Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (1): 77-83.doi: 10.13229/j.cnki.jdxbgxb20190114

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Fuzzy self⁃adaptive PID control for fused deposition modeling 3D printer heating system

Xing-tian QU(),Xue-xu WANG,Hui-chao SUN(),Kun ZHANG,Long-wei YAN,Hong-yi WANG   

  1. College of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
  • Received:2019-02-16 Online:2020-01-01 Published:2020-02-06
  • Contact: Hui-chao SUN E-mail:quxt@jlu.edu.cn;sunhc@jlu.edu.cn

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Abstract:

In the process of FDM 3D printing, it is necessary to heat the printing nozzle and the heating bed of the printing platform until the required temperature of the printing material is reached. The heating process is time-consuming and energy-wasting due to the time lag and poor stability of the heating system. In order to solve the above problems, the paper adopts the fuzzy self-adaptive PID control method to control the heating process of the printing nozzle and the heating bed of the printing platform, and establishes the Matlab/Simulink simulation model of the control system. The simulation results show that the control effect of the fuzzy self-adaptive PID control method on the heating system of FDM 3D printer is better than that of the traditional PID control method. It has the advantages of small overshoot, fast response and more stable control effect.

Key words: automatic control technology, 3D printing, fuzzy PID, temperature control, simulation model

CLC Number: 

  • TP18

Fig.1

Principle of FDM 3D printing process"

Fig.2

Structural principle of fuzzy adaptive PID control system"

Table 1

Variable fuzzy field details"

变量名称 模糊论域
e [-3,3]
ec [-3,3]
? K P [-0.3,0.3]
? K I [-0.06,0.06]
? K D [-3,3]

Fig.3

Membership function of input variable e "

Fig.4

Membership function of input variable ec "

Fig.5

Membership function of output variable ? K P "

Fig.6

Membership function of output variable ? K I "

Fig.7

Membership function of output variable ? K D "

Table 2

Fuzzy control rule of ΔK P "

ec
NB NM NS ZO PS PM PB
e NB PB PB PM PM PS ZO ZO
NM PB PB PM PS PS ZO NS
NS PM PM PM PS ZO NS NS
ZO PM PM PS ZO NS NM NM
PS PS PS ZO NS NS NM NM
PM PS ZO NS NM NM NM NB
PB ZO ZO NM NM NM NB NB

Table 3

Fuzzy control rule of ΔK I "

ec
NB NM NS ZO PS PM PB
e NB NB NB NM NM NS ZO ZO
NM NB NB NM NS NS ZO ZO
NS NB NM NS NS ZO PS PS
ZO NM NM NS ZO PS PM PM
PS NM NS ZO PS PS PM PB
PM ZO ZO PS PS PM PB PB
PB ZO ZO PS PM PM PB PB

Table 4

Fuzzy control rule of ΔK D "

ec
NB NM NS ZO PS PM PB
NB PS NS NB NB NB NM PS
e NM PS NS NB NM NM NS ZO
NS ZO NS NM NM NS NS ZO
ZO ZO NS NS NS NS NS ZO
PS ZO ZO ZO ZO ZO ZO ZO
PM PB PS PS PS PS PS PB
PB PB PM PM PM PS PS PB

Fig.8

? K P output surface diagram"

Fig.9

? K I output surface diagram"

Fig.10

? K D output surface diagram"

Fig.11

"

Fig.12

Simulation diagram of fuzzy adaptive PID model system"

Fig.13

"

Fig.14

"

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