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Kunststoffe international 2018/06-07

Process Set-Up through Machine Learning

Purposefully Selected Injection Molding Parameters from Simulations and Practical Experiments

Process Set-Up through  Machine Learning

Machine learning methods have great potential in production – however, so far only a few concrete application examples exist. For the process set-up in injection molding, machine learning enables objective and specifically optimized parameter settings. Neural networks and a combination of simulations and practical experiments help to find suitable models for the parameter optimization as quickly as possible and independently of the experience of the operator.

Prof. Dr.-Ing. Christian Hopmann; Dr.-Ing. Matthias Theunissen; Jens Wipperfürth, M.Sc.,; Julian Heinisch, M.Sc.,

Beitrag auf Deutsch lesen
These articles might be interesting for you

1 C. Fernandes, A.J. Pontes, J.C. Viana, A. Gaspar-Cunha: Modeling and Optimization of the Injection-Molding Process: A Review. In: Advances in Polymer Technology 21683 (2016), pp. 1-21

2 S. Kashyap, D. Datta: Process parameter optimization of plastic injection molding: A review. In: International Journal of Plastics Technology 19 (2015), pp. 1-18

3 M. Bichler: Prozessgrößen beim Spritzgießen. Beuth Verlag: Berlin, Vienna, Zürich, 2012, ISBN 978-3-410-22778-6

4 N.C. Fei, N.M. Mehat, S. Kamaruddin: Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review. In: ISRN Industrial Engineering Vol. 2013 (2013), pp. 1-11

5 O. Schnerr-Häselbarth: Automatisierung der Online-Qualitätsüberwachung beim Kunststoffspritzgießen. Dissertation, 2000

6 P. Liedl, M. Friede, D. Fick: Softwaregestützte Optimierung des Prozessfensters bei attributiven Qualitätsmerkmalen. In: Kunststoffe 105 (2015), pp. 40-42

7 J. Schultz, in: VDI (Ed.), VDI-Berichte 1282, GMA-Kongress ‘96, Mess- und Automatisierungstechnik, VDI-Verlag, Düsseldorf, 1996, pp. 733-742

8 M. Fasching, G. Berger, W. Friesenbichler , P. Filz, B. Helbich: Robust process control for rubber injection moulding with use of systematic simulations and improved material data. In: International Polymer Science and Technology 41 (2014), pp. 640-644

9 F. Shi, Z.L. Lou, J.G. Lu, Y.Q. Zhang: Optimisation of Plastic Injection Moulding Process with Soft Computing. In: The International Journal of Advanced Manufacturing Technology 21 (2003), pp. 656-661

10 C. Hopmann, M. Theunissen, J. Heinisch: Von der Simulation in die Maschine - Objektivierte Prozesseinrichtung durch maschinelles Lernen, VDI Jahrestagung Spritzgießen, Baden-Baden, 2018

11 C. Hopmann, J. Wahle, M. Theunissen, J. Heinisch, P. Bibow, N. Lammert, F. Kessler: Flexibilisierung der Spritzgießfertigung durch Digitalisierung, Umdruck zum 29. Internationalen Kolloquium Kunststofftechnik, Aachen, 2018

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Institut für Kunststoffverarbeitung IKV in Industrie und Handwerk an der RWTH Aachen

Seffenter Weg 201
DE 52074 Aachen
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Fax: 0241 80-92262

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International Polymer Processing

International Polymer Processing, the journal of the Polymer Processing Society, is a discussion forum for the world-wide community of engineers and scientists in the field of polymer processing.

The journal covers research and industrial application in the very specific areas of designing polymer products, processes, processing machinery and equipment.

International Polymer Processing

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