Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm

Abstract

Plastic injection processes comprise plastication, injection, packing, cooling, ejection and process/part quality control applications. These steps are followed for the parts, which are designed to be produced by plastic injection method. Having initial knowledge about the process that will be undertaken is necessary because of present-day competitive conditions that forces us to produce faster and cheaper with a higher quality, such as minimum warpage, sink marks, etc. Computer-aided analysis and engineering softwares are used in order to meet this necessity. For plastic injection process, one of the commercial computer-aided engineering softwares is the MoldFlow Plastic Insight. In this study, best gate location, filling and flow, warpage applications have done for minimum warpage of plastic part with this tool. Process parameters such as mold temperature, melt temperature, packing pressure, packing time, cooling time, runner type and gate location are considered as model variables. The effects of process parameters for thin shell plastic part were exploited using both design of experiment (DOE), Taguchi orthogonal array and finite element software MoldFlow (FE). The most important process parameters influencing warpage are determined using finite element analysis results based on analysis of variance (ANOVA) method. Artificial neural network (ANN) is interfaced with an effective GA to find the minimum warpage value.