The need to revise the current methods to measure and assess static recrystallization behavior
Introduction
Static recrystallization (SRX) occurs when a sufficient stored energy developed during a hot deformation process has not dynamically recrystallized the material. A classical nucleation and growth process is responsible to initiate and to develop the phenomenon, and there is an incubation time after deformation for initiation of SRX (Hodgson et al., 2008).
Non-standard tests have been used extensively in the literature to physically simulate the occurrence of SRX and to indirectly measure it during hot deformation. Examples of these include use of the torsion (Zhou and Clode, 1998) and compression test (Mirzadeh, 2014) to generate raw load-deformation data. Neither of the tests can produce homogeneous hot deformation. As a result, the physical simulation and the indirect measurement of SRX using the tests data become even more complex. This is partly due to a need to convert the post interruption/dwelling load-deformation measurements of the tests into strength data. Commonly used data reduction techniques, such as the Fields’ technique (Fields and Backofen, 1957) in the case of torsion, are only valid for homogeneous deformation.
Since the advent of back extrapolation technique (Andrade et al., 1983) in 1983 to measure static recrystallization, it has been used by several researchers for more than two decades (e.g. Gómez et al. (2009) and Fan et al. (2011)). Other techniques proposed to measure SRX also rely on use of the effective stress changes before and after the interruption. We refer to these techniques in the current article as “conventional methods”. In such methods, it is assumed that the post interruption effective stress values, required to “define” the softening fraction, are proportional to the measured load–displacement data (e.g. torque–twist for the hot torsion case). Therefore the strength changes in the conventional methods are regarded as “measured data”. Skipping the complex post processing step for the “SRX load–displacement data” in these methods, the need to verify the step and subsequently their identified SRX models have been overlooked. In order to assess the merits of a chosen method, it is important to consider their impact on the “accuracy” and “computational time” in a compromising way. However the method’s ability to verify its identified SRX model quantitatively is uncompromisable. A multi-layer theory (Khoddam and Hodgson, 2014) was proposed recently to interpret the post interruption torque–twist data in the torsion test and convert them to the effective stress. The multi-layer theory is quite complex and compared to the conventional methods is computationally expensive to implement. Nevertheless, as it will be explained later, the theory enables to verify its identified SRX model.
Conventional post processing of the test results relies on significant simplifications. Joun et al. (2008) and Gavrus et al. (1998) used inverse numerical post processing techniques to identify the constitutive parameters of material during hot deformation. Yanagida and Yanagimoto (2008) employed an inverse computational technique to calibrate parameters of post interruption annealing during hot compression. Such “computer aided characterization methods” are necessary to assess the validity of the conventional SRX simulations and measurements.
In this work, the multi-layer theory (Khoddam and Hodgson, 2014) will be applied to the hot torsion torque–twist data of a type 304 austenitic stainless steel deformed at and strain rate of with a number of interruption scenarios. Next, the SRX kinetics is computationally identified using the inverse method. The validity of an existing conventional SRX model for the above conditions is assessed and the SRX measurements based on the computational and conventional techniques will be compared and discussed.
Section snippets
Modified back extrapolation technique
The Back extrapolation technique (Andrade et al., 1983) has been commonly used to estimate fractional softening, , for indirect measurement of the static recrystallization. The technique has been shown schematically in Fig. 1. It involves graphical overlaying of the first and second flow curves. The residual strain for the second curve, , is also an indication of the degree of softening after an interval of hot working. To perform the technique analytically, mathematical expressions of the
Results and discussion
In order to explore the extends of the unsaturated zone, a numerical approach (Khoddam and Hodgson, 2014) was employed here to calibrate the SRX parameters; a 304 type stainless steels for the current numerical study, the chemical compositions of which is presented in Table 1. This is an austenitic stainless steel, with no discernible phase transformation reported during its hot deformation over a wide range of temperatures (Dehghan-Manshadi, 2007).
The chosen temperature and strain rate in the
Conclusion
Common hot deformation physical tests, torsion and compression, cannot produce homogeneous deformation. Consequently, their produced load–displacement-holding time data require new and well developed post processing methods to accurately measure static recrystallization and to characterize its kinetics. A numerical–experimental inverse solution of the hot torsion test has been presented here to investigate the extent of heterogeneity and to assess the accuracy of the previous characterizations
Acknowledgments
The authors are grateful to Dr. H. Beladi for many useful discussions and also to Dr. A. Dehghan-Menshadi for providing the experimental data used in this study. One of the authors (PDH) acknowledges the support of the Australian Research Council through his ARC Laureate Fellowship.
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