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Influence of injection parameters and mold materials on mechanical properties of ABS in plastic injection molding
International Communications in Heat and Mass Transfer 37 (2010) 1359–1365
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International Communications in Heat and Mass Transfer j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i c h m t
Influence of injection parameters and mold materials on mechanical properties of ABS in plastic injection molding☆ Babur Ozcelik a,⁎, Alper Ozbay a, Erhan Demirbas b a b
Department of Mechanical Engineering, Gebze Institute of Technology 41400 Gebze-Kocaeli/Turkey Department of Chemistry, Gebze Institute of Technology, 41400 Gebze-Kocaeli/Turkey
a r t i c l e
i n f o
Available online 1 August 2010 Keywords: Plastic injection molding ABS Mechanical properties DOE Taguchi optimization method Regression analysis
a b s t r a c t This study optimized effect of injection parameters such as melt temperature, packing pressure, cooling time and injection pressure on the mechanical properties of Acrylonitrile–Butadiene–Styrene (ABS) moldings. Mold materials having two different thermal conductivities, 191 W/mK for aluminum 2000 series and 50 W/ mK for AISI 1020 at 25 °C were selected to use in experimental studies. Taguchi's L9(34) orthogonal array design was employed for the experimental plan. Mechanical properties of ABS specimens such as elasticity module, tensile strength and tensile strain at yield, tensile strain at break, flexural modules and izod impact strength (notched) were measured by using some test methods. Signal to noise ratio for mechanical properties of ABS using the Taguchi method was calculated and effect of the parameters on mechanical properties was determined using the analysis of variance. Linear mechanical models were also created by using regression analysis. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Mold materials with the different thermal properties affect the mechanical properties of the plastic parts in injection molding. Aluminum (Al) mold shows advantages in terms of weight, heat transfer and low production cost as compared to steel materials. Aluminum molds are used to produce between 5,000 and 50,000 plastic parts [1]. Researches on the mechanical properties of injection molded of fiber reinforced thermoplastics and injection molding parameters are realized. Sadabadi and Ghasemi investigated the effects of the injection molding process parameters including injection flow rate, mold wall temperature, packing pressure using short fiber reinforced polystyrene composites which could affect fiber orientation and tensile modulus of injection molded parts [2]. Yang studied tribological behaviors and mechanical properties of polycarbonate reinforced with 20% short glass fiber and 6% polytetrafluoroethylene using Taguchi's orthogonal arrays and analysis of variance (ANOVA) under different conditions of injection molding such as filling time, melt and mold temperatures and packing pressure [3]. Yang also examined the mechanical properties and tribological behaviors mainly ultimate stress and surface roughness using grey relational analysis [4].
☆ Communicated by W.J. Minkowycz. ⁎ Corresponding author. E-mail address: [email protected] (B. Ozcelik). 0735-1933/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.icheatmasstransfer.2010.07.001
Guerrica-Echevarria et al. determined the mechanical properties of injection molded parts using injection rate, melt temperature and screw rotation rate during plasticization [5]. Kenig et al. analyzed the effect of process parameters namely, mold and melt temperatures, packing pressure, injection speed and cooling time for tensile modulus using both multivariate regression analysis and artificial neural network [6]. Bociaga examined mechanical and thermal properties of HDPE moldings for mold temperature and injection velocity. He noticed that higher tensile strength, yield stress, tensile modulus and hardness were obtained when the mold temperature was increased [7]. Nagaoka et al. [8] studied tensile and three-point flexural tests from the sandwich and normal injection moldings for polypropylene material. Shie [9] investigated optimization of injection molding process for mechanical properties of polypropylene with regression neural network. The effect of the process parameters on warpage [10] and sink marks [11] in plastic injection molding and on drill bit [12] in drilling were studied with the Taguchi method. In this study, changes in mechanical properties of Acrylonitrile– Butadiene–Styrene (ABS) material were optimized with injection parameters for two mold materials (aluminum 2000 series and AISI 1020). Taguchi's L9(34) orthogonal array design was used for experimental plan. Signal to noise ratio (S/N) for mechanical properties of each mold material was determined and effect of injection parameters on mechanical properties was carried out using analysis of variance (ANOVA). Linear mechanical models were also obtained from regression analysis.
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Table 1 An orthogonal array L9(34) of Taguchi. A
B
C
D
Melt temperature (°C)
Packing pressure (MPa)
Cooling time (sn)
Injection pressure (MPa)
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
1 2 3 2 3 1 3 1 2
1 2 3 3 1 2 2 3 1
Fig. 2. Specimen dimensions for three-point flexural test (ISO 178).
Table 2 The process parameters and levels. Process parameters Melt temperature (°C) Packing pressure (MPa) Cooling time (s) Injection pressure (MPa)
A B C D
Level 1
Level 2
Level 3
200 28 16 36
240 34 19 43
280 39 22 50
Fig. 3. Specimen dimensions for izod impact test (ISO 180).
parameters on mechanical properties. In this way, optimal levels of the process parameters can be estimated.
3. Experimental Table 3 Physical and mechanical properties of ABS.
3.1. Taguchi's orthogonal arrays
Physical properties Melt flow index
ASTM D1238
200 C/5 kg
1.8 g/10 min
Mechanical properties Tensile strength Flexural strength Flexural modules Izod impact strength
ASTM D638 ASTM D790 ASTM D790 ASTM D256
5 mm/min 2.8 mm/min 2.8 mm/min 1/4 in.
47 MPa 66 MPa 2200 MPa 235 J/m
2. Definition of the Taguchi method The Taguchi method developed by Taguchi consists of three stages which are system, parameters, and tolerance designs, respectively [13]. The system design involves the application of scientific and engineering knowledge required in manufacturing of a product. The parameter design is employed to find optimal process values for improving of the quality characteristics. The tolerance design is used for determining and analyzing of the tolerances in optimal settings recommended by the parameter design. By applying the Taguchi method based on orthogonal arrays, time and cost required for conducting of the experiments can be reduced. Taguchi recommends the use of the S/N ratio for determination of the quality characteristics implemented in engineering design problems. The S/N ratio characteristics with signed-target type can be divided into three stages: the smaller is the better, the nominal is the best, and the larger is the better [13]. In this study, the larger is better approach was used for maximizing mechanical properties of the product. In addition to the S/N ratio, ANOVA was employed to obtain for effect of the process
The effects of several process parameters based on Taguchi's orthogonal design could be determined effectively from matrix experiments [13]. Minitab 14 software was used for statistical calculations [14]. Tests were organized in using Taguchi's L9(34) orthogonal array (Table 1). An experimental plan for four parameters with three levels was organized by the Taguchi method (Table 2). Melt temperature taken from the ABS data sheet was set to 200 °C for level 1 and 280 °C for level 3, and level 2 was set to 240 °C which was an average value of these two levels. Values of packing pressure, cooling time and injection pressure for level 1 was obtained from Mold Flow Inside (MPI 6.1) software [15] and parameters for levels 2 and 3 were selected as 1.2 and 1.4 times of level 1, respectively [16].
3.2. Material ABS (Strarex ABS-SD-0150) compound was used for this study. The properties of ABS compound were shown in Table 3.
