Hybrid model and method for determining manufacturing...

Details Hybrid model and method for determining manufacturing... Hybrid model and method for determining manufacturing...

2005-01-18

2005-01-18

Picard, Leo (Department: 2125)

Data processing: generic control systems or specific application

Specific application, apparatus or process

Product assembly or manufacturing

C700S029000, C700S048000, C706S023000, C706S904000

Reexamination Certificate

active

06845289

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.

REFERENCES:
patent: 5434783 (1995-07-01), Pal et al.
patent: 5671335 (1997-09-01), Davis et al.
patent: 5673368 (1997-09-01), Broese et al.
patent: 5796920 (1998-08-01), Hyland
patent: 5914884 (1999-06-01), Gur Ali et al.
patent: 6047221 (2000-04-01), Piche et al.
patent: 6600961 (2003-07-01), Liang et al.
patent: 20010051858 (2001-12-01), Liang et al.
patent: 195 14 535 (1996-10-01), None
patent: 197 43 600 (1999-04-01), None
Yarlagadda, P K D V “Prediction of processing parameters for injection moulding by using a hybrid neural network” Proc. of th Institution of Mechanical Engineers Part B Engineering Manufacture Oct. 16, 2001 vol. 215 Issue 10 pp. 1465-1470.*
Neural Networks, vol. 5, (month unavailable) 1992, pp. 139-144, Frank Bärmann and Friedrich Biegler-König, On a Class of Efficient Learning Algorithms for Neural Networks.
Psichogios D C et al: “Process Modeling Using Structured Neural Networks” Proceeding Of The American Control Conference (ACC). Chicago, Jun. 24-26, 1992, New York, IEEE, US, Bd. 3, Jun. 6, 1992, Seiten 1917-1921, XP000343627, ISBN: 0-7803-0210-9 *Seite 1918, linke Spalte, Zeile 19—rechte Spalte, Zeile 28.
Smith J et al: “An artificial nerual network model of certain aspects of fighter pilot cognition” Decision Aiding for Complex Systems. Charlottesville, VA., Oct. 13-16, 1991, Proceeding of the International Conference on Systems, Man And Cybernetics, New York, IEEE, US, Bd. 1_, Oct. 13, 1991, Seten 1545-1550, XP010054640 ISBN: 0-7803-0233-8 Seite 1546, rechte Spalte, Zeile 11—Zeile 32.

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