WARPAGE PREDICTION IN PLASTIC INJECTION MOLDED PART USING ARTIFICIAL NEURAL NETWORK

Abstract

The main objective of this paper is to predict the warpage of a circular injection moldedpart based on different processing parameters. The selected part is used as spacers in automotive,transmission, and industrial power generation industries. The second goal is facilitating the setupof injection molding machine without (any) need for trial and error and reducing the setup time. Tomeet these objectives, an artificial neural network (ANN) model was presented. This model iscapable of warpage prediction of injection molded plastic parts based on variable processparameters. Under different settings, the process was simulated by Moldflow and the warpage ofthe part was obtained. Initially, the effects of the melt temperature, holding pressure and the moldtemperature on warpage were numerically analyzed. In the second step, a group of data that hadbeen obtained from analysis results was used for training the ANN model. Also, another group ofdata was applied for testing the amount of ANN model prediction error. Finally, maximum error ofANN prediction was determined. The results show that the R-Squared value for data used fortraining of ANN is 0.997 and for the test data, is 0.995.