pertanika j. sci. & technol. 25 (s): 233 - 240 (2017 ... papers/jst vol... · pertanika j. sci....

Pertanika J. Sci. & Technol. 25 (S): 233 - 240 (2017)

SCIENCE & TECHNOLOGYJournal homepage: http://www.pertanika.upm.edu.my/

ISSN: 0128-7680 © 2017 Universiti Putra Malaysia Press.

ARTICLE INFO

Article history:Received: 28 September 2016Accepted: 03 February 2017

E-mail addresses: [email protected] (Najiy Rizal Suriani Rizal),[email protected] (Aidah Jumahat),[email protected] (Azuddin Mamat) *Corresponding Author

Analysis of Fill Time and Injection Pressure of Multiple 20 gram Parisons during Injection Moulding Process

Najiy Rizal Suriani Rizal1, Azuddin Mamat2 and Aidah Jumahat1*1Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM), 40450, Shah Alam, Selangor, Malaysia2Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya (UM),50603 Kuala Lumpur, Malaysia

ABSTRACT

In recent years, injection moulding process is one of the most advanced and efficient manufacturing processes for mass production of plastic bottles. However, a good quality of parison is difficult to achieve due to uncontrollable humidity, pressure inlet and water inlet velocity. This paper investigates the effect of using multiple mould cavities to improve the process fill time and injection pressure in the production of PET plastic bottles using MoldFlow software. The modelling of parison was developed using CATIA with the consideration of every part of the parison. MoldFlow software was used to analyse the flow of 20 g parison with different cavity numbers (1, 8, 16, 24 cavity), as well as its corresponding runner size towards its fill time and injection pressure. Other important parameters that affect the production of parison, such as melting temperature, mould temperature, atmospheric temperature and cooling time, were remained constant. The fill time required to produce 24 moulds was improved by 60% compared to using 8 mould cavity only, and this enable the production of more plastic bottles in a day. Therefore, fill time and injection pressure are two important parameters to be considered in the injection moulding process, especially to reduce parison defect and increase its production rate.

Keywords: Injection moulding, MoldFlow, mould design, parison, thermoplastic

INTRODUCTION

The plastic industry is one of the most vibrant sectors in Malaysia. The growth of domestic downstream plastic processing activities is attributed to the tremendous development in the petrochemical sector in the country. However, higher production costs and environmental concerns are plaguing the

Najiy Rizal Suriani Rizal, Azuddin Mamat and Aidah Jumahat

234 Pertanika J. Sci. & Technol. 25 (S): 233 - 240 (2017)

industry. The conventional injection moulding machine that is exported to Malaysia is small in size and limited in shape, both critical factors that affect the production process of parison, also called preform (Rizal et al., 2015).

Daver and Demirel (2012) examined the effects of preform deformation behaviour and the optimum cooling time on the quality of bottle preform. These effects were determined by conducting structural analysis on the actual bottles. Hedia, Aldousari and Zager (2010) studied the optimal design for PET bottle in order to maximise its reliability by using numerical simulation to analyse the effect of the design ( Hedia, Aldousari, & Zager, 2010). De Miranda et al. (2011) studied the design optimisation and weight reduction of 500 mL CSD PET bottle through FEM simulations. These studies examined the simulation of the process and the mechanical demands related to the PET bottle application with the aim of assessing the efficiency of packaging design and enabling efficient and correct sizing. Chen (2011) used a genetic algorithm method to simulate and analyse optimisation process parameters for Multi-cavity injection moulding parts warpage. It was found that multi-cavity mould runner arrangements could seriously affect the warpage changes of the parts.

Li and Jia (2011) studied the structural characteristics of mould for precise injection moulding. The current paper examined the influence of mould structure on the quality of injection-moulded parts. Taghizadeh et al. (2013) focused on warpage prediction in plastic injection moulded part using artificial neural network. Similarly, Nian et al. (2015) studied warpage control of thin-walled injection moulding using local mould temperatures. Wang et al. (2013) investigated the reduction of sink mark and warpage of the moulded part in rapid heat cycle moulding process.

