A comprehensive experimental study and numerical modeling of parison formation in extrusion blow molding

Parison dimensions in extrusion blow molding are affected by two phenomena, swell due to stress relaxation and sag drawdown due to gravity. It is well established that the parison swell and sag are strongly dependent on the die geometry and the operating conditions. The availability of a modeling technique ensures a more accurate prediction of the entire blow molding process, as the proper prediction of the parison formation is the input for the remaining process phases. This study considers both the simulated and the experimental effects of the die geometry, the operating conditions, and the resin properties on the parison dimensions using high density polyethylene. Parison programming with a moving mandrel and the flow rate evolution in intermittent extrusion are also considered. The parison dimensions are measured experimentally by using the pinch-off mold technique on two industrial scale machines. The finite element software BlowParison® developed at IMI is used to predict the parison formation, taking into account the swell, sag, and nonisothermal effects. The comparison between the predicted parison/part dimensions and the corresponding experimental data demonstrates the efficiency of numerical tools in the prediction of the final part thickness and weight distributions. POLYM. ENG. SCI., 47:1–13, 2007. © 2006 Society of Plastics Engineers     

Overall numerical simulation of extrusion blow molding process

This paper focuses on the overall numerical simulation of the parison formation and inflation process of extrusion blow molding. The competing effects due to swell and drawdown in the parison formation process were analyzed by a Lagrangian Eulerian (LE) finite element method (FEM) using an automatic remeshing technique. The parison extruded through an annular die was modeled as an axisymmetric unsteady nonisothermal flow with free surfaces and its viscoelastic properties were described by a K‐BKZ integral constitutive equation. An unsteady die‐swell simulation was performed to predict the time course of the extrudate parison shape under the influence of gravity and the parison controller. In addition, an unsteady large deformation analysis of the parison inflation process was also carried out using a three‐dimensional membrane FEM for viscoelastic material. The inflation sequence for the parison molded into a complex‐shaped mold cavity was analyzed. The numerical results were verified using experimental data from each of the sub‐processes. The greatest advantage of the overall simulation is that the variation in the parison dimension caused by the swell and drawdown effect can be incorporated into the inflation analysis, and consequently, the accuracy of the numerical prediction can be enhanced. The overall simulation technique provides a rational means to assist the mold design and the determination of the optimal process conditions.     

On-line prediction of final part dimensions in blow molding: A neural network computing approach

Control over final part thickness distributions in extrusion blow molding would be very useful in resin optimization. An on-line measurement is essential for process monitoring and control of the part dimensions. Excessive resin usage results in material waste and increased cycle times because of increased cooling requirements. An inadequate thickness results in decreased mechanical strength, especially in regions along the part where large blow ratios or complex geometries exist. Neural networks are investigated as a method for the on-line prediction of the final part distribution from the parison dimensions. The purpose of this work is to demonstrate the feasibility, for preliminary use, of neural networks for this application. The network inputs include the initial parison thickness and tempera-ture profiles, the bottle mold geometry and a rheological parameter representative of the material. Varying blow-up ratios are obtained from the bottle mold geome-try. The network accesses data from a pool of eighty data sets for the training sequence. The data sets are broadly distributed with regard to the operating conditions, so as to give the network a wide range of applicability. The simulations are performed on data sets not present in the access pool used for training.     

Noncontact measurement of parison thickness profiles affected by swell and sag in continuous extrusion blow molding

Optimization of final part thickness distributions is crucial in the extrusion blow molding process in order to minimize resin usage. Prediction of part thickness distributions from basic process and material parameters would be ideal. However, attempts to do so have been unsuccessful, largely because of the inability to predict parison thickness profiles. One must therefore resort to measurement of the parison thickness profile and estimation of the final part thickness distribution by computational methods. This paper describes a new technique for the noncontact estimation of parison thickness profiles in continuous extrusion blow molding. The method accounts for sag and requires no previous knowledge of rheological data. It can be employed on-line for the purposes of process monitoring and control. The approach is based on the measurement of the parison length evolution with time during extrusion, the parison diameter profile, the flow rate, and the melt temperature gradient along the length of the parison. These parameters are utilized in conjunction with a theoretical approach that describes the extrusion of a parison under the effects of swell, sag, and extrusion into ambient conditions. Results are presented for three resins of various molecular weight distributions. The degree of sag is minimal at the top and bottom of the parison, and reaches a maximum near the center of the parison. Results are also presented to demonstrate the versatility of the method under other process conditions, such as varying flow rate, die temperature, and die gap.     

