Abstract: The present invention relates to door modules comprising at least one single-layer, continuous fiber-reinforced semifinished fiber matrix product which is obtained by impregnating semifinished fiber products with at least one thermoplastic, and to the use thereof in vehicles or in buildings.

Abstract: The present invention relates to door modules comprising at least one single-layer, continuous fiber-reinforced semifinished fiber matrix product which is obtained by impregnating semifinished fiber products with at least one thermoplastic, and to the use thereof in vehicles or in buildings.

Abstract: The present invention relates to a multilayer composite material, to a process for the production thereof and to a housing part or a housing of an electronic device comprising such a multilayer composite material.

Abstract: The present invention relates to halogen-free flame-retardants for thermoplastic polyesters with UL 94 V-0 classification and with particularly good mechanical properties and high tracking resistance.

Abstract: The present invention relates to halogen-free flame-retardants for thermoplastic polyesters with UL 94 V-0 classification and with particularly good mechanical properties and high tracking resistance.

Abstract: A stress/strain curve is established by means of neural networks 1 to N and 4. To that end, parameters are input into the input 50, from which the neural networks 1 to N respectively establish the principal components of characteristic points. The curve type is selected on the basis of the output of the neural network 4. The principal components of the characteristic points of the corresponding curve type are then inverse-transformed. The stress/strain curve is then calculated by the generator 59 on the basis of the inverse transformation.

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.

Abstract: A method of predicting the properties (e.g., mechanical and/or processing properties) of an injection-molded article is disclosed. The method makes use of a hybrid model which includes at least one neural network. In order to forecast (or predict) properties with respect to the manufacture of a plastic molded article, a hybrid model is used in the present invention, which includes: one or more neural networks NN1, NN2, NN3, NN4, . . . , NNk; and optionally 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 networks are used to map processes whose relationship is present only in the form of data, as it is in effect impossible to model such processes rigorously. As a result, a forecast relating to properties including the mechanical, thermal and rheological processing properties and relating to the process time of a plastic molded article is obtained.

Abstract: The relationship between the stress &sgr; and the strain &egr; is firstly established in step 100 with short-term tests as a function of the temperature T. In steps 101 to 104, a Findley model is extended in such a way as to obtain a relationship between the strain &egr; and the stress &sgr; as a function of the time t and the temperature T. The two models are combined in steps 105 and 106, so as to obtain overall a relationship between the stress &sgr; and the strain &egr; as a function of the time t and the temperature T.

Abstract: A method of predicting the properties (e.g., mechanical and/or processing properties) of an injection-molded article is disclosed. The method makes use of a hybrid model which includes at least one neural network. In order to forecast (or predict) properties with respect to the manufacture of a plastic molded article, a hybrid model is used in the present invention, which includes: one or more neural networks NN1, NN2, NN3, NN4, . . . , NNk; and optionally 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 networks are used to map processes whose relationship is present only in the form of data, as it is in effect impossible to model such processes rigorously. As a result, a forecast relating to properties including the mechanical, thermal and Theological processing properties and relating to the process time of a plastic molded article is obtained.

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.

Abstract: The relationship between the stress &sgr; and the strain &egr; is firstly established in step 100 with short-term tests as a function of the temperature T. In steps 101 to 104, a Findley model is extended in such a way as to obtain a relationship between the strain &egr; and the stress &sgr; as a function of the time t and the temperature T. The two models are combined in steps 105 and 106, so as to obtain overall a relationship between the stress &sgr; and the strain &egr; as a function of the time t and the temperature T.

Abstract: A stress/strain curve is established by means of neural networks 1 to N and 4. To that end, parameters are input into the input 50, from which the neural networks 1 to N respectively establish the principal components of characteristic points. The curve type is selected on the basis of the output of the neural network 4. The principal components of the characteristic points of the corresponding curve type are then inverse-transformed. The stress/strain curve is then calculated by the generator 59 on the basis of the inverse transformation.