Reduced Order Constitutive Modeling of Directionally Solidified Ni Base Superalloys

Abstract

Temperaturedependent crystal viscoplasticity models are ideal for modeling largegrained, directionally solidified Nibase superalloys but are computationally expensive. This work explores the use of reducedorder models that are potentially more efficient with similar predictive capability of capturing temperature and orientation dependence. First, a transversely isotropic viscoplasticity model is calibrated to a directionally solidified Nibase superalloy using the response predicted by a crystal viscoplasticity model. The unified macroscale model is capable of capturing isothermal and thermomechanical responses in addition to secondary creep behavior over the temperature range of 20–1050 آ°C. A second approach is an extreme reducedorder microstructuresensitive constitutive model that uses an artificial neural network to provide a set of parameters that depend on orientation, temperature, and strain rate to give a firstorder approximation of the material response using a simple constitutive model. This simple relationship is then used in a Neubertype fatigue notch analysis to predict the local response.