Optimization of Injection Molding Process to Minimize Weld-line and Sink-mark Defects Using Taguchi based Grey Relational Analysis

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ScienceDirect Materials Today: Proceedings 5 (2018) 12615–12622

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ICMMM - 2017

Optimization of Injection Molding Process to Minimize Weld-line and Sink-mark Defects Using Taguchi based Grey Relational Analysis Sreedharan J and A.K Jeevanantham * Department of Manufacturing Engineering, School of Mechanical Engineering, VIT University, Vellore – 632 014, India

Abstract The study gives an insight of the material ABS stare UT-0510T material with MFI OF 16G/10MIN by conducting experiment on an injection molding machine. The part that is considered for this V3 window transparent used in air conditioners. This study is basically done to identify the effect of molding parameters on defects such as weld line and sink mark. The experiments have been performed by using L27 ORTHOGONAL ARRAY and these have been normalized by Grey relational analysis (GRA).The input variables are considered by using ANOVA. To determine optimal molding parameters Taguchi method constructed GRA was used. On these experiments we got optimized parameters which have reduced the weld-line width of 56.4% and sink mark depth of 68.9. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

Keywords: Injection molding, Weld-line; Sink-mark; Taguchi Method; Grey relational analysis

1. Introduction Now a day, experiments are performed in manufacturing organizations to increase productivity/process quality, since; it is the basic criteria to understand the process behavior. Variables and their effect on process estimation refer to determining the effects of process variables or factors on the output performance characteristics. Design of Experiment (DOE) can also help in understanding the sensitivities of change in levels of the factors. This approach works particularly well for injection molding product and process design for choosing the best combination of levels of the factors which affect the process, quality and short shot. Plastic injection molding is the process where the molten plastic material is injected into a mold to take its shape.

* Corresponding author. Tel.: +91 9488044330. E-mail address: [email protected]. 2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

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A weld on plastics parts can cause structural problems and/or be visibly unacceptable. Research is to optimize the process for the occurrence of weld-line or knoit-line and sink-mark that occurs in injection molding. Weld-lines are formed when plastic melts pass through different gates and intersect at some location. This is inevitable during complex welding parts where many gate points are being used. The restriction in the part creates weld-lines or knitlines when the molten material is forced to flow through multiple gate points while filling of the cavity. The molten material will unite again after the flow restrictions and solidify in the lower temperature on the mold surface with less melt pressure which will create the weld lines on the molded parts. The weak strength of weld-line can be due to one of the following factors: low temperature which can prevent resin from knitting together across a weld-line; low temperature of mold surface, material flowing into cavity will be cooled excessively and prevent knitting; high temperature of resin can cause degrading and gassing which can also prevent good knitting; runners and gates may be too small resulting in low injection pressure. Presence of weld-line introduces an element of uncertainty to the performance of injection molded parts [1]. It is exaggerated particularly in multiphase materials by component mismatch leading additional weakening [2]. A multi feed injection mechanism was proposed [4] to improving the strength of weld-line in injection molding process. Prediction of weld-line strength was constructed on dissemination and the Flory-Huggins theory of the free energy of mixing [5]. It was validated by an experimental results performed for pure polycarbonate. The sensitivity of Moldflow's weld-line prediction algorithm was examined [6] on the effects of changes in viscosity, density for a poly (methyl methacrylate). It was proved that the changes in the mold and die temperatures were at odds with the experiments. The morphological analysis of the weld-line using scanning electron microscopy and image analysis were established [7] for high-density polyethylene. Weld-line characteristics of polypropylene (PP) and high density polyethylene (HDPE) structures with different cross-sections were investigated [8] and showed that the weld-line strong point from a regular test is not relevant in microinjection molding. The effects of gate dimension of the micro injection mold on mechanical properties of weld-line were studied [9] by experiments with PP and HDPE. Mold filling with multi-inlets in the cavity was simulated [10] with an improved Level Set Method using finite volume and finite difference methods. It was shown that a smaller size insert corresponds to a relatively shorter weld-line and with the increase in the size of the insert, the dimension of the weld-line increases correspondingly. Sink-mark is one of the major defects which is normally seen on the molded part it is a narrow depression which appears due to the closure of the pneumatic gates which in turn caused the shrinkage on the gate area after sealing of the gates after injection cycle which caused residual stresses and it may also cause failure of the part. An experimental design with two-way interaction was investigated [11] to the sink-mark depth. Numerical simulation was combined with DOE technique to study the influence of cavity geometry on sink-mark of the injection molded part [12]. Experimental investigation can be done to understand the development contrivance of sink-marks where it helps in improving the superficial eminence [13]. With contemporary improvements in various aspects of injection molding process design advances and parts. DOE is the best practice which gives influential and capable methods for synchronized study on factors which contributes on the quality of the end product by which we can conclude on the process parameters [14]. In most of the studies Taguchi method (TM) is used to analyze and optimize the experimental designs [15]. There have been other issues with the Taguchi methods which are being resolved by conducting series of means and experiments to get an optimal solution by using Taguchi methods. We have used GRA (Gray Relational Analysis) which was first suggested by Deng in 1989[17].It can be arrived by calculating the arithmetic means of regularized multi objective which is used to find the GRG. It is calculated by using weighting values of the experiments relatively than using averages. Here we have used Experimental Design, Examination method and optimization of process parameters based on Taguchi methods based on GRA. 2. Experimental Procedure In this study, commercial grade of Acrylonitrile Butadiene Styrene (ABS) Starex UT-0510T, with melt index of 16g/10min was selected for the injection molding process. V-3 grill window transparent part which is used in the air conditioner was chosen for the analysis. Multi injection points and gate valve which allow the parts to be molded with or without weld-line was used. We have conducted the research on a 900t Woojin brand molding machine which is complete closed loop central processing unit with Hot Runner controller (Yudo with 6 Zones) is taken to be