3.3. Injection molding machine A double cavity of the mold was manufactured in the CNC machine according to ASTM D638 standards. A test part shown in Fig. 1 was injected by a plastic injection machine (MIR, Turkey) which has a clamping force of 637 kN and an injection pressure of 1480 bar, respectively.
Fig. 1. Specimen dimensions for tensile strength (ASTM D638).
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Table 4 Experimental and S/N ratio results of mechanical properties for AISI 1020. Exp. no
1 2 3 4 5 6 7 8 9
Elasticity module (MPa)
Tensile strength at yield (MPa)
Tensile strain at yield (mm/mm)
Tensile strain at break (mm/mm)
Flexural modules (MPa)
Izod impact strength (notched) (J)
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
2782 2679 2621 2458 2502 2368 1870 2217 2128
68.89 68.56 68.37 67.81 67.96 67.49 65.44 66.92 66.56
39.09 40.00 39.24 39.64 38.12 38.06 36.75 36.37 36.48
31.84 32.04 31.87 31.96 31.62 31.61 31.31 31.21 31.24
0.02963 0.03132 0.03012 0.03155 0.03171 0.03160 0.03167 0.03037 0.03110
− 30.56 − 30.08 − 30.42 − 30.02 − 29.98 − 30.00 − 29.99 − 30.35 − 30.14
0.03016 0.04258 0.05846 0.09336 0.06136 0.07799 0.31779 0.06472 0.06875
− 30.41 − 27.42 − 24.66 − 20.60 − 24.24 − 22.16 − 9.96 − 23.78 − 23.25
1823 1885 2098 2158 1903 1970 2122 2375 1844
65.22 65.50 66.44 66.68 65.59 65.89 66.53 67.51 65.32
0.564 0.571 0.593 0.641 0.586 0.601 0.720 0.710 0.592
− 4.97 − 4.87 − 4.54 − 3.86 − 4.64 − 4.42 − 2.85 − 2.97 − 4.55
injection parameters on mechanical properties of the product. The formula of S/N ratio was shown in Eq. (1)
3.4. Experimental testing methods and equipment A suitable dimension was cut from Fig. 1 to carry out three-point flexural and impact tests. Three-point flexural and impact tests were performed with ISO 178 and ISO 180 standards. The specimens for the tensile and three-point flexural test were shown in Figs. 1 and 2. The tensile and three-point flexural test speeds were found as 10 mm/min and 2 mm/min (ASTM D638). 10 J of the impact hammer was used in the impact test and specimen used for the test was shown in Fig. 3. Tensile strength, three-points flexural and impact test were determined from INSTRON 4411, INSTRON 5560 and Ceast 6545, respectively. The tests were repeated three times and the mean values were presented.
" S = N = −10 log10
1 n 1 ∑ n i = 1 y2i
# ð1Þ
where S is the standard deviation, yi is the measured experimental results and n explain to the number of samples in each test trial [17]. Result from measurements of elasticity module, tensile strength and tensile strain at yield, tensile strain at break, flexural modules and izod impact strength were presented for mechanical properties of the product (Tables 4 and 5) which determined the optimal levels of four process parameters for both mold materials [16].
4. Results and discussion 4.2. Analysis of the mechanical results by the thermal conductivity 4.1. Analysis of the S/N ratio Kovacs and Bercsey examined the influence of the mold thermal conductivity on the warpage of the mold. In their study, the typical mold thermal conductivity using conventional steel tool was between
The test results were evaluated in terms of signal/noise (S/N) ratio. The S/N was calculated by larger is better for determining effect of
Table 5 Experimental and S/N ratio results of mechanical properties for Al mold. Exp. No
1 2 3 4 5 6 7 8 9
Elasticity module (MPa)
Tensile strength at yield (MPa)
Tensile strain at yield (mm/mm)
Tensile strain at break (mm/mm)
Flexural modules (MPa)
Izod impact strength (notched) (J)
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
2746 2595 2548 2620 2772 2564 2411 2831 2621
68.77 68.28 68.12 68.36 68.85 68.19 67.64 69.04 68.37
34.38 41.02 41.03 40.42 40.27 40.08 39.59 38.99 38.58
30.73 32.26 32.26 32.13 32.10 32.21 31.95 31.82 31.73
0.03445 0.03434 0.03380 0.03094 0.03088 0.03203 0.03235 0.03187 0.03132
− 29.26 − 29.28 − 29.42 − 30.19 − 30.21 − 29.89 − 29.80 − 29.93 − 30.08
0.15599 0.17153 0.19370 0.07041 0.07538 0.18574 0.09773 0.09520 0.20044
− 16.14 − 15.31 − 14.26 − 23.05 − 22.45 − 14.62 − 20.12 − 20.43 − 13.96
2252 2276 2273 2141 2154 2160 1984 1926 1935
67.05 67.14 67.13 66.61 66.67 66.69 65.95 65.69 65.73
0.683 0.640 0.698 0.677 0.654 0.651 0.643 0.643 0.659
− 3.31 − 3.88 − 3.12 − 3.39 − 3.69 − 3.73 − 3.84 − 3.84 − 3.62
Table 6 ANOVA results for elasticity module for AISI 1020 mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
67.55 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
68.60 67.38 67.76 67.80
67.76 67.81 67.64 67.16
66.30 67.47 67.26 67.70
Sum of square (s)
Variance (V)
P (%)
8.13 0.31 0.42 0.71 0.00 9.58
4.07 0.16 0.21 0.36 0.00
84.90 3.28 4.37 7.45 0.00 100.00
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Table 7 ANOVA results for tensile strength at yield for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
31.64 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
31.92 31.70 31.56 31.57
31.73 31.63 31.75 31.65
31.25 31.58 31.60 31.68
Sum of square (s)
Variance (V)
P (%)
0.71 0.02 0.06 0.02 0.00 0.81
0.35 0.01 0.03 0.01 0.00
86.78 3.06 7.53 2.64 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
0.19 0.01 0.08 0.10 0.00 0.38
0.10 0.00 0.04 0.05 0.00
50.05 1.51 22.08 26.35 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
110.05 35.68 53.95 56.31 0.00 255.99
55.02 17.84 26.98 28.15 0.00
42.99 13.94 21.08 22.00 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
0.82 0.18 0.26 3.44 0.00 4.69
0.41 0.09 0.13 1.72 0.00
17.41 3.76 5.57 73.26 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
2.73 0.55 0.28 1.39 0.00 4.96
1.37 0.28 0.14 0.69 0.00
55.13 11.19 5.73 27.95 0.00 100.00
Table 8 ANOVA results for tensile strain at yield for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
− 30.17 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 30.36 − 30.19 − 30.31 − 30.23
− 30.00 − 30.14 − 30.08 − 30.03
− 30.16 − 30.19 − 30.13 − 30.27
Table 9 ANOVA results for tensile strain at break for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
− 22.94 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 27.50 − 20.32 − 25.45 − 25.97
− 22.33 − 25.15 − 23.76 − 19.84
− 19.00 − 23.36 − 19.62 − 23.01
Table 10 ANOVA results for flexural module for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
66.08 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
65.72 66.14 66.21 65.37
66.05 66.20 65.84 65.98
66.46 65.88 66.19 66.88
Table 11 ANOVA results for izod impact limit for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
− 4.19 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 4.79 − 3.90 − 4.13 − 4.72
− 4.31 − 4.16 − 4.43 − 4.05
− 3.46 − 4.50 − 4.01 − 3.79
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4.3. ANOVA results
Table 12 The highest S/N values for AISI 1020 mold. Table No.