The amount of materials injected into the parison mould is difficult to control, contributing to low quality products. This leads to an increase in the number of rejected products, which translates into high production cost and wastage. There has been no comprehensive study on the effect of number of cavity on fill time and injection pressure.

The present work used MoldFlow with Polyethylene Terephthalate (PET) mechanical properties and parameters to stimulate and investigate the effect of 20 g parison on fill time and injection pressure. A robust design of parison mould with greener injection moulding system was modelled using CATIA V5R20.

METHOD

The Geometrical Acquisition and general parameter of actual mould 20 g parison was done and analysed. The mould is taken from Bakal Sejati to undergo Coordinate Measuring Machine (CMM) BEYOND707 Mitutoyo to get the geometrical measurement of the actual size of the parison. The general parameter for the injection moulding process was also acquired from the same company as shown in Table 1.

Parameter Analysis during Injection Moulding Process

235Pertanika J. Sci. & Technol. 25 (S): 233 - 240 (2017)

After the measurement was collected form CMM, the geometrical measurement is modelled using CATIA V5R20 Dassault System to produce a CAD model of 20 g parison as show in Figure 1. The measurement is based on the actual model with dimensions drawn in mm.

Table 1 General acquired parameters for 20 g parison

Factor Parameter of 20 g ParisonMould Temperature 100°CMelt Temperature 280°CPacking time 10 SecPacking Pressure 140 MPaCooling time 17 SecCooling temperature 30°CAmbient temperature 35°C

Cooling time 17 Sec

Cooling temperature 30°C

Ambient temperature 35°C

After the measurement was collected form CMM, the geometrical measurement is

modelled using CATIA V5R20 Dassault System to produce a CAD model of 20 g parison as

show in Figure 1. The measurement is based on the actual model with dimensions drawn in

mm.

Figure 1. The geometry of the 20 g parison

The modelled geometry was then transferred to Autodesk Moldflow Insight 2011

Educational Edition software to analyse the parison model by following the actual injection

moulding process. The parameter and material properties are input into this software.

Simulation of the injection moulding process was conducted using a variation of number

cavities consisting of 1 cavity, 8 cavities, 16 cavities and 24 cavities respectively, with the

Figure 1. The geometry of the 20 g parison

The modelled geometry was then transferred to Autodesk MoldFlow Insight 2011 Educational Edition software to analyse the parison model by following the actual injection moulding process. The parameter and material properties are input into this software. Simulation of the injection moulding process was conducted using a variation of number cavities consisting of 1 cavity, 8 cavities, 16 cavities and 24 cavities respectively, with the process parameter in Table 1. Figure 2 below is the parison modelled using MoldFlow simulation. The input parameters for simulation are shown in Table 2.



The properties of materials correlate with the process parameters of injection moulding and mould modelling. The Polyethylene Terephthalate (PET) used in this research is Eastar Copolyester EN067 that was supplied by Eastman Chemical Products. PET is a thermoplastic polymer that has good ductility, strength, hardness and stiffness while the amorphous PET has better ductility.

20 g parison on the viscous plastic flow and the rate of filling time is analysed. The effect of number of cavities and on fill time and on injection pressure is examined.

RESULTS AND DISCUSSION

Four different numbers of cavities (1 cavity, 8 cavities, 16 cavities and 24 cavities) were analysed using MoldFlow to determine the fill time and injection pressure. The number of cavities affect the fill time and injection pressure of the parison. The runner size also varies as the number of cavities increases. Figure 3 shows the time taken to fill 1, 8, 16 and 24 cavities are 0.5236 s, 0.7745 s, 0.8737 s and 1.145 s respectively. The result shows the increase in number of cavities actually increases production. The difference in time between these four cavities was an increase of about 60%.

Table 2 The parameter input for MoldFlow software

Factor Parameter of 20 g ParisonMould temperature 100°CMelt Temperature 280°CAtmospheric temperature 35°CCooling time 17 sRunner Size 8 mm

process parameter in Table 1. Figure 2 below is the parison modelled using Moldflow

simulation. The input parameters for simulation are shown in Table 2.

