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Inverse estimation on elastic parameter of Particulate Reinforced Composites based on CAx

机译：基于CAx的颗粒增强复合材料弹性参数的反演。

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In this paper, General Regression Neural Network (GRNN) is introduced to establish inverse estimation method on elastic parameter for solid rocket motor (SRM) grain. Particulate Reinforced Composites (PRC) such as propellant has been applied widely in space launcher. Considering the fluctuating of components as well as the complexity of molding technology, dispersion of mechanics performance which is interesting for both designer and fabricant, is distinct for PRC vessel. Experiential expression can be formulated between capability and structual as well as performable parameters. However, batch diversity and single product's peculiarity was hardly obtained in manufactory. Basing displacement information from characteristic points, sample building method is put forward by finite element numerical experiment. Multidisciplinary software such as ABAQUS, EXCEL, are integrated in Matlab. After error evaluating, the inverse estimation method is performed to analyze vertical storage test of a specific grain. Consequently, the flexibility and versatility of the method are distinctly.
机译：本文采用广义回归神经网络（GRNN）建立固体火箭发动机（SRM）晶粒弹性参数的逆估计方法。推进剂等颗粒增强复合材料（PRC）已在航天发射器中得到广泛应用。考虑到组件的波动以及成型技术的复杂性，对于设计者和制造者而言，机械性能的分散对于PRC船舶而言是独特的。经验表达可以在能力，结构以及可执行参数之间制定。但是，在工厂中很难获得批次的多样性和单一产品的特殊性。基于特征点的位移信息，通过有限元数值实验提出了样本建立方法。 Matlab中集成了多学科软件，例如ABAQUS，EXCEL。在误差评估之后，执行逆估计方法以分析特定谷物的垂直存储测试。因此，该方法的灵活性和多功能性非常明显。

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