1、M.Kamel et al.(Eds.):AIS 2011,LNAI 6752,pp.102111,2011.Springer-Verlag Berlin Heidelberg 2011 Thermal Dynamic Modeling and Control of Injection Moulding Process Jaho Seo,Amir Khajepour,and Jan P.Huissoon Department of Mechanical and Mechatronics Engineering University of Waterloo j7seoengmail.uwater
2、loo.ca Abstract.Thermal control of a mould is the key in the development of high ef-ficiency injection moulds.For an effective thermal management system,this re-search provides a strategy to identify the thermal dynamic model for the design of a controller.Using neural networks and finite element an
3、alysis,system iden-tification is carried out to deal with various cycle-times for moulding process and uncertain dynamics of the mould.Based on the system identification,a self-adaptive PID controller with radial basis function(RBF)is designed to tune controller parameters.The controllers performanc
4、e is studied in terms of track-ing accuracy under different moulding processes.Keywords:Plastic injection moulding;thermal dynamic modeling;cycle-times,neural networks;finite element analysis;RBF based self-adaptive PID control.1 Introduction Injection moulding is a primary manufacturing process to
5、produce parts by injecting molten plastic materials into a mould.Thermal control is a key issue in this process since uniform temperature in the moulds contributes to production quality by reducing problems such as shrink porosity,poor fill and prolonged cycle-times for part solidification 1,2.Many
6、approaches have been proposed to deal with the thermal control in mould(or die)systems.A PI 3 and PID algorithms 1 were applied to manage the cavity temperature on a plastic injection moulding and high-pressure die-casting,respectively.To improve limitation of PID control in presence of uncertain or
7、 nonlinear dynamics,a Dahlin controller 4 and the Model Predictive Control(MPC)have been utilized in diverse range of mould systems for thermal control 5,6,7,8.Despite of improved performance compared to PID control,the addressed controllers are not robust in some circumstances with more complex non
8、linearities Specifically,because these controllers are based on a linear“best-fit”approximation(e.g.,ARX and ARMAX),the performance of the controllers is affected largely by modeling errors arisen from uncertain dynamics.Although accurate modeling of the thermal dynamics of moulds is a prerequisite
9、to successful thermal control,it is a difficult task in practice due to mould uncertainties.For example,a mould is a complex continuous system with cooling and heating Thermal Dynamic Modeling and Control of Injection Moulding Process 103 channels causing the modeling and control to be quite complic
10、ated.Unmodeled thermal dynamics of moulds(such as convection and radiation)also provide further modeling challenges.To deal with inherent challenges of modeling in a mould system,this paper considers a neural network(NN)approach.By applying NN techniques,the thermal dynamics with uncertainties in a
11、plastic injection mould is modeled.In addition,our modeling covers various cycle-times for plastic moulding process that has not been studied(i.e.,most documented approaches for thermal management of mould systems have only considered a fixed cycle-time).In this study,the system identification is co
12、nducted using the temperature distribution obtained through a finite element analysis(FEA).Based on this system identification,a controller is designed using the RBF based self-adaptive PID control.Section 2 describes the mould system.Section 3 presents a methodology for modeling thermal dynamics us
13、ing FE simulation and NN techniques.In Section 4,a controller is designed and its performance is discussed.Finally,Section 5 provides concluding remarks.2 Injection Mould and Various Cycle-Times Figure 1 shows a plastic injection mould used for the analysis in this study.Hot polymer injected into th
14、e mould cavity is cooled to the demoulding temperature by heat transfer to coolant through the cooling channel in close proximity to the cavity.Since the dominant heat for the injection moulding process is transferred by means of the conduction and convection by the coolant 3,9,the flow rate and tem
15、perature of the coolant are chosen as the control parameters.Finite element simulations for ther-mal dynamic analysis of the mould are carried out based on this input-output model.In the plastic injection moulding process,the cycle-time is allocated for the main phases of injection,packing,holding,c
16、ooling and ejection.Since the cooling phase among these phases takes a large portion of the cycle-time to cool the polymer down to solidification temperature 10,the cooling time plays an important role in the cycle.The previous studies dealing with the system identification and thermal control in the injection moulding process have used a predetermined cooling time(thus cycle-time).However,the system identification using a fixed cycle-time cannot cope with the wide range of thermal dynamics vari
