Prediction of thermal field dynamics of mould in casting using artificial neural networks

Abstract

Manufacturing a large number of cast parts made of aluminium alloy led to an increased interest in developing and applying new control techniques of the casting process. Anyway, the difficulty in estimating some important process parameters only allowed the use of some approaches which are limited to a few geometric models. Many researchers made great efforts to find the best method for monitoring and measuring thermal field dynamics of the cast and mould during solidification and cooling of the melt alloy. Acquiring very accurate data leads to best approach for solving the heat transfer problem in casting. The paper presents the prediction of thermal field dynamics of mould in permanent mould casting using artificial neural networks and based on thermal history of the cast part and the way this thermal history influences the thermal changes of the mould. It is very important to identify the relation between the thermal fields' dynamics of both cast and mould in order to create and use a control technique of the cast solidification and cooling. The necessity of controlling the cast solidification is due to the large demand of cast parts with improved mechanical properties.