Warpage Analyses On Ultra Thin Shell Biology Essay

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Warpage is always considered as a common issue related with injection molding process and always be the main target by mold designers to avoid it. Increasingly difficult to control warpage with too thin plastic products. Therefore many researches and publications were made on this topic purposely to study the most significant factors influence warpage in plastic parts. In this study, an ultra thin shell plastic product is decided to be a subject of analysis. The part with dimension aa x bb x cc and thickness dd is evaluate by using edge gate. Thermoplastic Polycarbonate/Acrylonitrile Butadiene Styrene (PC/ABS) is used as a plastic material. Taguchi Method is applied in identifying the optimum value of injection molding parameters and Moldflow Plastic Insight software is used to simulate the injection molding process. Two experiments have conducted to compare the result of warpage in ultra thin shell parts with same and differences temperature in core and cavity plate (mold temperature). The results shows the different temperature on core and cavity plates has no significant factors in ultra thin shell parts molded but mold temperature is the most significant factor in ultra thin shell parts. This finding is absolutely a good thing to learn of warpage effect in the ultra thin shell produced.

Injection Molding Process is defined as a process used in producing plastic-based products. Efficiency of the injection molding process is highly influenced by correct settings of particular processes. This process generally begins from the stage of designing plastic parts, followed by designing molds, fabricating molds, and setting parameters before an injection molding process takes place and this causes a number of unwanted defects such as weld lines, warpage, sink marks which reduce value of the molded components.

As far as quality is concerned, warpage is one of common effects on molded parts. It is important for the product design engineer to simulate the warpage effect during design process to minimize the modification cost on the mold and to maximize the quality of parts producing. Many researches and publications were made on this topic, both on theoretical simulation and on experimental results to study the behavior of warpage occurred at molded parts.

Tang [1] has applied Taguchi method to minimize warpage on thin plate parts. The gate dimension and the mold temperature were eliminated while ANOVA was used to determine the most significant factor affected warpage. As a result, melting temperature was found to be the most important factor that contributes to the existence of warpage.

Huang and Tai [2] studied the effects of warpage that is seen in thin shell parts produced by injection molding using simulation software. Taguchi method used to determine the optimum value of injection parameters and this led to a finding that packing pressure is the most significant factor that affects warpage and gate locations as well as filling time have only small effects over warpage.

A result acquired by Liao et al. [3] also agrees that packing pressure is the most influential parameter in injection moulding process. His study was done purposely to determine the reactions of a thin walled part according to shrinkage and warpage issues where mould temperature, melt temperature, packing pressure and injection speed were taken as the injection parameters [4]. From the research, it is found that packing pressure is a big factor contributes to the occurrence of warpage.

Z.Shayfull et al. [5] proved that the temperature differences of the core and cavity sides have effects on the quality of plastic part molded.

Yu et al. [6] have studied the injection molding of thin plates of micro sized features. Zhao et al. [7] and Shen et al. [8] have investigated the effects of the process parameters on the micromolding process and part quality, however, investigations for ultra thin shell injection molding is rarely to be reported.

M.C. Song et al. [8] have explored effects of injection process parameters on the molding process for ultra thin wall plastic parts. The ultra-thin wall plastic part is a particular kind of micro plastic parts. It is conventionally defined as the part that have a nominal wall thickness of 1mm or less and a surface area of at least 50 cm2 with a flow length/thickness ratio above 100 or 150.

As far as this issue is concerned, the challenge for the manufacturing engineers nowadays is to produce ultra thin molded parts at minimum warpage. The aim is to find out the most influential factor in ultra thin shell injection molding, so as to reduce the times of trial molding, improve part quality and can be a reference in further investigation of molding defects (warpage, weld line, e.g.) of ultra thin shell plastic parts.

GATING SYSTEM DESIGN

Product design engineers should be able to identify types of to be used. It can be allocate at the surface which user can't see the gate marks after assembly. However sometimes it had to be placed on cosmetic surfaces and normally it will covered by sticking label. Fig. 1 shows a thin shell parts (A x B x C) mm with thickness XX mm and gating system with pin point gate (three-plate mold).

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The details of gating system with complete dimensions are shown in details in Fig. 2.

Fig. 3 shows the cooling channel with Ø6mm designed for the mold.

EXPERIMENT

There are many factors that affect the quality of plastic products produced from injection molding process. It includes a design of plastic product, plastic material used, types of cavity insert materials, types of machines, machine parameters, coolant design, coolant size, coolant liquid and room temperature. In this study, only a few major factors are taken into considerations and some assumptions are made which are;

Gate dimension factor is neglected because of its design is not identical for every part.

