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Polymer Testing 21 (2002) 745749www.elsevier.com/locate/polytest

Property Modelling

Prediction of parison swell in plastics extrusion blowmolding using a neural network method

H.-X. Huang *, C.-M. Liao

College of Industrial Equipment and Control Engineering, South China University of Technology, Guangzhou, PR China

Received 8 November 2001; accepted 2 January 2002

Abstract

A neural network-based model approach is presented in which the effects of the die temperature and flow rate onthe diameter and thickness swells of the parison in the continuous extrusion blow molding of high-density polyethylene(HDPE) are investigated. Comparison of the neural network model predictions with experimental data yields very goodagreement and demonstrates that the neural network model can predict the parison swells at different processing para-meters with a high degree of precision (within 0.001). 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Plastics; Extrusion blow molding; Parison swell; Neural network method

1. Introduction

Blow molding is the third largest plastics processingtechnique worldwide and has undergone rapid growthand diversification with regard to potential applications[1]. It has evolved from being a technique for the pro-duction of plastic containers into a manufacturing pro-cess for the production of industrial parts of automobiles,office automation equipment, etc.

The extrusion blow molding process involves threemain stages, namely, parison formation, parisoninflation, and part solidification. Parison formation is acritical stage and is rather complex in that it is affectedby two phenomena known as swell and sag. Parisonswell, occurring both in diameter and thickness, is dueto the nonlinear viscoelastic deformation of the polymermelt in the extrusion die. Sag is caused by gravitationalforces that act on the suspended parison.

Predicting the parison dimensions just prior toinflation will be useful for minimizing resin usage whileproviding the necessary strength and rigidity of blow

* Corresponding author. Tel.: +8620-8711-4273; fax: +8620-

8711-0562.E-mail address: [email protected] (H.-X. Huang).

0142-9418/02/$ - see front matter 2002 Elsevier Science Ltd. All rights reserved.

PII: S0142-9418(02)00005-3

molded parts. The use of numerical techniques for the

simulation of the parison formation stage has seen arapid growth in the last decade [26]. Numerical tech-niques help to minimize machine setup times and toolingcosts as well as optimize processing parameters to yielddesired final part specifications. Modeling the parisonformation with numerical methods, however, has the fol-lowing shortcomings:

1. Modeling generally requires many simplifyingassumptions, thereby leading to a limited accuracy ofsimulation results.

2. A constitutive equation must be used. Clearly,reliable constitutive equations for adequately describ-ing the nonlinear viscoelastic behavior of the polymermelt during extrusion are still lacking. Otsuki et al.[6] carried out numerical simulations of parisonswells extruded through straight, divergent and con-vergent dies. Several important viscoelastic models,the K-BKZ, the PTT and the Larson models, whichcan express well the shear flow characteristics ofhigh-density polyethylene (HDPE), were used. Theirstudies demonstrated that there are remarkable differ-ences among the results of these models. Moreover,there are some difficulties in obtaining relevant rheol-ogical data for constitutive equations.

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Fig. 2. Experimentally determined diameter swell (a) and thickness swell (b) for a 250-mm-long parison at seven different flowrates (g/min): , 13.4; , 18; , 22; , 26.2; , 29.2; +, 33.2; , 37.4.

Fig. 3. Experimentally determined diameter swell (a) and thickness swell (b) for a 250-mm-long parison at four different die tempera-

tures (C): , 160; +, 180; , 200; , 220.

at seven different extrusion flow rates and four differentdie temperatures, respectively.

As mentioned previously, the neural network was

trained by accessing a pool of 20 training data sets which

incorporated the coupled effects of the die temperature

and flow rate. The trained neural network model wasthen tested with eight testing data sets. Predicted parison

diameter and thickness swells from the trained network

model are compared with corresponding experimental

results in Fig. 4. Comparison yields very good agreement

between the two. Moreover, the sum of the squared error

between the predicted network output value and the

experimental value could be obtained after testing the

trained network. For diameter swell or thickness swell,

the sum of the squared error is very small (less than

0.001), that is, the trained neural network model shows

a high degree of prediction precision.

Once trained, the neural network model has been

identified and can be utilized to forecast the outputsexpected for new levels of input variables. Figs. 5 and

6 portray the parison diameter and thickness swells pre-

dicted by the trained neural network model at four differ-

ent flow rates and die temperatures, respectively. It canbe seen that an approximately linearly increasing

relationship exists between the diameter swell and the

distance from the die. The thickness swell increases sig-

nificantly with the distance from the die near the die exit,but slowly at greater distances from the die.

The trained neural network model ascertains the quan-

titative relationships between the diameter and thickness

swells of the parison and processing parameters. Thus,

the diameter and thickness swells, or diameter and thick-

ness profiles, along the parison can be predicted fromprocessing parameters under the effect of sag, thereby

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Fig. 4. Comparison of predicted parison diameter swell (a) and thickness swell (b) from the network model with experimental

results.

Fig. 5. Predicted parison diameter swell (a) and thickness swell (b) from the network model at four different flow rates (g/min):(1) 11; (2) 24.2; (3) 31.1; (4) 39.5.

Fig. 6. Predicted parison diameter swell (a) and thickness swell (b) from the network model at four different die temperatures (C):

(1) 150; (2) 170; (3) 190; (4) 215.

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reducing the amount of experimental work. The predic-

tions can be made on line for the purposes of process

monitoring and control.

5. Conclusions

The parison swells of HDPE were investigated as a

function of the processing parameters including the die

temperature and flow rate. A neural network-basedapproach was applied to the experimental data, leading to

a model for predicting the parison diameter and thickness

swells from the processing parameters. The comparison

of the experimentally determined parison swells with the

predicted ones using the trained neural network model

showed very good agreement between the two. The sum

of the squared error for the predictions by this proposed

model was within 0.001.

Acknowledgements

Financial support provided by the National Natural

Science Foundation of China (29804004) Excellent Tal-

ent Foundation of the Education Department of Guang-

dong Province and Doctorial foundation of colleges and

The University of China (200110561002) is gratefully

acknowledged. The authors would like to thank S. M.

Wang and S. L. Yang for their valuable contributions to

this work.

References

[1] D.V. Rosato, D.V. Rosato (Eds.), Blow molding hand-

book, Hanser, Munich, 1989.

[2] R.W. DiRaddo, A. Garcia-Rejon, L. Pecora, D. Poirier,

SPE ANTEC Tech. Papers 40 (1994) 1026.

[3] S. Tanoue, Y. Kuwano, T. Kajiwara, K. Funatsu, K. Ter-

ada, M. Yamabe, Polym. Eng. Sci. 35 (1995) 1546.

[4] Y. Otsuki, T. Kajiwara, K. Funatsu, Polym. Eng. Sci. 37

(1997) 1171.

[5] S. Tanoue, T. Kajiwara, Y. Iemoto, K. Funatsu, Polym.

Eng. Sci. 38 (1998) 409.

[6] Y. Otsuki, T. Kajiwara, K. Funatsu, Polym. Eng. Sci. 39

(1999) 1969.

[7] D. Laroche, K.K. Kabanemi, L. Pecora, R.W. DiRaddo,Polym. Eng. Sci. 39 (1999) 1223.

[8] R.W. DiRaddo, A. Garcia-Rejon, Polym. Eng. Sci. 33

(1993) 653.

[9] R.W. DiRaddo, A. Garcia-Rejon, Adv. Polym. Technol.

12 (1993) 3.

[10] S. Eggen, A. Sommerfeldt, Polym. Eng. Sci. 36 (1996)

336.

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