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An Anisotropic Failure Characteristic- and Damage-Coupled Constitutive Model
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An Anisotropic Failure Characteristic- and Damage-Coupled Constitutive Model
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An Anisotropic Failure Characteristic- and Damage-Coupled Constitutive Model
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An Anisotropic Failure Characteristic- and Damage-Coupled Constitutive Model
An Anisotropic Failure Characteristic- and Damage-Coupled Constitutive Model
Journal Article

An Anisotropic Failure Characteristic- and Damage-Coupled Constitutive Model

2025
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Overview
This study proposes a coupled constitutive model that captures the anisotropic failure characteristics and damage evolution of nickel-based single-crystal (SX) superalloys under various temperature conditions. The model accounts for both creep rate and material damage evolution, enabling accurate prediction of the typical three-stage creep curves, macroscopic fracture morphologies, and microstructural features under uniaxial tensile creep for specimens with different crystallographic orientations. Creep behavior of SX superalloys was simulated under multiple orientations and various temperature-stress conditions using the proposed model. The resulting creep curves aligned well with experimental observations, thereby validating the model’s feasibility and accuracy. Furthermore, a finite element model of cylindrical specimens was established, and simulations of the macroscopic fracture morphology were performed using a user-defined material subroutine. By integrating the rafting theory governed by interfacial energy density, the model successfully predicts the rafting morphology of the microstructure at the fracture surface for different crystallographic orientations. The proposed model maintains low programming complexity and computational cost while effectively predicting the creep life and deformation behavior of anisotropic materials. The model accurately captures the three-stage creep deformation behavior of SX specimens and provides reliable predictions of stress fields and microstructural changes at critical cross-sections. The model demonstrates high accuracy in life prediction, with all predicted results falling within a ±1.5× error band and an average error of 14.6%.