The temperature of the environment is assumed constant.

The coolant is assumed as pure water.

The effects of other minor factors (Other than melting temperature, mold temperature, filling and packing processes) are not to be under the topic of discussion.

The layout of the cooling channels is assumed to maintain a constant temperature.

The effects due to the shape and size of the mold and product are neglected due to various shapes of product.

The plastic material used in all of the simulations is amorphous thermoplastic PC/ABS blend, Cycoloy C2950HF from GE. Its viscosity is between 102 and 104 poise where the shear rate is in 102-103 s-1 range. The range of melt temperature is between 220 oC and 400oC approximately.

Basic physical and mechanical properties of PC/ABS are shown in Table 1.

Table 1: The physical properties of PC/ABS

Specific heat, Cp(J/kgoC)

1871

Glass transition temperature, Tg(oC)

112

Thermal expansion coefficient, α (mm/moC)

74

Elastic modulus, E (MPa)

2.63 x 103

Poisson's ratio, Ï…

0.23

Thermal conductivity, K(w/moC)

0.27

Fig. 4 shows the two cavities of thin shell generated with 67,270 pieces of 1mm length triangular.

Two experiments have been conducted to get the best setting parameters and to determine the most significant factors affected warpage on ultra thin shell parts. Four factors (A-D) are identified to be controlled in Experiment-I and five factors (A-E) controlled in Experiment-II. L9 34 is chosen for experiment-I and L16 45 are chosen for experiment-II. These orthogonal array variance and parameters control factors are shown in Table 2, 3,4,5,6 and 7 respectively. The differences between Experiment-I and II is Experiment-I considered only mold temperature, that means the temperature core and cavity side is same but Experiment-II considered the different temperature for cavity and core side. The injection time is set 0.1s. This is because, from the simulation, if the injection time more than 0.1s, some of combination parameters will result short-shot on the molded parts.

The Signal-to-noise (S/N) ratio is calculated according to results (deflection in z-direction) form warpage analysis of thin shell plate as shown in Table 8 and 9 in order to obtain the best parameter setting arrangement. From this technique, the percentage of contribution is calculated in determining which of the factor has significant effect on part's warpage.

Table 2: The three level of effective factor for experiment-I variance

Factor

Level

1

2

3

Mold temperature, A (°C)

60

70

80

Melt temperature, B (°C)

260

270

280

Packing pressure, C (MPa)

70%

80%

90%

Packing time, D (s)

0.7

0.8

0.9

Table 3: The four level of effective factor for experiment-II variance

Factor

Level

1

2

3

4

Mold temperature, A (°C)

65

70

75

80

Core temperature, B (°C)

65

70

75

80

Melt temperature, C (°C)

265

270

275

280

Packing pressure, D (MPa)

75%

80%

85%

90%

Packing time, E (s)

0.75

0.80

0.85

0.90

Table 4: L9 Orthogonal array variance

for experiment-I

Trial No.

Control Factor

A

B

C

D

1

1

1

1

1

2

2

1

2

2

3

3

1

3

3

4

1

2

2

3

5

2

2

3

1

6

3

2

1

2

7

1

3

3

2

8

2

3

1

3

9

3

3

2

1

Table 5: L16 Orthogonal array variance

for experiment-II

Trial No.

Control Factor

A

B

C

D

E

1

1

1

1

1

1

2

1

2

2

2

2

3

1

3

3

3

3

4

1

4

4

4

4

5

2

1

2

3

4

6

2

2

1

4

3

7

2

3

4

1

2

8

2

4

3

2

1

9

3

1

3

4

2

10

3

2

4

3

1

11

3

3

1

2

4

12

3

4

2

1

3

13

4

1

4

2

3

14

4

2

3

1

4

15

4

3

2

4

1

16

4

4

1

3

2

Table 6: The combination parameters for the control factors for experiment-I

Trial No.

Control Factor

A

B

C

D

1

60

260

70

0.7

2

70

260

80

0.8

3

80

260

90

0.9

4

60

270

80

0.9

5

70

270

90

0.7

6

80

270

70

0.8

7

60

280

90

0.8

8

70

280

70

0.9

9

80

280

80

0.7

Table 7: The combination parameters for the control factors for experiment-II

Trial No.

