OPTIMISATION OF STEEL PRODUCTION INCORPORATING ECONOMIC FACTORS

Over the last four years efforts have been devoted towards the development and validation of mechanical test result models relating to a range of alloy steels. Several neural-network based models have been developed, two of which are related to the mechanical test results of Ultimate Tensile Strength (UTS), Reduction of Area (ROA), Elongation, etc. The ultimate aim of developing these models is to pave the way to process optimisation through better predictions of mechanical properties. In this research we propose to exploit such neural network models in order to determine the optimal alloy composition and heat treatment temperatures required, given certain predefined mechanical properties such as the UTS by including certain economic factors such as the price of composites and the energy necessary for tempering. Genetic Algorithms, with their power of searching a relatively large space without requiring the gradient of a function, are used for this purpose. The results obtained are very encouraging in that steels with adequate properties and optimised costs are obtained.