# Tempcore Process Simulator to Analyze Microstructural Evolution of Quenched and Tempered Rebar
## 3 Results and Discussion
### Performance Validation of TPS
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Cooling time (sec)**} & \multicolumn{2}{c}{**1.5**} \\ \hline \multicolumn{2}{c}{**Alloy type**} & \multicolumn{2}{c}{**Type 1**} & \multicolumn{2}{c}{**Type 2**} \\ \hline \multirow{6}{*}{**Chemical composition (wt\%)**} & C & 0.28 & 0.27 \\ \cline{2-4}  & Si & 0.24 & 0.22 \\ \cline{2-4}  & Mn & 1.43 & 1.22 \\ \cline{2-4}  & V & 0.031 & 0.020 \\ \cline{2-4}  & P & \textless{}0.040 & \textless{}0.040 \\ \cline{2-4}  & S & \textless{}0.040 & \textless{}0.040 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance test with respect to alloy type. Figure 11: Microstructures obtained from TPS after cooling time of 2.0 s. (A: surface layer; B: transition layer; C: center core.). of microhardness for Cases 1 and 2. The tempered martensite rim obtained from TPS have been well confirmed by hardness profiles. Figure 12: Tempered martensite rim obtained from TPS for (**a**) Type 1 and (**b**) Type 2. As shown above, the effects of cooling time and chemical composition on the microstructural evolution and mechanical properties of quenched and tempered rebar have been successfully analyzed. These results show that the need to relate the microstructures to the resulting properties of quenched and tempered rebar can be satisfied by proposed TPS. [PERSON] et al. [31] developed a thermal model to predict the temperature distribution across the bar over its whole quenching and self-tempering rout. The output of this model has been used to calculate the area of martensite formed in outer layer and ferrite-pearlite zone. [PERSON] et al. [32] also predicted hardness of the tempered martensitic rim of Tempcore rebars. They developed a model for calculating the hardness of the tempered martensitic rim as a function of chemical composition and non-isothermal tempering parameters. These approaches can be advantageous to estimate rebar strength without experiments, but the accuracy of the thermal model is essentially required. The information on such a thermal model essentially requires precise temperature history, and it can be obtained by TPS. For the validation of the TPS developed in this study, the area of martensite predicted from TPS was compared with the experimentally obtained area of martensite. From the hardness distribution across the rebar, the martensite volume fraction \(V_{M}\), which is an important parameter in the Tempcore process as it affects the final mechanical properties of the rebar, was determined using the following equation:  
\[V_{M}\ =\ 1-4\left(\frac{R_{M}}{D}\right)^{2} \tag{1}\]  
Here, \(V_{M}\) denotes the martensite volume fraction, RM is the distance of the martensite layer from the center, and \(D\) is the diameter of the rebar, as shown in Figure 14a. As shown in Figure 14b, strong correlation has been observed between martensite volume fractions obtained from shop floor operation and TPS. It strongly confirms that TPS can simulate the shop floor Tempcore process with a high degree of fidelity and reliability. Figure 13: Variation of microhardness obtained from TPS for Cases 1 and 2.