Control Factor

A

B

C

D

E

1

65

65

265

75

0.75

2

65

70

270

80

0.80

3

65

75

275

85

0.85

4

65

80

280

90

0.90

5

70

65

270

85

0.90

6

70

70

265

90

0.85

7

70

75

280

75

0.80

8

70

80

275

80

0.75

9

75

65

275

90

0.80

10

75

70

280

85

0.75

11

75

75

265

80

0.90

12

75

80

270

75

0.85

13

80

65

280

80

0.85

14

80

70

275

75

0.90

15

80

75

270

90

0.75

16

80

80

265

85

0.80

The deflection of thin shell parts in z-direction obtained from the simulation process are also analyzed using Analysis of Variance (ANOVA) where the level of confidence is set at 0.05. The results from ANOVA are compared with SN ratio method. The interaction effect of factors is identified and the contribution of each factor towards the total effect is analyzed.

RESULT AND DISCUSSION

Table 8 and 9 show the results of warpage analysis (deflection in z-direction) for thin shell parts. In this case, 'the smaller the better quality' equation from Taguchi method is chosen. The equation of S/N is shown below;

MSD is the mean square deviation and stands for the number of observations where , is the number of tests in one trial. The summary of S/N values for the warpage of thin shell parts is shown in Tables 8 and 9.

Table 8: Summary of the Results of warpage in thin shell parts

Trial No.

Control Factor

Deflection

Z-Direction

S/N Ratio

A

B

C

D

1

60

260

70

0.7

0.0659

23.6223

2

70

260

80

0.8

0.0579

24.7464

3

80

260

90

0.9

0.0469

26.5765

4

60

270

80

0.9

0.0533

25.4655

5

70

270

90

0.7

0.0617

24.1943

6

80

270

70

0.8

0.0527

25.5638

7

60

280

90

0.8

0.0559

25.0518

8

70

280

70

0.9

0.0509

25.8656

9

80

280

80

0.7

0.0448

26.9744

Table 9: Summary of the Results of warpage in thin shell parts

Trial No.

Control Factor

Deflection Z-Direction

S/N Ratio

A

B

C

D

E

1

65

65

265

75

0.75

0.0562

25.0053

2

65

70

270

80

0.80

0.0533

25.4655

3

65

75

275

85

0.85

0.0545

25.2721

4

65

80

280

90

0.90

0.0478

26.4114

5

70

65

270

85

0.90

0.0545

25.2721

6

70

70

265

90

0.85

0.0546

25.2561

7

70

75

280

75

0.80

0.0517

25.7302

8

70

80

275

80

0.75

0.0454

26.8589

9

75

65

275

90

0.80

0.0561

25.0207

10

75

70

280

85

0.75

0.0452

26.8972

11

75

75

265

80

0.90

0.0536

25.4167

12

75

80

270

75

0.85

0.0488

26.2316

13

80

65

280

80

0.85

0.0458

26.7827

14

80

70

275

75

0.90

0.0530

25.5145

15

80

75

270

90

0.75

0.0438

27.1705

16

80

80

265

85

0.80

0.0590

24.5830

The data of deflection in z-direction for the thin shell parts also analyzed using Analysis of Variance (ANOVA). The relative percentage contribution of all factors is determined by comparing the relative variance. Then the degrees of freedom, variance, F-ratio, sums of squares, pure sum of square and percentage contribution are computed. The examples of calculations are shown below and the results of S/N ratio for warpage in thin shell are listed in Tables 8 and 9.

Table 10: The response table of S/N ratio for WARPAGE in THIN SHELL FROM EXPERIMENT-i

Level

A

B

C

D

1

24.7132

24.9818

25.0172

24.9303

2

24.9355

25.0745

25.7288

25.0731

3

26.3716

25.9639

25.2742

25.9692

Diff.

1.6584

0.9822

0.7115

1.0389

Table 11: The response table of S/N ratio for WARPAGE in Thin SHELL FROM EXPERIMENT-II

Level

A

B

C

D

E

1

25.5386

25.5202

25.0653

25.6204

26.4830

2

25.7793

25.7833

26.0349

26.1309

25.1998

3

25.8916

25.8974

25.6665

25.5061

25.8856

4

26.0127

26.0212

26.3176

25.9647

25.6537

Diff.

0.4741

0.5010

1.2523

0.6249

1.2831

Fig. 5 - 8 show S/N response diagrams constructed for the warpage in a thin shell (Experiment-I) based on data acquired from Table 10. Fig. 9 - 13 show S/N response diagrams constructed for the warpage in a thin shell (Experiment-II) based on data acquired from Table 11.

