Injection Molded Nylon Pa66 Side Arms Biology Essay

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Side arm is used as a mould in a latex-dipping process of manufacturing urinary catheters. A research previously found that a new side arm can be made from Nylon PA66 using an injection moulding process which was much more cost effective. However, there is an unwanted warpage issue that frequently occurred on each of the Nylon PA66 side arms after the process takes place. Therefore, this paper discusses effects of a number of parameters involved in producing Nylon PA66 side arms that lead to this warpage issue by means of an injection moulding process. The parameters concerned are melt temperature, filling time, packing pressure and packing time. A model of side arm is designed and simulated using simulation software to imitate the real operation of an injection moulding process. These parameters are then analyzed with respect to the deflections occurred using Taguchi method and further verified by using Analysis of Variance (ANOVA) technique. At the end of this study, it is found that melt temperature and packing time play the most significant role to the existence of warpage.

EARLIER studies on aluminum side arms has confirmed that the material which is Aluminum led to high reject rate of manufacturing latex based urinary catheters[1]. As an alternative, new material Nylon PA66 had been chosen to replace the Aluminum body side arms as shown in Fig. 1 and it had been successfully tested and passed the same requirement as needed by the manufacturer [2].

Fig. : Nylon PA66 side arm [1].

Warpage was always found to be the most frequent defect in producing Nylon PA66 side arms. The newly injected side arm bodies always tend to warp due to uneven residual stress right after it cools down due to temperature variations affected from varying wall sections throughout cooling period. M.F. Ghazali [1] in his previous study highlighted 4 critical parameters which are melt temperature, filling time, packing pressure and packing time as the factors to be emphasized and any other factors were kept constant. All 4 factors were chosen with 3 levels in constructing an L9 34 array and as a result, the warpage effects with respect to z-axis are shown in Table 1.

TABLE : L9 ORTHOGONAL ARRAY RESULTS OF

WARPAGES AND S/N VALUES [1]

Trial No.

Control Factors

Max z

Melt temp

Filling time

Packing Pressure

Packing Time

1

260

0.1

60

0.6

2.073

2

260

0.3

75

0.8

1.322

3

260

0.5

90

1.0

0.9588

4

275

0.1

75

1.0

1.605

5

275

0.3

90

0.6

2.691

6

275

0.5

60

0.8

1.759

7

290

0.1

90

0.8

2.958

8

290

0.3

60

1.0

1.956

9

290

0.5

75

0.6

2.925

TABLE : PERCENTAGE OF CONTRIBUTIONS USING ANOVA [1]

Factors

f

S

V

F

P(%)

Melt Temperature, (°C)

2

2.024817

1.012409

-

50.50

Filling time, (s)

2

0.17086

0.08543

-

4.26

Packing pressure, (MPa)

2

0.1386

0.0693

-

3.46

Packing time, (s)

2

1.674922

0.837461

-

41.78

Pooled error

0

0

Total

8

4.0092

2.0046

100

From this studies, it can be seen clearly that in a process of injecting Nylon PA66 side arms, melt temperature is the first parameter needs to be prioritized as it gives the most significant factor of 50.5%. Packing time gives 41.78% percentage of contributions which is the second largest value in giving significant impact on the process and followed by filling time 4.26% and lastly by packing pressure 3.46%.

However, the analysis made by M.F. Ghazali assumed core and cavity temperatures were at uniform value. In contrast, as far as the temperature differential in cavity and core is concerned, it can severely cause a resin skin effect on a part which causes uneven surface stress of the part's body injected [3].

These 2 parameter has been commonly neglected by so many researchers. For an instance, Ming-Chih Huang [4] in his analysis to identify the most significant factors affecting an injection-molded part of thin shell - warping issue only considered filling time, mold temperature, gate dimension, melt temperature, packing pressure and packing time as the potential factors.

Likewise, Yin F et. al [5] applied Back Propagation neural network modeling to predict warpage by considering mold temperature, melt temperature, packing pressure, packing time and cooling time.

