Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process

Abstract

In this study, an artificial neural network (ANN)–based method is presented to predict the experimental effective demolding forces (EDFs) produced during the injection molding of a polycarbonate polymer material. To evaluate the prediction accuracy and capability of the proposed method, three different algorithms, namely Levenberg–Marquardt (lm), BGFS quasi-Newton (bfg), and scale conjugate gradient (scg), were included in the proposed model. The generated models were validated by comparing the experimental and ANN results, which showed good quantitative agreement. The performance of the algorithms was evaluated using the R² and root mean square error (RMSE) values, which indicated that scg exhibited the best performance with an R² of 0.9655 and an RMSE of 0.0223. The relative contribution plot of the ANN models showed that packing pressure had a stronger influence than mold temperature, filling time, and melt temperature. These results will form the basis for predicting the EDF of a comparable molded part using ANN and will help to significantly improve the demolding properties of polymer materials.