Feasible pressure and axial feed path determination for fuel filler tube hydroforming by genetic algorithm

Abstract

Successful fuel filler tube hydroforming largely depends on proper loading paths, that is, application of internal pressure and axial feeding during the forming time duration. Generally, two part quality criteria are considered in selecting the feasible loading paths: (a) minimum part wall thinning and (b) part wrinkle free. Due to the highly nonlinear nature of the tube hydroforming process, iterative finite element analyses with adjustments based on forming experience are typically conducted to design the loading paths. In this research, genetic algorithm was integrated into the finite element analysis–based optimization, resulting in enhanced determination of the feasible loading paths. Genetic algorithm is a heuristic search based on mechanics of natural selection. A pair of pressure and axial feeding profiles was represented by connecting genes making up to be a chromosome. In each search, mutation and crossover operations generated a new generation of chromosomes. Fitness functions were formulated to assess performance of the chromosomes reflecting the part quality. Generations after generations, the optimal chromosomes are found only when the evaluated fitness function value falls within a user-defined tolerance. Unlike the typical iterative finite element analysis approach, it was shown that the iterative finite element analysis augmented with genetic algorithm was able to determine feasible pressure and axial feeding paths autonomously. The current approach still requires a lot of simulation runs, which must be offset by high-performance computing resources.