Sreedharan J &A.K Jeevanantham / Materials Today: Proceedings 5 (2018) 12615–12622

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used to formulate the trials at several conditions. The trials are done with preheated raw material, machine and cad data on part which is fed into a CAE software package. Initially, the ABS UT-0510T was preheated at the temperature of 85±5°C for 3 hours to remove moisture present in the material. The nozzle temperatures were set at 240±10, 230±10 and 225±10 °C. The process parameters were set and the melt was injected. The mold chiller temperature is used as 18±5 °C for the cooling time at 45secs. The part cycle time was 75secs. Then the injected part was checked for shrinkage in the front view and short fill in rib area, also for the short fill and flash in locking rib. The present study is done in order to evade the sink mark and weld-line in the part. Since this part is at the face of the air conditioner, these defects become critical in terms of the aesthetics. The parts trials are performed in order to get a good quality of parts are got. The chosen input parameters for the experimental study were: Barrel melt temperature, mould temperature, injection pressure, holding pressure, cooling time, back pressure, holding time and ambient temperature. The table 1 represents the molding constraints and their levels which have been considered in this study. Table 1. Process Parameter levels in experiment. Symbol Molding parameter

Level 1

Level 2

Level 3

A

Melt temperature (°C)

250

235

215

B

Mold temperature (°C)

80

70

60

C

Injection pressure (MPa)

45

52

56

D

Holding pressure (MPa)

20

25

30

E

Cooling time (sec)

20

25

30

F

Back pressure (MPa)

5

8

10

G

Holding time (sec)

3

5

7

H

Ambient temperature (°C)

25

30

35

In the present study, we have considered eight input parameters but we haven’t considered the collaboration effect. These seven parameters are set at 3 levels. Degree of freedom which is taken for conducting the experiments is [8× (3–1)] + 1 = 17. The Taguchi method states that the considered experimentation should be same or higher than the selected. Thus, the L27 (33) OA was selected and the eight more contributing factors have been taken in the columns. During the trials the experimentation is done on each process conditions for 45minutes in order to get a stabilized process condition and 15nos are taken after that which is taken for detailed study. We have considered 3 samples on each trial run. It has been noted for all the injection for each injection phase, the changeover from the injection phase to the holding phase has been observed in order to confirm the set injection pressure has attained. Since ABS, a high flow transparent TRABS material, analyzing of the parts are done after 24hrs in order to get the best results. After 24hrs measuring of the parts are done. The relationship of the mean (signal) to the standard deviation (noise) called S/N ratio is employed based on the characteristic of the response variable as Nominal the best (NB), lower the better (LB), and higher the better (HB). The parameter combination that has the highest S/N ratio is considered as optimal setting. To ensure the process stability and a precise outcome each permutation is done three times. In this work, 2 distinguishing factors have been taken such as tensile strength and hardness for quality measurement and to acquire the higher-the-better characteristics. The S/N ratio for higher-the-better is shown in Eq.1.

