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Research on Model Partition for Large-scale Rapid Prototyping

机译:大规模快速原型的模型划分研究

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To build the large-scale model efficiently based on RP,an optimal partition method was described with no need to extend the machine’s work volume. By the proposed edgebased partitioning,the large and complex model can be decomposed into small and simple sub-models according to its structure. In this paper,the best fit loop selecting and the hierarchical decomposing are also presented. Several experimental results demonstrate the reliability and efficiency of the edge-based partitioning.
机译:为了有效地基于RP构建大型模型,描述了一种无需扩展机器工作量的最佳分区方法。通过提出的基于边缘的划分,可以将大型和复杂模型根据其结构分解为小型和简单子模型。本文还提出了最佳拟合循环选择和分层分解方法。几个实验结果证明了基于边缘的分割的可靠性和效率。

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