基于ANN的FGH96合金热变形行为的模拟电路模型

在锻造领域，多采用数字模型进行工艺设计和过程控制，但数字模型无法完成材料成形过程的实时控制。因此，有必要建立一种适合于实时控制的材料动态流动行为模型，以提高生产效率和锻件质量。通过热模拟试验，对细晶态FGH96合金的高温流动特性进行了研究，用BP（Back Propagation）网络建立了FGH96合金热变形行为的人工神经网络模型，根据电模拟理论，利用模拟电路的快速反应与易于控制等特点，建立了基于ANN的FGH96合金的模拟电路模型。测试结果表明，所建立的ANN模型和模拟电路模型均具有较高的预测精度，能很好地反映材料热成形过程的动态流动行为，可用于材料热成形过程的实时控制。

Analog-Circuit Model of FGH96 Superalloy Hot Deformation Behaviors Based on Artificial Neural Network

At the present time, numerical models (such as, numerical simulation based on FEM) adopted broadly in technological design and process control in forging field can not implement the real-time control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network (ANN) model of FGH96 superalloy during hot deformation is established by using back propagation (BP) network. Then according to electrical analogy theory, its analog-circuit (AC) model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy's dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.