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An equation for flow stress as a function of true strain and grain size has been derived from these results.
Here, a quantitative, parameter-free model to predict the flow stress as a function of temperature and strain rate of such alloys is presented.
Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator, an artificial neural network model of high temperature flow stress as a function of strain, strain rate and temperature was established.
The mechanical properties predicted include flow stress as a function of temperature and strain-rate, as well as time for 0.1-0.2 0.1-0.2strain as a function of stress and temperature.
Comparison with data shows that, whereas calculations capture satisfactorily the evolution of the average composite flow stress as a function of notch radius at small strains, the notched samples damage faster and fail at strains lower than predicted.
The model is then used to predict the flow stress as a function of temperature and grain size and strain rate sensitivity (m) for a wide range of strain rates.
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Based on these blood flows, the wall shear stress as a function of vessel diameter was calculated at each age for each strain.
Flow analysis yielded an empirical formula indicating shear stress as a function of the formulation concentrations of bentonite and sodium carboxymethyl cellulose, stirring time and shear rate.
The stress as a function of strain at 0.15 mg ml−1 is plotted in Fig. 1a.
(D) The image depicts stress as a function of distance from the apex of the dermal papillary structure.
Figure 26 Horizontally averaged stress components and second invariant of stress as a function of depth.
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