3D打印路径规划研究  

Research on 3D Printing Path Planning

作　　者：崔凤英[1] 李晓微 CUI Fengying;LI Xiaowei(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学自动化与电子工程学院,山东青岛266061

出　　处：《青岛科技大学学报（自然科学版）》2020年第2期101-105,共5页Journal of Qingdao University of Science and Technology:Natural Science Edition

基　　金：山东省研究生教育联合培养基地建设项目(SDYJD18029).

摘　　要：以机械制造为背景,针对机械中多封闭轮廓部件在3D打印路径扫描过程中产生空行程的问题,提出了一种结合遗传算法和蚁群算法的路径规划优化算法,合理规划各轮廓的扫描顺序及扫描起始点,缩短了3D打印扫描过程中轮廓路径的总长度,减少了轮廓路径的空行程,从而提高成型的速度和质量.该融合算法既解决了蚁群算法初期盲目性大的问题,又解决了遗传算法启发信息利用不足和容易陷入局部最优解的缺点.实验结果表明:所提出的方法能够减少空行程,提高成型速度和质量.In this paper,a path planning optimization algorithm combining genetic algorithm ant colony algorithm is proposed.The algorithm is based on the background of mechanical manufacturing.It aims at the problem of empty travel of multi-closed contour components in the process of 3D printing path scanning.The scanning sequence and starting point of each contour are reasonably planned,and the contour path in the process of 3D printing and scanning is shortened.The total length of the diameter reduces the empty travel of the contour path,thus improving the speed and quality of forming.The fusion algorithm not only solves the problem of blindness in the initial stage of ant colony algorithm,but also solves the shortcomings of insufficient utilization of heuristic information and easy to fall into local optimal solution of genetic algorithm.The experimental results show that the proposed method can reduce the empty travel and improve the forming speed and quality.

关 键 词：3D打印 路径规划 蚁群算法 遗传算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]