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Heterogeneous model integration of complex mechanical parts based on semantic feature fusion

机译：基于语义特征融合的复杂机械零件异构模型集成

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摘要

Due to the rapid development of computer, sensor, and automatic control technologies, the amount of data generated during product design and manufacturing is increasing significantly. The product data bank is large, complex, heterogeneous, and often fast-changing; it is difficult to integrate heterogeneous models using the conventional method. Therefore, a semantic feature fusion-based heterogeneous model integration method is proposed. First, the error in the geometric dimensions and position are extracted using model registration. Second, the basic geometric feature is obtained using slippage analysis. Third, the extracted data, such as the basic geometric feature and the error in the geometric dimensions and position, are fused into the design model using the level set method. Finally, the marching cubes method is introduced to reconstruct the surface of the fused model. The empirical results demonstrate that the proposed algorithm can integrate all types of semantic features and geometric features into a basic product model effectively and efficiently.
机译：由于计算机，传感器和自动控制技术的飞速发展，在产品设计和制造过程中生成的数据量显着增加。产品数据库庞大，复杂，种类繁多，并且经常快速变化；使用常规方法很难集成异构模型。因此，提出了一种基于语义特征融合的异构模型集成方法。首先，使用模型配准提取几何尺寸和位置的误差。其次，使用滑移分析获得基本几何特征。第三，使用级别设置方法将提取的数据（例如基本几何特征以及几何尺寸和位置的误差）融合到设计模型中。最后，引入了行进立方体方法来重建融合模型的表面。实验结果表明，该算法可以有效地将各种类型的语义特征和几何特征集成到基本产品模型中。

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