Genetic Algorithm based Multi-Objective Optimization of Solidification in Die Casting using Deep Neural Network as Surrogate Model

In this paper, a novel strategy of multi-objective optimization of die casting is presented. The cooling of molten metal inside the mold is achieved by passing a coolant, typically water through the cooling lines in the die. Depending on the cooling line location, coolant flow rate and die geometry, nonuniform temperatures are imposed on the molten metal at the mold wall. This boundary condition along with the initial molten metal temperature affect the product quality quantified in terms of micro-structure parameters and yield strength. A finite volume based numerical solver is used to correlate the inputs to outputs. The objective of this research is to estimate the initial and wall temperatures so as to optimize the product quality. The non-dominated sorting genetic algorithm (NSGA--II) which is popular for solving multi-objective optimization problems is used. The number of function evaluations required for NSGA--II can be of the order of millions and hence, the finite volume solver cannot be used directly for optimization. Thus, a neural network trained using the results from the numerical solver is used as a surrogate model. Simplified versions of the actual problem are designed to verify results of the genetic algorithm. An innovative local sensitivity based approach is used to rank the final Pareto optimal solutions and choose a single best design.