Temperature control method for dual-nozzle FDM 3D printer based on genetic algorithm-fuzzy PID

doi:10.3785/j.issn.1006-754X.2024.03.169

Chinese Journal of Engineering Design

2024, Vol. 31

Issue (2): 151-159 DOI: 10.3785/j.issn.1006-754X.2024.03.169

Theory and Method of Mechanical Design

Temperature control method for dual-nozzle FDM 3D printer based on genetic algorithm-fuzzy PID

Binghui JI¹(

),Jian MAO^1,²(

),Bo QIAN¹

^1.School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201600, China
^2.Sichuan Research Institute, Shanghai Jiaotong University, Chengdu 610213, China

Download:

HTML

PDF(2336KB)
Export: BibTeX | EndNote (RIS)

Abstract

Fused deposition modeling (FDM) 3D printing requires the print nozzle to be heated the desired temperature of the material before printing begins. Due to the low printing efficiency of the single nozzle FDM 3D printer, and the large lag and poor stability of its heating system, the whole forming process is time-consuming and wasteful of resources, and the quality of the formed parts is not high. In order to solve the above problems, a temperature control method based on genetic algorithm-fuzzy PID (proportional-integral-derivative) was proposed to control the heating method of dual-nozzle FDM 3D printer, which combined the differences in physical and chemical properties of printing materials. The MATLAB/Simulink simulation model of the temperature control system was established to verify the reliability of the proposed control method. The simulation and experimental results showed that compared with the traditional PID control and fuzzy PID control, the response time of the genetic algorithm-fuzzy PID control was shortened by 36.03% and 32.45%, and the adjustment time was shortened by 28.06% and 20.99%, which had the advantages of fast response, short adjustment time, small overshoot and stable control effect. The research results can provide reference for dual-nozzle FDM 3D printing of composite materials.

Key words： fused deposition modeling dual-nozzle temperature control genetic algorithm fuzzy PID

Received: 17 May 2023 Published: 26 April 2024

CLC:

TH 164

Corresponding Authors: Jian MAO E-mail: 18635072871@163.com;jmao@sues.edu.cn

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Binghui JI
	Jian MAO
	Bo QIAN

Cite this article:

Binghui JI,Jian MAO,Bo QIAN. Temperature control method for dual-nozzle FDM 3D printer based on genetic algorithm-fuzzy PID. Chinese Journal of Engineering Design, 2024, 31(2): 151-159.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2024.03.169 OR https://www.zjujournals.com/gcsjxb/Y2024/V31/I2/151

基于遗传算法-模糊PID的双喷头FDM型3D打印机温度控制方法

熔融沉积成形（fused deposition modeling, FDM）3D打印需要将打印喷头加热至材料所需温度后才能开始打印。由于单喷头FDM型3D打印机的打印效率较低，以及其加热系统的滞后性较大且稳定性差，使得整个成形过程既耗时又浪费资源，且成形件的质量不高。为解决上述问题，结合打印材料物理性质和化学性质的差异性，提出了一种基于遗传算法-模糊PID（proportional-integral-derivative，比例-积分-微分）的温度控制方法，以实现对双喷头FDM型3D打印机加热方法的控制，并建立温度控制系统的MATLAB/Simulink仿真模型，以验证所提出的控制方法的可靠性。仿真和实验结果表明，与传统PID控制、模糊PID控制相比，遗传算法-模糊PID控制的响应时间缩短了36.03%和32.45%，调节时间缩短了28.06%和20.99%，具有响应速度快、调节时间短、超调量小和控制效果稳定等优势。研究结果可为复合材料的双喷头FDM 3D打印提供参考。

关键词： 熔融沉积成形, 双喷头, 温度控制, 遗传算法, 模糊PID

Fig.1 Test model of print nozzle temperature control system

变量	模糊论域
$e$	[-6, 6]
e_c	[-6, 6]
$K p$	[-6, 6]
$K i$	[-6, 6]
$K d$	[-6, 6]

