Papers by Keyword: Martensite Start Temperature

Abstract: In order to investigate the effect of austenite grain size on martensite start temperature of Nb-V-Ti micro-alloyed ultra-high strength steel, the phase transformation features of Nb-V-Ti micro-alloyed steel was investigated. It has been found that martensite start temperature increased with the increase of austenite grain size as a consequence of the increase of austenitizing temperature. Based on microstructure observation, two types of MX carbonitrides with different compositions and morphologies have been identified. With the increase of the austenite grain size, both the volume fraction of precipitates and the dislocation density decreased, which may be induced by the strengthening of the austenite matrix directly and increasing the resistance of austenite to plastic deformation. Hence, the increase of martensite start temperature could be attributed to a decrease in volume fraction of precipitates and dislocation density.

Abstract: The martensite start temperature (Ms) of a Fe-0.40%C-1.87%Mn-1.65%Si alloy with bainite and martensite dual phases was studied. The phase transformation dilation curves were measured and Ms temperatures of deformed and non-deformed specimens were obtained. Finally the effect of bainitic transformation upon Ms was investigated. The results show that bainitic phase transition affects the Ms and the increase of transformation results in the decrease of Ms. The mechanical stabilization of austenite due to ausforming is partially eliminated by bainitic transformation, which weakens the effect of ausforming on Ms.

Abstract: Martensite phase and its formation are quite attractive and important in industrial steels for reasons of having good properties such as high strength and high hardness. As such, determining the martensite formation start temperature in steel heat treatment operations is extremely important. Some parameters including chemical composition and grain size are effective factors on this temperature. In this investigation, we have made an attempt to determine this temperature with regard to chemical composition of steels. To reach this goal, we have explored the use of feedforward Artificial Neural Network (ANN) with the Back Propagation (BP) learning algorithm. A comparison is made between the Ms temperatures predicted with this model and those from the empirical equation as well as the experimental values obtained from costly and time-consuming tests in scientific and industrial centers for various steels. This comparison indicates that a better agreement exists between the ANN-predicted results and experimental values than the results from the empirical equation and experimental values.

Abstract: There are many empirical equations for predicting martensite start temperature (Ms) and the kinetics models of martensitic transformation of plain carbon and low alloy steels. The Ms temperature equations are only dependent upon the chemistry, while the martensite transformation kinetics models are based on the degree of undercooling below Ms temperature. However, the prior austenite grain size (AGS) is also expected to influence both Ms temperature and martensite transformation kinetics as it does in diffusive transformations. In this study, herefore, both Ms temperature and martensite transformation kinetics of a low alloy steel with different austenite grain sizes were investigated using a dilatometer. The new Ms equation and martensite transformation kinetics model including the AGS effect are proposed.