Similarly, B. Ozcelik [6] also did not take in cavity and core temperatures in analyzing the influence of different parameters and mold materials on the properties of injected molded ABS parts.

Realizing the importance of counting in two parameters; core temperature and cavity temperature, this research extends the study made by M.F. Ghazali [1][2] in analyzing the most significant parameters effecting the injected parts of Nylon PA66 side arms. Therefore, the new factors considered are cavity temperature, core temperature, melt temperature, filling time, packing time and packing pressure.

There are 6 factors with 5 levels each. The details of the factors and levels are shown in Table 3.

TABLE : BEST SETTING OF COMBINATION

Factors

Symbols

Levels

1

2

3

4

Cavity temperature, (°C)

A

50

60

70

80

Core temperature, (°C)

B

50

60

70

80

Melt Temperature, (°C)

C

250

260

270

280

Filling time, (s)

D

0.1

0.2

0.3

0.4

Packing time, (s)

E

0.6

0.7

0.8

0.9

Packing pressure, (MPa)

F

50

60

70

80

TABLE : L25 ORTHOGONAL ARRAY

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

1

5

5

5

5

6

2

1

2

3

4

7

2

2

3

4

5

8

2

3

4

5

1

9

2

4

5

1

2

10

2

5

1

2

3

11

3

1

3

5

2

12

3

2

4

1

3

13

3

3

5

2

4

14

3

4

1

3

5

15

3

5

2

4

1

16

4

1

4

2

5

17

4

2

5

3

1

18

4

3

1

4

2

19

4

4

2

5

3

20

4

5

3

1

4

21

5

1

5

4

3

22

5

2

1

5

4

23

5

3

2

1

5

24

5

4

3

2

1

25

5

5

4

3

2

Mold Fabrication

Fig.2 shows a mold set made by an AISI1050 type which is prepared in injecting Nylon PA66 resin and the edge gate is set at 1mm thickness with a 6mm diameter size of cooling channel. The design of its gating system is shown in Fig.3 whereas a complete gating system and cooling channel dimensions are shown in Fig. 4.

Fig. : AISI 1050 mold set used to inject nylon PA66 in manufacturing side arms [1].

Fig. : Design of gating system

Fig. : Gating system and cooling channel dimensions

Experimental Method

In identifying the best parameter setting for this experimentation, a few steps are considered which are:

Factors selected

Orthogonal Array and Factor Levels

Material used

Assumptions

Simulatio.

Factors Selected

Mould temperature is ignored due to the presence of ambient temperature and eventually the factors taken into considerations are only core temperature, cavity temperature, melt temperature, filling time, packing time and packing pressure

Selection of Orthogonal Array and Factor Levels

Three levels of each factor are conducted in an L9 34 array where the selection of the array is because of its suitability for four factors with three Levels [6]. The L9 34 orthogonal array is shown in Table 1. The three-different levels of filling time, packing pressure and packing time are taken according to the value recommended by M.C. Huang [5] and the levels of melt temperature is chosen based on the thermal properties of Nylon PA66 as well as recommended processing value by Autodesk Moldflow Insight 2011 software. The levels and factors suggested are all shown in Table 2.

Assumptions

There are several factors that have significant effects on an

injection molding process, which are types of materials, machine specifications, dimensional and shape of products, types of mould materials, location of coolant runners and the selection of the coolant liquid [7][8].

However, for a simple conduction of experimental simulation, only the factors with respect to the control of the process are considered which are:

The effects due to the geometrical dimensions of the mold and the product are neglected due to a variety of shapes of side arm.

Only the effects of the filling time, packing pressure, packing time and melt temperature are considered and evaluated.

Both core and cavity moulds are assumed to have the same and uniform temperature.

The temperature surrounded the cooling channels is assumed to be constant.

The ejection temperature is set at 158°C and the temperature of the environment is 27°C.

The coolant is considered as pure water.

Material.

Material which is simulated in all of the analyses is Nylon PA66 which is a homo-polymer and generally applied where impact resistance and strength are required. Its melt temperature is from 260°C -290°C. It also has good strength and stiffness which can be retained at elevated temperatures.

