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公开(公告)号：US20090234051A1
公开(公告)日：2009-09-17
申请号：US12083185
申请日：2006-10-12
CPC分类号： C08L67/00 , B29C45/0001 , C08K3/013 , C08K3/22 , C08K3/24 , C08K3/30 , C08K5/34928 , C08K5/5313 , C08L67/02 , C08L2666/18
摘要： 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.
摘要翻译： 本发明涉及具有UL 94 V-0分类且具有特别好的机械性能和高跟踪阻力的热塑性聚酯的无卤阻燃剂。
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2.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.
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公开(公告)号：US07058618B2
公开(公告)日：2006-06-06
申请号：US10107762
申请日：2002-03-27
IPC分类号： G06N3/02
CPC分类号： G01N3/08 , G01N2203/0017 , G01N2203/0067 , G01N2203/0075 , G01N2203/0218 , G06N3/0454
摘要： 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.
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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
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。
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5.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
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）。
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