Monideepa Mukherjee

Models for Austenite to Martensite Transformation in TRIP-Aided Steels: A Comparative Study

Low alloy transformation induced plasticity (TRIP)-aided steels are of particular interest to the automotive industry as they offer an excellent combination of strength and ductility at affordable costs. These unique properties depend primarily on the deformation induced transformation behavior of retained austenite to martensite, which is therefore, the most important aspect of TRIP-aided steels. As such, it is important to develop a mathematical model for the transformation of retained austenite. This would not only help in a better understanding of the deformation induced transformation behavior of retained austenite but also aid in a better design of the microstructure of TRIP-aided steels. A large number of empirical as well as semi-empirical models have been developed over the years to describe and predict the variation of retained austenite with strain. A comparative study of the different available models will be presented in this article.

Artificial Neural Network : Some Applications in Physical Metallurgy of Steels

Artificial neural networks are parameterized nonlinear models used for empirical regression and classification modelling. Their flexibility enables them to discover very complex relationships among large number of variables with complex interdependencies. Hence it is a very appropriate technique for developing predictive models in the short term. This article discusses the development of neural network models based on a Bayesian framework and some applications of the same. These examples include: (i) prediction of yield and tensile strengths of hot-rolled low-carbon ferrite-pearlite steel plates as a function of composition and rolling parameters, (ii) estimation of bainite plate thickness, (iii) estimation of retained austenite as a function of process parameters in Transformation Induced Plasticity aided (TRIP-aided) steels, and (iv) analysis of strain-induced transformation behavior of retained austenite during uniaxial tensile testing of TRIP-aided steels. While examples (i), (ii), and (iv) provide an overview of the work reported earlier, example (iii) is reported here in open literature for the first time. In all these four cases, it has been shown that the results are consistent with the established physical metallurgy principles.

Strain induced transformation of retained austenite in TRIP aided steels: a neural network model

Abstract A neural network model has been developed to predict the strain induced transformation behaviour of retained austenite in transformation induced plasticity aided steels, as a function of the driving force for martensitic transformation, initial retained austenite content, matrix microstructure and forming conditions. The model was found to make realistic predictions that generally agree with established metallurgical principles and other published data.

Evaluation of factors affecting the edge formability of two hot rolled multiphase steels

In this study, the effect of various factors on the hole expansion ratio and hence on the edge formability of two hot rolled multiphase steels, one with a ferrite-martensite microstructure and the other with a ferrite-bainite microstructure, was investigated through systematic microstructural and mechanical characterization. The study revealed that the microstructure of the steels, which determines their strain hardening capacity and fracture resistance, is the principal factor controlling edge formability. The influence of other factors such as tensile strength, ductility, anisotropy, and thickness, though present, are secondary. A critical evaluation of the available empirical models for hole expansion ratio prediction is also presented.