【摘要】 提出了一种正交试验设计与人工神经网络相结合的镀铬工艺参数优化方法。样本极差结果表明,对镀铬层的厚度及阴极电流效率影响因素依次为电流密度、电镀时间、电镀温度;且最佳电镀温度为45℃。通过神经网络建立电镀工艺参数与性能之间的模型,预测得出的镀铬层的厚度和阴极电流效率与实际试验的结果接近,训练精度较高,预测值与试验值的相对误差小于1.20%。通过建立镀铬层多指标综合评价模型,对镀铬层的厚度及阴极电流效率两个指标进行综合评价,通过对两个指标权重值的调整,确定镀铬层的综合性能值,得出最优的工艺参数。更多还原

【Abstract】 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.更多还原