Comparison of multivariate analysis techniques in plastic injection moulding process

Abstract

In this paper, we present a comparison between several statistical discriminant analysis techniques applied to a plastic injection moulding process for monitoring quality of injected moulded parts. Comparison among different ways of training the system can provide useful conclusions about the behaviour of the different models in poor conditions. The goal of this paper is to establish a baseline for comparing the performance between different algorithms. A wide variety of research objectives throughout the literature makes it difficult to provide a feasible comparison between results. The evaluation is intended to provide detailed, empirical information on the effectiveness and impact of different model parameters on the performance of the different approaches. The pros and cons of the approaches used are discussed. In order to predict the quality of a plastic part, we extract a set of salient features that characterise an injection cycle and then match these features against a database of stored examples of predefined classes by using supervised classification. The database was created from 199 real plastic injections without any overlap between training and testing datasets.