Figure 2. The parison of multi-cavity using Moldflow software

Table 2

The parameter input for Moldflow software

Factor Parameter of 20 g Parison

Mould temperature 100°C

Melt Temperature 280°C

Atmospheric temperature 35°C

Cooling time 17 s

Runner Size 8 mm

The properties of materials correlate with the process parameters of injection moulding

and mould modelling. The Polyethylene Terephthalate (PET) used in this research is Eastar

Copolyester EN067 that was supplied by Eastman Chemical Products. PET is a thermoplastic

Figure 2. The parison of multi-cavity using MoldFlow software



Table 3 shows the percentage of volume filling at specific flow time for 1 cavity, 8 cavities, 16 cavities and 24 cavities. Data shows that it takes a longer time to fill 24 cavities. However, in terms of production, the 24 cavity produced much higher parison compared with 8 cavities. The efficiency of the 24 cavity parisons in terms of production is better than a 16 cavity and 8 cavity parisons but in terms of overhead cost, it is more expensive to produce a 24 cavity parison.

Figure 3. Viscous plastic flow analysis of PET material for four different models; (i) 1 cavity; (ii) 8

cavities; (iii) 16 cavities; and (iv) 24 cavities

Table 4 shows the result for the fill time and injection pressure for the four cavities (1

cavity, 8 cavities, 16 cavities and 24 cavities). From the tabulation below, the fill time for 24

cavity parison is within 1.145s which is higher compared with a single cavity. However, if the

comparison is made in terms of fill time per parison, a 24-mould cavity parison filled much

faster compared with single cavity, which is 0.0475 s per parison and 0.5236 s per parison

respectively.

Figure 3. Viscous plastic flow analysis of PET material for four different models; (i) 1 cavity; (ii) 8 cavities; (iii) 16 cavities; and (iv) 24 cavities

Table 4 shows the result for the fill time and injection pressure for the four cavities (1 cavity, 8 cavities, 16 cavities and 24 cavities). From the tabulation below, the fill time for 24 cavity parison is within 1.145s which is higher compared with a single cavity. However, if the comparison is made in terms of fill time per parison, a 24-mould cavity parison filled much faster compared with single cavity, which is 0.0475 s per parison and 0.5236 s per parison respectively.



Table 3 Percentage of volume filling at specific flow time of parison by number of cavity

Cavity & percentageFill Time (s)

1 (%) 8 (%) 16 (%) 24 (%)

0

0% at 0s

0% at 0s

0% at 0s

0% at 0s

0.1

20.84% at 0.1091s

16.67% at 0.1291s

12.46% at 0.1089s

12.50% at 0.1431s

0.3

58.35% at 0.3055s

41.67% at 0.3227s

37.50% at 0.3276s

29.16% at 0.3339s

0.5

100% at 0.5236s

66.66% at 0.5163s

58.33% at 0.5097s

45.83% at 0.5247s

0.7 100% at 0.7745s

83.34% at 0.7281s

62.49% at 0.7155s

0.8

100% at 0.8737s

79.16% at 0.9064s

1.0 100% at 1.1450s

Table 4 Parameter analysis for number of cavity

Number of Cavity 1 8 16 24Fill time (s) 0.5236 0.7745 0.8737 1.145Fill time / parison 0.5236 0.0968 0.0546 0.0475Injection pressure (MPa) 20.1371 36.8453 32.6802 30.6973



Figure 4 shows that when the number of cavity increases the fill time also increases. The number of cavities affects the fill time in mass production. Increasing the number of cavities will produce more parison in terms of mass production. In order to produce 24 parison, the process will take 1.145 s when using 24 cavities mould, whereas it takes 12.5664 s for single cavity and 2.3235 s for 8 cavities to produce 24 parison respectively.

