Design optimization using soft computing techniques for extrusion blow molding processes

The control of thickness distribution in extrusion blow molded parts is critical to assure product quality and reduce manufacturing cost. This study applies the soft computing strategy to determine the optimal die gap in the parison programming of extrusion blow molding process. Two types of optimization problem are addressed in this study. The process optimization objective is obtaining a uniform thickness of blown parts, and the design optimization objective is minimizing part weight subject to stress constraints. The finite element software, BlowView, 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 Neural-Taguchi and Genetic Algorithm (FUNTGA), first establishes a back propagation network using Taguchi's experimental array to predict the relationship between the design variables and the 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 the Genetic Algorithm using fuzzy rules to modify the fitness function and thus improve search efficiency. The design of a HDPE bottle is used to illustrate the application of the process optimization, and the design of a gas tank is used to illustrate the application of performance. The design optimization uses ANSYS to find the stress distribution of blown parts under loads. The comparison of results using the optimization module of BlowView, Taguchi, and FUNTGA demonstrates the effectiveness of the proposed strategy.Peer reviewed: YesNRC publication: Ye