Determination of martensite start temperature in engineering steels part I. Empirical relations describing the effect of steel chemistry

The dependency of the martensite start (Ms) temperature upon composition of engineering steels has been examined by analyzing the results predicted by an artificial neural network (ANN) model and thermodynamic data. Two new f ormulas, the simple linear and binary interaction ones, have been statistic ally derived and applied to predict the Ms temperature in an Fe-C-Si-Mn-Cr- Mo system. It is shown that the separation of the influence of interactions from that of individual alloying elements is successful since most of the statistical results are reasonable and thus have been physically interprete d. The thermodynamic calculations show that the alloying elements have simi lar influence upon the Ms and Art temperatures. The apparent effect of carb on depends largely on C-X interactions. C-Mn and C-Mo interactions weaken t he effect of carbon while that of C-Si interaction intensifies the role of C. This is supported by phenomenological results and has been physically in terpreted. The interactions between substitutional alloying elements have a lso significant influence upon the Ms temperature. The Si-Mn interaction st rongly increases the Ms, while Si-Mo interaction significantly decreases th e Ms. So far, there is no proper physical explanation for this though suppo rtive evidence has been obtained from phenomenological results. Mn and Mo h ave the weakest apparent interaction, mat is, their influence can be simply added up. Moreover, a semi-physical model has been built to predict the Ms temperature from a critical temperature, which can be calculated thermodyn amically. It shows that the semi-physical method gives a satisfactory predi ction of Ms with a standard error of 15.3 degrees C. Evaluation of nine com mon empirical methods indicates that the Kung and Rayment (KR) formula give s the best predicting results amongst them.