Thermodynamic modelling ofmartensite start temperature in commercial steels

University essay from KTH/Materialvetenskap

Abstract: Firstly, an existing thermodynamic model for the predicting of martensite start temperature of commercial steels has been improved to include more elements such as N, Si, V, Mo, Nb, W, Ti, Al, Cu, Co, B, P and S and their corresponding composition ranges for Martensitic transformation. The predicting ability of the existing model is improved considerably by critical assessment of different binary and ternary systems i.e. CALPHAD approach which is by wise selection of experimental data for optimization of the interaction parameters. Understanding the degree of variation in multi-component commercial alloys, various ternary systems such as Fe-Ni-X and Fe-Cr-X are optimized using both binary and ternary interaction parameters. The large variations between calculated and the experimental values are determined and reported for improvements in thermodynamics descriptions.Secondly, model for the prediction of Epsilon martensite start temperature of some commercial steels and shape memory alloys is newly introduced by optimizing Fe-Mn, Fe-Mn-Si and other Fe-Mn-X systems considering the commercial aspects in the recent development of light weight steels alloyed with Al and Si.Thirdly, the effect of prior Austenite grain size (pAGS) on martensite start temperature is introduced into the model in the form of non-chemical contribution which will greatly influence the Gibbs energy barrier for transformation. A serious attempt has been made to describe the dependency of transition between lenticular and thin-plate martensite morphologies on the refinement of prior Austenite grain size.Finally, the model is validated using a data-set of 1500 commercial and novel alloys. Including the newly modified thermodynamic descriptions for the Fe-based TCFE9 database by Thermo-Calc software AB, the model has the efficiency to predict the martensite start temperature of Multi-component alloys with an accuracy of (±) 35 K. The model predictability can be further improved by critical assessment of thermodynamic factors such as stacking faults and magnetism in Fe-Mn-Si-Ni-Cr systems.

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