Songklanakarin Journal of Science and Technology SJST R1 Suvanjumrat

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1 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat The Novel Simulation of Bottle Blow Molding to Determine Suitable Parison Thicknesses for Die Shaping Journal: Songklanakarin Journal of Science and Technology Manuscript ID SJST-0-0.R Manuscript Type: Original Article Date Submitted by the Author: 0-Apr-0 Complete List of Authors: Suvanjumrat, Chakrit; Mahidol University Faculty of Engineering, Mechanical Engineering Keyword: Simulation, Bottle, Blow Molding, Parison, Die Shaping

2 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 Original Article The Novel Simulation of Bottle Blow Molding to Determine Suitable Parison Thicknesses for Die Shaping Abstract Chakrit Suvanjumrat,*, Nathaporn Ploysook, Ravivat Rugsaj and Watcharapong Chookaew Department of Mechanical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, 0, Thailand Laboratory of Computer Mechanics for Design (LCMD), Department of Mechanical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, 0, Thailand * Corresponding author, address: chakrit.suv@mahidol.ac.th This research proposed artificial intelligence (AI) method to determine suitable parison thicknesses at the horizontal cross-section of bottle for the die shaping. The cross-section of the bottle was emphasized for the blow molding simulation by the finite element method (FEM) in order to achieve the accurate results more than the full bottle shape simulation. The absolute average error of cross-section simulation was.0% when it was compared with the experimental data. Subsequently, the artificial neural network (ANN) was trained using the FEM data of cross-section blow molding. The ANN was good agreement with the experimental data for the more complex shape of two implementable bottles and obtained an absolute average error of.%. Eventually, the genetic algorithm (GA) method was developed to determine the uniform perimeter thickness of bottle which passed the top load testing. The predictable parison thickness by AI gave an absolute average error of.% from the desirable thickness.

3 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 Keywords: Simulation, bottle, blow molding, parison, die shaping. Introduction Lubricant oil bottles are attractive with the color and shape. There are many complex shapes of lubricant oil bottle which were launched in the automotive market. These bottles are produced using the extrusion blow molding process. There are two extrusion methods which comprise of the die-gap programming and die shaping have been used to prepare the plastic tube or parison for blowing lubricant oil bottles. The suitable parison thickness is important to use for forming the lubricant oil bottles by inflation to completely contact on the surface of bottle mold cavity. Parison thickness is regulated by the extrusion die methods to obtain the bottle thickness regarding to the requirement of bottle design. However, the blow molding theory is employed to calculate the initial thickness of parison (Lee, ), the trial and error method is often used to adjust the die gap for the complex shape of bottles. The thickness of parison is difficult to control properly to form the lubricant oil bottles which principally have a reflective symmetry shape. It is often depended on the experience of die makers to shape the extrusion die. Consequently, the significant amount of wasteful plastic, cost and time are lost at the parison setting. In the present days, the computer aided engineering (CAE) which involves the finite element method (FEM) to simulate the extrusion blow molding process is referred for the engineering design to reduce the trial and error method for setting up the parison thickness (Marcmann, Verron, & Peseux, 00). Nevertheless, the FEM is started by preparing the extrusion blow molding model which is difficult to perform with the

4 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 complex shape bottles. Particularly, it has limitation to suddenly optimize the parison thickness for the desirable thickness of bottle design; therefore, the simulating time is also entirely consumed for the trial and error method to prepare the blow molding model and to determine the suitable thickness of parison. The optimal techniques had been applied to the finite element analysis (FEA) to search an appropriated parison thickness of hollow products (Biglione, Bereaux, Charmeau, Balcaen, & Chhay, 0). The gradient-based optimization scheme had been proposed to determine the optimal bottle thickness that satisfied to resist two types of loading, top load and internal pressure (Gauvin, Thibault, & Laroche, 00). The parison thickness had been controlled using parison programming or die gap opening. The variable thickness of parison enforced researchers to prepare many results of FEA for the optimization of each shape of bottles. The optimal die gap programming of extrusion blow molding process could use the neural network to determine for the uniform part thickness after the parison inflation. Taguchi s method was applied to reduce the number of die gap data of FEA to prepare for training by the artificial neural network (ANN). The parison thickness which was assumed to be equal in the cross section area was predicted by the neural network. The genetic algorithm (GA) was then applied to search for the optimal parison thickness (Jyh-Cheng, Xiang-Xian, Tsung- Ren, & Thibault, 00). The fuzzy neural-taguchi network with GA (FUNTGA) was established to help the parison programming of the bottle which should be an axis symmetric shape. Huang and Huang (00) used POLYFLOW. software to prepare the blow molding data of bottles for the ANN which was combined to find the optimal parison thickness using the GA. Yu and Juang (0) proposed fuzzy reasoning of the prediction reliability to evolve the ANN training to guide the GA searching. The small

