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Creep-Plasticity Interaction

  • E. Krempl
Part of the International Centre for Mechanical Sciences book series (CISM, volume 399)

Abstract

Inelastic analyses are needed for predicting the lifetime of one-of-a-kind components operating under severe conditions of loading and environment. Examples are power generation and propulsion machinery and chemical processing plants. The components are subjected to steady and cyclic loading which involves nonlinear monotonic and cyclic behavior. In addition, rate dependence, creep and relaxation are present. These phenomena must be captured in a constitutive equation suitable for inelastic analyses.

The “unified” viscoplasticity theory based on overstress (VBO) was formulated using the experiment-based approach. Servo-controlled testing machines and strain measurements on the gage length are employed to measure the response to a given input. The responses contain the influence of the changing microstructure. In VBO no yield surface is used and rate dependence is present at every temperature. The high temperature formulation has a static recovery term and provides for softening. Softening is the result of diffusion, which counteracts the hardening due to inelastic straining. Low and high homologous temperature qualitative properties such as the behavior under very fast, very slow monotonic, rate-dependent loading are investigated in addition to creep, relaxation and cyclic loading. The low homologous temperature formulation admits a long-time asymptotic solution, which is thought to apply to the “flow” stress region of the stress-strain diagram where special relaxation properties are observed in real materials and in the model. References to papers by the author and his students show that primary, secondary and tertiary creep can be modeled with VBO in addition to anomalous behavior. Inelastic strains are observed and modeled in recovery experiments at zero stress.

Keywords

Creep Rate Creep Test Creep Strain Dynamic Strain Aging Relaxation Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 1999

Authors and Affiliations

  • E. Krempl
    • 1
  1. 1.Rensselaer Polytechnic InstituteTroyUSA

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