Optimizing injection molding parameters to minimize and prediction potential for flashing defects

Abstract

The injection molding process is a manufacturing process that can produce products in a short time in large quantities, in the injection molding process the factor of setting process parameters plays a significant role in product quality, so it requires special treatment. The purpose of this study is to find the optimal parameters in the injection moulding process of yogurth container lid with polypropylene material, so that the process can reduce the incidence of flashing defects that result in the emergence of initial waste in the industrial environment. The method used in this research is to create a Response Surface Methodology Box-Behnken Design (RSM - BBD) optimization model and an Artificial Neural Network (ANN) model approach in analyzing optimal parameters and predicting the appearance of flashing defects in a designed cycle. The results obtained from this research are the optimal parameters from the RSM and ANN model recommendations, namely the clamping force setting of 70 tons, holding time 0.1 seconds, and holding pressure. The ANN model provides the highest level of prediction accuracy with an R2 value of 100% and a prediction error rate of 7.9689E-09. In comparison, the RSM model obtains a prediction accuracy level with an R2 of 71% with an error rate of 0.24315.