Table 1 Fuzzy domain of each variable in fuzzy PID controller

Fig.2 Membership function of fuzzy input variable

e

Fig.3 Membership function of fuzzy output variable

K p

Table 2 Fuzzy control rules for

K p

，

K i

，

K d

参数符号	含义	说明
$t r i s e$	上升时间	从开始升温到第1次达到目标温度所需的时间，初始误差为90%
$t s e t$	调节时间	从开始升温到误差稳定在3 $? ℃$ 以内所需的时间
$e o v e r, ?? m a x$	最大超调量	目标温度的最大超调量
$e d e l t a$	稳态误差	调节后的平均误差

Table 3 Symbol and meaning of performance indicator for fuzzy PID controller

Fig.4 Optimization flow of fuzzy PID control parameters based on genetic algorithm

Fig.5 Simulation models of temperature controller based on different algorithms

Fig.6 Comparison of control effect of three temperature controllers

控制器	$t r i s e / s$	$t s e t / s$	$e o v e r, ?? m a x / ℃$	$e d e l t a / ℃$
传统PID	199.21	308.29	24.57	0.51
模糊PID	188.66	280.71	19.15	0.33
遗传算法-模糊PID	127.44	221.78	10.89	0.04

Table 4 Comparison of performance indicator of three temperature controllers

Fig.7 Comparison of control effect of three temperature controllers after adding disturbance

Table 5 Comparison of adjustment time for three temperature controllers after adding disturbance

Fig.8 Dual-nozzle FDM 3D printer

Fig.9 Circular-table formed part

Fig.10 Temperature rise curve of print nozzle 1 (heating up to 230 ℃)

Fig.11 Temperature rise curve of print nozzle 2 (heating up to 280 ℃)

Fig.12 Aircraft fork component formed part


[1]	唐通鸣，张政，邓佳文，等.基于FDM的3D打印技术研究现状与发展趋势［J］.化工新型材料，2015，43（6）：228-230，234. TANG T M， ZHANG Z， DENG J W， et al. Research status and trend of 3D printing technology based on FDM［J］. New Chemical Materials， 2015， 43（6）： 228-230， 234.

[2]	KIM S， SEONG H， HER Y， et al. A study of the development and improvement of fashion products using a FDM type 3D printer［J］. Fashion and Textiles， 2019， 6： 9（1）-9（24）.

[3]	徐佳.大型FDM双喷头3D打印机设计及工艺参数研究［D］.秦皇岛：燕山大学，2016：1-37. XU J. Large double nozzle FDM 3D printer design and process parameters study［D］. Qinhuangdao： Yanshan University， 2016： 1-37.

[4]	李晓桐.FDM式3D打印机控制系统设计及工艺参数优化［D］.哈尔滨：哈尔滨理工大学，2021：1-39. LI X T. Control system design of FDM 3D printer and process parameter optimization［D］. Harbin： Harbin University of Science and Technology， 2021： 1-39.

[5]	ALTAN A， HACIOĞLU R. The algorithm development and implementation for 3D printers based on adaptive PID controller［J］. Journal of Polytechnic， 2018， 21（3）： 559-564.

[6]	LIU Z， WANG G， HUO Y， et al. Research on precise control of 3D print nozzle temperature in PEEK material［J］. AIP Conference Proceedings， 2017， 1890（1）： 040076.

[7]	LIAO H H. Simulations research on Smith predictive adaptive fuzzy-PID compound controller in the temperature control system of microchip level PCR instrument［J］. Applied Mechanics and Materials， 2013， 373-375： 1324-1331.

[8]	张金立.3D打印机智能温控系统及路径规划算法的研究［D］.上海：上海工程技术大学，2020：35-42. ZHANG J L. Research on intelligent temperature control system and path planning algorithm of 3D printer［D］. Shanghai： Shanghai University of Engineering Science， 2020： 35-42.