5 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 number of training samples was performed using Taguchi s orthogonal array. This methodology balance reliability and optimality for the FEA of extrusion blow molding process. Even though the AI method (ANN, fuzzy logic and GA) was used to optimize the parison thickness for extrusion blow molding process, it was just effective to the axis symmetry bottle shape. Otherwise, the previous AI method did not perform to prepare a suitable parison from the die shaping (Lee, ) for the blow molding process. Unfortunately, the AI method could not be accomplished when the FEA could not perform to prepare the training samples for the complex bottle shape such as rectangular shape bottles, one handle bottles, and two handle bottles. This research proposed the novel simulation technique for the die shaping of the complex shape bottles in the blow molding process. The horizontal cross-section of bottle shape was reasonable to simulate using FEM instead of the full shape bottle for the blow molding process with the shaping die. The ANN and GA were applied for the optimal parison thickness to obtain the uniform cross-section thickness of bottle which could resist the top load specification (Suvanjumrat & Chaichanasiri, 0). The flow chart of the novel approach which is used to determine the suitable parison thickness for the die shaping is shown in Fig... Experiment The lubricant oil bottle is shown in Fig. a. This lubricant oil bottle has a reflective symmetry shape. It was produced with the high density polyethylene (HDPE) grade B of SCG Plastic Company Ltd. The extrusion blow molding process was employed to produce the lubricant oil bottles and set up the producing conditions which were comprised the parison outer diameter of mm, initial temperature of C, die

6 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 gap opening of mm, die closing speed of. mm/sec, blow-up pressure of bar, and blow-up time of 0. sec. The blow molding machine was set three shaping dies without the parison programming. The five parisons which were extruded through the shaping dies had been selected to measure the thickness (Fig. b). The twelve columns (A to F) around parison perimeter were assigned to be the reference line for measuring parison thickness. There were started by A at degrees from the front parting line of bottle in the counterclockwise (CCW) rotation and started by A at - degrees from the front parting line of bottle in the clockwise (CW) rotation. The thickness of parison every level of 0 mm along its height was assigned points to measure the thickness. The thickness gauge model MiniTest 00 FH of ElektroPhysik which had the resolution of ± µm had been used to measure the parison thickness at the reference points. Figure a shows the average thickness graph of parisons which are blew to be the lubricant oil bottles. The parison had been the circular hollow cylinder with the outerdiameter of.00 mm and did not have the uniform thickness around its perimeter. The thickness of parison increased from top to bottom which was affected from the gravity force. The correlation of parison height and thickness can be written as Eq.. = h+ () where is the parison thickness, is the parison height, is. -, and is constant regarding to the initial thickness of parison flow through the die gap. The label area on bottle surface at the bottle height of.00 mm was happened the maximum blow ratio of parison along the bottle height and affected by the unsuitable die shaping which did not blow the uniform thickness around the bottle perimeter. The fifteen bottle samples were selected and measured the wall thickness. The bottle also assigned column positions regarding to the parison columns. Figure b

