Numerical Simulation of Microstructure and Mechanical Properties of Controlled Cooling Process After Hot Rolling of Seamless Steel Tubes

Literature Overview

This study by Wang Minting, Li Xuetong, Rao Weiqiang, and Du Fengshan (2013), published in the journal Iron and Steel, addresses the controlled cooling process in the hot rolling production line for seamless steel tubes. Specifically, the research focuses on the cooling stage between the sizing (or reducing) mill and the straightening operation, which is a critical process window for controlling the final microstructure and mechanical properties of the tube. The authors employed a finite difference method (FDM) to establish a comprehensive prediction system for the temperature field, phase transformation, and mechanical properties during the cooling process of 20 steel tubes, and validated the model against experimental data obtained from actual cooling trials.

Process Background and Technical Challenge

In the seamless steel tube hot rolling process, the tube exits the sizing mill at a high temperature (typically 850-950°C) and must be cooled to a temperature suitable for straightening and coiling. The cooling rate and cooling medium during this stage have a profound effect on the final microstructure and mechanical properties. Too rapid cooling can lead to the formation of undesirable phases such as martensite or bainite, resulting in excessive hardness and poor toughness. Too slow cooling can result in coarse grain growth and reduced strength. The challenge is to achieve a controlled cooling profile that produces the desired balance of strength, toughness, and formability.

Process Parameters and Their Effects

Numerical Simulation Methodology

Heat Transfer Coefficient Determination

The first step in the simulation was to determine the heat transfer coefficient between the steel tube surface and the cooling medium. This was accomplished by conducting experiments with steel probes immersed in different cooling media. The temperature-time curves of the probes were recorded, and the inverse heat transfer method was used to calculate the heat transfer coefficient as a function of the workpiece surface temperature. The inverse heat transfer method solves the heat conduction equation backward in time, using the measured temperature data to determine the boundary condition (heat transfer coefficient).

The resulting heat transfer coefficient data showed that the heat transfer coefficient varies significantly with the surface temperature of the workpiece. At higher temperatures, the heat transfer coefficient is higher due to enhanced convection and radiation. As the surface temperature decreases, the heat transfer coefficient drops, particularly in the range where steam formation and film boiling occur in water cooling.

Finite Difference Method Model

The FDM model was established to solve the three-dimensional heat conduction equation for the cylindrical geometry of the steel tube. The model accounts for:

1. Temperature-dependent thermal properties: The thermal conductivity, specific heat, and density of the steel are functions of temperature and phase composition.
2. Phase transformation kinetics: The transformation from austenite to ferrite, pearlite, and bainite was modeled using the JMA (Johnson-Mehl-Avrami) equation, with transformation kinetics parameters determined from continuous cooling transformation (CCT) diagrams for 20 steel.
3. Thermo-mechanical coupling: The model accounts for the effect of plastic deformation on the transformation kinetics and the effect of transformation on the flow stress.
4. Boundary conditions: The surface boundary condition uses the heat transfer coefficient determined from the probe experiments, and the internal boundary condition accounts for the hollow geometry of the tube.

Microstructure and Property Prediction

The phase transformation model predicts the volume fractions of ferrite, pearlite, and bainite as a function of position within the tube wall and the cooling time. The mechanical properties (yield strength, tensile strength, hardness) are then predicted using the Hall-Petch relationship for grain size strengthening and the mixture rule for phase contributions. The model was validated against experimental data obtained from cooling trials, where the tube was cooled in air and the temperature at various times and positions was measured, along with the mechanical properties of the cooled tube.

Validation and Results

The comparison between the calculated and measured temperature data showed good agreement, with deviations within acceptable limits for engineering applications. The calculated temperature profiles captured the key features of the cooling process, including the cooling rate variation across the wall thickness and the temperature gradient between the outer and inner surfaces. The predicted mechanical properties also showed good correlation with the experimental measurements, confirming the validity of the phase transformation model and the property prediction approach.

Key Simulation Results

• The outer surface of the tube cools faster than the inner surface, resulting in a temperature gradient across the wall thickness.
• The phase transformation starts at the outer surface and progresses inward as the cooling continues.
• The final microstructure varies across the wall thickness, with finer grains and higher strength at the outer surface.
• The cooling time has a significant effect on the final microstructure, with longer cooling times resulting in coarser grains and lower strength.

Engineering Practice Implications

The simulation results provide valuable guidance for optimizing the controlled cooling process in seamless steel tube production:

1. Cooling medium selection: Air cooling is suitable for achieving a balanced microstructure with adequate strength and toughness. Water cooling can be used for higher strength applications but requires careful control to avoid excessive hardness and potential cracking.
2. Cooling time optimization: The cooling time should be selected to achieve the desired transformation temperature range. For 20 steel, a cooling time of 20-40 seconds in air typically produces a fine pearlite-ferrite microstructure with good mechanical properties.
3. Wall thickness consideration: For thicker-walled tubes, the temperature gradient across the wall is more pronounced, and the property variation between the outer and inner surfaces is greater. This may require additional process control measures such as staged cooling or internal cooling.
4. Process monitoring: The simulation model can be used as a predictive tool for process optimization, allowing engineers to evaluate the effect of process parameter changes before implementing them in production. This reduces the trial-and-error approach and accelerates process development.

Study Insights and Outlook

This paper demonstrates the power of numerical simulation in understanding and optimizing the controlled cooling process for seamless steel tubes. The integration of heat transfer, phase transformation, and mechanical property prediction into a single FDM framework provides a comprehensive tool for process design and optimization. The validation against experimental data confirms the reliability of the approach for engineering applications. For engineers involved in seamless steel tube production, this type of simulation can significantly reduce development time and improve product quality consistency. Future work should extend the simulation to include the effect of rolling deformation on the transformation kinetics, the effect of tube geometry on the cooling uniformity, and the prediction of residual stresses and distortion resulting from the cooling process. The development of such predictive capabilities is essential for the continued improvement of seamless steel tube quality and performance.