From the S/N ratio response in Tables 10 and 11, the highest value from each factor is considered the best and chosen as the finest grouping of parameters. Tables 12 and 13 show the summary of best parameter settings for the thin shell based on Experiment-I and II. The results can also be seen from S/N response diagram shown in Fig. 5-8 for Experiment-I and Fig. 9-13 for Experiment-II.

Table 12: Best setting of combination parameters FOR EXPERIMENT-I

Factor

Parameters

Mold temperature, (°C)

80

Melt temperature, (°C)

280

Packing pressure, (MPa)

80%

Packing time, (s)

0.9

Table 13: Best setting of combination parameters FOR EXPERIMENT-II

Factor

Parameters

Cavity temperature, (°C)

80

Core temperature, (°C)

80

Melt temperature, (°C)

280

Packing pressure, (MPa)

80%

Packing time, (s)

0.75

Furthermore the difference between levels in Table 10 and 11 also shows which factor is most significant that give effects on warpage thin shell parts. From Table 10 for Experiment-I, the most major factor that affects on length of weld line in thin plate is mold temperature (A) and followed by packing time (D), melt temperature (B) and packing pressure (C). Otherwise, for Experiment-II, the most major factor that affects on length of weld line in thin plate is melt temperature (C) and followed by packing time (E), packing pressure (D), core temperature (B) and cavity temperature (A).

Table 14: MOST SIGNIFICANT FACTORS AFFECTED ON WARPAGE THIN SHELL PARTS

Most Significant Factor

Experiment-I

Experiment-II

1.

Mold temperature (A)

Melt temperature (C)

2.

Packing time (D)

Packing time (E)

3.

Melt temperature (B)

Packing pressure (D)

4.

Packing pressure (C)

Core temperature (B)

5.

N/A

Cavity temperature (A).

The differences results of analysis in Table 14 are due to a different level, and factor in the analysis that was done. In Experiment-I only four factors are considered and the level of factors is three, but in Experiment-II, five factors are considered and the level of factors is four. In Experiment-II, the melting temperature is the most significant factors compared to the mold temperature in Experiment-I. This is because, in Experiment-II, mold temperature has divide into core and cavity temperature to study the effect of difference temperature of mold on warpage in thin shell parts. Two more simulation have been done based on best setting of combination parameters in Tables 12 and 13 to get the very best setting combination parameters in order to get the minimum warpage. Table 14 shows the results of deflection z-direction for the best setting combination parameters.

Table 14: Best setting of combination parameters

Parameters Setting

Factor

Parameters-I

Parameters-II

Mold Temperature, (°C)

80

N/A

Cavity temperature, (°C)

N/A

80

Core temperature, (°C)

N/A

80

Melt temperature, (°C)

280

280

Packing pressure, (MPa)

80%

80%

Packing time, (s)

0.90

0.75

Deflection

Z-Direction, (mm)

0.0449

0.0449

Results in Table 14 shows the deflection in z-direction is same if only packing time is changed. So, from the experiment conducted, the best parameters and warpage for thin shell parts as per Table 14.

The deflection z-direction data in Tables 8 and 9 are also analyzed using Analysis of Variance (ANOVA) that computes the sums of squares, degrees of freedom, variance and percentage contribution. The examples of calculations are shown below and the results ANOVA of warpge in thin shell parts are summarized in Table 15 and 16.

Table 15: ANOVA TABLE FOR THIN SHELL PARTS FROM EXPERIMENT-I

Source

f

S

V

F

P(%)

Mold Temperature, (°C)

2

18.300 x 10-05

9.138 x 10-05

-

50.14

Melt

temperature, (°C)

2

7.034 x 10-05

3.517 x 10-05

-

19.27

Packing

pressure, (MPa)

2

3.110 x 10-05

1.553 x 10-05

-

8.52

Packing time, (s)

2

8.060 x 10-05

4.031 x 10-05

-

22.08

Pooled error

0

0.000

100.00

Total

8

36.500 x 10-05

Table 16: ANOVA TABLE FOR THIN SHELL PARTS FROM EXPERIMENT-II

Source

f

S

V

F

P(%)

Cavity Temperature, (°C)

3

1.455 x 10-05

4.851 10-06

-

4.45

Core

Temperature, (°C)

3

1.855 x 10-05

6.184 10-06

-

5.68

Melt

temperature, (°C)

3

1.458 x 10-05

4.860 10-05

-

44.63

Packing

pressure, (MPa)

3

3.538 x 10-05

1.179 10-05

-

10.83

Packing time, (s)

3

1.124 x 10-05

3.746 10-05

-

34.40

Pooled error

0

0.000

100.00

Total

12

3.267 x 10-04

The percentage of contribution for each factor is listed at the last column in Tables 15 and 16. The percentage contribution of each factor for warpage in thin shell parts can be seen clearly in Table 17.