Variables affecting parison diameter swell and their correlation with rheological parameters

An important factor in the selection of blow molding resins for producing handled bottles is the effective diameter swell of the parison. Ideally, the diameter swell is directly related to the weight swell and would require no separate consideration. In actual practice, the existence of gravity, the finite parison drop time and the anisotropic aspects of the blow molding operation prevent reliable prediction of the parison diameter swell directly from the weight swell. The parison diameter swell is a complex function of the weight swell, the rate of swell and the melt strength. Elements of this function are presented which show the effect of extrusion rate, parison drop time and parison weight. A technique is presented which allows the estimation of local weight and diameter swell ratios. Their direct relationship is confirmed by data obtained on several blow molding resins. The relationship between weight swell and diameter swell is definitely anisotropic. A mathematical model for swell is proposed which incorporates experimentally determined rate constants and swell coefficients. Correlations are given which suggest fundamental relationships between these derived coefficients and basic variables such as resin properties or process conditions. The model's predictive capability is demonstrated by using it to back calculate parison dimensions.     

基于RBF神经网络的挤出吹塑中型坯尺寸的预测

在利用BP神经网络预测挤出吹塑中型坯尺寸工作的基础上,采用径向基神经网络(RBF)来预测挤出吹塑中型坯尺寸,并与BP神经网络的预测结果进行了比较。结果表明,虽然RBF与BP神经网络均能较好地预测挤出吹塑中型坯尺寸,RBF网络的训练时间比BP少很多,只是BP的0.7%。     

聚碳酸酯的吹塑成型

简介聚碳酸酯吹塑容器的特性与用途,着重分析其吹塑(包括挤出吹塑、共挤吹塑和注射吹塑)成型的机械(螺杆、机筒、型坯机头与吹塑模具)设计要点及工艺条件。     

挤出吹塑型坯吹胀的CAD/CAE技术

对挤出吹塑型坯吹胀过程的CAD／CAE技术进行了初步研究。此技术可通过对吹塑CAD几何造型，CAD／CAE间信息传递，以及CAE分析的集成，实现对挤出吹塑型坯吹胀成型工艺过程的模拟和分析。最后用实例验证了此技术的可行性，为塑料制品的设计、材料选择、模具设计、吹塑成型工艺的制定及吹塑成型工艺过程的控制提供了科学依据。     

Modeling parison formation in extrusion blow molding by neural networks

A series of experiments were carried out on the parison formation stage in extrusion blow molding of high‐density polyethylene (HDPE) under different die temperature, extrusion flow rate, and parison length. The drop time of parison when it reached a given length and its swells, including the diameter, thickness, and area swells, were determined by analyzing its video images. Two back‐propagation (BP) artificial neural network models, one for predicting the length evolution of parison with its drop time, the other predicting the swells along the parison, were constructed based on the experimental data. Some modifications to the original BP algorithm were carried out to speed it up. The comparison of the predicted parison swells using the trained BP network models with the experimentally determined ones showed quite a good agreement between the two. The sum of squared error for the predictions is within 0.001. The prediction of the parison diameter and thickness distributions can be made online at any parison length or any parison drop time within a given range using the trained models. The predicted parison swells were analyzed. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 2230–2239, 2005     

New Strategies for Predicting Parison Dimensions in Extrusion Blow Molding

In this work, two new strategies were proposed for predicting the parison thickness and diameter distributions in extrusion blow molding. The first one was a finite-element-based numerical simulation for the parison extruded from a varying die gap. The comparison of simulated and experimental parison thickness distributions indicates that the new method has certain accuracy in predicting the parison thickness from a varying die gap. The second one was an artificial neural network (ANN) approach, the characteristics of which are in sufficient patterns that can be obtained without doing too many experiments. The diameter and thickness swells of the parisons extruded under different flow rates were obtained by a well-designed experiment. The obtained data were then used to train and test the ANN model. The dimension of one location on the parison can provide one pattern to train the ANN model. Trained and tested ANN model can be used to predict the dimensions at any location on the parison within a given range. The proposed two strategies can help search the processing conditions to obtain optimal parison thickness distributions.     

