Abstract: A storage device (100) for storing a sample body (200) is described, wherein the storage device (100) has an opening, wherein the opening comprises an upper region (101) and a lower region (103), wherein the opening is designed to ac-commodate the sample body (200), wherein the sample body (200) to be accom-modated has an upper part (201) and, along the axis of symmetry (202) thereof, a central part (203) adjoining the upper part (201), wherein the walls of the opening are designed such that, when the sample body (200) is accommodated, the central part (203) of the sample body (200) to be accommodated is located at a distance from the walls of the opening, perpendicularly to the axis of symmetry (202), wherein the walls of the upper region (101) form a lateral guide (105) for the upper part (20 I) of the sample body (200) in order to enable the upper part (20 I) of the sample body (200) to move along the axis of symmetry (202) of the sample body (200), wherein the lateral guide (105) is given by the walls of

Abstract: The invention relates to a method and a device for testing the fire hazard of a material. According to one embodiment of the method, a plane region of the surface of a specimen made of the material is brought in contact for at most a predetermined contact time with a glow wire, which has been heated to a predetermined temperature. Image data of the specimen are furthermore acquired by at least a first camera at least while the specimen is in contact with the glow wire. Image processing of the acquired image data of the specimen is furthermore carried out, preferably in realtime, ignition of the specimen by the glow wire being detected if applicable. A first duration is then determined, which corresponds to the length of time between the application of the tip of the glow wire on the specimen and the ignition of the specimen.

Abstract: The image data capture of a test piece is performed with at least one first camera. A reference point of the test piece is determined by processing the image data recorded with the at least one first camera. A burner is positioned at a prescribed first distance from the reference point for the flame exposure of the test piece. At least one second camera may also detect whether the test piece exposed to a flame is burning or incandescing. At least one third camera may be used for monitoring the burner flame and at least one fourth camera may be used for determining whether the test piece drips while burning. Furthermore, at least one detector may used for detecting whether the drips ignite a pad of wadding positioned under the test piece.

Abstract: The invention relates to a method and a device for testing the fire hazard of a material. According to one embodiment of the method, a plane region of the surface of a specimen made of the material is brought in contact for at most a predetermined contact time with a glow wire, which has been heated to a predetermined temperature. Image data of the specimen are furthermore acquired by at least a first camera at least while the specimen is in contact with the glow wire. Image processing of the acquired image data of the specimen is furthermore carried out, preferably in realtime, ignition of the specimen by the glow wire being detected if applicable. A first duration is then determined, which corresponds to the length of time between the application of the tip of the glow wire on the specimen and the ignition of the specimen.

Abstract: The image data capture of a test piece is performed with at least one first camera. A reference point of the test piece is determined by processing the image data recorded with the at least one first camera. A burner is positioned at a prescribed first distance from the reference point for the flame exposure of the test piece. At least one second camera may also detect whether the test piece exposed to a flame is burning or incandescing. At least one third camera may be used for monitoring the burner flame and at least one fourth camera may be used for determining whether the test piece drips while burning. Furthermore, at least one detector may used for detecting whether the drips ignite a pad of wadding positioned under the test piece.

Abstract: A method for controlling the quality of thermoplastic molding compositions in granular form is disclosed. The method entails obtaining a sample from a batch of granules, producing at least one transparent plastics article from the sample, examining the article for visible defects and determining, on the basis of the examination whether said article meets at least one predetermined quality acceptance criterion. Also disclosed is a device for carrying out the inventive method.

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 weathering a sample over a period of time, and measuring color shifts of the sample is described. The method includes: (a) weathering the sample over a first interval of time; (b) determining the color coordinates of the sample after the first interval of time; and (c) repeating steps (a) and (b) for at least one successive interval of time until the completion of the period of time. In an embodiment of the present invention, step (b) further includes: (i) measuring the color coordinates of the sample after the first interval of time; (ii) providing access to at least one color master having color coordinates, the access being obtained by means of the color coordinates of the sample; and (iii) comparing the color master with the sample for verification of the color coordinates.

Abstract: A network (in particular a neuronal network) which is used for determining at least one proportion of at least one component of a formulation, and subsequently for the production of a product (e.g., a molded article) having a pre-selected (or desired) color is described. The neuronal network comprises: (a) a neuronal network (or neuronal processor); (b) at least one of, (i) an input for inputting at least one color coordinate of a component of the formulation into the neuronal processor, and (ii) an input for inputting at least one preselected color coordinate of the product into the neuronal processor; and (c) an output for outputting a proportion of at least one component of the formulation from the neuronal processor. Inputs (b)(i) and (b)(ii), and output (c) are each independently connected (e.g., electronically and/or digitally) to the neuronal processor. Also described is a method of determining the proportions of components in a formulation for producing a product (e.g.

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.

Abstract: The present invention relates to thermoplastic graft polymer moulding compositions for high-gloss applications, which exhibit a reduced tendency of the additives to exude out of the thermoplastic composition during processing, and which exhibit reduced deposit build-up in the mould.

Abstract: Thermoplastic polycarbonate/graft polymer moulding compositions containingA) 5 to 95 parts by weight of an aromatic polycarbonate,B) 1 to 50 parts by weight of at least one thermoplastic homopolymer, copolymer or terpolymer of styrene, .alpha.-methylstyrene, acrylonitrile, N-substituted maleimide or mixtures thereof,C) 1 to 50 parts by weight of at least one graft polymer ofC.1) 5 to 90 parts by weight of styrene, .alpha.-methylstyrene, acrylonitrile, N-substituted maleimide or mixtures thereof, onC.2) 95 to 10 parts by weight of a rubber with a glass transition temperature .ltoreq.0.degree. C. andD) 1 to 25 parts by weight (per 100 parts by weight A+B+C) of at least one additive selected from flame retardants, lubricants, antistatic agent, mould release agents or mixtures thereof,characterised in that the component B) is produced by bulk, solution or suspension polymerisation and has an oligomer content .ltoreq.1 wt.

Abstract: The invention relates to polyarylene sulfides (PAS), preferably polyphenylene sulfide (PPS), reduced in viscosity by aftertreatment of the PAS formed in the production process with disulfides in the melt.

Abstract: Thermoplastic polyalkyleneterephthalate moulding compounds prepared by blendingA) 99.9 to 80.0% by weight of one or more thermoplastic polyalkyleneterephthalates andB) 0.1 to 20.0% by weight of an epoxy/isocyanurate resin in the melt.