Optimization of casting parameters is essential in terms of quality factors in foundries. Nowadays, to optimize process parameters, new approaches such as artificial neural networks method are being used. In this study, a neural network model has been developed to control the grain size in aluminum casting alloys. Some of the important grain refinement parameters such as casting temperature, holding time and addition level have been evaluated as inputs for the model. The network training architecture was optimized at 241 training cycles with quasi-Newton algorithm with a single hidden layer and 6 neurons. With modeling, mean absolute percent error was found at 0.99 between experimental measurements and model estimation. R² value has been calculated as 99.2%. The minimum grain size was measured for the parameter of 680 °C casting temperature, 0.25% Ti, 25-min holding time. It was found that there was a good agreement between experimental measurements and artificial neural network predictions.

就铸造厂的质量因素而言，优化铸造参数至关重要。如今，为了优化过程参数，正在使用诸如人工神经网络方法的新方法。在这项研究中，已经开发了一种神经网络模型来控制铝铸造合金的晶粒尺寸。一些重要的晶粒细化参数，例如铸造温度，保温时间和添加量已被评估为模型的输入。网络训练架构在241个训练周期中使用具有单个隐藏层和6个神经元的准牛顿算法进行了优化。通过建模，实验测量和模型估计之间的平均绝对百分比误差为0.99。R ²值已计算为99.2％。在680°C铸造温度，0.25％Ti，25分钟保持时间的参数下测量了最小晶粒尺寸。发现在实验测量和人工神经网络预测之间有很好的一致性。