7 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 shows the average thickness graphs of the lubricant oil bottles along the bottle height. The die shaping had been performed to control the parison thickness to form the complete shape of bottle; therefore the parison thickness which was regarded to be the bottle corner was more thicker than the other column in parison tube. The column of parison which was formed to be the bottle wall as far from the parison axis had thickness more than other parison column. The parison thickness which was obtained by the die shaping method depended on the experience of die maker. The number of shaping to obtain the final shape of die gap related to the time and cost consuming for setting up the extrusion blow molding process of lubricant oil bottles.. The Bottle Blow Molding Models The D model of.0 liter lubricant oil bottle as shown in Fig. is carried out to create finite element model of the blow molding process. The full shape bottle would be created by starting at the mold clamping on parison until the parison was blown to from the bottle shape. The cross-section shape bottle would be investigated at the horizontal section which was high from the bottle bottom of.00 mm. The bottle mold did not clamp on parison to blow the bottle wall at the height of.00 mm; therefore, the clamping process did not consider for creating the finite element model. The bottle blow molding simulation was performed using the personal computer with Core-i CPU. MHz and GB DDR III SDRAM memory.. Full Shape Bottle The blow molding model was prepared using three parts of finite element model, two mold parts and one parison part. Figure c shows the finite element model of full

8 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 shape bottle blow molding process. The parison part was generated by rectangular elements which were total amount of,0 elements. The plate element had the width and length of. and. mm, respectively. The mold parts were placed on two sides of parison for clamping and blowing, respectively. The surface model was assigned to be the bottle mold because the parison was clamped and blown to contact just only the inner surface of mold. The material model of parison was assigned regarding to Thusneyapan and Rugsaj (0). The thickness of parison used the average thickness of the measurement results around parison perimeter to assign the parison thickness model. The fixed boundary condition was defined on nodes at the top edge of parison model. The surface molds were assigned to clamp parison and to hold until the parison was blown to form the final shape of bottle. The blowing pressure of. kpa was defined on the inner surface of parison. The temperature of parison was C while the environmental temperature was C. The simulation result has been shown by the sequent images of blow molding process in Fig. a. The color contour was represented by the thickness of the bottle from start to finish blow molding process. The final thickness of bottle of the simulation result was compared with the experimental data. Figure b shows graphs of bottle thickness for experiment and simulation. The simulation graphs have trended as like as the experimental graphs. The finite element analysis (FEA) obtained an absolute average error of.%.. Cross-Section Shape Bottle The finite element model of cross-section shape bottle blow molding process was more advanced than the full shape bottle blow molding simulation. The interesting

9 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 horizontal section which was high from the bottom of.00 mm was cut to simulate using FEM. This bottle section did not concern the clamping step in the extrusion blow molding process. The initial section of parison was in the clamped mold at the initial step of simulation; therefore, the parison model was prepared in D finite element model. The hollow circular shape of parison model was divided by the rectangular plate elements. The parison thicknesses were defined as same as the experimental data at the bottle height of.00 mm. Total elements to create the parison model were 0 elements. The material property of parison was defined as same as the parison model in the full shape bottle blow molding process. Otherwise conditions comprised internal pressure and environment temperature also defined as same as the full shape bottle. Figure a shows the simulation results of the cross-section shape bottle blow molding process. The cross-section of parison was inflated to form the cross-section shape of bottle at its height of.00 mm. The deformation of parison could be represented by the color contour. The thinner area would be yellow and the thickener area was blue. The bottle thickness could be measured by the distance between inner and outer perimeter of finite element model at the final step of simulation. Figure b shows the comparison graph of bottle thickness between experiment and cross-section shape bottle simulation. The FEA results of the cross-section shape bottle were more agreement with the experiment than the full shape bottle blow molding simulation. The absolute average error of the cross-section shape bottle blow molding simulation which compared with the experiment was.0%. The blow molding simulation using the cross-section shape bottle can improve for more accuracy by setting other material models such as K-BKZ, following works by (Rasmussen & Yu, 00). However, the cross-section shape bottle blow molding

