DESIGN OF ALLOY STEELS USING MULTI-OBJECTIVE OPTIMIZATION

In this chapter, a multi-objective optimization approach is used to address the alloy design problem, which concerns finding optimal processing parameters and the corresponding chemical compositions to achieve certain pre-defined mechanical properties of alloy steels. Neurofuzzy modeling has been used to establish the property prediction models for use in the multi-objective optimal design approach which is implemented using Particle Swarm Optimization (PSO). PSO is used as the search algorithm, because its populationbased approach fits well with the needs of multi-objective optimization. An evolutionary adaptive PSO algorithm is introduced to improve the performance of the standard PSO. Based on the established tensile strength and impact toughness prediction models, the proposed optimization algorithm has been successfully applied to the optimal design of heat-treated alloy steels. Experimental results show that the algorithm can locate the constrained optimal solutions quickly and provide a useful and effective guide for alloy steels design.