Online pattern‐based part quality monitoring of the injection molding process

Abstract

AbstractThe quality of injection molded parts is currently monitored in the plant using techniques that focus on the statistical analysis of discrete data and, in particular, peak values. This paper presents an alternative online technique for part quality monitoring that focuses on the analysis of complete data patterns. Specifically, this paper discusses the application of artificial neural networks (ANNs) as part quality monitoring tools. The method of approach is to train a back propagation network (BPN) to associate part quality with the corresponding data pattern produced during injection. In Part I of this work, the data pattern consists of a series of discrete values and the part quality measure is defined as part weight. In Part II, the data pattern is the measurement profile observed from a pressure sensor placed in the mold cavity and the part quality measure is defined as part length. Results show that ANNs are successful in predicting part quality based on data patterns when an entire sensor profile is analyzed. Furthermore, demonstrations show that the approach is superior in predicting part quality when compared to statistical techniques now widely practiced by the injection molding process industry.