Search

The present work deals with the preparation of near full density Cu-5 wt-%W, Cu-20 wt-%Wand Cu-40 wt-%W nanocomposite strips from elemental copper and tungsten powders. The proposed route consists of preparing Cu-W nanocomposite powders via high energy ball milling of mixture of elemental powders followed by die compaction of the milled powder, sintering of the powder preforms at 1273 K and hot rolling of the unsheathed sintered preforms at 1273 K under protective atmosphere. The microstructural characteristics of the ball milled Cu-W composite powder as well as finished hot rolled strips of Cu-W composites have been investigated, and the mechanism underlying such microstructural evolution has been discussed. Ball milled composite powder particles as well as hot rolled finished Cu-W composite strips consisted of uniform distribution of submicrometre sized and nanosized tungsten particles embedded in the copper matrix. The microhardness of the finished Cu-W composite strips increased with increasing W content, whereas electrical conductivity decreased with increasing tungsten content in the finished Cu-W nanocomposite strips. [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) model was developed to predict the microstructural evolution of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel (Alloy D9) during dynamic recrystallization (DRX). The input parameters were strain, strain rate, and temperature whereas microstructural features namely, %DRX and average grain size were the output parameters. The ANN was trained with the database obtained from various industrial scale metal-forming operations like forge hammer, hydraulic press, and rolling carried out in the temperature range 1173-1473 K to various strain levels. The performance of the model was evaluated using a wide variety of statistical indices and the predictability of the model was found to be good. The combined influence of temperature and strain on microstructural features has been simulated employing the developed model. The results were found to be consistent with the relevant fundamental metallurgical phenomena. [ABSTRACT FROM AUTHOR]

The paper discusses the kinetics, mechanism and modelling of the microstructural evolution of a 15Cr–15Ni–2.2Mo–0.3Ti modified austenitic stainless steel (alloy D9) during dynamic recrystallization (DRX). The experimental methodology included different hot working operations employing industrial equipment such as forge hammer, hydraulic press and rolling carried out in the temperature range 1,173–1,473 K to various strain levels. The kinetics of DRX has been investigated employing modified Johnson–Mehl–Avrami–Kolmogorov (JMAK) model. It has been found that the value of Avrami exponent varies in a close range of 1.17–1.34 which implies that D9 exhibits growth controlled DRX. Optical metallography has revealed that nucleation of DRX grains occurred along the prior grain boundaries by bulging mechanism. Microstructural characterization has shown that a significant correlation between microstructural features and processing parameters exists. However, this interrelation is ambiguous and fuzzy in nature. Therefore an artificial neural network model has been developed to predict the microstructural features, namely fraction of DRX and grain size, at different processing conditions. A good correlation between experimental findings and predicted results has been obtained. An instantaneous microstructure, therefore, can be designed in order to optimize the process parameters based on microstructural evolution. [ABSTRACT FROM AUTHOR]