Patents by Inventor Martin Wanders

Martin Wanders has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210008849
    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.
    Type: Application
    Filed: July 8, 2020
    Publication date: January 14, 2021
    Applicant: LANXESS Deutschland GmbH
    Inventors: Martin Wanders, Stefan Seidel, Lukas Schroer
  • Publication number: 20160090481
    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.
    Type: Application
    Filed: November 17, 2015
    Publication date: March 31, 2016
    Applicant: LANXESS DEUTSCHLAND GMBH
    Inventors: Jochen Endtner, Matthias Bienmuller, Martin Wanders
  • Publication number: 20090234051
    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.
    Type: Application
    Filed: October 12, 2006
    Publication date: September 17, 2009
    Inventors: Jochen Endtner, Matthias Bienmüller, Martin Wanders
  • Patent number: 7058618
    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.
    Type: Grant
    Filed: March 27, 2002
    Date of Patent: June 6, 2006
    Assignee: Bayer Aktiengesellschaft
    Inventors: Roland Loosen, Thomas Mrziglod, Martin Wanders, Klaus Salewski, Bahman Sarabi
  • Patent number: 6845289
    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.
    Type: Grant
    Filed: April 22, 2002
    Date of Patent: January 18, 2005
    Assignee: Bayer Aktiengesellschaft
    Inventors: Klaus Salewski, Thomas Mrziglod, Martin Wanders, Roland Loosen, Jürgen Flecke, Bahman Sarabi
  • Patent number: 6839608
    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.
    Type: Grant
    Filed: April 22, 2002
    Date of Patent: January 4, 2005
    Assignee: Bayer Aktiengesellschaft
    Inventors: Bahman Sarabi, Thomas Mrziglod, Klaus Salewski, Roland Loosen, Martin Wanders
  • Patent number: 6687624
    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.
    Type: Grant
    Filed: March 27, 2002
    Date of Patent: February 3, 2004
    Assignee: Bayer Aktiengesellschaft
    Inventors: Bahman Sarabi, Martin Wanders, Dietmar Wächtler, Andreas Wende
  • Publication number: 20030050728
    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.
    Type: Application
    Filed: April 22, 2002
    Publication date: March 13, 2003
    Inventors: Bahman Sarabi, Thomas Mrziglod, Klaus Salewski, Roland Loosen, Martin Wanders
  • Publication number: 20030014152
    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.
    Type: Application
    Filed: April 22, 2002
    Publication date: January 16, 2003
    Inventors: Klaus Salewski, Thomas Mrziglod, Martin Wanders, Roland Loosen, Jurgen Flecke, Bahman Sarabi
  • Publication number: 20020178832
    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.
    Type: Application
    Filed: March 27, 2002
    Publication date: December 5, 2002
    Inventors: Bahman Sarabi, Martin Wanders, Dietmar Wachtler, Andreas Wende
  • Publication number: 20020152426
    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.
    Type: Application
    Filed: March 27, 2002
    Publication date: October 17, 2002
    Inventors: Roland Loosen, Thomas Mrziglod, Martin Wanders, Klaus Salewski, Bahman Sarabi