Abstract: Due to the characteristics of layer by layer accumulation in additive manufacturing, it was necessary to consider the correct forming of overhanging structures in the forming processes to avoid the formation of redundant support structures in the structural optimization processes. An optimization method for 3D model structure was proposed herein. Firstly, the point cloud data method was used to process the 3D model to obtain the contour features. Then the model was divided according to different features. Finally, the corresponding internal ellipsoid structures were established according to different external features, and the self-supporting structures were rebuilt in the interior in order to ensure that the optimized structure molding process would not generate extra support because of the special structure. The experimental results show that the method proposed herein may reduce the molding time and material cost by 16.4% and 12% on average respectively on the premise of guaranteeing the performance of the solid model with four different characteristic models as examples.

Key words: additive manufacturing, point cloud data, structure optimization, self-supporting algorithm

摘要： 增材制造因其逐层累加的特点，在进行结构优化时，需要考虑悬垂结构在成形过程中正确成形，避免在过程中形成冗余支撑结构。提出了一种三维模型结构优化方法，利用点云数据方法对三维模型进行处理获得其轮廓特征，将模型按照不同特征分割，针对其不同外部特征建立对应的内部椭球结构，同时在其内部重新构建自支撑结构，以保证优化后的结构成形过程中不会因为结构特殊而生成额外的支撑。实验结果表明，以四个不同特征模型为例，提出的方法在保证实体模型性能的前提下，平均减少了16.4%的成形时间及12%的材料成本。

关键词: 增材制造, 点云数据, 结构优化, 自支撑算法