Cycle‐based Adaption of a Model‐Predictive Control Strategy for Injection Molding Machines. Issue 1 (18th November 2019)

• Record Type: Journal Article
• Authors: Froehlich, Christoph; Kemmetmüller, Wolfgang; Kugi, Andreas
• Other Names: Eberhardsteiner J. guestEditor.; Schöberl M. guestEditor.
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 19:Issue 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-18
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.201900317
• Languages: English
• ISSNs: 1617-7061

Abstract

Abstract: Injection molding is one of the most important manufacturing processes in the polymer industry. It is a cyclic process, where the phases of the process are repeated for each produced part. An accurate control of the process variables (e.g. the injection pressure and speed) to their desired values is essential to obtain a constant high product quality. In a recent publication [5], it was shown that the control accuracy can be significantly improved compared to the state of the art by means of a nonlinear model‐predictive control (MPC) strategy. Thereby, an essential point to achieve a high control accuracy is the estimation of the (bascially unknown) volume flow into the mold. In the present work, the benefits of utilizing the measurement information of the previous and the current cycles are examined. An estimator concept is presented which combines the advantages of the high accuracy of an offline estimation with the capability to account for variations of the injection process by an online estimation. The improved control accuracy of the proposed method is shown by simulation studies.