Highest S/N values
Table Table Table Table Table Table
A1 A1 A2 A3 A3 A3
6 7 8 9 10 11
B2 B1 B2 B1 B2 B1
C1 C2 C2 C3 C1 C3
The influence of process parameters on the mechanical properties of ABS material was analyzed by ANOVA which determined how many parameters to be affected by the mechanical properties. The percentage contribution of variance was calculated by the following equations [17]
D1 D3 D2 D2 D3 D3
n
2
SST = ∑ ðηi −ηm Þ
ð2Þ
i=1
25 and 80 W/mK [18]. The selective laser sintered tool insert's thermal conductivity was less than 15 [W/mK] and the unfilled epoxy resins' thermal conductivity was around 0.5 [W/mK], which cause increased warpage of the part [18]. Silva et al. examined that the characterization of the performance of the products produced by RIM (Reaction Injection Molding) was done using molds in several materials (aluminum, silicone and resin with graphite) and polyurethane (PUR) [1]. Thermal conductivity values of the mold materials were 0.2 W/mK for lab 850, 0.2 W/mK for prolab 65, 100 W/mK for resin with graphite and 162 W/mK for alumec 89, respectively. They found that the tensile strength at yield decreased with the increasing of thermal conductivity of the mold materials and the flexural modules decreased for high values of thermal conductivity of the mold material [1]. Nagaoka et al. [8] stated that the molding conditions such as injection speed, cylinder temperature, and mold temperature conferred on the mechanical properties of the sandwich moldings for PP material. Tensile strength increased with increasing mold temperature. When the core material cylinder temperature was set to 230 °C, an increase in tensile strength with increasing mold temperature was observed. However, at 270 °C core cylinder temperature, the opposite tendency had been observed whereby tensile strength decreased with increasing mold temperature. Values of elasticity module and tensile strength at yield for Al mold were higher than that of steel mold in this study when melt temperature and cooling time were high as shown in Tables 4 and 5. There was hardly any difference observed for values of tensile strain at yield and at break. Values of flexural modules and izod impact strength were found to be higher for Al mold when melt temperature and cooling time were low. Results from this study and literature works showed that mechanical properties of products from molds were not changed proportionally as thermal conductivities of mold temperature or mold material. There was a big difference in thermal conductivities of mold materials but changes in mechanical properties of injected products from mold materials were hardly noticeable. Thermal conductivity of steel mold material used in this study was 4 times higher than that of aluminum mold material. There was a 10–20% change in mechanical properties when molding materials had different thermal conductivities. Nagaoka et al. [8] had obtained similar results in their studies. Silva et al. [1] explained that there was some noticeable changes occurred in mechanical properties at high values of thermal conductivities when there was a big difference in values of thermal conductivities of mold materials.
• • • •
Total sum of squared deviations SST ŋi = S/N ratio, ŋm = average S/N ratio, and n = number of test. SST is also expressed in Eq. (3)
SST = SSM ðSSA + SSB + SSC: …Þ + SSE
ð3Þ
where SSE is the sum of squared error. Eq. (4) is illustrated for SSM i kA h 2 SSM ðSSB ; SSC ;::::Þ = ∑ nAi *ðηAi −ηm Þ
ð4Þ
i=1
where kA nAi ηAi
number of level for factor A experiment number in i level for factor A S/N ratio in i level for factor A
4.3.1. ANOVA results for steel and aluminum mold materials Results from ANOVA were given for steel and aluminum mold materials in Tables 6–11 and 13–18. In these tables, F value was not compared with F-test table because calculated error was too small and F value was infinite. Results from the parameters (elasticity module, tensile strength and tensile strain at yield, tensile strain at break, flexural modules and izod impact strength (notched) limit) for steel aluminum mold materials were illustrated in Tables 6–12 and in Tables 13–19, respectively. F0.05;2;9 was 4.26. F ratio was accurately computed corresponding 95% confidence level in calculation of process parameters. It can be calculated from the ratio of the mean sum of squared deviations. P value refers to the significance level. It was seen from Tables 6–9 and 11 that the most important parameter for elasticity module, tensile strength and tensile strain at yield, tensile strain at break, was melt temperature and its percentage was obtained for the above parameters by 84.90%, 86.78%, 50.05%, and 42.99%, respectively. The most important parameter affecting flexural module in Table 10 was injection pressure by 73.26% [16]. In case of aluminum mold material, percentage of injection pressure was 44.21% for elasticity module, 35.32% for tensile strength at yield and 36.93 for izod impact limit (Tables 13, 14 and 18), and percentages of melt
Table 13 ANOVA results for elasticity module for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
68.40 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
68.39 68.26 68.66 68.67
68.47 68.72 68.34 68.04
68.35 68.22 68.21 68.51
Sum of square (s)
Variance (V)
P (%)
0.02 0.47 0.33 0.65 0.00 1.46
0.01 0.23 0.16 0.32 0.00
1.40 31.88 22.50 44.21 0.00 100.00
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Table 14 ANOVA results for tensile strength at yield for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
31.89 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
31.75 31.60 31.53 31.52
32.10 32.06 32.04 32.09
31.83 32.02 32.10 32.07
Sum of square (s)
Variance (V)
P (%)
0.20 0.38 0.58 0.63 0.00 1.80
0.10 0.19 0.29 0.32 0.00
11.00 21.15 32.53 35.32 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
1.01 0.01 0.04 0.07 0.00 1.13
0.50 0.00 0.02 0.04 0.00
89.39 0.53 3.65 6.43 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
35.25 56.77 6.13 10.04 0.00 108.19
17.62 28.39 3.06 5.02 0.00
32.58 52.48 5.66 9.28 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
2.68 0.00 0.02 0.03 0.00 2.73
1.34 0.00 0.01 0.01 0.00
98.29 0.08 0.69 0.95 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
0.16 0.18 0.01 0.21 0.00 0.57
0.08 0.09 0.01 0.10 0.00
28.74 32.00 2.33 36.93 0.00 100.00
Table 15 ANOVA results for tensile strain at yield for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
− 29.78 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 29.32 − 29.75 − 29.69 − 29.85
− 30.09 − 29.81 − 29.85 − 29.66
− 29.94 − 29.80 − 29.81 − 29.85
Table 16 ANOVA results for tensile strain at break for Al mold. Average S/N
− 17.82 Degree of freedom (DOF)
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
2 2 2 2 1 9
Average S/N values Level 1
Level 2
Level 3
− 15.24 − 19.79 − 17.06 − 17.52
− 20.04 − 19.40 − 17.44 − 16.71
− 18.20 − 14.28 − 18.97 − 19.24
Table 17 ANOVA results for flexural module for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
66.52 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
67.11 66.54 66.48 66.48
66.66 66.50 66.50 66.60
65.79 66.52 66.58 66.48
Table 18 ANOVA results for izod impact limit for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
− 3.60 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 3.44 − 3.51 − 3.63 − 3.54
− 3.60 − 3.80 − 3.63 − 3.81
− 3.77 − 3.49 − 3.55 − 3.45
B. Ozcelik et al. / International Communications in Heat and Mass Transfer 37 (2010) 1359–1365 Table 19 The highest S/N values for Al mold.