Numerical simulation of blow molding—Viscoelastic flow analysis of parison formation

The simulation of the parison formation process in blow molding has been studied. The flow field was divided into two regions, namely, the extrudate swell region near the die lip and the parison formation region after the exit swell. In the swell region, we predicted the swelling ratio and residual stress distribution for high Weissenberg numbers for steady planar well using the 1-mode Giesekus model. In the parison formation region, the flow is assumed to be an unsteady unaxial elongational flow including drawdown and recoverable swell and is modeled using the 10-mode Giesekus model. We calculated the time course of parison length and thickness distribution, and compare the calculation results of parison length with experimental data. It was found that the predicted values agreed rather well with the experimental values. The calculation results could especially predict the shrink-back, which is the phenomenon where the parison length becomes shorter after the cessation of extrusion, and it was found tat this was caused by the recoverable swell of the parison, which depends on the tensile stress generation in the die. Various flow rates and die geometries were studied and confirmed the reliability and usefulness of the method.     

An experimental investigation of capillary extrudate swell in relation to parison swell behavior in blow molding

An experimental study was carried out to study and characterize the capillary extrudate swell and parison swell behavior in extrusion blow molding of two commercial blow molding grade high density polyethylene resins. The capillary extrudate swell behavior of these resins were determined employing a capillary rheometer and a special thermostatting chamber. Parison swell behavior was determined using an Impco A13-R12 reciprocating screw blow molding machine in conjunction with cinematography and pinch-off. The experimental conditions under which capillary extrudate and parison swell data can be related are elucidated. Excellent agreement is found between the area swell values determined on the basis of capillary and parison swell experiments.     

现代设计方法在型坯成型研究中的应用

系统地介绍了几种现代设计方法：有限元法、计算机辅助设计和人工神经网络方法；并阐述了几种现代设计方法在塑料挤出吹塑的型坯成型阶段中的应用现状。     

Fast online acquisition and analysis for parison swell and sag in blow molding

It is critical to quantitatively and reliably characterize the effects of swell and sag phenomena on the final parison dimensions in extrusion blow molding. To achieve this goal, an online image acquisition and analysis technique was developed. The successive images of parison were automatically taken using the online acquisition apparatus. These images were then analyzed by the combined use of the conventional digital image processing method and the new one developed by the authors. So the development of parison diameter and thickness swells with the extrusion time could be determined online. On the basis of the online obtained actual swell values, the pure swell and sag components were quantitatively determined. The developed technique was tested through a series of experiments using several resins under different processing parameters and die types. Shown in the present article were the results for a converging die under three different die gaps and a high‐density polyethylene. Some new phenomena were observed using the proposed technique. The results showed that the technique yields fast and accurate determination of the evolution of diameter, thickness, and length of parison during its extrusion. The technique can be employed as a part of the closed loop control for blow molded part thickness. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2399–2406, 2006     

On-Line measurement of parison geometry during blow molding: Parison swelling for three high-density polyethylenes with different molecular weights and molecular weight distributions

An image analysis technique has been developed to measure diameter and thickness distribution of a parison during the extrusion stage in blow molding. The system operates on-line during extrusion on any commercial blow-molding machine. The system has been developed to help development of new blow-molding resins by increasing our understanding of the connection between polymer structure and parison shape. The system can also be used for die design during optimization of a production process. The combined use of experimental design and multivariate projection techniques makes this an efficient tool for the practical processing engineer. Experiments done on three high-density polyethylene blow-molding resins show the importance of measuring the time dependence of the diameter and thickness distribution under different extrusion conditions for a given polymer. Our results show that many of the swell and sag related properties we see cannot be directly inferred from standard laboratory swell-experiments.     

