US 6,845,289 B2 · Hybrid model and method for determining manufacturing properties of an injection-molded part

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.

Claims18

Dependent claims

A method of determining properties relating to the manufacture of an injection-molded part comprising:

(a) providing a hybrid model comprising,
(i) a first input (1) for inputting parameters,
(ii) at least one neural network (NN1, . . . NNk) having an input for inputting at least one of said parameters into the neural network, and having a first output for outputting a forecast value,
(iii) at least one rigorous model (R1, R2 , R3;
R4, R5, . . . ) having a second input for inputting at least one of said parameters and said forecast value, and a second output for outputting a calculated value relating to the properties,
(iv) a means for mapping an input vector of parameters (P1, P2, P3, P4, P5 . . . P1) onto a subvector (PN1, PN2, PN3 . . . . PN1), and
(v) a means (4) for mapping the subvector onto corresponding inputs of the neural networks;
(b) selecting said parameters;
(c) inputting said parameters into said hybrid model;
and
(d) obtaining at least one of said forecast value and said calculated value relating to at least one of said properties.

8 more elements

A method of determining properties relating o the manufacture of an injection-molded part comprising:

(a) providing a hybrid model comprising,
(i) a first input (1) for inputting parameters,
(ii) at least one neural network (NN1, . . . NNk) having an input for inputting at least one of said parameters into the neural network, and having a first output for outputting a forecast value, and
(iii) at least one rigorous model (R1, R2, R3;
R4, R5, . . . having a second input for inputting at least one of said parameters and said forecast value, and a second output for outputting a calculated value relating to the properties,
(b) selecting said parameters;
(c) inputting said parameters into said hybrid model;
and
(d) obtaining at least one of said forecast value and said calculated value relating to at least one of said properties
wherein each rigorous model independently outputs at least one calculated value relating to at least one of the following properties:
cycle time, mold-filling time, injection time, metering time, cooling time, customer-specific filling pressure, Carreau melt viscosity, effective thermal conductivity, modulus of transverse elasticity, transverse tearing stress, and transverse breaking strain.

8 more elements

Dependencies are inferred from the claim text.

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

Life of this patent

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

Life of this patent

Abstract

Claims18