[9]	赵立柱，苏东海，左伟，等.基于粒子群模糊PID控制的风机盘车液压缸同步控制系统［J］.机电工程，2022，39（7）：961-966. doi：10.3969/j.issn.1001-4551.2022.07.013 ZHAO L Z， SU D H， ZUO W， et al. Synchronous control system of hydraulic cylinder of fan coil car controlled by particle swarm fuzzy PID［J］. Journal of Mechanical & Electrical Engineering， 2022， 39（7）： 961-966. doi: 10.3969/j.issn.1001-4551.2022.07.013

[10]	刘艺炜.皮肤3D打印三维移动平台搭建与温度控制系统设计［D］.太原：太原理工大学，2021：21-32. LIU Y W. Design of 3D mobile platform for skin 3D printing and temperature control system［D］. Taiyuan： Taiyuan University of Technology， 2021： 21-32.

[11]	周婧，高红飞，卢林，等.变论域模糊PID控制微流挤出型3D打印机的挤压力研究［J］.工程设计学报，2022，29（5）：572-578. doi：10.3785/j.issn.1006-754X.2022.00.075 ZHOU J， GAO H F， LU L， et al. Research on extrusion force of micro-flow extrusion 3D printer controlled by variable universe fuzzy PID［J］. Chinese Journal of Engineering Design， 2022， 29（5）： 572-578. doi: 10.3785/j.issn.1006-754X.2022.00.075

[12]	曲兴田，王学旭，孙慧超，等.熔融沉积成形技术3D打印机加热系统的模糊自适应PID控制［J］.吉林大学学报（工学版），2020，50（1）：77-83. QU X T， WANG X X， SUN H C， et al. Fuzzy self⁃adaptive PID control for fused deposition modeling 3D printer heating system［J］. Journal of Jilin University （Engineering and Technology Edition）， 2020， 50（1）： 77-83.

[13]	王自立.电动汽车电机驱动控制系统研究［D］.南宁：广西大学，2020：18-26. WANG Z L. Research on electric vehicle motor drive control system［D］. Nanning： Guangxi University， 2020： 18-26.

[14]	张宝峰，张燿，朱均超，等.基于模糊PID的高精度温度控制系统［J］.传感技术学报，2019，32（9）：1425-1429. doi：10.3969/j.issn.1004-1699.2019.09.022 ZHANG B F， ZHANG Y， ZHU J C， et al. High precision temperature control system based on fuzzy PID［J］. Chinese Journal of Sensors and Actuators， 2019， 32（9）： 1425-1429. doi: 10.3969/j.issn.1004-1699.2019.09.022

[15]	樊宁.面向熔融混合材料3D打印的过程控制系统设计与实现［D］.南京：南京师范大学，2021：48-61. FAN N. Design and implementation of process control system for 3D printing of fused mixed materials［D］. Nanjing： Nanjing Normal University， 2021： 48-61.

[16]	SAHOO B P， PANDA S. Improved grey wolf optimization technique for fuzzy aided PID controller design for power system frequency control［J］. Sustainable Energy， Grids and Networks， 2018， 16： 278-299.

[17]	GOLDBERG D E. Genetic algorithms in search， optimization and machine learning［M］. Boston： Addison-Wesley Longman Publishing Co.， Inc.， 1989： 1-191.

[18]	王婷婷，王宏志，刘清雪，等.遗传算法优化的无刷直流电机模糊PID控制器设计［J］.吉林大学学报（理学版），2020，58（6）：1421-1428. WANG T T， WANG H Z， LIU Q X， et al. Design of fuzzy PID controller for brushless DC motor optimized by GA［J］. Journal of Jilin University （Science Edition）， 2020， 58（6）： 1421-1428.

[19]	孙嘉梁，符晓.遗传算法优化的移相全桥变换器模糊PID控制［J］.测控技术，2022，41（5）：113-118. SUN J L， FU X. Fuzzy PID control of phase shift full-bridge converter optimized by genetic algorithm［J］. Measurement & Control Technology， 2022， 41（5）： 113-118.

[20]	CHEN J， LU Q， BAI J， et al. A temperature control method for microaccelerometer chips based on genetic algorithm and fuzzy PID control［J］. Micromachines， 2021， 12（12）： 1511-1524.

[21]	刘金琨.先进PID控制MATLAB仿真［M］.3版.北京：电子工业出版社，2011：288-296． LIU J K. MATLAB simulation of advanced PID control［M］. 3rd ed. Beijing： Electronic Industry Press， 2011： 288-296.

