Study on Flow Stress Constitutive Relationship of GH738 Superalloy Based on Machine Learning

Abstract:

Hot compression experiments were conducted on GH738 superalloy using Gleeble 3500 thermal simulation machine at deformation temperature of 980–1100 °C and strain rate of 0.001–0.1 s^-1 to study the flow stress behavior of the alloy. Three machine learning algorithms, namely random forest (RF), support vector machine (SVM), and genetic algorithm-back propagation (GA-BP) neural networks, were employed to establish constitutive relationship models for the flow stress behavior of GH738 superalloy. Subsequently, these models were compared and analyzed in terms of their predictive accuracy. The results indicate that the flow stress of GH738 superalloy decreases with the increase in deformation temperature and the decrease in strain rate. The correlation coefficients for the RF, SVM, and GA-BP constitutive relationship models are determined as 0.921, 0.998, and 0.999, while the average absolute relative errors as 14.587%, 2.112%, and 0.901%, respectively. The results demonstrate that SVM and GA-BP constitutive relationship models have better prediction accuracy than RF model in predicting the flow stress behavior of GH738 superalloy. It can provide a theoretical basis for the calculation of deformation resistance and forging tonnage under different deformation conditions, and it can also provide reliable flow stress data for numerical simulation of forging process.