Prediction of Quality Index of Injection-Molded Parts by Using Artificial Neural Networks

Abstract

Based on the deep analysis of injection molding process and factors influenced on the quality of injection-molded parts, and considering of the non-linear, multi-variable coupling of process and the problem of data deficiencies, a feed-forward three-layer neural network was presented to predict the part weight based on the melt temperature, holding pressure and holding time, which is the most important quality index of injection-molded parts. Furthermore, an architecture optimization approach for neural networks was used and the performance of ANN was evaluated and tested by its application to verification tests with process parameters randomly selected which all of them were not used in the network training. Results showed that the ANN predictions yield mean absolute percentage error (MAPE) in the range of 0.1％ and the maximum relative error (MRE) in the range of 0.47％ for the test data set, and which can comparatively accurately reflect the influence relation of the injection process parameters on part’s quality indexs under the circumstance of data deficiencies.