Optimization of CCT Equations Using Calculated Grain Boundary Soluble Compositions for the Simulation of Austenite Decom
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INTRODUCTION
THE prediction of CCT diagrams based on steel compositions has been the subject of several earlier studies as described in references.[1–4] Typically, these predictions are based on statistical analysis of measured CCT curves. Trzaska et al. proposed an artificial neural network method for the calculation of CCT diagrams based on steel composition.[1] Kirkaldy et al. optimized
JYRKI MIETTINEN is with the Process Metallurgy Research Group, University of Oulu, P.O. Box 8000, 90014, Oulu, Finland. SAMI KOSKENNISKA, MAHESH SOMANI, AARNE POHJONEN, JARI LARKIOLA, and JUKKA KO¨MI is with the Material and Mechanical Engineering, University of Oulu, P.O. Box 8000, 90014, Oulu, Finland. Contact e-mail: sami.koskenniska@oulu.fi SEPPO LOUHENKILPI is with the Process Metallurgy Research Group, University of Oulu and also with the Material and Mechanical Engineering, University of Oulu and also with the Department of Materials Science and Engineering, Aalto University, Vuorimiehentie 2, 02150 Espoo, Finland. Manuscript submitted May 8, 2019.
METALLURGICAL AND MATERIALS TRANSACTIONS B
their model parameters over experimental TTT diagrams in References 2 and 3. After optimization, the model can be used to calculate the CCT diagram applying the Scheil–Avrami or additivity rule.[5] The same methodology is used in commercial software JMatPro.[4] Since 1984, a thermodynamic-kinetic software package, IDS (InterDendritic Solidification), has been developed to simulate the phase change, compound formation/dissolution and solute distribution during the solidification of steels, and the subsequent cooling/ heating process after solidification.[6–8] IDS can also be used as a sub-tool in online calculations of continuous casting by coupling it to a proper heat-transfer model of the process.[9] In such a case, the impacts of the casting conditions are considered, by using the calculated nodal cooling rates of the strand as input data for IDS. The solid-state phase transformations related to the austenite decomposition process can also be simulated and predicted fairly accurately using an additional module called ADC. The ADC module is used for low-alloy steels in the temperature range of 900 C to 25 C in order to simulate the decomposition of austenite into
proeutectoid ferrite or cementite, pearlite, bainite, and also martensite. During the simulation, the formation or dissolution of different precipitates is included. The ADC module applies thermodynamic chemical potential equality equations of paraequilibrium condition,[10] the material balance equations for the ferrite/austenite interface advancing in a spherical austenite grain[11] (previously for carbon only, but later extended to nitrogen and boron as well), and the continuous cooling transformation (CCT) equations optimized from the measurements made in Germany[12,13] and Great Britain.[14] In these equations, the influencing solutes are C, Si, Mn, Cr, Mo, and Ni. The ADC module works reasonably well for typical low-alloyed steels, but its results are not as smooth as tho
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