Temperature control method for dual-nozzle FDM 3D printer based on genetic algorithm-fuzzy PID
Binghui JI1(),Jian MAO1,2(),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
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.
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.
Fig.1 Test model of print nozzle temperature control system
变量
模糊论域
[-6, 6]
ec
[-6, 6]
[-6, 6]
[-6, 6]
[-6, 6]
Table 1Fuzzy domain of each variable in fuzzy PID controller
Fig.2 Membership function of fuzzy input variable
Fig.3 Membership function of fuzzy output variable
e
ec
NB
NM
NS
Z
PS
PM
PB
NB
Z/NB/PS
Z/NB/NS
NM/NM/NB
NM/NM/NB
NM/NS/NB
NB/Z/NM
NB/Z/PS
NM
PS/NB/PS
Z/NB/NS
NS/NM/NB
NM/NS/NM
NM/NS/NM
NM/Z/NS
NB/Z/Z
NS
PS/NB/Z
PS/NM/NS
Z/NS/NM
NS/Z/NM
NS/Z/NS
NM/PS/NS
NM/PS/Z
Z
PM/NM/Z
PM/NM/NS
PS/NS/NS
Z/Z/NS
NS/PS/NS
NM/PM/NS
NM/PM/Z
PS
PM/NM/Z
PM/NS/Z
PM/Z/NS
PS/PS/Z
Z/PS/Z
NS/PM/Z
NS/PB/Z
PM
PB/Z/PB
PB/Z/PS
PMZ/PS
PS/PS/PS
PS/PM/PS
Z/PB/PS
NS/PB/PB
PB
PB/Z/PB
PB/Z/PM
PM/PS/PM
PM/PM/PM
PS/PM/PS
Z/PB/PS
Z/PB/PB
Table 2Fuzzy control rules for ，，
参数符号
含义
说明
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%
调节时间
从开始升温到误差稳定在3以内所需的时间
最大超调量
目标温度的最大超调量
稳态误差
调节后的平均误差
Table 3Symbol 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
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 4Comparison of performance indicator of three temperature controllers
Fig.7 Comparison of control effect of three temperature controllers after adding disturbance
控制器
tset/s
传统PID
287.63
模糊PID
253.16
遗传算法-模糊PID
201.24
Table 5Comparison 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
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