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。
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.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2015年第10期2365-2373,共9页
Journal of System Simulation
基金
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)
关键词
三维打印
模型分层方向
遗传算法
多目标优化
3D printing
part orientation
genetic algorithm
multi-objective optimization
