Research Engineer, Structural Fatigue Branch, National Aeronautics and Space Administration, Lewis Research Center, Cleveland, OH
Professor, University of Akron, Akron, OH
Research Engineer, Fatigue & Thermal Group, NYMA, Inc., NASA Lewis Research Center Group, Brook Park, OH
Pages: 28 Published: Jan 1996
Specific forms for both the Gibb's and complementary dissipation potentials are chosen such that a complete (i.e., fully associative) potential based multiaxial, nonisothermal unified viscoplastic model is obtained. This model possess one tensorial internal state variable (that is, associated with dislocation substructure) and an evolutionary law that has nonlinear kinematic hardening and both thermal and strain induced recovery mechanisms. A unique aspect of the present model is the inclusion of nonlinear hardening through the use of a compliance operator, derived from the Gibb's potential, in the evolution law for the back stress. This nonlinear tensorial operator is significant in that it allows both the flow and evolutionary laws to be fully associative (and therefore easily integrated), greatly influences the multiaxial response under non-proportional loading paths, and in the case of nonisothermal histories, introduces an instantaneous thermal softening mechanism proportional to the rate of change in temperature. In addition to this nonlinear compliance operator, a new consistent, potential preserving, internal strain unloading criterion has been introduced to prevent abnormalities in the predicted stress-strain curves, which are present with nonlinear hardening formulations, during unloading and reversed loading of the external variables. The specific model proposed is characterized for a representative titanium alloy commonly used as the matrix material in SiC fiber reinforced composites, i.e., TIMETAL 21S. Verification of the proposed model is shown using ”specialized” non-standard isothermal and thermomechanical deformation tests.
viscoplasticity, nonlinear hardening, TIMETAL, 21S, nonisothermal, deformation, multiaxial, correlations, predictions
Paper ID: STP16452S