Table 21 Linear model between parameters and mechanical properties.
Tables No.
Highest S/N values
Table Table Table Table Table Table
A2 A2 A2 A2 A1 A3
13 14 15 16 17 18
B2 B2 B2 B1 B1 B2
C1 C3 C2 C3 C3 C1
D1 D2 D3 D3 D2 D2
temperature were 89.39% for tensile strain at yield and 98.29% for flexural module (Tables 15 and 17), respectively. In Table 16 the most important parameter affecting tensile strain at break was packing pressure by 52.48% [16]. The highest S/N values in Tables 6–11 showed the most suitable injection parameters for each parameter were listed in Table 12. 4.4. Regression analysis results for steel and aluminum mold materials Regression analysis was a statistical tool for the investigation of relationships between variables. A linear model between injection parameters and mechanical properties were created by using MINITAB. The model result was best explained by values of regression coefficient, r2, close to 1. The results were given in Table 20 for steel and Table 21 for aluminum mold materials. The elasticity module, tensile strength at yield, flexural module and izod impact strength for steel and flexural module for aluminum mold materials had linear relationships between the injection parameters whereas other mechanical properties resulted in non linear relationships. 5. Conclusions In this study, changing of mechanical properties of ABS material was optimized by ANOVA and regression analysis with respect to injection parameters and two mold materials. The most important parameter affecting the elasticity module, tensile strength and tensile strain at yield, tensile strain at break was melt temperature and its effect was determined for steel as 84.90%, 86.78%, 50.05% and 42.99%, respectively. The other parameter affected by flexural module (73.26%) was injection pressure. In case of aluminum mold material, percentages of injection pressure were found as 44.21% for elasticity module, 35.32% for tensile strength at yield and 36.93% for izod impact limit, and percentages of melt temperature were 89.39% for tensile strain at yield and 98.29% for flexural module, respectively. The most important parameter affecting tensile strain at break was packing pressure by 52.48%. The elasticity module, tensile strength at yield, flexural module and izod impact strength for steel and flexural module for aluminum mold materials gave linear relationships (based on values of r2) with injection parameters whereas other mechanical properties resulted in non linear relationships.
Table 20 Linear model between parameters and mechanical properties. Mechanical properties
The equations obtained from Minitab
R-sq (%)
R-sq (Adj) (%)
Elasticity module
70.4 − 1.15 A + 0.047 B − 0.255 C − 0.052 D 32.3 − 0.332 A − 0.0650 B + 0.0233 C + 0.0567 D 64.1 + 0.367 A − 0.130 B − 0.010 C + 0.750 D − 5.95 + 0.668 A − 0.305 B + 0.055 C + 0.465 D
87.40
74.80
87.40
74.80
91.80
83.60
91.60
83.20
Tensile strength at yield Flexural module Izod impact strength
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Mechanical properties
The equations obtained from Minitab
R-sq (%)
R-sq (Adj) (%)
Flexural module
3013 – 3.98 A – 0.65 B + 4.06 C – 0.05 D
96.60
93.20
Values of elasticity module and tensile stress at yield for Al mold were higher than that of steel mold when melt temperature and cooling time were high as shown in Tables 4 and 5. There was hardly any difference observed for values of tensile strain at yield and at break. Value of flexural modules and izod impact strength were found to be higher for Al mold when melt temperature and cooling time were low. Acknowledgements The authors would like to thank to Gebze Institute of Technology for financial support of the project, 2007-A17. The authors also thank to Dr. T. Sinmazcelik from Kocaeli University, Galsan Co. and College of PAGEV in Turkey for the experimental equipments. References [1] M. Silva, A. Mateus, P. Bartolo, A.S. Pouzada, A.J. Pontes, The effect of mould materials in the performance of products moulded by RIM, IV International Material Symposium, Porto, Portugal, 1–4 April 2007. [2] H. Sadabadi, M. Ghasemi, Effects of some injection molding process parameters on fiber orientation tensor of short glass fiber polystyrene composites (SGF/PS), Journal of Reinforced Plastics and Composites 26 (17) (2007) 1729–1741. [3] Y.K. Yang, Optimization of injection–molding process of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites via design of experiments method: a case study, Materials and Manufacturing Processes 21 (8) (2006) 915–921. [4] Y.K. Yang, Optimization of injection–molding process for mechanical and tribological properties of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites with grey relational analysis: a case study, Polymer-Plastics Technology and Engineering 45 (7) (2006) 769–777. [5] G. Guerrica-Echevarria, J.I. Eguiazabal, J. Nazabal, Influence of the preparation method on the mechanical properties of a thermotropic liquid crystalline copolyester, Polymer Testing 20 (4) (2001) 403–408. [6] S. Kenig, A. Ben-David, M. Omer, A. Sadeh, Control of properties in injection molding by neural networks, Engineering Applications of Artificial Intelligence 14 (6) (2001) 819–823. [7] E. Bociaga, The effect of mold temperature and injection velocity on selected properties of polyethylene moldings, Polimery 45 (11–12) (2000) 830–836. [8] T. Nagaoka, U.S. Ishiaku, T. Tomari, H. Hamada, S. Takashima, Effect of molding parameters on the properties of PP/PP sandwich injection moldings, Polymer Testing 24 (8) (2005) 1062–1070. [9] J.-R. Shie, Optimization of injection–molding process for mechanical properties of polypropylene components via a generalized regression neural network, Polymers for Advanced Technologies 19 (1) (2008) 73–83. [10] S.H. Tang, Y.J. Tan, S.M. Sapuan, S. Sulaiman, N. Ismail, R. Samin, The use of Taguchi method in the design of plastic injection mould for reducing warpage, Journal of Materials Processing Technology 182 (2007) 418–426. [11] S.-J. Liu, C.H. Lin, Y.C. Wu, Minimizing the sink marks in injection–molded thermoplastics, Advances in Polymer Technology 20 (3) (2001) 202–215. [12] M. Savaşkan, Y. Taptık, M. Ürgen, Deney tasarımı yöntemi ile matkap uçlarında performans Optimizasyonu, itüdergisi/d, mühendislik, 3 (6) (2004) 117–128 (in Turkish). [13] G. Taguchi, Introduction to Quality Engineering, McGraw-Hill, New York, 1990. [14] Minitab 14 User Manual. [15] MoldFlow Plastic Insight, Release 6.1, MPI 6.1, 2001. [16] A. Ozbay, The investigation of the mechanical properties in the plastic products produced using different mold and polymer materials, MSc Thesis, Gebze Institute of Technology, 2008 (in Turkish). [17] P.J. Ross, Taguchi Techniques for Quality Engineering, second ed.McGraw-Hill, New York, 1996. [18] J.G. Kovacs, T. Bercsey, Influence of mold properties on the quality of injection molded parts, Periodica Polytechnica Ser. Mech. Eng. 49 (2) (2005) 115–122.
