro-arc oxidation coating of magnesium alloy was taken as the evaluation standard. Three electric parameters of current density, frequency and duty cycle, The processing time is the optimization object, the neural network and genetic algorithm are combined to optimize the electrical parameters and processing time in the micro-arc oxidation process of magnesium alloy, the uniform design of the learning samples of the neural network achieves the goal of uniform distribution and reducing the number of experiments, The optimized results are: current density 1.3A / dm2, frequency 700Hz, duty cycle 20%, processing time 20min.