[1]	Yaqin TIAN,Menghui HU,Wentao LIU,Yinzhi HOU. Path planning of autonomous mobile robot based on jump point search-genetic algorithm[J]. Chinese Journal of Engineering Design, 2023, 30(6): 697-706.

[2]	Di ZHAO,Guo CHEN,Xiaoli CHEN,Xiongjin WANG. Terrain adaptive mechanism design and obstacle-surmounting performance analysis of wheeled search and rescue robot[J]. Chinese Journal of Engineering Design, 2023, 30(5): 579-589.

[3]	Fangjian DOU,Qingying QIU,Cheng GUAN,Jinjie SHAO,Haifeng WU. Optimization design of acceleration and deceleration curve of winding machine with large moment of inertia[J]. Chinese Journal of Engineering Design, 2023, 30(4): 503-511.

[4]	Jun GUAN,Yihua DING,Qingtao GE,Shuai ZHAO,Yang LU,Jie ZHANG. Rapid manufacturing of RFID antennas based on multi-material 3D printing technology[J]. Chinese Journal of Engineering Design, 2023, 30(3): 288-296.

[5]	Yi-nan CHEN,Zhi-xin PU,Zhen-ni ZHENG. A novel vascular interventional surgery robot with force detection mechanism[J]. Chinese Journal of Engineering Design, 2023, 30(1): 20-31.

[6]	Xin MI,Hong LI,Yan-qing GUO,Hong-wei GAO,Hao-nan WANG,Yi-fan NING. Parameter optimization of single plunger pump check valve based on linear regression[J]. Chinese Journal of Engineering Design, 2022, 29(6): 705-712.

[7]	Jing ZHOU,Hong-fei GAO,Lin LU,Guo-lin DUAN. Research on extrusion force of micro-flow extrusion 3D printer controlled by variable universe fuzzy PID[J]. Chinese Journal of Engineering Design, 2022, 29(5): 572-578.

[8]	Qin LI,Ying-qi JIA,Yu-feng HUANG,Gang LI,Chuang YE. A multi-objective trajectory optimization algorithm for industrial robot[J]. Chinese Journal of Engineering Design, 2022, 29(2): 187-195.

[9]	DING Shu-yong, ZHANG Zheng, DING Wen-jie, LIN Yong. Optimization design of multi-lane stereo garage and research on vehicle access strategy[J]. Chinese Journal of Engineering Design, 2021, 28(4): 443-449.

[10]	YAN Guo-ping, ZHOU Jun-hong, ZHONG Fei, LI Zhe, ZHOU Hong-di, PENG Zhen-ao. Design and optimization of magnetic compression correction device for paper-plastic composite bag[J]. Chinese Journal of Engineering Design, 2021, 28(3): 367-373.

[11]	WANG Chao, SUN Wen-xu, MA Xiao-jing, CHEN Ji-yang, LUAN Yi-zhong, MA Si-le. Temperature control system of HVPE growth equipment based on fuzzy control[J]. Chinese Journal of Engineering Design, 2020, 27(6): 765-770.

[12]	ZHANG Shuai, HAN Jun, TU Qun-zhang, YANG Xiao-qiang, YANG Xuan. Multi-objective optimization design of deployable mechanism of scissor folding bridge based on GA-NLP[J]. Chinese Journal of Engineering Design, 2020, 27(1): 67-75.

[13]	KONG De-shuai, HU Gao-feng, ZHANG Guan-wei, ZHANG Da-wei. Design and performance analysis of variable preload spindle based on piezoelectric actuator[J]. Chinese Journal of Engineering Design, 2019, 26(6): 743-752.

[14]	LIU Chun-qing, WANG Wen-han. Parameter optimization of generating method spherical precision grinding based on ANN-GA[J]. Chinese Journal of Engineering Design, 2019, 26(4): 395-402.

[15]	ZHENG Hua-lin, YANG Shun-bo, PAN Sheng-hu, WANG Chao. Research on control system of fixed length cutting and automatic layout for flap discs[J]. Chinese Journal of Engineering Design, 2019, 26(4): 461-468.

Viewed

Full text

Abstract

Cited

Shared

Discussed