Metaheuristic multiobjective optimization in steel welds
Strength and toughness of steel welds have always been considered as inversely related. This contradicting combination of these properties has made it difficult for designers to achieve an optimum combination. Optimizing the control variables like chemistry and process parameters for obtaining highest combination of strength and toughness is the target of this research. Nondominated Sorting Genetic Algorithms (NSGA) was used to arrive at the pareto-front. It was observed that for the interpass temperatures 150, 200, and 250°C the manganese concentration was decreased while increasing the nickel concentrations. However, for interpass temperatures greater than 200°C, the concentration of manganese was increased with decrease in nickel concentration, as interpass temperatures raised. An empirical relation between the martensite start temperatures and the interpass temperature was derived to highlight how the algorithm has moved the chemistry to obtain the optimum combination of strength and toughness.
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