基于特征的模型多目标优化分割算法

为了减少人为因素对分割结果的影响，提高计算精度和效率，保证分割方案的合理性，提出一种基于特征的模型分割算法，通过特征识别、可加工性分析，分割面获取及分割方案多目标优化4个主要步骤完成模型分割。由于分割过程中需要考虑多个目标函数，故将遗传算法多目标优化应用于分割过程中，通过对分割面和分割顺序的优化获得最优化分割结果，并以具体实例验证了算法的可行性。

Feature-based Multi-objective Optimization Model Partitioning Scheme

To get the automation of model partitioning, reduce the influence of human factors to partitioning results, increase the precision and efficiency of computation and ensure the rationality of partitioning scheme, a feature-based partitioning algorithm for complex model is proposed. Model partitioning schemes are gained through four main steps：feature recognition, manufacturability analysis, partitioning faces abstraction and multi-objective optimization of partitioning schemes. For there are many objective functions in partitioning process, multi-objective optimization of genetic algorithm is applied in algorithm. The optimal partitioning scheme is obtained by partitioning face and partitioning sequence optimization and the feasibility of this algorithm is verified by concrete examples.