1

n



j 1

ijLB  10 log

y n

2 ij

   

.....(1)

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Where ηij is the jth S/N ratio of the ith experiment, yij is the ith experiment at the jth test, n is the total number of experiments. Weld-line width and sink-mark depth were measured using an optical microscope (Nikon Epiphot 300/200) and tabulated as shown in Table 2. Table 2. L27 OA for Experimental and response variable

Run

Response variable

Molding parameter and their levels A

B

C

D

E

F G H

Weld-line width

S/N Ratio

Sink-mark depth

1

2

3

1

2

3

Weld-line width

Sink-mark depth

1

250 80 45 20 20

5

3 25

0.995

1.045

1.210

0.1456

0.1450

0.1450

-0.72636

16.76065

2

250 80 45 20 25

8

5 30

0.990

1.054

1.240

0.1466

0.1460

0.1462

-0.82620

16.69708

3

250 80 45 20 30 10 7 35

1.015

1.100

1.150

0.1450

0.1452

0.1456

-0.74661

16.75667

4

250 70 52 25 20

5

3 30

1.024

1.105

1.201

0.1476

0.1472

0.1474

-0.92487

16.63004

5

250 70 52 25 25

8

5 35

0.863

1.065

0.910

0.1470

0.1456

0.1460

0.44618

16.70098

6

250 70 52 25 30 10 7 25

0.885

1.100

0.930

0.1486

0.1480

0.1481

0.21040

16.58107

7

250 60 56 30 20

5

3 35

0.872

1.070

0.917

0.1455

0.1452

0.1459

0.38393

16.74073

8

250 60 56 30 25

8

5 25

0.808

1.060

0.854

0.1490

0.1491

0.1492

0.78178

16.53045

9

250 60 56 30 30 10 7 30

0.895

1.093

0.940

0.1482

0.1472

0.1470

0.17838

16.62607

10

235 80 52 30 20

7 25

0.950

1.105

0.990

0.1455

0.1454

0.1459

-0.14748

16.73676

11

235 80 52 30 25 10 3 30

0.875

1.145

1.150

0.1442

0.1447

0.1450

-0.54249

16.79461

12

235 80 52 30 30

5

5 35

0.920

1.158

1.220

0.1510

0.1475

0.1482

-0.88226

16.54166

13

235 70 56 20 20

8

7 30

1.025

1.100

0.980

0.1456

0.1450

0.1450

-0.30873

16.76065

14

235 70 56 20 25 10 3 35

0.859

1.057

0.904

0.1550

0.1442

0.1465

0.50229

16.55733

15

235 70 56 20 30

5

5 25

0.800

1.000

0.850

0.1551

0.1550

0.1545

1.03749

16.20083

16

235 60 45 25 20

8

7 35

0.883

1.081

0.928

0.1442

0.1445

0.1450

0.28502

16.79861

17

235 60 45 25 25 10 3 25

0.875

1.073

0.920

0.1462

0.1471

0.1465

0.35685

16.67729

18

235 60 45 25 30

5 30

0.901

1.080

1.100

0.1475

0.1477

0.1480

-0.26424

16.61042

19

215 80 56 25 20 10 5 25

0.987

0.980

1.210

0.1521

0.1510

0.1545

-0.54188

16.33231

20

215 80 56 25 25

5

7 30

1.020

1.100

0.950

0.1491

0.1485

0.1480

-0.21589

16.56348

21

215 80 56 25 30

8

3 35

0.882

1.080

0.926

0.1460

0.1457

0.1459

0.29684

16.72088

22

215 70 45 30 20 10 5 30

0.962

1.064

1.100

0.1512

0.1515

0.1498

-0.37100

16.42995

23

215 70 45 30 25

5

7 35

0.889

1.090

0.926

0.1532

0.1542

0.1521

0.24431

16.29658

24

215 70 45 30 30

8

3 25

0.920

1.050

0.976

0.1469

0.1459

0.1462

0.14502

16.69310

25

215 60 52 20 20 10 5 35

1.020

1.100

0.890

0.1498

0.1512

0.1510

-0.06109

16.43958

26

215 60 52 20 25

5

7 25

0.885

1.083

0.930

0.1489

0.1487

0.1480

0.26716

16.56349

27