10 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 method has the final thickness of bottle well and can use the cross-section parison thickness at the interested level for determining the initial parison thickness around the die gap using Equation. Particularly, this FEM can use to guide mold makers for die shaping of the extrusion blow molding process in the future work.. Artificial Neural Network The cross-section bottle blow molding simulation was trained using ANN. The back propagation learning method which improved using Levenberg-Marquardt algorithm was defined for the training technique. The ANN architecture with three hidden layers was designed to train the cross-section blow molding process. The multiple input neuron and three hidden layers network was applied to learn the blow molding simulation of the cross-section bottle. The ANN gave the final thickness of cross-section bottle at the desirable height when the parison thicknesses were entered for the input of network. The thicknesses of parison and bottle at,,,, and were defined to be the input and output data, respectively. The Log- Sigmoid transfer function (Rajasekaran & Vijayalakshmi Pai, 00) which is written in Equation is used to be the first and second hidden layer. The linear transfer function was the third hidden layer of network. The first, second and third hidden layer has, and neurons, respectively. ( )= + ( ) () where is the x-value of the sigmoid s midpoint, is the curve s maximum value, and is the steepness of the curve. This work was created the novel orthogonal array to reduce the input data preparation from the FEA. The orthogonal array had a simple concept to arrange

11 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 thickness of parison for the input data. The three level thicknesses around parison perimeter were defined which comprised of the high, middle and low thickness. The parison thickness at two consecutive columns was random for three level thicknesses meanwhile the residual columns were the middle thickness. The level of thickness was arranged for each set of the input data; therefore, the input data of set for training the ANN were defined to prepare for prediction of the cross-section thickness of bottle wall at the interesting bottle height. The ANN which was trained using orthogonal array could be used to predict thickness of the cross-section shape bottle blow molding simulation. The thicknesses around cross-section perimeter of ANN prediction are compared with the FEA which is shown by graphs in Fig.. The absolute average error of ANN when compared with the FEA using five unseen initial parison thicknesses was less than.%. The ANN showed the good agreement of result with the FEA and could use for predicting of the bottle thickness rapidly. The parison tubes of two different shapes of one-liter bottle from the extrusion blow molding process were collected for the ANN testing. The A-shape and B-shape bottle is shown positions to measure parison thickness in Fig. (right). These parisons are blown to be the A-shape and B-shape bottle which is shown in Fig. (left). The interesting cross-section of A-shape and B-shape bottle was at the bottle height of. and.0 mm, respectively. The ANN was tested by input the average thickness of parisons, five parison tubes for each bottle shape, for the input data. The output data of ANN which was the bottle thickness at the interesting horizontal cross-section was compared with the experimental data. Figure a and b shows the comparison graph between the ANN and experiment of A-shape and B shape bottle, respectively. The

12 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 ANN could predict the bottle thickness in good agreement with experiment. The absolute average error of ANN was.% and.0% when compared cross-section thickness with the A-shape and B-shape bottle, respectively.. Genetic Algorithm The top load simulation was performed on the one-liter bottle model to determine the target thickness. The one-liter bottle thickness of. mm was required to support the kg top load. The bottle thickness around perimeter of the interesting cross-section which was equal to the desirable thickness was the optimal solution for the GA. The objective function as the fitness function in GA was designed for determining the initial parison column thicknesses which gave the final bottle thickness as equal to the desirable thickness. This study used the trained ANN prediction model for the fitness function. The goal output of ANN prediction can be written by the following fitness function. ( )= ( ) where is the desirable bottle thickness, is the bottle column thickness, and is the number of bottle column. The GA was applied to search the parison thickness which was blown the uniform cross-section bottle thickness. The parison thicknesses at A to F column of the one-liter bottle are described in Table. The suitable parison thicknesses were specified for the finite element model of the cross-section shape bottle blow molding. The results of blow molding simulation are shown in Fig.. The final thicknesses of parison which were blown to be the one-liter bottle were the uniform thickness. The bottle thickness could be predicted using the ANN. The final thicknesses of bottle at the bottle height of ()

13 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0.00 mm by the FEA and ANN by specifying the suitable parison thicknesses using GA are described in Table. The thickness of bottle of ANN had an absolute average difference from the FEA of 0. mm and was deviated from the target (. mm) by the absolute value of.%. The suitable parison thicknesses which were blown to be the uniform bottle thickness at the desirable bottle height can use to calculate the initial parison thicknesses at the die gap using Equation. The parison thicknesses at the die gap will be used for die shaping of the blow molding process in the further work. It is the good guidance for the die maker of the lubricant oil bottle manufacturing.. Conclusions This research had been developed a novel approach to optimize the parison thickness using hybrid method which was combined the FEM, ANN, and GA. The horizontal cross-section of bottle was developed to simulate the blow molding process using the FEM. It was demonstrated that this FEM was more advantage than the finite element model of the full shape bottle. Otherwise, the cross-section of bottle blow molding simulation was validated with the physical experiment. The thicknesses of cross-section bottle at the interesting height were defined the error and were obtained the simulated results in the good agreement with the experimental data which had an absolute average error less than.0%. The horizontal cross-section bottle blow molding simulation was used to prepare the initial parison and final bottle thickness data to train ANN instead the physical experiment. The novel orthogonal array was developed based on logical connectivity of parison at each zone to optimize amount of data which was required to train ANN. The ANN was demonstrated by comparing with

