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Optimizing process robustness in Injection Moulding

In Injection Molding the part quality is defined by many different machine parameters. The complexity of interactions and effects of these parameters on the part quality cannot be calculated precisely but only as tendencies. So only with experiments quality characteristics e.g. dimensions, surface roughness, mechanical characteristics can only be analysed by experimental runs. In the thesis different systematic approaches of process analysis and optimization are discussed. These are the conventional Design of Experiments (DOE) but also methods of Taguchi and Shainin. On the base of a comination of these methods an optimizing procedure is proposed and tested, modified for the special requirements of the injection molding process. It is shown that the process robustness can depend strongly on the setting of the machine parameters. So using this procedure enables to find a machine setting which make sure that the part quality is robust against process distortion, e.g. variation of the material lot quality. Also the variation of the part quality characteristics can be reduced systematically. This is shown for parts dimensions and mechanical characteristics. Inner stress in parts of amorphous thermoplastic materials can also be reduced. A relation to the inner characteristics like cristalinity distribution or DSC characteristics could not be found.

Lesen Sie die deutsche Zusammenfassung auf Kunststoffe.de
Author

Rainer Bourdon
Lehrstuhl für Kunststofftechnik (LKT)
Universität Erlangen-Nürnberg

Information

Free keywords: Injection Molding, quality, DOE (Design of Experiments), quality optimization, process optimization, process robustness, Taguchi, Shainin
Institute / chair: Technische Fakultät der Universität Erlangen-Nürnberg
Language: German
Technical consultant for expert services: Prof. Dr.-Ing. Dr. h.c. Gottfried W. Ehrenstein (Betreuer), Prof. Dr.-Ing. Dieter Dengel, Prof. Dr.-Ing. Johannes Wortberg
Publication year: 1994
Provider: Technische Fakultät der Universität Erlangen-Nürnberg

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