Comparison of the Warm Deformation Constitutive Model of GH4169 Alloy Based on Neural Network and the Arrhenius Model

Abstract

In order to realize a better description of plastic flow behavior in the warm deformation process of GH4149, the GH4169 superalloy was compressed by Gleeble-3800 at a temperature of 700–900 °C and a strain rate of 0.01–10 s−1. The constitutive model of GH4169 superalloy was established using artificial neural network (ANN) and the Arrhenius equation, and the accuracy of the model was compared. The results show that the average absolute relative error (AARE) of the ANN constitutive model is 4.34%. The AARE of the Arrhenius equation constitutive model is 29.95%. The ANN constitutive model is more accurate than the Arrhenius constitutive model, and has consistent accuracy in the whole parameter range. The stress–strain curve obtained by the model is in good agreement with the experimental curve. The process of the warm compression test is simulated by finite element software importing the ANN constitutive material model. The results verified the reliability of the model. The ANN constitutive model can effectively predict the flow stress of GH4169 superalloy during the warm deformation process.

Keywords

• GH4169 alloy
• deformation process
• artificial neural network
• flow stress