Figure 4. Fill time against number of cavities

Figure 4 shows that when the number of cavity increases the fill time also increases. The

number of cavities affects the fill time in mass production. Increasing the number of cavities

will produce more parison in terms of mass production. In order to produce 24 parison, the

process will take 1.145 s when using 24 cavities mould, whereas it takes 12.5664 s for single

cavity and 2.3235 s for 8 cavities to produce 24 parison respectively.

Number of Cavity 1 8 16 24

Fill time (s) 0.5236 0.7745 0.8737 1.145

Fill time / parison 0.5236 0.0968 0.0546 0.0475

Injection pressure (MPa) 20.1371 36.8453 32.6802 30.6973

Figure 4. Fill time against number of cavities

Figure 5. Injection pressure against number of cavities

Figure 5 shows the injection pressure affects the number of cavities from 8 to 24 cavities.

There was a decrease in injection pressure from 8 to 24 cavities mould. This was due to the

increase of sprue, runner and gate as they automatically change with the increase of mould

cavity to reduce part malfunction from excessive pressure.

CONCLUSION

The main objective of this study was to investigate the effect of number of cavity on fill time

and injection pressure. The simulation using Moldflow was carried out to study the effect of

cavity numbers on fill time and injection pressure. The process parameters considered during

the analysis are melting temperature, mould temperature, atmospheric temperature and

cooling time. The change in fill time and injection pressure differs based on Figure 4 and

Figure 5. Future studies can look at how to overcome the change in mould temperature.

ACKNOWLEDGEMENTS

Figure 5. Injection pressure against number of cavities

Figure 5 shows the injection pressure affects the number of cavities from 8 to 24 cavities. There was a decrease in injection pressure from 8 to 24 cavities mould. This was due to the increase of sprue, runner and gate as they automatically change with the increase of mould cavity to reduce part malfunction from excessive pressure.



CONCLUSION

The main objective of this study was to investigate the effect of number of cavity on fill time and injection pressure. The simulation using MoldFlow was carried out to study the effect of cavity numbers on fill time and injection pressure. The process parameters considered during the analysis are melting temperature, mould temperature, atmospheric temperature and cooling time. The change in fill time and injection pressure differs based on Figure 4 and Figure 5. Future studies can look at how to overcome the change in mould temperature.

ACKNOWLEDGEMENTS

The authors thank Institute of Research Management and Innovation (IRMI), Ministry of Education Malaysia and Institute of Graduate Studies (IPSIS) UiTM for their financial support. This research was performed at the Faculty of Mechanical Engineering, UiTM Malaysia under Grant BESTARI 600-IRMI/DANA 5/3/BESTARI (0006/2016).

REFERENCESChen, W. J. (2011). Simulation and Analysis of Optimization Process Parameters for Multi Cavity

Injection Molding Parts Warpage by Genetic Algorithm Method. Applied Mechanics and Materials, 142, 54–57.

Daver, F., & Demirel, B. (2012). A simulation study of the effect of preform cooling time in injection stretch blow molding. Journal of Materials Processing Technology, 212(11), 2400–2405. https://doi.org/10.1016/j.jmatprotec.2012.06.004

De Miranda, C. A. S., Camara, J. J. D., Monken, O. P., & Dos Santos, C. G. (2011). Design Optimization and Weight Reduction of 500 mL CSD PET Bottle through FEM Simulations. Journal of Materials Science and Engineering B, 1(7), 947–959.

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Suriani Rizal, N. R., Jumahat, A., Hashim, U. R., & Omar, M. S. (2015). Effect of Pet Parison Size on Injection Moulding Mould Design. Jurnal Teknologi, 76(6), 115-119. https://doi.org/10.11113/jt.v76.5702

Taghizadeh, S., Ozdemir, A., & Uluer, O. (2013). Warpage Prediction in Plastic Injection Molded Part using Artifical Neural Network, 37, 149–160.

Wang, X., Zhao, G., & Wang, G. (2013). Research on the reduction of sink mark and warpage of the molded part in rapid heat cycle molding process. Materials and Design, 47, 779–792. https://doi.org/10.1016/j.matdes.2012.12.047

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