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

Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods

Minimization of warpage and sink index in injection-molded thermoplastic parts using Taguchi optimization method

Warpage and structural analysis of thin shell plastic in the plastic injection molding

In this paper, an effective optimization method using the Kriging model is proposed to minimize the warpage in injection molding. The warpage deformations are nonlinear, implicit functions of the process conditions, which are typically evaluated by the solution of finite element (FE) equations, a complicated task which often involves huge computational effort. The Kriging model can build an approximate function relationship between warpage and the process conditions, replacing the expensive FE reanalysis of warpage in the optimization. In addition, a “space-filing” sampling strategy for the Kriging model, named rectangular grid, is modified. Moldflow Corporation’s Plastics Insight software is used to analyze the warpage deformations of the injection-molded parts. As an example, the warpage of a cellular phone cover is investigated, where the mold temperature, melt temperature, injection time, and packing pressure are regarded as the design variables. The result shows that the proposed optimization method can effectively decrease the warpage deformations of the cellular phone cover and that the injection time has the most important influence on warpage in the chosen range.

An effective warpage optimization method in injection molding based on the Kriging model

Optimal process conditions of thin-wall injection molding of a cellular phone cover were investigated with the consideration of interaction effects between process parameters. L27 experimental tests based on Taguchi's method were performed, and then Cyclone Scanner, PolyCAD and PolyWorks were used to measure the shrinkage and warpage of the thin-wall injected parts to determine the optimal process conditions. Based on the results of the analysis of variables and the F-test, interaction effects for each observed factor were determined. The results indicated that the packing pressure was the most important process parameter affecting the shrinkage and warpage of the thin-wall part. The optimal process conditions were different for the shrinkage and the warpage. This was because during the injection process, the mechanisms affecting shrinkage or warpage were different. Compared with the results obtained with simplified thin-wall parts in the literature, it was found that the geometry of a real commercial part did affect the optimal process conditions and the order of influence of process parameters. The optimal process conditions determined by Taguchi's method for reducing the shrinkage and warpage were verified experimentally in this work. Polym. Eng. Sci. 44:917–928, 2004. © 2004 Society of Plastics Engineers.

Optimal process conditions of shrinkage and warpage of thin-wall parts

This study demonstrates the successful use of back-propagation artificial neural networks (BPANNs) in predicting the shrinkage and warpage of injection-molded thin-wall parts. The effects of structural parameters of a BPANN on the predictionaccuracy and the capability of a BPANN in determining the optimal process condition are also discussed. The training and testing data are obtained experimentally based on a Taguchi L27 (313) test schedule. The results show that the trained BPANN can successfully predict the shrinkage and warpage of injection-molded thin-wall parts. Comparing the prediction accuracies of the trained BPANN and C-Mold software, it is noted that the trained BPANN predicts more accurately. In terms of determining the optimal process condition for minimizing the shrinkage and warpage of injected thin-wall parts, the trained BPANN is also shown to give a better optimal process condition than Taguchi's method. Polym. Eng. Sci. 44:2029–2040, 2004. © 2004 Society of Plastics Engineers.

Y. C. Su

Papers

Optimal process conditions of shrinkage and warpage of thin-wall parts

Shrinkage and warpage prediction of injection-molded thin-wall parts using artificial neural networks