Hybrid model and method for determining manufacturing properties of an injection-molded part

US6845289B2 · kind B2 · utility

Assignee

• BAYER HEALTHCARE LLC · DE

Inventors

• Klaus Salewski · Greifswald, DE
• Thomas Mrziglod · Wittlich, DE
• Martin Wanders · Neschen, DE
• Roland Loosen · Lechenich, DE
• Jürgen Flecke · Dormagen, DE
• Bahman Sarabi · Krefeld, DE

Key dates

• Filing date: Apr 22, 2002
• Grant date: Jan 18, 2005
• Expiry date: Nov 7, 2022

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y10S706/904
• WIPO field: Control
• WIPO sector: Instruments

Abstract

A method of determining properties relating to the manufacture of an injection-molded article is described. The method makes use of a hybrid model which includes at least one neural network and at least one rigorous model. In order to forecast (or predict) properties relating to the manufacture of a plastic molded part, a hybrid model is used which includes: one or more neural networks NN1, NN2, NN3, NN4, . . . , NNk; and one or more rigorous models R1, R2, R3, R4, . . . , which are connected to one another. The rigorous models are used to map model elements which can be described in mathematical formulae. The neural model elements are used to map processes whose relationship is present only in the form of data, as it is typically impossible to model such processes rigorously. As a result, a forecast (or prediction) relating to properties including, for example, the mechanical, thermal and rheological processing properties and relating to the cycle time of a plastic molded part can be made.