非支配排序遗传算法的三维打印分层方向优化

系统仿真学报 ›› 2015, Vol. 27 ›› Issue (10): 2365-2373.

非支配排序遗传算法的三维打印分层方向优化

戴宁, 欧立松, 黄仁凯, 刘浩

南京航空航天大学机电学院,南京 210016

收稿日期:2015-05-26 修回日期:2015-07-24 出版日期:2015-10-08 发布日期:2020-08-07
作者简介:戴宁(1978-),男,南京人,博士,副教授,研究方向为CAD/CAM;欧立松(1988-),男,陕西人,研究生,研究方向为三维打印。
基金资助:
国家863项目(SS2013AA040801); 江苏省科技支撑计划(BE2014009-3); 江苏省三维打印装备与制造重点实验室开放课题(BM2013006)

3D Printing Orientation Optimization Based on Non-dominated Sorting Genetic Algorithm

Dai Ning, Ou Lisong, Huang Renkai, Liu Hao

College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received:2015-05-26 Revised:2015-07-24 Online:2015-10-08 Published:2020-08-07

摘要/Abstract

摘要： 三维打印技术中模型的分层方向是影响着模型表面精度、加工时间和加工成本的重要关键技术之一。减少加工时间和提高表面精度相互制约,当分层厚度较小时,模型表面精度提高,但降低了加工效率;当分层厚度增加,减少了加工时间,但模型表面精度下降。针对这一问题,提出了基于非支配排序遗传算法的模型分层优化方法。建立了表面精度和加工时间两个目标函数;设计了模型姿态方向的染色体模型和自适应拥挤度算子;通过选择、交叉和变异实现迭代求解。实验表明：该方法可以有效地解决三维打印过程中模型分层方向优化问题。

关键词: 三维打印, 模型分层方向, 遗传算法, 多目标优化

Abstract: Part orientation is one of the key technologies in 3D Printing,which has important influence on the surface precision, machining time and machining cost of the part. This problem is a research hot point of how to balance the surface precision and machining time. The improved Non-dominated Sorting Genetic algorithm was proposed to solve the problem of part orientation optimization. The mathematical model of part surface accuracy and machining time were constructed. The chromosome model of part orientation and the adaptive crowding distance were established. The genetic operators of select, crossover and mutation were used to get a set of iterative solution. The experiments show that this method can effectively solve the problem of optimum part orientation.

Key words: 3D printing, part orientation, genetic algorithm, multi-objective optimization

中图分类号:

TP391.72

戴宁, 欧立松, 黄仁凯, 刘浩. 非支配排序遗传算法的三维打印分层方向优化[J]. 系统仿真学报, 2015, 27(10): 2365-2373.

Dai Ning, Ou Lisong, Huang Renkai, Liu Hao. 3D Printing Orientation Optimization Based on Non-dominated Sorting Genetic Algorithm[J]. Journal of System Simulation, 2015, 27(10): 2365-2373.

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