Performance Optimization of Extrusion Blow Molded Parts Using Fuzzy Neural-taguchi Method and Genetic Algorithm

This paper presents a soft computing strategy to determine the optimal die gap parison programming of extrusion blow molding process. The design objective is to minimize part weight subject to stress constraints. The finite-element software, BlowSim, is used to simulate the parison extrusion and the blow molding processes. However, the simulations are time consuming, and minimizing the number of simulation becomes an important issue. The proposed strategy, Fuzzy NeuralTaguchi and Genetic Algorithm (FUNTGA), first establishes a back propagation network using Taguchi’s experimental array to predict the relationship between design variables and response. Genetic algorithm is then applied to search for the optimum design of parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The Reliability Distance is proposed and introduced to genetic algorithm using fuzzy rules to modify the fitness function and thus improve search efficiency. This study uses ANSYS to find the stress distribution of blown parts under loads. The comparison of results demonstrates the effectiveness of the proposed strategy.