Abstract
Nowadays significant part of plastic and, in particular, thermoplastic products of different sizes is manufactured using injection molding process. Due to the complex nature of changes that thermoplastic materials undergo during different stages of the injection molding process, it is critically important to control parameters that influence final part quality. In addition, injection molding process requires high repeatability due to its wide application for mass-production. As a result, it is necessary to be able to predict the final product quality based on critical process parameters values. The following paper investigates possibility of using Artificial Neural Networks (ANN) and, in particular, Multilayered Perceptron (MLP), as well as Decision Trees, such as J48, to create models for prediction of quality of dog bone specimens manufactured from high density polyethylene. Short theory overview for these two machine learning methods is provided, as well as comparison of obtained models’ quality.