Quality Control in Injection Molding based on Norm-optimal Iterative Learning Cavity Pressure Control

IFAC-PapersOnLine, vol. 53, pp. 10380–10387

Abstract

Plastic injection molding is characterized by high design flexibility of the manufactured parts. Consequently, it is one of the most important processes for mass production of plastic parts. The setup of the manufacturing process is very complex due to numerous impact factors. In addition, material fluctuations or changing ambient conditions require the adaption of the setup during manufacturing to guarantee a constant product quality. In order to reduce the setup effort and to control the quality, the concept of model-based self-optimization is applied to injection molding. Therefore, a model-based Norm-Optimal Iterative Learning Controller (NOILC) is used to track a desired reference for the cavity pressure during the entire cycle. This reference is determined by the so-called pvT-optimization which considers the cooling behavior of the melt within the cavity. It is shown by experiments that the cavity pressure can be controlled with high accuracy using the presented NOILC. Furthermore, the accuracy of the quality, especially the part weight is improved by combining the NOILC with an additional pvT-optimization.

Authors 7

1. Sebastian Stemmler corresponding Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Germany, 52062 Aachen

2. Marko Vukovic Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Germany, 52062 Aachen

3. Muzaffer Ay Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Germany, 52062 Aachen

4. Julian Heinisch Aachen Institute for Plastics Processing (IKV)

   RWTH Aachen University

   Affiliation as printed

   Institute for Plastics Processing at RWTH Aachen University, 52062 Aachen

5. Yannik Lockner Aachen Institute for Plastics Processing (IKV)

   RWTH Aachen University

   Affiliation as printed

   Institute for Plastics Processing at RWTH Aachen University, 52062 Aachen

6. Dirk Abel Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Germany, 52062 Aachen

7. Christian Hopmann Aachen Institute for Plastics Processing (IKV)

   RWTH Aachen University

   Affiliation as printed

   Institute for Plastics Processing at RWTH Aachen University, 52062 Aachen