Contents lists available at ScienceDirect
International Communications in Heat and Mass Transfer j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i c h m t
Influence of injection parameters and mold materials on mechanical properties of ABS in plastic injection molding☆ Babur Ozcelik a,⁎, Alper Ozbay a, Erhan Demirbas b a b
Department of Mechanical Engineering, Gebze Institute of Technology 41400 Gebze-Kocaeli/Turkey Department of Chemistry, Gebze Institute of Technology, 41400 Gebze-Kocaeli/Turkey
a r t i c l e
i n f o
Available online 1 August 2010 Keywords: Plastic injection molding ABS Mechanical properties DOE Taguchi optimization method Regression analysis
a b s t r a c t This study optimized effect of injection parameters such as melt temperature, packing pressure, cooling time and injection pressure on the mechanical properties of Acrylonitrile–Butadiene–Styrene (ABS) moldings. Mold materials having two different thermal conductivities, 191 W/mK for aluminum 2000 series and 50 W/ mK for AISI 1020 at 25 °C were selected to use in experimental studies. Taguchi's L9(34) orthogonal array design was employed for the experimental plan. Mechanical properties of ABS specimens such as elasticity module, tensile strength and tensile strain at yield, tensile strain at break, flexural modules and izod impact strength (notched) were measured by using some test methods. Signal to noise ratio for mechanical properties of ABS using the Taguchi method was calculated and effect of the parameters on mechanical properties was determined using the analysis of variance. Linear mechanical models were also created by using regression analysis. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Mold materials with the different thermal properties affect the mechanical properties of the plastic parts in injection molding. Aluminum (Al) mold shows advantages in terms of weight, heat transfer and low production cost as compared to steel materials. Aluminum molds are used to produce between 5,000 and 50,000 plastic parts [1]. Researches on the mechanical properties of injection molded of fiber reinforced thermoplastics and injection molding parameters are realized. Sadabadi and Ghasemi investigated the effects of the injection molding process parameters including injection flow rate, mold wall temperature, packing pressure using short fiber reinforced polystyrene composites which could affect fiber orientation and tensile modulus of injection molded parts [2]. Yang studied tribological behaviors and mechanical properties of polycarbonate reinforced with 20% short glass fiber and 6% polytetrafluoroethylene using Taguchi's orthogonal arrays and analysis of variance (ANOVA) under different conditions of injection molding such as filling time, melt and mold temperatures and packing pressure [3]. Yang also examined the mechanical properties and tribological behaviors mainly ultimate stress and surface roughness using grey relational analysis [4].
☆ Communicated by W.J. Minkowycz. ⁎ Corresponding author. E-mail address: [email protected] (B. Ozcelik). 0735-1933/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.icheatmasstransfer.2010.07.001
Guerrica-Echevarria et al. determined the mechanical properties of injection molded parts using injection rate, melt temperature and screw rotation rate during plasticization [5]. Kenig et al. analyzed the effect of process parameters namely, mold and melt temperatures, packing pressure, injection speed and cooling time for tensile modulus using both multivariate regression analysis and artificial neural network [6]. Bociaga examined mechanical and thermal properties of HDPE moldings for mold temperature and injection velocity. He noticed that higher tensile strength, yield stress, tensile modulus and hardness were obtained when the mold temperature was increased [7]. Nagaoka et al. [8] studied tensile and three-point flexural tests from the sandwich and normal injection moldings for polypropylene material. Shie [9] investigated optimization of injection molding process for mechanical properties of polypropylene with regression neural network. The effect of the process parameters on warpage [10] and sink marks [11] in plastic injection molding and on drill bit [12] in drilling were studied with the Taguchi method. In this study, changes in mechanical properties of Acrylonitrile– Butadiene–Styrene (ABS) material were optimized with injection parameters for two mold materials (aluminum 2000 series and AISI 1020). Taguchi's L9(34) orthogonal array design was used for experimental plan. Signal to noise ratio (S/N) for mechanical properties of each mold material was determined and effect of injection parameters on mechanical properties was carried out using analysis of variance (ANOVA). Linear mechanical models were also obtained from regression analysis.
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Table 1 An orthogonal array L9(34) of Taguchi. A
B
C
D
Melt temperature (°C)
Packing pressure (MPa)
Cooling time (sn)
Injection pressure (MPa)
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
1 2 3 2 3 1 3 1 2
1 2 3 3 1 2 2 3 1
Fig. 2. Specimen dimensions for three-point flexural test (ISO 178).
Table 2 The process parameters and levels. Process parameters Melt temperature (°C) Packing pressure (MPa) Cooling time (s) Injection pressure (MPa)
A B C D
Level 1
Level 2
Level 3
200 28 16 36
240 34 19 43
280 39 22 50
Fig. 3. Specimen dimensions for izod impact test (ISO 180).
parameters on mechanical properties. In this way, optimal levels of the process parameters can be estimated.
3. Experimental Table 3 Physical and mechanical properties of ABS.
3.1. Taguchi's orthogonal arrays
Physical properties Melt flow index
ASTM D1238
200 C/5 kg
1.8 g/10 min
Mechanical properties Tensile strength Flexural strength Flexural modules Izod impact strength
ASTM D638 ASTM D790 ASTM D790 ASTM D256
5 mm/min 2.8 mm/min 2.8 mm/min 1/4 in.
47 MPa 66 MPa 2200 MPa 235 J/m
2. Definition of the Taguchi method The Taguchi method developed by Taguchi consists of three stages which are system, parameters, and tolerance designs, respectively [13]. The system design involves the application of scientific and engineering knowledge required in manufacturing of a product. The parameter design is employed to find optimal process values for improving of the quality characteristics. The tolerance design is used for determining and analyzing of the tolerances in optimal settings recommended by the parameter design. By applying the Taguchi method based on orthogonal arrays, time and cost required for conducting of the experiments can be reduced. Taguchi recommends the use of the S/N ratio for determination of the quality characteristics implemented in engineering design problems. The S/N ratio characteristics with signed-target type can be divided into three stages: the smaller is the better, the nominal is the best, and the larger is the better [13]. In this study, the larger is better approach was used for maximizing mechanical properties of the product. In addition to the S/N ratio, ANOVA was employed to obtain for effect of the process
The effects of several process parameters based on Taguchi's orthogonal design could be determined effectively from matrix experiments [13]. Minitab 14 software was used for statistical calculations [14]. Tests were organized in using Taguchi's L9(34) orthogonal array (Table 1). An experimental plan for four parameters with three levels was organized by the Taguchi method (Table 2). Melt temperature taken from the ABS data sheet was set to 200 °C for level 1 and 280 °C for level 3, and level 2 was set to 240 °C which was an average value of these two levels. Values of packing pressure, cooling time and injection pressure for level 1 was obtained from Mold Flow Inside (MPI 6.1) software [15] and parameters for levels 2 and 3 were selected as 1.2 and 1.4 times of level 1, respectively [16].
3.2. Material ABS (Strarex ABS-SD-0150) compound was used for this study. The properties of ABS compound were shown in Table 3.
3.3. Injection molding machine A double cavity of the mold was manufactured in the CNC machine according to ASTM D638 standards. A test part shown in Fig. 1 was injected by a plastic injection machine (MIR, Turkey) which has a clamping force of 637 kN and an injection pressure of 1480 bar, respectively.
Fig. 1. Specimen dimensions for tensile strength (ASTM D638).