It can be observed that in Experiment-I, mold temperature contributes the most which is 50.14% followed by packing time 22.08%, melt temperature 19.27% and packing pressure 8.52%. In Experiment-II, the most contribution factors is melt temperature 44.63%, packing time 34.40%, packing pressure 10.83%, core temperature 5.68% and cavity temperature 4.45%.

Table 17: Summary of PERCENTAGE CONTRIBUTION for WARPAGE IN THIN SHELL PARTS

Experiment-I

Experiment-II

Parameters

%

Parameters

%

Mold Temperature, (°C)

50.14

Melt

Temperature, (°C)

44.63

Packing Time, (s)

22.08

Packing Time, (s)

34.40

Melt

Temperature, (°C)

19.27

Packing

Pressure, (MPa)

10.83

Packing

Pressure, (MPa)

8.52

Core Temperature, (°C)

5.68

Cavity Temperature, (°C)

4.45

From the results, it can be seen that's although mold temperature is the most significant factor in Experiment-I, It is no longer significant factor in experiment-II when the mold temperature is divided into cavity and core temperature. This is proved by setting parameters as Shown in Table 14. The best setting parameters from Experiment-I and II is same except packing time but the result of warpage for both best setting parameters is same that is 0.0449mm.

CONCLUSION

Previous studies in injection molding process used fixed temperature value for mold temperature (cavity temperature and core temperature). For instance, M.C. Song et al. [X] did not considered mold temperature in their study in research on effects of injection process parameters on the molding process for ultra-thin wall plastic parts. Tang [3] and Huang and Tai [4] maintained same temperature for cavity and core temperature in simulation and experimental of warpage on thin plate and thin shell plate. In contrast, this research focuses the comparison on effect of same temperature in cavity and core side and effect of difference value of temperature on core and cavity on thin shell parts. The conclusions of the research are as follows;

Taguchi orthogonal array can effectively reduce the number of trials in mold testing. The effective factors can be determined using ANOVA.

For thin shell parts, results show that the differences cavity and core temperature is not a significant factors as shown in Experiment-II but the mold temperature (cavity and core temperature are same) is the most significant factors in contribution warpage as can be seen in Experiment-I.

The influence of all factors that contributes to warpage has been characterized believed to be helpful in determining more precise process conditions in determining injection molding parameters.

There are several factors affected warpage on the molded part such as feed systems design, cooling channel size, cooling channel positions and gate sizes that need to be determined first in order to design a plastic injection mold. This study has proven that the differences cavity and core side has no significant effects on warpage of thin shell and simulation software can help us reducing time taken to test the mold with the optimum quality of part produced.

[1] Huang MC, Tai CC. The effective factors in the warpage problem of an injectionmolded part with a thin shell feature. Journal of Material Processing Technology 110 (2001) 1-9.

[2] S.H. Tang, Y.J. Tan, S.M.Sapuan, S.Sulaiman, N. Ismail, R. Samin, The use of Taguchi method in the design of platis injection mould for reducing warpage, Journal of Material Processing Technology 182 (2007) 418-426.

[3] Liao SJ, Chang DY, Chen HJ, Tsou LS, Ho JR, Yau HT, et al. Optimal process conditions of shrinkage and warpage of thin-wall parts. Polym Eng Sci 2004;44(5):917-28.

[4] Think Thin, Asian Plastics News, July/August 1996, pp. 12-14.

[5] Z. Shayfull, M.F. Ghazali, M. Azaman, S.M. Nasir, N.A. Faris, Effect of Differences Core and Cavity Temperature on Injection Molded Part and Reducing the Warpage by Taguchi Method, International Journal of Engineering & Technology, Vol: 10, 2010, pp. 133-140.

[6] L.Y. Yu, C.G. Koh, L.J. Lee, Experimental investigation and numerical simulation of injection molding with micro-features, Polym. Eng. Sci. 42 (5) (2002) 871-888.

[7] J. Zhao, R.H. Mayes, G. Chen, Effects of process parameters on micro molding process, Polym. Eng. Sci. 43 (9) (2003) 1542-1554.

[8] Y.K. Shen, S.L. Yeh, S.H. Chen, Three-dimensional non-newtonian computations of micro-injection molding with the finite element method, Int. Comm. Heat Mass Transfer 29 (5) (2002) 643-652.

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