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The temperature distribution and cooling rate during the welding process have signi®cant e ects on the mechanical and metallurgical properties of a weldment. The change in microstructure, grain growth, hardness and residual stress in a weldment are very dependent on the temperature distribution and cooling rate. In the present work a three-dimensional transient ®nite element analysis of heat transfer in arc welding has been done to predict the di erent zones of microstructures. The problem was solved by taking several important factors into consideration (such as temperature dependence of material properties, a Gaussian distribution for the welding arc, enthalpy, etc.). The model was indirectly veri®ed by conducting some experiments on submerged arc welding. The weld metal zone, coarse-grained zone and ®ne-grained zone were theoretically estimated by using three-dimensional ®nite element analysis and compared with the experimentally measured values. It was found that theoretically estimated zones corresponded well to the experimentally measured zones. A correlation was also developed to estimate the arc radius from the weld metal zone.

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In the present work, design of experiments (DOE) technique with response surface methodology was used to develop both linear and non-linear models, to establish the input-output relationships in green sand mould system. Grain fineness number (GFN), amount of clay, amount of water and number of strokes (degree of ramming) were considered as the input variables (parameters), which control the outputs (i.e. mould properties). Full factorial DOE was utilised for the linear model, whereas central composite design and Box-Behnken designs were used to develop the non-linear models. Experiments were conducted to measure the green sand mould properties, such as permeability, green compression strength, mould hardness and bulk density. The adequacy of all the developed models was checked through statistical analysis. Twenty random test cases were considered, to validate the models and compare their performances. A model that is statistically adequate and gives minimum percentage of deviation in prediction was adjudged as the best model for a particular response.