B. Ozcelik et al. / International Communications in Heat and Mass Transfer 37 (2010) 1359–1365
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Table 4 Experimental and S/N ratio results of mechanical properties for AISI 1020. Exp. no
1 2 3 4 5 6 7 8 9
Elasticity module (MPa)
Tensile strength at yield (MPa)
Tensile strain at yield (mm/mm)
Tensile strain at break (mm/mm)
Flexural modules (MPa)
Izod impact strength (notched) (J)
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
2782 2679 2621 2458 2502 2368 1870 2217 2128
68.89 68.56 68.37 67.81 67.96 67.49 65.44 66.92 66.56
39.09 40.00 39.24 39.64 38.12 38.06 36.75 36.37 36.48
31.84 32.04 31.87 31.96 31.62 31.61 31.31 31.21 31.24
0.02963 0.03132 0.03012 0.03155 0.03171 0.03160 0.03167 0.03037 0.03110
− 30.56 − 30.08 − 30.42 − 30.02 − 29.98 − 30.00 − 29.99 − 30.35 − 30.14
0.03016 0.04258 0.05846 0.09336 0.06136 0.07799 0.31779 0.06472 0.06875
− 30.41 − 27.42 − 24.66 − 20.60 − 24.24 − 22.16 − 9.96 − 23.78 − 23.25
1823 1885 2098 2158 1903 1970 2122 2375 1844
65.22 65.50 66.44 66.68 65.59 65.89 66.53 67.51 65.32
0.564 0.571 0.593 0.641 0.586 0.601 0.720 0.710 0.592
− 4.97 − 4.87 − 4.54 − 3.86 − 4.64 − 4.42 − 2.85 − 2.97 − 4.55
injection parameters on mechanical properties of the product. The formula of S/N ratio was shown in Eq. (1)
3.4. Experimental testing methods and equipment A suitable dimension was cut from Fig. 1 to carry out three-point flexural and impact tests. Three-point flexural and impact tests were performed with ISO 178 and ISO 180 standards. The specimens for the tensile and three-point flexural test were shown in Figs. 1 and 2. The tensile and three-point flexural test speeds were found as 10 mm/min and 2 mm/min (ASTM D638). 10 J of the impact hammer was used in the impact test and specimen used for the test was shown in Fig. 3. Tensile strength, three-points flexural and impact test were determined from INSTRON 4411, INSTRON 5560 and Ceast 6545, respectively. The tests were repeated three times and the mean values were presented.
" S = N = −10 log10
1 n 1 ∑ n i = 1 y2i
# ð1Þ
where S is the standard deviation, yi is the measured experimental results and n explain to the number of samples in each test trial [17]. Result from measurements of elasticity module, tensile strength and tensile strain at yield, tensile strain at break, flexural modules and izod impact strength were presented for mechanical properties of the product (Tables 4 and 5) which determined the optimal levels of four process parameters for both mold materials [16].
4. Results and discussion 4.2. Analysis of the mechanical results by the thermal conductivity 4.1. Analysis of the S/N ratio Kovacs and Bercsey examined the influence of the mold thermal conductivity on the warpage of the mold. In their study, the typical mold thermal conductivity using conventional steel tool was between
The test results were evaluated in terms of signal/noise (S/N) ratio. The S/N was calculated by larger is better for determining effect of
Table 5 Experimental and S/N ratio results of mechanical properties for Al mold. Exp. No
1 2 3 4 5 6 7 8 9
Elasticity module (MPa)
Tensile strength at yield (MPa)
Tensile strain at yield (mm/mm)
Tensile strain at break (mm/mm)
Flexural modules (MPa)
Izod impact strength (notched) (J)
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
Exp. result
S/N
2746 2595 2548 2620 2772 2564 2411 2831 2621
68.77 68.28 68.12 68.36 68.85 68.19 67.64 69.04 68.37
34.38 41.02 41.03 40.42 40.27 40.08 39.59 38.99 38.58
30.73 32.26 32.26 32.13 32.10 32.21 31.95 31.82 31.73
0.03445 0.03434 0.03380 0.03094 0.03088 0.03203 0.03235 0.03187 0.03132
− 29.26 − 29.28 − 29.42 − 30.19 − 30.21 − 29.89 − 29.80 − 29.93 − 30.08
0.15599 0.17153 0.19370 0.07041 0.07538 0.18574 0.09773 0.09520 0.20044
− 16.14 − 15.31 − 14.26 − 23.05 − 22.45 − 14.62 − 20.12 − 20.43 − 13.96
2252 2276 2273 2141 2154 2160 1984 1926 1935
67.05 67.14 67.13 66.61 66.67 66.69 65.95 65.69 65.73
0.683 0.640 0.698 0.677 0.654 0.651 0.643 0.643 0.659
− 3.31 − 3.88 − 3.12 − 3.39 − 3.69 − 3.73 − 3.84 − 3.84 − 3.62
Table 6 ANOVA results for elasticity module for AISI 1020 mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
67.55 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
68.60 67.38 67.76 67.80
67.76 67.81 67.64 67.16
66.30 67.47 67.26 67.70
Sum of square (s)
Variance (V)
P (%)
8.13 0.31 0.42 0.71 0.00 9.58
4.07 0.16 0.21 0.36 0.00
84.90 3.28 4.37 7.45 0.00 100.00
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Table 7 ANOVA results for tensile strength at yield for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
31.64 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
31.92 31.70 31.56 31.57
31.73 31.63 31.75 31.65
31.25 31.58 31.60 31.68
Sum of square (s)
Variance (V)
P (%)
0.71 0.02 0.06 0.02 0.00 0.81
0.35 0.01 0.03 0.01 0.00
86.78 3.06 7.53 2.64 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
0.19 0.01 0.08 0.10 0.00 0.38
0.10 0.00 0.04 0.05 0.00
50.05 1.51 22.08 26.35 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
110.05 35.68 53.95 56.31 0.00 255.99
55.02 17.84 26.98 28.15 0.00
42.99 13.94 21.08 22.00 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
0.82 0.18 0.26 3.44 0.00 4.69
0.41 0.09 0.13 1.72 0.00
17.41 3.76 5.57 73.26 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
2.73 0.55 0.28 1.39 0.00 4.96
1.37 0.28 0.14 0.69 0.00
55.13 11.19 5.73 27.95 0.00 100.00
Table 8 ANOVA results for tensile strain at yield for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
− 30.17 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 30.36 − 30.19 − 30.31 − 30.23
− 30.00 − 30.14 − 30.08 − 30.03
− 30.16 − 30.19 − 30.13 − 30.27
Table 9 ANOVA results for tensile strain at break for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
− 22.94 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 27.50 − 20.32 − 25.45 − 25.97
− 22.33 − 25.15 − 23.76 − 19.84
− 19.00 − 23.36 − 19.62 − 23.01
Table 10 ANOVA results for flexural module for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
66.08 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
65.72 66.14 66.21 65.37
66.05 66.20 65.84 65.98
66.46 65.88 66.19 66.88
Table 11 ANOVA results for izod impact limit for AISI 1020 mold. Average S/N
Melt temperature (°C) Packing pressure (MPa) Cooling time (sn) Injection pressure (MPa) Error Total
− 4.19 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 4.79 − 3.90 − 4.13 − 4.72
− 4.31 − 4.16 − 4.43 − 4.05
− 3.46 − 4.50 − 4.01 − 3.79
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4.3. ANOVA results
Table 12 The highest S/N values for AISI 1020 mold. Table No.
