Semi-Automatic Optimization of Steel Heat Treatments for Achieving Desired Microstructure

Abstract

The thermo-mechanical processing history together with the steel composition defines the final microstructure, which in turn produces the macroscopic mechanical properties of the final product. In many industrial processes it is therefore of paramount importance to find the optimal thermal path that produces the desired microstructure. In the current study an optimization method has been developed to calculate the optimal thermal path for producing desired amounts of microstructural constituents (ferrite, bainite, martensite) of a medium carbon, low-alloy steel, and a low carbon microalloyed steel. The optimization is performed for two separate industrial processes: induction hardening of a pipeline steel and a water cooling of hot rolled steel strip. The optimization workflow consists of first setting the desired amounts of microstructural constituents, and subsequent optimization of the thermal path, which produces these desired amounts. For the water cooling of a steel strip we additionally employed previously developed tool to calculate the cooling water fluxes that are needed to realize the optimized cooling path in water cooling line after hot rolling. To demonstrate the applicability of the method, we present results that were obtained for different case studies related to the industrial processes.

Application of two-grid interpolation to enhance average gradient method for solving partial differential equations

“…For this reason, it is very important to obtain the capability of modelling the microstructure evolution in detail. Although the mean field models, such as the ones used in [1,2,3,4], are useful in rapid modelling and optimization of the relative quantities related to the microstructure, they lack the capability of including local effects, which can play important role in the microstructure evolution. For example in [2], it was recently observed that in order to understand and to model stabilization of austenite regions in steel processing, it is necessary to consider local carbon enrichment and the effect of local mechanical strains.…”

In-Situ Sem Characterization and Numerical Modelling of Bainite Formation and Impingement of a Medium-Carbon, Low-Alloy Steel

“…We have previously developed a coupled heat conduction and mean field (MF) phase transformation model [14][15][16][17], that has been applied for simulating austenite decomposition in cooling of a steel coil [15] as well as in slow cooling of low temperature ausformed steel, which leads to the formation of fine bainitic structure [18]. The mean field phase transformation model has been also used for simulating microstructure evolution to aid the design of induction hardening of a medium carbon pipeline material [19] where the model can be used for optimizing the cooling path and strategy so that desired fractions of different phases can be obtained [20,21] through the pipe thickness. Using this model, it is possible to quantitatively estimate the phase fraction and final hardness.…”

Numerical Simulation of Jominy End Quench Test Using Coupled Heat Transfer and Phase Transformation Model

“…Ferrite and pearlite are softer phases than bainite and martensite, where untempered martensite is the hardest. The steel composition affects the potency to form the soft phases, ferrite and pearlite, and a suitable composition can be chosen [4][5][6][7][8] to create the desired properties of the steel product, in addition to optimizing the cooling path [9][10][11][12] for this purpose [13].…”