Use of genetic algorithms to optimize gate and vent locations for the resin transfer molding process

Abstract Resin infusion processes are finding increasing applications in the manufacture of composite parts that have geometric and material complexities. In such cases, the placement of gates and vents is non‐intuitive and may require expensive repetitive experimentation. Finite element based resin flow simulation codes have been successfully used for modeling and analysis of the mold filling process. Such filling simulations, when coupled with a search algorithm, can also prove useful for optimal design of the filling process. Genetic algorithms (GAs) mimic natural selection and can efficiently “evolve” near‐global optimal solutions from a large number of alternative solutions. In this paper, GAs are used to optimize gate and vent locations for the resin transfer molding process in order to minimize fill times and dry spot formation. A process performance index or cost function is defined that incorporates the fill time and dry spot formation as primary variables. A part having material and geometric complexities was chosen for a case study. A genetic algorithm and mold filling simulations were used interactively to search for optimal gate and vent locations to locate near optimal solutions. The GA was able to find good solutions using less than 1% of simulations of the possible permutations of gates and vents. The case study was also repeated in the presence of recetracking channels. Again, the optimal locations were found by the GA using less than 1% of all possible combinations. Thus, GAs can be efficiently used for minimization of fill time and dry spot formation through optimal location of gates and vents in RTM processes. However, the optimal location will be a function of the cost function, the choice of which depends on the trade‐offs between different factors and the quality of the part desired.