A principal component analysis model-based predictive controller for controlling part warpage in plastic injection molding. Issue 6 (15th April 2015)

• Record Type: Journal Article
• Authors: Zhang, Songtao; Dubay, Rickey; Charest, Meaghan
• Is Part Of: Expert systems with applications. Volume 42:Issue 6(2015)
• Page Start: 2919
• Page End: 2927
• Publication Date: 2015-04-15
• Subjects: On-line quality control -- Data mining -- MPC -- Injection molding -- Mathematical modeling; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.11.030
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Quality measurement on injection molded parts are related to process parameter data. The data experimentation data is analyzed using statistical methods (PCA), (ICA) and (ANOVA). Models are constructed to predict a quality index based on data analysis results. A MPC scheme that allows for on-line quality control was developed using the models. The presented scheme is extendable to a variety of manufacturing processes. Abstract: Quality control is an important aspect of manufacturing processes. Product quality of injection molded parts is influenced by the injection molding process. In this study statistical tools were used to develop a model that relates injection molding process variables to part quality. A statistically based model predictive control algorithm was developed for controlling part quality with manipulated variables coolant flow rate and coolant temperature. This approach replaces the need of off-line quality measurement and provides real-time injection modeling quality control.