Highest S/N values
Table Table Table Table Table Table
A1 A1 A2 A3 A3 A3
6 7 8 9 10 11
B2 B1 B2 B1 B2 B1
C1 C2 C2 C3 C1 C3
The influence of process parameters on the mechanical properties of ABS material was analyzed by ANOVA which determined how many parameters to be affected by the mechanical properties. The percentage contribution of variance was calculated by the following equations [17]
D1 D3 D2 D2 D3 D3
n
2
SST = ∑ ðηi −ηm Þ
ð2Þ
i=1
25 and 80 W/mK [18]. The selective laser sintered tool insert's thermal conductivity was less than 15 [W/mK] and the unfilled epoxy resins' thermal conductivity was around 0.5 [W/mK], which cause increased warpage of the part [18]. Silva et al. examined that the characterization of the performance of the products produced by RIM (Reaction Injection Molding) was done using molds in several materials (aluminum, silicone and resin with graphite) and polyurethane (PUR) [1]. Thermal conductivity values of the mold materials were 0.2 W/mK for lab 850, 0.2 W/mK for prolab 65, 100 W/mK for resin with graphite and 162 W/mK for alumec 89, respectively. They found that the tensile strength at yield decreased with the increasing of thermal conductivity of the mold materials and the flexural modules decreased for high values of thermal conductivity of the mold material [1]. Nagaoka et al. [8] stated that the molding conditions such as injection speed, cylinder temperature, and mold temperature conferred on the mechanical properties of the sandwich moldings for PP material. Tensile strength increased with increasing mold temperature. When the core material cylinder temperature was set to 230 °C, an increase in tensile strength with increasing mold temperature was observed. However, at 270 °C core cylinder temperature, the opposite tendency had been observed whereby tensile strength decreased with increasing mold temperature. Values of elasticity module and tensile strength at yield for Al mold were higher than that of steel mold in this study when melt temperature and cooling time were high as shown in Tables 4 and 5. There was hardly any difference observed for values of tensile strain at yield and at break. Values of flexural modules and izod impact strength were found to be higher for Al mold when melt temperature and cooling time were low. Results from this study and literature works showed that mechanical properties of products from molds were not changed proportionally as thermal conductivities of mold temperature or mold material. There was a big difference in thermal conductivities of mold materials but changes in mechanical properties of injected products from mold materials were hardly noticeable. Thermal conductivity of steel mold material used in this study was 4 times higher than that of aluminum mold material. There was a 10–20% change in mechanical properties when molding materials had different thermal conductivities. Nagaoka et al. [8] had obtained similar results in their studies. Silva et al. [1] explained that there was some noticeable changes occurred in mechanical properties at high values of thermal conductivities when there was a big difference in values of thermal conductivities of mold materials.
• • • •
Total sum of squared deviations SST ŋi = S/N ratio, ŋm = average S/N ratio, and n = number of test. SST is also expressed in Eq. (3)
SST = SSM ðSSA + SSB + SSC: …Þ + SSE
ð3Þ
where SSE is the sum of squared error. Eq. (4) is illustrated for SSM i kA h 2 SSM ðSSB ; SSC ;::::Þ = ∑ nAi *ðηAi −ηm Þ
ð4Þ
i=1
where kA nAi ηAi
number of level for factor A experiment number in i level for factor A S/N ratio in i level for factor A
4.3.1. ANOVA results for steel and aluminum mold materials Results from ANOVA were given for steel and aluminum mold materials in Tables 6–11 and 13–18. In these tables, F value was not compared with F-test table because calculated error was too small and F value was infinite. Results from the parameters (elasticity module, tensile strength and tensile strain at yield, tensile strain at break, flexural modules and izod impact strength (notched) limit) for steel aluminum mold materials were illustrated in Tables 6–12 and in Tables 13–19, respectively. F0.05;2;9 was 4.26. F ratio was accurately computed corresponding 95% confidence level in calculation of process parameters. It can be calculated from the ratio of the mean sum of squared deviations. P value refers to the significance level. It was seen from Tables 6–9 and 11 that the most important parameter for elasticity module, tensile strength and tensile strain at yield, tensile strain at break, was melt temperature and its percentage was obtained for the above parameters by 84.90%, 86.78%, 50.05%, and 42.99%, respectively. The most important parameter affecting flexural module in Table 10 was injection pressure by 73.26% [16]. In case of aluminum mold material, percentage of injection pressure was 44.21% for elasticity module, 35.32% for tensile strength at yield and 36.93 for izod impact limit (Tables 13, 14 and 18), and percentages of melt
Table 13 ANOVA results for elasticity module for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
68.40 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
68.39 68.26 68.66 68.67
68.47 68.72 68.34 68.04
68.35 68.22 68.21 68.51
Sum of square (s)
Variance (V)
P (%)
0.02 0.47 0.33 0.65 0.00 1.46
0.01 0.23 0.16 0.32 0.00
1.40 31.88 22.50 44.21 0.00 100.00
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Table 14 ANOVA results for tensile strength at yield for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
31.89 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
31.75 31.60 31.53 31.52
32.10 32.06 32.04 32.09
31.83 32.02 32.10 32.07
Sum of square (s)
Variance (V)
P (%)
0.20 0.38 0.58 0.63 0.00 1.80
0.10 0.19 0.29 0.32 0.00
11.00 21.15 32.53 35.32 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
1.01 0.01 0.04 0.07 0.00 1.13
0.50 0.00 0.02 0.04 0.00
89.39 0.53 3.65 6.43 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
35.25 56.77 6.13 10.04 0.00 108.19
17.62 28.39 3.06 5.02 0.00
32.58 52.48 5.66 9.28 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
2.68 0.00 0.02 0.03 0.00 2.73
1.34 0.00 0.01 0.01 0.00
98.29 0.08 0.69 0.95 0.00 100.00
Sum of square (s)
Variance (V)
P (%)
0.16 0.18 0.01 0.21 0.00 0.57
0.08 0.09 0.01 0.10 0.00
28.74 32.00 2.33 36.93 0.00 100.00
Table 15 ANOVA results for tensile strain at yield for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
− 29.78 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 29.32 − 29.75 − 29.69 − 29.85
− 30.09 − 29.81 − 29.85 − 29.66
− 29.94 − 29.80 − 29.81 − 29.85
Table 16 ANOVA results for tensile strain at break for Al mold. Average S/N
− 17.82 Degree of freedom (DOF)
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
2 2 2 2 1 9
Average S/N values Level 1
Level 2
Level 3
− 15.24 − 19.79 − 17.06 − 17.52
− 20.04 − 19.40 − 17.44 − 16.71
− 18.20 − 14.28 − 18.97 − 19.24
Table 17 ANOVA results for flexural module for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
66.52 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
67.11 66.54 66.48 66.48
66.66 66.50 66.50 66.60
65.79 66.52 66.58 66.48
Table 18 ANOVA results for izod impact limit for Al mold. Average S/N
Melt temperature (°C) (A) Packing pressure (MPa) (B) Cooling time (sn) (C) Injection pressure (MPa) (D) Error Total
− 3.60 Degree of freedom (DOF)
Average S/N values Level 1
Level 2
Level 3
2 2 2 2 1 9
− 3.44 − 3.51 − 3.63 − 3.54
− 3.60 − 3.80 − 3.63 − 3.81
− 3.77 − 3.49 − 3.55 − 3.45
B. Ozcelik et al. / International Communications in Heat and Mass Transfer 37 (2010) 1359–1365 Table 19 The highest S/N values for Al mold.