215 60 52 20 30

8

3 30

0.765

0.963

0.810

0.1545

0.1550

0.1535

1.40923

16.23073

8

5

3. Grey Relational Analysis In GRA, the function of variables is ignored if the response variable or standard value is high. S/N ratios are normalized between zero and one. Here we see larger the S/N ratio, better the experimental run, larger-the-better normalization was adopted using Eq. (2). Then the normalized S/N ratio xij can be expressed as shown in Table 2.

Sreedharan J &A.K Jeevanantham / Materials Today: Proceedings 5 (2018) 12615–12622

xij 

yij  min y ij j

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…….(2)

max y ij  min y ij j j

Where, yij is the S/N ratio of the ith will track for the jth response variable. In this the larger normalized results which are obtained gives better consistency and considered to be in par with the experimental outcome. Grey relational coefficient (GRC) on all experiments is carried on the optimal and actual outcome. GRC ξij is shown in Eq.3.

0 0 min min xi  xij   max max xi  xij i j i j ij  0 0 xi  xij   max max xi  xij i j

…….(3)

Where xi0 represents the best normalized results for ith response variable. ς is the distinguishing coefficient the values will be lying between 0 and 1 but always set at 0.5. GRCs have been done for this experimental study and being represented in Table 3. For our understanding GRG is being conducted on each experimental process and it is tabulated by using the individual response variable which is represented in the Eq. (4). If the value of the GRG is high it is seen that the experimental run is vital.

Where,

i 

1 n  w j ij n j 1

n

w j 1

j

 1,

…..(4)

Where γi is the GRG for the ith experiment, n = response variables, wj = weighting value of the jth response variable. Using Eq. (4), GRCs and GRGs values in Table 3 have been arrived by calculating and symbolized. Optimisation design is always done on single relational parameters than on complex numerous response parameters. In table 4, GRG is calculated for the every molding parameter which are considered by the use of Taguchi Methods for L27 OA and the average GRG has been tabulated. Enhanced equivalent multiple response variable means the GRG is considered to be high. We have selected the biggest typical GRGs were taken and idyllic permutation of the molding parameter levels are attained through A2 (Melt temperature of 235°C), B3 (Mold temperature of 60°C), C1 (Injection pressure of 45 MPa), D1 (Holding pressure of 20 MPa), E3 (30 sec of Cooling time), F2 (Back pressure of 8 MPa), G1 (3 sec of Holding time) and H3 (Ambient temperature of 35°C). On comparison in Table 4 we have seen GRG on factor G is largest and next comes the other ranges in the order F, A, B, H, D, E and C. This study proves loading time plays a major role trailed by back pressure, melt temperature, mold temperature, ambient temperature, holding pressure, cooling time and injection pressure.

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Table 3. Calculation for 27runs GRC and GRG Run