Integrated numerical modeling of the blow molding process

The numerical modeling of the extrusion blow molding of a fuel tank is considered in this work. The integrated process phases are consecutively simulated, namely, parison formation, clamping, and inflation, as well as part solidification, part deformation (warpage), and the buildup of residual stresses. The parison formation is modeled with an integral type viscoelastic constitutive equation for the sag behavior and a semi-empirical equation for the swell behavior. A nonisothermal viscoelastic formulation is employed for the clamping and inflation simulation, since parison cooling during extrusion strongly affects the inflation behavior. Once the parison is inflated, it solidifies while in the mold and after part ejection. Warpage and residual stress development of the part are modeled with a linear viscoelastic solid model. Numerical predictions are compared with experimental results obtained on an industrial scale blow molding machine. Good agreement is observed. A process optimization based on a desired objective function, such as uniform part thickness distribution and/or minimal part weight, is performed. The integrated clamping, inflation, and cooling stages of the process are considered. The optimization is done by the systematic manipulation of the parison thickness distribution. Iterations are performed employing a gradient based updating scheme for the parison thickness programming, until the desired objective of uniform part thickness is obtained.     

熔体挤出速度对共挤吹塑型坯离模膨胀影响的数值模拟

基于三维非等温黏弹性熔体多相分层流动有限元数值模拟技术,模拟研究了熔体挤出速度对多层共挤吹塑成型环坯离模膨胀和初始温度场的影响规律,揭示了型坯离模膨胀的产生机理。结果表明,多层共挤吹塑成型环坯离模膨胀是由熔体的二次流动诱发而产生,与熔体流出机头进入自由膨胀段的二次流动强度成正比,而其二次流动强度随着熔体挤出速度的增大而增强,因而导致环坯离模膨胀随着熔体挤出速度的增加而增大;多层共挤吹塑成型熔体的二次流动强度与其第二法向应力差成正比关联关系,这与Debbaut的试验研究结论完全吻合,表明二次流动是由第二法向应力差驱动而产生。     

Measurement and calculation of parison dimensions and bottle thickness distribution during blow molding

Experimental data are reported regarding the dynamics of the blow molding process, including parison formation, growth, and inflation. These data have been obtained with the aid of high speed cinematography and pinch mold experiments, in conjunction with two commercial blow molding polyethylene resins. It is shown that pinch mold experiments alone do not yield accurate data regarding thickness and diameter swell. Furthermore, the inflation process involves decreasing rates of inflation with time, as a result of the rise in viscosity due to the cooling of the parison during inflation. Mathematical procedures are proposed for a first-order estimation of parison length and swell as a function of time and the inflation behavior after clamping. In the absence of more dependable basic procedures, the proposed treatment is employed to estimate the effective transient swell functions for the parison using experimental data obtained under the specified conditions. The mathematical treatment is extended to determine the thickness distribution of the bottle. Good agreement is obtained between experimental and calculated results.     

基于神经网络的挤出吹塑中型坯尺寸预测

延续了本课题组在挤出吹塑中利用人工神经网络(ANN)预测型坯尺寸的工作,建立一个新的ANN模型。经过样本训练和检验后,模型能在一定范围内预测型坯任意位置上的尺寸(直径和厚度);与以往工作相比,相同的实验量能提供更丰富的训练样本。     

The dynamics of parison free inflation in extrusion blow molding

Parison free inflation behavior, associated with the extrusion blow molding process, is considered both experimentally and theoretically. Experimental observations indicate1 that the parison assumes a rather complex shape under conditions of unrestricted inflation. In particular, the time-dependent shape is markedly ellipsoidal rather than cylindrical in nature. This nonuniform behavior, however, becomes more prominent in relation to the entire length as the parison-length-to-diameter ratio is decreased. Based on the experimental observations, a simplified analytical treatment of the free inflation of a viscoelastic parison is presented. The theoretical results illuminate the influence of material properties and process conditions on the inflation process. Expectedly, inflation is enhanced by an increase in the pressure driving force as well as by a decrease in viscosity. However, melt elasticity is also found to exert a significant influence on the inflation behavior. Moreover, the theoretical analysis suggests that the initial parison dimensions play a central role in controlling the inflation process.