Table 21 Linear model between parameters and mechanical properties.
Tables No.
Highest S/N values
Table Table Table Table Table Table
A2 A2 A2 A2 A1 A3
13 14 15 16 17 18
B2 B2 B2 B1 B1 B2
C1 C3 C2 C3 C3 C1
D1 D2 D3 D3 D2 D2
temperature were 89.39% for tensile strain at yield and 98.29% for flexural module (Tables 15 and 17), respectively. In Table 16 the most important parameter affecting tensile strain at break was packing pressure by 52.48% [16]. The highest S/N values in Tables 6–11 showed the most suitable injection parameters for each parameter were listed in Table 12. 4.4. Regression analysis results for steel and aluminum mold materials Regression analysis was a statistical tool for the investigation of relationships between variables. A linear model between injection parameters and mechanical properties were created by using MINITAB. The model result was best explained by values of regression coefficient, r2, close to 1. The results were given in Table 20 for steel and Table 21 for aluminum mold materials. The elasticity module, tensile strength at yield, flexural module and izod impact strength for steel and flexural module for aluminum mold materials had linear relationships between the injection parameters whereas other mechanical properties resulted in non linear relationships. 5. Conclusions In this study, changing of mechanical properties of ABS material was optimized by ANOVA and regression analysis with respect to injection parameters and two mold materials. The most important parameter affecting the elasticity module, tensile strength and tensile strain at yield, tensile strain at break was melt temperature and its effect was determined for steel as 84.90%, 86.78%, 50.05% and 42.99%, respectively. The other parameter affected by flexural module (73.26%) was injection pressure. In case of aluminum mold material, percentages of injection pressure were found as 44.21% for elasticity module, 35.32% for tensile strength at yield and 36.93% for izod impact limit, and percentages of melt temperature were 89.39% for tensile strain at yield and 98.29% for flexural module, respectively. The most important parameter affecting tensile strain at break was packing pressure by 52.48%. The elasticity module, tensile strength at yield, flexural module and izod impact strength for steel and flexural module for aluminum mold materials gave linear relationships (based on values of r2) with injection parameters whereas other mechanical properties resulted in non linear relationships.
Table 20 Linear model between parameters and mechanical properties. Mechanical properties
The equations obtained from Minitab
R-sq (%)
R-sq (Adj) (%)
Elasticity module
70.4 − 1.15 A + 0.047 B − 0.255 C − 0.052 D 32.3 − 0.332 A − 0.0650 B + 0.0233 C + 0.0567 D 64.1 + 0.367 A − 0.130 B − 0.010 C + 0.750 D − 5.95 + 0.668 A − 0.305 B + 0.055 C + 0.465 D
87.40
74.80
87.40
74.80
91.80
83.60
91.60
83.20
Tensile strength at yield Flexural module Izod impact strength
1365
Mechanical properties
The equations obtained from Minitab
R-sq (%)
R-sq (Adj) (%)
Flexural module
3013 – 3.98 A – 0.65 B + 4.06 C – 0.05 D
96.60
93.20
Values of elasticity module and tensile stress at yield for Al mold were higher than that of steel mold when melt temperature and cooling time were high as shown in Tables 4 and 5. There was hardly any difference observed for values of tensile strain at yield and at break. Value of flexural modules and izod impact strength were found to be higher for Al mold when melt temperature and cooling time were low. Acknowledgements The authors would like to thank to Gebze Institute of Technology for financial support of the project, 2007-A17. The authors also thank to Dr. T. Sinmazcelik from Kocaeli University, Galsan Co. and College of PAGEV in Turkey for the experimental equipments. References [1] M. Silva, A. Mateus, P. Bartolo, A.S. Pouzada, A.J. Pontes, The effect of mould materials in the performance of products moulded by RIM, IV International Material Symposium, Porto, Portugal, 1–4 April 2007. [2] H. Sadabadi, M. Ghasemi, Effects of some injection molding process parameters on fiber orientation tensor of short glass fiber polystyrene composites (SGF/PS), Journal of Reinforced Plastics and Composites 26 (17) (2007) 1729–1741. [3] Y.K. Yang, Optimization of injection–molding process of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites via design of experiments method: a case study, Materials and Manufacturing Processes 21 (8) (2006) 915–921. [4] Y.K. Yang, Optimization of injection–molding process for mechanical and tribological properties of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites with grey relational analysis: a case study, Polymer-Plastics Technology and Engineering 45 (7) (2006) 769–777. [5] G. Guerrica-Echevarria, J.I. Eguiazabal, J. Nazabal, Influence of the preparation method on the mechanical properties of a thermotropic liquid crystalline copolyester, Polymer Testing 20 (4) (2001) 403–408. [6] S. Kenig, A. Ben-David, M. Omer, A. Sadeh, Control of properties in injection molding by neural networks, Engineering Applications of Artificial Intelligence 14 (6) (2001) 819–823. [7] E. Bociaga, The effect of mold temperature and injection velocity on selected properties of polyethylene moldings, Polimery 45 (11–12) (2000) 830–836. [8] T. Nagaoka, U.S. Ishiaku, T. Tomari, H. Hamada, S. Takashima, Effect of molding parameters on the properties of PP/PP sandwich injection moldings, Polymer Testing 24 (8) (2005) 1062–1070. [9] J.-R. Shie, Optimization of injection–molding process for mechanical properties of polypropylene components via a generalized regression neural network, Polymers for Advanced Technologies 19 (1) (2008) 73–83. [10] S.H. Tang, Y.J. Tan, S.M. Sapuan, S. Sulaiman, N. Ismail, R. Samin, The use of Taguchi method in the design of plastic injection mould for reducing warpage, Journal of Materials Processing Technology 182 (2007) 418–426. [11] S.-J. Liu, C.H. Lin, Y.C. Wu, Minimizing the sink marks in injection–molded thermoplastics, Advances in Polymer Technology 20 (3) (2001) 202–215. [12] M. Savaşkan, Y. Taptık, M. Ürgen, Deney tasarımı yöntemi ile matkap uçlarında performans Optimizasyonu, itüdergisi/d, mühendislik, 3 (6) (2004) 117–128 (in Turkish). [13] G. Taguchi, Introduction to Quality Engineering, McGraw-Hill, New York, 1990. [14] Minitab 14 User Manual. [15] MoldFlow Plastic Insight, Release 6.1, MPI 6.1, 2001. [16] A. Ozbay, The investigation of the mechanical properties in the plastic products produced using different mold and polymer materials, MSc Thesis, Gebze Institute of Technology, 2008 (in Turkish). [17] P.J. Ross, Taguchi Techniques for Quality Engineering, second ed.McGraw-Hill, New York, 1996. [18] J.G. Kovacs, T. Bercsey, Influence of mold properties on the quality of injection molded parts, Periodica Polytechnica Ser. Mech. Eng. 49 (2) (2005) 115–122.