14 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 the FEA of the cross-section bottle blow molding process using five unseen sets of initial parison thickness. It was found that the absolute average error of ANN was.%. Particularly, it was validated with two complex shape bottles and had an absolute average error less than.% when compared with the physical experiment. Subsequently, the trained ANN was used as the fitness function for the parison thickness optimization using GA. The objective of the GA is the searching of suitable initial parison thickness which could give the uniform thickness around the horizontal cross-section perimeter at the interesting height of the reflective shape bottle. The optimized parison thickness which was obtained by GA was validated using FEM. The cross-section bottle blow molding was simulated using the initial parison thickness which was optimized and searched by GA. The simulated result indicated that the optimized parison thicknesses were successful well to blow the uniform bottle thickness regarding to a thickness target. The absolute deviation of thickness was.% from the target. The optimization of parison thickness by the novel method which was developed in this research could be a guide for shaping the extrusion die by using with the simplified relationship equation between the parison height and thickness. This novel approach to determine initial thickness of parison which was extruded through the die will be useful to guide the die makers for shaping of the die gap in the further works. Acknowledgments The authors wish to thank Panjawattana Plastic Public Company Limited for supporting the experimental devices. References

15 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 Biglione, J., Bereaux, Y., Charmeau, J. Y., Balcaen, J., & Chhay, S. (0). Numerical simulation and optimization of the injection blow molding of polypropylene bottles a single stage process. International Journal of Material Forming, (), -. Gauvin, C, Thibault, F., & Laroche, D. (00). Optimization of blow molded part performance through process simulation. Polymer Engineering & Science, (), -. Huang, G. Q., & Huang, H. X. (00). Optimizing parison thickness for extrusion blow molding by hybrid method. Journal of Materials Processing Technology, (-), -. Jyh-Cheng, Y., Xiang-Xian, C., Tsung-Ren, H., & Thibault, F. (00). Optimization of extrusion blow molding processes using soft computing and Taguchi s method. Journal of Intelligent Manufacturing, (), -. Lee, N. C. (). Plastic Blow Molding Handbook. New York, USA: Van Nostrand Reinhold. Lee, N. C. (). Blow Molding Design Guide. Munich, Germany: Hanser. Marcmann, G., Verron, E., & Peseux, B. (00). Finite element analysis of blow molding and thermoforming using a dynamic explicit procedure. Polymer Engineering & Science, (), -. Rajasekaran, S., & Vijayalakshmi Pai, G. A. (00). Neural Networks, Fuzzy Logic, and Genetic Algorithms. New Delhi, India: Prentice-Hall. Rasmussen, H. K., & Yu, K. (00). On the burst of branched polymer melts during inflation. Rheologica Acta, (), -.

16 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 Suvanjumrat, C., & Chaichanasiri, E. (0). Finite element method for creep testing of high density polyethylene lubricant oil bottles. Kasetsart Journal (Natural Science), (), -. Thusneyapan, S., & Rugsaj, R. (0). Non-linear finite element analysis of timevarying thickness and temperature during the extrusion blow molding process. Journal of Research and Applications in Mechanical Engineering, (), -. Yu, J. C., & Juang, J. Y. (0). Design optimization of extrusion-blow-molded parts using prediction-reliability-guided search of evolving network modelling. Journal of Applied Polymer Science,, -.

