"Statistical Modeling of Austenite Formation in Steels"
The formation of most steels products depends on chemical compositions and operating heating rates. Therefore, the need to know temperature of formation a phase a necessity in order to decrease extra costs. In this work we use than statistical modeling of austenite formation in steels and the present investigation introduces the statistical processes models for the empirical modeling of the formation of austenite during the continues heating of steels. At the previous works has examined the application of neural networks and Guassian process model to this problem, but the Guassian and Gamma Inverse processes. Models are a more general probabilistic models and are somewhat more amenable to interpretation. It is demonstrated that the models lead to an improvement in the significance of the trends of the temperatures as a function of the chemical composition and heating rate. In some cases, these predicted trends are more plausible than those obtained with neural network and Guassian process analysis’s. Additionally it is shown that many of the trace alloying elements present in steels are irrelevant in determining the austenite formation temperatures