GRC

GRG

Weld-line width

Sink-mark depth

Grade

Rank

1

0.35337

0.88730

0.62034

9

2

0.34300

0.74644

0.54472

17

3

0.35122

0.87693

0.61407

11

4

0.33333

0.63940

0.48636

22

5

0.54788

0.75378

0.65083

6

6

0.49328

0.57876

0.53602

18

7

0.53233

0.83777

0.68505

2

8

0.65035

0.52709

0.58872

13

9

0.48670

0.63400

0.56035

14

10

0.42847

0.82855

0.62851

8

11

0.37420

0.98678

0.68049

3

12

0.33744

0.53772

0.43758

24

13

0.40452

0.88730

0.64591

7

14

0.56271

0.55332

0.55801

15

15

0.75842

0.33333

0.54588

16

16

0.50935

1.00000

0.75467

1

17

0.52583

0.71129

0.61856

10

18

0.41086

0.61364

0.51225

20

19

0.37428

0.39061

0.38244

27

20

0.41797

0.55970

0.48884

21

21

0.51199

0.79360

0.65279

5

22

0.39598

0.44774

0.42186

26

23

0.50046

0.37318

0.43682

25

24

0.48002

0.73909

0.60955

12

25

0.44250

0.45430

0.44840

23

26

0.50541

0.55971

0.53256

19

27

1.00000

0.34483

0.67242

4

4. Analysis of variance for GRGs The analysis of variance (ANOVA) on GRG’s is carried out to find out the major contributors on the output to the different inputs which will enable us to find out the best input variable plays a major role in influencing the output and it has been tabulated in Table 5. In this the GRGs are restrained by the sum of squared deviations from the total mean of GRG, and are categorized into 2 bases: the summation of squared deviations on each parameter considered and the summation of squared error. The fisher’s F-test [16] is a tool to find out the molding parameters which have major say on the GRG. The outcome is that three molding parameters back pressure (impact: 30.23%), holding time (27.39%) and melt temperature (15.54%) are considered to be playing a major role on the weld-line width and sink-mark depth.

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Table 4. Response table for GRGs Average of GRGs Molding parameter

Symbol

Rank Level 1

Level 2

Level 3

Range

Melt temperature

A

5.28646

5.38187*

4.64568

0.73619

3

Mold temperature

B

5.04978

4.89125

5.37297*

0.48172

4

Injection pressure

C

5.13284*

5.07318

5.10799

0.05966

8

Holding pressure

D

5.18231*

5.08277

5.04893

0.13338

6

Cooling time

E

5.07354

5.09955

5.14091*

0.06737

7

Back pressure

F

4.74567

5.74813*

4.82021

1.00246

2

Holding time

G

5.58358*

4.53268

5.19775

1.05090

1

Ambient temperature

H

5.06258

5.01319

5.23824*

0.22505

5

Overall mean GRG = 0.56719 * Optimum level

Table 5. ANOVA Results for GRG

Sources of variation

Sum of

Mean sum of

square

square

Dof

Fcalculated

Fcritical α=0.05

% contribution

Melt temperature

2

0.03562

0.01781

4.09375*

4.10

15.54

Mold temperature

2

0.01339

0.00670

1.53946

4.10

5.84

Injection pressure

2

0.00020

0.00010

0.02294

4.10

0.09

Holding pressure

2

0.00107

0.00053

0.12279

4.10

0.47

Cooling time

2

0.00026

0.00013

0.02948

4.10

0.11

Back pressure

2

0.06932

0.03466

7.96683*

4.10

30.23

Holding time

2

0.06280

0.03140

7.21784*

4.10

27.39

Ambient temperature

2

0.00311

0.00155

0.35734

4.10

1.36

Error

10

0.044

0.00435

Total

26

0.22926

18.98

* Significant at 95% confidence level

5. Validation Experiment Since the ideal molding parameter is found now the subsequent step is to identify optimum parameter levels which are used to envisage and authenticate response variables using the ideal process parameter permutation. The estimated GRG using ideal process parameter permutation can be derived from Eq.(5).

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

q



  m   i m i 1



……. (5)

Where γm = total mean of the GRG, = average GRG of the optimum level of the ith molding parameter and q is the number of molding parameters which seriously marks the multiple response variables. The table 6 provides the outcomes of confirmation tests. The weld-line width is minimized from 1.0830 to 0.4720 (56.4 % of improvement) and the sink-mark depth is reduced from 0.1452 to 0.0451 (68.9% of improvement). It is seen from these experiments we see that predicted and Experimental GRGs have close affiliation. This proves the helpfulness of TM based GRA to multiple response optimizations which have reduced to a reduction in weld-line and sink-mark defects. 6. Conclusion Over this study we have found the Taguchi based GRA provides us an opportunity to improve and optimize the injection molding parameters. ABS Starex UT-0510T with melt index of 16g/10Min molded as a V-3 window transparent air-conditioner component was chosen for the study. In this work weld-line width and sink-mark were taken as multiple response variables which are converted by GRA to a single GRG which clearly helps in getting an ideal process parameter to minimize the weld-line and sink-mark defects and by getting the ideal combination of the molding parameters as A2 B3 C1 D1 E3 F2 G1 and H3. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]

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