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作 者:屈瑞娜 颜翠翠[1] QU Ruina;YAN Cuicui(Xi’an Siyuan university,Xi’an 710038)
机构地区:[1]西安思源学院,西安710038
出 处:《自动化与仪器仪表》2023年第1期85-90,共6页Automation & Instrumentation
基 金:省教育厅科技项目《基于机器视觉的微型镜头缺陷检测与识别方法研究》(20JK0857)。
摘 要:针对传统3D打印机存在打印自动化控制效果不佳,导致打印时间和打印成本增加的问题,提出基于改进遗传算法的一体式3D打印自动化控制技术。首先构建基于3D打印任务多目标优化调度模型,并确定优化调度的约束条件和目标函数;然后基于传统遗传算法的3D打印任务多目标优化调度问题,基于遗传算法的子代选择,引入浓度平衡机制和抑制条件,将三轮迭代缩减至一轮,得到基于改进遗传算法的3D打印任务多目标优化调度方法。仿真结果表明,改进后的遗传算法在47次、88次、86次和95次时即可实现收敛,收敛速度明显优于粒子群优化算法和传统遗传算法,寻优求解效率更高。实际应用发现,3D打印机的总打印时间由16.8小时降低至9小时,打印成本由2 759.3元下降至1 762.4元。由此说明,改进的遗传算法可减少3D打印机的打印时间和打印成本、提高打印效率,可实现3D打印自动化控制。In view of the problem that the traditional 3D printer has poor printing automatic control effect,which leads to the increase of printing time and printing cost,the integrated 3D printing automatic control technology based on the improved genetic algorithm is proposed.Firstly,construct the multi-objective optimal scheduling model based on 3D printing task,and determine the constraints and objective functions for optimal scheduling based on traditional genetic algorithm,introduce the concentration balance mechanism and suppression condition,reduce the three rounds of iteration to one round,and obtain the multi-objective optimal scheduling method for 3D printing tasks based on improved genetic algorithm.The simulation results show that the improved genetic algorithm can achieve convergence in 47,88,86 and 95 times,the convergence rate is obviously better than the particle swarm optimization algorithm and the traditional genetic algorithm,and the optimization solution efficiency is higher.Practical application shows that the total printing time of 3D printers has been reduced from 16.8 hours to 9 hours,and the printing cost has decreased from 2,759.3 yuan to 1,762.4 yuan.This shows that the improved genetic algorithm can reduce the printing time and cost of 3D printers,improve the printing efficiency,and realize the automatic control of 3D printing.
关 键 词:遗传算法 3D打印 多目标任务 优化调度 自动化控制
分 类 号:TP392[自动化与计算机技术—计算机应用技术]
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