Integrated model for thermo-mechanical controlled process in rod (or bar) rolling

This study presents an integrated model for computing the thermo-mechanical parameters (cross-sectional shape of workpiece, the pass-by-pass strain and strain rate and the temperature variation during rolling and cooling between inter-stands) and metallurgical parameters (recrystallisation behaviour and austenite grain size—AGS), to assess the potential for developing “Thermo-Mechanical Controlled Process” technology in rod (or bar) rolling, which has been a well-known technical terminology in strip (or plate) rolling since 1970s.

The advantage of this model is that metallurgical and mechanical parameters are obtained simultaneously in a short computation time compared with other models. The model has been applied to a rod mill to predict the exit cross-sectional shape, area and AGS per pass by incorporating the equations for AGS evolution being used in strip rolling. At the finishing train of rod mills, the strain rates reach as high as 1000–3000 s−1 and the inter-pass times are around 10–60 ms.

The results show that the proposed model is an efficient tool for evaluating the effects of process-related parameters on product quality and dimensional tolerance of the products in rod (or bar) rolling. The results of the simulation demonstrated that the equation for AGS evolution being used in strip rolling might have limitations when applied directly to rod rolling at a high strain rate.

Numerical investigation of austenite grain size distribution in square-diamond pass hot bar rolling

In this study, the austenite grain size (AGS) for hot bar rolling of AISI4135 steel was predicted based on two different AGS evolution models available in the literature. In order to predict the AGS more accurately, both models were integrated with a three-dimensional non-isothermal finite element program by implementing a modified additivity rule. The predicted results based on two models for the square-diamond (S-D) and round-oval (R-O) pass bar rolling processes were compared with the experimental data available in the literature. Then, numerical predictions depending on various process parameters such as interpass time, temperature, and roll speed were made to compare both models and investigate the effect of these parameters on the AGS distributions. Such numerical results were found to be beneficial to understand the effect of the microstructure evolution model on the rolling processes better and control the processes more accurately.