利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

15 条记录 (耗时 0.016 秒)

    Method and computer system for establishing a relationship between a stress and a strain
    1.
    发明授权
    Method and computer system for establishing a relationship between a stress and a strain 有权
    建立应力与应变关系的方法和计算机系统

    公开(公告)号：US06687624B2

    公开(公告)日：2004-02-03

    申请号：US10108150

    申请日：2002-03-27

    申请人： Bahman Sarabi ,  Martin Wanders ,  Dietmar Wächtler ,  Andreas Wende

    发明人： Bahman Sarabi ,  Martin Wanders ,  Dietmar Wächtler ,  Andreas Wende

    IPC分类号： G01L100

    CPC分类号： G06F17/5018 ,  G01N2203/0214 ,  G06F2217/80

    摘要： 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.

    摘要翻译： 应力σ和应变ε之间的关系首先在步骤100中建立，其中短期测试作为温度T的函数。在步骤101至104中，将Findley模型扩展为获得 应变ε和应力σ作为时间t和温度T的函数。两个模型在步骤105和106中组合，以便总体上获得应力σ和应变ε之间的关系作为函数 时间t和温度T.

    Hybrid model and method for determining manufacturing properties of an injection-molded part
    2.
    发明授权
    Hybrid model and method for determining manufacturing properties of an injection-molded part 有权
    用于确定注射成型部件的制造性能的混合模型和方法

    公开(公告)号：US06845289B2

    公开(公告)日：2005-01-18

    申请号：US10127367

    申请日：2002-04-22

    申请人： Klaus Salewski ,  Thomas Mrziglod ,  Martin Wanders ,  Roland Loosen ,  Jürgen Flecke ,  Bahman Sarabi

    发明人： Klaus Salewski ,  Thomas Mrziglod ,  Martin Wanders ,  Roland Loosen ,  Jürgen Flecke ,  Bahman Sarabi

    IPC分类号： B29C45/76 ,  G05B13/02 ,  G05B17/02 ,  B29C45/00

    CPC分类号： B29C45/76 ,  B29C2945/7604 ,  B29C2945/76066 ,  B29C2945/7611 ,  B29C2945/76257 ,  B29C2945/76381 ,  B29C2945/76414 ,  B29C2945/76979 ,  G05B13/027 ,  G05B17/02 ,  Y10S706/904

    摘要： 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.

    摘要翻译： 描述了确定与注射成型制品的制造有关的性能的方法。 该方法利用包括至少一个神经网络和至少一个严格模型的混合模型。 为了预测（或预测）与塑料模制部件的制造有关的属性，使用混合模型，其包括：一个或多个神经网络NN1，NN2，NN3，NN4。 。 。 ，NNk; 和一个或多个严格的型号R1，R2，R3，R4。 。 。 ，它们彼此连接。 严格的模型用于绘制可以用数学公式描述的模型元素。 神经模型元素用于映射其关系仅以数据形式存在的过程，因为通常不可能严格地对这些过程进行建模。 因此，可以进行与包括例如机械，热和流变加工性质以及与塑料模制部件的循环时间有关的特性的预测（或预测）。

    Hybrid model and method for determining mechanical properties and processing properties of an injection-molded part
    4.
    发明授权
    Hybrid model and method for determining mechanical properties and processing properties of an injection-molded part 有权
    用于确定注塑部件的机械性能和加工性能的混合模型和方法

    公开(公告)号：US06839608B2

    公开(公告)日：2005-01-04

    申请号：US10127280

    申请日：2002-04-22

    申请人： Bahman Sarabi ,  Thomas Mrziglod ,  Klaus Salewski ,  Roland Loosen ,  Martin Wanders

    发明人： Bahman Sarabi ,  Thomas Mrziglod ,  Klaus Salewski ,  Roland Loosen ,  Martin Wanders

    IPC分类号： B29C45/76 ,  G05B13/02 ,  G05B17/02 ,  B29C45/00

    CPC分类号： G05B13/027 ,  B29C45/76 ,  B29C2945/76006 ,  B29C2945/7602 ,  B29C2945/761 ,  B29C2945/76153 ,  B29C2945/76257 ,  B29C2945/7629 ,  B29C2945/76505 ,  B29C2945/76531 ,  B29C2945/76979 ,  B29C2945/76989 ,  G05B17/02

    摘要： 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.

    摘要翻译： 公开了一种预测注射成型制品的性能（例如机械和/或加工性能）的方法。 该方法利用包括至少一个神经网络的混合模型。 为了预测（或预测）关于塑料模制品的制造的性质，本发明使用混合模型，其包括：一个或多个神经网络NN1，NN2，NN3，NN4，...。 。 。 ，NNk; 和可选的一个或多个严格的模型R1，R2，R3，R4，...。 。 。 ，它们彼此连接。 严格的模型用于绘制可以用数学公式描述的模型元素。 神经网络用于映射只存在于数据形式中的关系的进程，因为实际上不可能严格地对这些进程进行建模。 结果，获得了关于包括机械，热和流变加工性能以及涉及塑料模塑制品的加工时间的性能的预测。