Prediction and Improvement of Mechanical Properties of Corrosion Resistant Superalloy K44 with Adjusting Minor Additions C, B and Hf

Abstract

The neural network (NN) method is applied to predict the influence of minor additions C, B and Hf on mechanical properties of cast superalloy K44 with heat treatment parameters considered. Furthermore, the influence of minor additions C, B and Hf on phase stability is estimated using d-electrons theory. The results from these two methods indicate that decreasing C content, increasing B content and doping 0.2–0.3 mass% Hf can balance the desired mechanical properties and phase stability in K44 alloy, which also is confirmed experimentally through the relation between mechanical properties and phase stability of base/modified K44 alloys.