A genetic algorithm (GA) based optimization of the composite desirability of the tensile properties of thermomechanically processed steel plates has been done. The quality of a steel-rolled product depends on its characteristics like tensile strength; yield strength; elongation and so on so forth. The concept of desirability scale arises when there is a need to combine the magnitude of several characteristics to a dimensionless scale (say, d). This desirability function is so constructed that any property value of a product is mapped between zero and one. Any value of d between zero and one gives an opportunity to improve the product quality depending on the closeness to these extreme two points. To provide a simultaneous balancing among physical properties of steel, viz. tensile strength, yield strength and elongation, a composite desirability scale (denoted by D) that represents average desirability is computed based on individual desirability values. The distribution of D is kept doubly bounded between zero and one to interpret the improvement aspect of product properties. The optimized solution of D provides a balance between multi-physical properties of steel under investigation. A neural network model has been developed to correlate between the tensile properties of steel and finally the desirability of the steel with its chemistry and several other rolling parameters in operation. Then, the model has been used for optimization of steel properties through the composite desirability by using GA. The GA technique, based on population search technique, has also been used in order to obtain the multiple feasible and implementable sets of solutions for the decision maker. The role of the variables on the work hardening behaviour of steel is also studied by varying the desirability function of yield strength and tensile strength. The results are validated from the existing knowledge of physical metallurgy in general and strengthening mechanisms in particular.

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