Simulation and Design.

Autodesk Moldflow Insight 2011 simulation software is used as an important tool in executing the experimentation. Results obtained together with the molding conditions can be useful in optimizing process parameters. It also can give predictions of what will happen before the real process takes place including predictions of warpage of side arms. The parts to be simulated are divided into 8522 pieces of surface triangles with 4326 nodes for both sides of side arm. The meshes of the parts and the design of the cooling channel can be seen in Fig. 6. Signal-to-noise (S/N) ratio is then calculated according to Table 4 and the deflection obtained is used to calculate the signal-to-noise (S/N) ratio to acquire the best setting of parameters arrangement. From this method, the percentage of contribution has been calculated to determine which of the factor will affect the warpage significantly.

Fig. : Cooling channel design for side arms

TABLE : CONTROL FACTORS AND LEVELS FOR

FACTOR A TO F

Trial No.

Control Factor

A

B

C

D

E

1

50

50

250

0.1

0.6

2

50

60

260

0.2

0.7

3

50

70

270

0.3

0.8

4

50

80

280

0.4

0.9

5

50

90

290

0.5

1.0

6

60

50

260

0.3

0.9

7

60

60

270

0.4

1.0

8

60

70

280

0.5

0.6

9

60

80

290

0.1

0.7

10

60

90

250

0.2

0.8

11

70

50

270

0.5

0.7

12

70

60

280

0.1

0.8

13

70

70

290

0.2

0.9

14

70

80

250

0.3

1.0

15

70

90

260

0.4

0.6

16

80

50

280

0.2

1.0

17

80

60

290

0.3

0.6

18

80

70

250

0.4

0.7

19

80

80

260

0.5

0.8

20

80

90

270

0.1

0.9

21

90

50

290

0.4

0.8

22

90

60

250

0.5

0.9

23

90

70

260

0.1

1.0

24

90

80

270

0.2

0.6

25

90

90

280

0.3

0.7

TABLE : SUMMARY OF RESULTS OF

Z-DEFLECTIONS AND S/N VALUES

Trial No.

