Multiobjective optimizations of strength and ductility of multiphase steels are made using genetic algorithm (GA) to find the role of the composition and process variables in the complicated work hardening process of such steel. Multi-phase steels offer attractive combinations of strength and ductility as a result of the different mechanical properties of the microstructural components and their interactions, and steadily increasing its usage in the automotive industries. Among the multi-phase steels the concepts of work hardening are different in case of microalloyed steels and that in transformation induced plasticity (TRIP) aided steel. The microalloyed steels show a very high work hardening respond only at low deformation, which gets continuously reduced with higher strain, which is almost constant over a wide range of deformation in TRIP-aided steel, due to transformation of retained austenite to martensite. The optimization of the two conflicting objectives viz. strength and ductility was attempted through rigorous experimental investigations by the researchers. Neural network based computational models, which can describe the complex correlations between the decision parameters for processing and materials chemistry of such steels, were developed using published data and were used for fitness functions. Two individual strength ductility maximization problems of multi-criterion nature were formulated for both types of steels. GA was performed to both these problems separately. The non-dominated strength-ductility Pareto optimal fronts were developed and compared. The role of the decision variables that ultimately affects the final properties in Pareto optimally balanced strength and ductility, as well as their role for work hardening behavior were investigated for both materials.

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