17 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 Figure The parison thickness optimization flow chart. Figure (a) The.0 liter lubricant oil bottle, (b) The samples of parison for blowing the.0 liter lubricant oil bottle. Figure The height vs. the average wall thickness of: (a) parison and (b) lubricant oil bottles. Figure The D model of.0 liter lubricant oil bottles in: (a) an isometric view, (b) the detailed dimension, and (c) the finite element model of extrusion blow molding. Figure (a) The sequent image of simulation result of extrusion blow molding process, (b) the height vs. the average wall thickness of lubricant oil bottles between experiment and simulation. Figure (a) The sequent image of simulation result of cross-section shape bottle extrusion blow molding, (b) the column line position vs. the average wall thickness of lubricant oil bottles between experiment and simulation. Figure The column line position vs. the wall thickness of lubricant oil bottles between ANN and simulation at bottle height of.00 mm. Figure The samples of lubricant oil bottle with its cross-section (left) and parison for blowing (right) of: (a) the A-shape and (b) the B-shape lubricant oil bottle.

18 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 Figure The column line position vs. the wall thickness of: (a) A-shape lubricant and (b) B-shape lubricant oil bottle between ANN and experiment at bottle height of. mm and.0 mm, respectively. Figure The sequent image of simulation result of cross-section shape bottle extrusion blow molding process using the suitable parison thickness of GA.

19 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 Table The suitable parison thicknesses for blowing the uniform thickness of cross-section bottle. Table The final thicknesses of bottle at the bottle height of.00 mm of the FEA and ANN by specifying the suitable parison thickness using GA.

20 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page 0 of 0 Targeted uniform bottle thickness Initial parison thickness Input Fitness function FEA ANN Evaluation Selection Crossover Mutation Satisfied? Parison thickness vs height relationship function = h+ Optimized die shape Final bottle thickness Figure Theparison thickness optimization flow chart. GA Optimized parison thickness Output

21 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 (a) Figure (a) The one-liter lubricant oil bottle, (b) The samples of parison for blowing the one-liter lubricant oil bottle. (a) (b) Figure The height vs. the average wall thickness of: (a) parison and (b) lubricant oil bottles. (b)

22 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0. mm mm Mold A A mm (a) (b) (c) Parison Figure TheD model of one-liter lubricant oil bottles in: (a) an isometric view, (b) the detailed dimension, and (c) the finite element model of extrusion blow molding. Mold B

23 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 t = 0 sec t = 0. sec t = 0. sec t = sec t = sec t = 0 sec (a) (b) Figure (a) The sequent image of simulation result of extrusion blow molding process, (b) the height vs. the average wall thickness of lubricant oil bottles between experiment and simulation. Thickness (mm)

24 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 t = 0 sec t = 0. sec t = 0. sec t = 0. sec t = sec t = sec (a) (b) Figure (a) The sequent image of simulation result of cross-section shape bottle extrusion blow molding, (b) the column line position vs. the average wall thickness of lubricant oil bottles between experiment and simulation. Strain

25 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 Figure The column line position vs. the wall thickness of lubricant oil bottles between ANN and simulation at bottle height of.00 mm.

26 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 (a) (b) Figure The samples of lubricant oil bottle with its cross-section (left) and parison for blowing (right) of: (a) the A-shape and (b) the B-shape lubricant oil bottle.

27 Page of Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat 0 (a) Figure The column line position vs. the wall thickness of: (a) A-shape lubricant and (b) B-shape lubricant oil bottle between ANN and experiment at bottle height of. mm and.0 mm, respectively. t = 0 sec t = 0. sec t = 0. sec t = 0. sec t = sec t = sec Figure The sequent image of simulation result of cross-section shape bottle extrusion blow molding process using the suitable parison thickness of GA. (b) Strain

28 Songklanakarin Journal of Science and Technology SJST-0-0.R Suvanjumrat Page of 0 Column Thickness (mm) A ( deg) B ( deg) C ( deg) D ( deg) E ( deg) F ( deg) Table The suitable parison thicknesses for blowing the uniform thickness of cross-section bottle. Bottle Column Thickness (mm) Average Method A ( deg) B ( deg) C ( deg) D ( deg) E ( deg) F ( deg) Thickness (mm) FEM ANN Different Thickness (mm) Table The final thicknesses of bottle at the bottle height of.00 mm of the FEA and ANN by specifying the suitable parison thickness using GA.