Control Factor

Max of z

deflections

A

B

C

D

E

F

1

50

50

250

0.1

0.6

50

1.541

2

50

60

260

0.2

0.7

60

1.606

3

50

70

270

0.3

0.8

70

1.654

4

50

80

280

0.4

0.9

80

1.693

5

50

90

290

0.5

1.0

90

1.758

6

60

50

260

0.3

0.9

90

1.097

7

60

60

270

0.4

1.0

50

1.436

8

60

70

280

0.5

0.6

60

2.538

9

60

80

290

0.1

0.7

70

3.096

10

60

90

250

0.2

0.8

80

1.062

11

70

50

270

0.5

0.7

80

1.697

12

70

60

280

0.1

0.8

90

2.637

13

70

70

290

0.2

0.9

50

2.536

14

70

80

250

0.3

1.0

60

1.258

15

70

90

260

0.4

0.6

70

1.66

16

80

50

280

0.2

1.0

70

1.681

17

80

60

290

0.3

0.6

80

3.078

18

80

70

250

0.4

0.7

90

1.071

19

80

80

260

0.5

0.8

50

1.415

20

80

90

270

0.1

0.9

60

1.624

21

90

50

290

0.4

0.8

60

2.473

22

90

60

250

0.5

0.9

70

1.174

23

90

70

260

0.1

1.0

80

1.113

24

90

80

270

0.2

0.6

90

2.585

25

90

90

280

0.3

0.7

50

2.502

TABLE : THE RESPONSE TABLE OF S/N RATIO FOR SIDE ARMS

Level

A

B

C

D

E

F

1

-4.3430

-4.3059

-1.6523

-5.4272

-6.8525

-5.1926

2

-4.4751

-5.3678

-2.6532

-5.0962

-5.4172

-5.2549

3

-5.4989

-4.4138

-4.9137

-4.9799

-4.8390

-4.8987

4

-4.4199

-5.5294

-6.7125

-4.1157

-3.8131

-4.0769

5

-5.2805

-4.4005

-8.0858

-4.3985

-3.0957

-4.5943

Diff

1.1559

1.2235

6.4336

1.3114

3.7569

1.1780

The z-deflections data obtained from the simulation process are the warpage concerned and to be analyzed using Analysis of Variance (ANOVA) with level of confidence is set at 0.05. The results are used by comparing it with the results obtained from the S/N ratio method. In addition, the interaction effect of factors is identified and the contribution of each factor to the total effect is calculated subsequently. After that, the percentage of contribution is calculated to identify which of the factor will majorly affect the warpage on side arms produced.

RESULT AND DISCUSSION

Warpage is one of unwanted defects in injection moulding process [9][10]. Therefore, the smaller warpage value, the better it is and for that reason in determining the S/N ratio of the warpage, Taguchi has outlined an equation in calculating S/N ratio for this case. The equation to obtain the values of S/N is shown below:

MSD = Mean Square Deviation,

= Observations

= No. of tests in a trial.

The data are also analyzed using Analysis of Variance (ANOVA) where the relative percentage contribution of all factors is determined by comparing with the relative variance. The examples of S/N calculations are shown below and the results of S/N ratio for the part are listed in Table 5.

TABLE : THE RESPONSE TABLE OF S/N RATIO FOR SIDE ARMS

From the S/N ratio response as shown in Table 5, the best combination of parameters can be identified by selecting the highest difference value from each factor. In this case, the most significant factor that has an effect on warpage for thin plate are cavity temperature (A) followed by packing time (D), core temperature (B) and packing pressure (C).

Table 6 shows the summary of best combinations of parameter. The result can also be observed from the graphs shown in Fig. 7-10.

TABLE : BEST SETTING OF COMBINATION

Factors

Symbols

Values

Cavity temperature, (°C)

A

90°C

Core temperature, (°C)

B

70°C

Melt Temperature, (°C)

C

250°C

Filling time, (s)

D

0.4s

Packing time, (s)

E

1.0s

Packing pressure, (MPa)

F

80MPa

Fig. : All factors with 5 levels vs S/N values

In ANOVA calculations, the degree of freedoms for all factors needs to be obtained first. The example of calculating degree of freedom is as below;

Total degree of freedom, f

For Factor A,

For Error, )

Sum of squares for all factors is then calculated and the example of calculating sum of squares is shown below;

Sum of squares, S

9.846174

For Factor A,

For Error,

The values of variances for all factors are then calculated. The example of calculating variance is shown below;

For Factor A,

For Variance Error,

F-ratio, F for all factors are calculated afterwards and the example of calculation is shown below;

For Factor A,

Last but not least, Percentage Contribution, PA for all factors are calculated and the example of the calculation is shown below)

For Factor A,

The percentage of contributions, PA for all factors are shown in Table 7. This results explain that melt temperature contribute the most by 50.50% and this is followed by packing time by 41.78%, filling time 4.26% and packing pressure 3.46%. This proves that melt pressure and packing time are the most significant parameters contribute to the development of warpages in the process while filling time and packing pressure only have small effects towards the existence of warpages on side arms. core temperature give significant effects on warpage defects.

TABLE : ANOVA TABLE

Factors

f

S

V

F

P(%)

A

4

0.3547

0.0886707

-

3.602239814

B

4

0.4386

0.1096513

-

4.454574945

C

4

6.4734

1.6183585

-

65.74567949

D

4

0.4031

0.1007755

-

4.093996308

E

4

2.0845

0.5211158

-

21.17028604

F

4

0.09189

0.0229717

 

0.933223402

Pooled error

0

0

0

100

Total

16

9.84617

CONCLUSION

In injection moulding process of Nylon PA66 side arms, melt temperature is found to be the most significant factor which contributes 50.50% followed by packing time by 41.78%, filling time 4.26% and packing pressure 3.46%. The influence of all factors has been identified and believed can be a key factor in helping mould designers in determining optimum process conditions injection moulding parameters.

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