The filling stage is a critical phenomenon in sand casting for making reliable castings. Latest research has demonstrated that for most liquid engineering alloys, the critical meniscus velocity of the melt at the ingate is in the range of 0.4–0.6 m s⁻¹. The work described in this research paper is to use neural network (NN) technology to propose digital twin approach for gating system design that allow to understand and model its performances faster and more reliable than traditional methods. This approach was applied in the case of sand casting of liquid aluminum alloy (EN AC-44200). The approach is based first on a digital representation of filling process to perform the melt flow simulations using a combination of the gating system design parameters, selected as a training cases from Taguchi orthogonal array (OA). The second step of the approach is the data capture of functional gating design system to train up the feed-forward back-propagation NN model. The validation of the well-trained NN model is assessed by interrogating predicted ingate velocity to it and making reliable predictions with high accuracy. The claim is that such digital twin approach is an effective solution to recognize the functional design parameters from the entire filling systems used during casting process.

填充阶段是砂型铸造中制造可靠铸件的关键现象。最新研究表明，对于大多数液态工程合金，入口处熔体的临界弯月面速度范围为0.4–0.6 m s ^-1。本研究论文中描述的工作是使用神经网络（NN）技术为门控系统设计提出数字孪生方法，该方法允许比传统方法更快，更可靠地了解和建模其性能。这种方法适用于液态铝合金的砂型铸造（EN AC-44200）。该方法首先基于填充过程的数字表示，以使用选通系统设计参数（从田口正交阵列（OA）作为训练案例中选择）的组合来执行熔体流动模拟。该方法的第二步是功能门控设计系统的数据捕获，以训练前馈反向传播NN模型。训练有素的NN模型的有效性是通过向其询问预测的门速度来进行评估的，并进行高精度的可靠预测。声称这种数字孪生方法是从铸造过程中使用的整个灌装系统中识别功能设计参数的有效解决方案。