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30120140505017 2-3

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 157 OPTIMIZATION OF PROCESS PARAMETERS OF PLASTIC INJECTION MOLDING FOR POLYPROPYLENE TO ENHANCE PRODUCTIVITY AND REDUCE TIME FOR DEVELOPMENT Mr. A.B. HUMBE(1) , Dr. M.S. KADAM(2) (1) Student, M.E. Manufacturing, Mechanical Engineering Department, Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra, India (2) Professor and Head, Mechanical Engineering Department, Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra, India ABSTRACT The injection molding process itself is a complex mix of time, temperature and pressure variables with a multitude of manufacturing defects that can occur without the right combination of processing parameters and design components. In this analysis input processing parameters are melt temperature (MT), Injection pressure(IP), holding pressure(HP) and cooling time(Cool Time) and responses considered for investigation of plastic injection molding process are cycle time and tensile strength. The material used for experimentation is polypropylene. The cycle time and tensile strength are obtained through series of experiments according to Taguchi’s L-9 Orthogonal Array to develop the equation. Experimental results are analyzed through Response Surface Method and the method is adopted to analyze the effect of each parameter on the cycle time and tensile strength to achieve minimum cycle time and maximum tensile strength. For optimization of input plastic injection molding processing parameters with responses RSM’s D-Optimal method. Keywords: Plastic Injection Molding, DOE, RSM’s D-Optimal Method. 1. INTRODUCTION Increasing the productivity and the quality of the plastic injected parts are the main challenges of plastic based industries, so there has been increased interest in the monitoring all aspects of the plastic injection molding parameters. Most production engineers have been using trial- and-error method to determine initial settings for a number of parameters, including melt temperature, injection pressure, injection velocity, injection time, packing pressure, packing time, cooling temperature, and cooling time which depend on the engineers’ experience and intuition to INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2014): 7.5377 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 158 determine initial process parameter settings. However, the trial-and-error process is costly and time consuming. Chang et al [1] studied the relationship between input process parameters for injection molded Dog-bone bar and outputs as weld line width and tensile impact using Taguchi method. He considered 7 input parameters such as melt and mold temperatures, injection and hold pressures, cooling and holding times, and back pressure and found that the melt and mold temperature, injection pressure, and holding time are the most effective, while hold pressure, holding time and back pressure are least important parameter. Loera et al [2] introduced a concept for deliberately varying the wall thicknesses of an injection molded part within recommended dimensional tolerance to reduce part warpage using Taguchi method. From the results, it is seen that varying wall thicknesses exhibited better warpage characteristics compared to the constant wall thicknesses. Instead of PIM, rotational molding (RM) is one of the most important polymer processing techniques for producing hollow plastic part. However, part warpage caused by inappropriate mould design and processing conditions is the problem that confounds the overall success of this technique. Yanwei Huyong [3] applied Taguchi method to systematically investigate the effects of processing parameters on the shrinkage behavior (along and across the flow directions) of three plastic materials. The results from the research shown that the mould and melt temperature, along with holding pressure and holding time, are the most significant factors to the shrinkage behavior of the three materials, although their impact is different for each material. Shuaib et al [4] studied the factors that contribute to warpage for a thin shallow injection-molded part. The process is performed by experimental method by Taguchi and ANOVA techniques are employed. The factors that been taking into considerations includes the mold temperature, melt temperature, filling time, packing pressure and packing time. The result shows that by S/N response and percentage contribution in ANOVA, packing time has been identified to be the most significant factors on affecting the warpage on thin shallow part. Longzhi et al [5] inestigated to avoid the surface sink marks on the automobile dashboard decorative covers, the combined effects of multi-molding process parameters are analyzed by the combination of orthogonal experiments and Mold flow simulation tests. By this method, it can gain the experiment data which can reflect the overall situation using fewer number of simulation test. Furthermore, the effects degree of different molding process parameters for surface sink marks are investigated, and the reasonable gate location and optimized parameter combination is obtained. It can solve the unreasonable appearance of process parameter settings. The mold design can fasten the mold developing schedule, thus shorten the cycle of product development, and improve the quality of products and the competitive ability of enterprise. Wen-ChinChen et al [6] in this research Taguchi method, back-propagation neural networks (BPNN), and Genetic algorithms (GA) are applied to the problem of process parameter settings for Multiple-Input Single-Output (MISO) Plastic injection molding. Taguchi method is adopted to arrange the number of experimental runs. Injection time, velocity pressure switch position, packing pressure, and injection velocity are engaged as process control parameters, and product weight as the target quality. Then, BPNN and GA are applied for searching the final optimal parameter settings. 2. EXPERIMENTAL WORK For plastic injection molding thermoplastics are used. The material used for this experimentation is polypropylene and L&T Demage plastic injection molding machine 40T to 250T capacity (shot capacity-50gm to 1000gm) is used while conducting experiments. We have selected 6 different parts which are of polypropylene material and divided them into two categories (large and small) and three parts in each. The cycle time and tensile strength are obtained through series of experiment according to Taguchi’s L-9 Orthogonal Array. The process parameters used and their levels are presented in table 1 and table 2.
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 159 Table 1: Factors and operating levels for large category components SR. NO. PART NAME / FACTORS MT (°c) IP(MPa) HP(MPa) COOL TIME(Sec) 1 Part Size L=125mm*B=55mm* H=15mm Thickness MIN=1mm; MAX=5mm 222 81 55 20 225 84 52 22 228 85 56 23 2 Part Size L=100mm* B=50mm* H=10mm Thickness MIN=1mm; MAX=2mm 217 71 50 19 219 76 48 21 221 79 53 22 3 Part Size L=90mm* B=40mm* H=10mm Thickness MIN=0.5mm;MAX=2mm 209 68 40 17 212 71 38 18 215 74 43 19 Table 2: Factors and operating levels for small category components SR. NO. PART NAME / FACTORS MT (°c) IP(MPa) HP(MPa) COOL TIME(sec) 4 Part Size L=70mm* B=30mm* H=15mm Thickness MIN=2mm; MAX=4mm 202 61 36 14 205 65 32 16 208 67 38 17 5 Part Size L=65mm* B=20mm* H=20mm Thickness MIN=1mm; MAX=3mm 198 55 27 15 200 58 26 14 202 62 31 16 6 Part Size L=60mm* B=25mm* H=15mm Thickness MIN=1mm; MAX=3mm 192 40 24 12 196 45 26 14 199 48 30 16 Result through experimental work is recorded and experimental data obtained for cycle time and tensile strength is analyzed. 3 DATA ANALYSIS 3.1 Mathematical equations for size L=125mm*B=55mm* H=15mm -The regression equation is for cycle time Cycle Time = - 121 + 0.611 MT + 0.038 IP+ 0.011 HP+ 0.857 Cool Time S = 0.614239 R-Sq = 95.28% R-Sq(adj) = 90.57% The parameter R2 describes the amount of variation observed in cycle time is explained by the input factors. R2 is obtained for above equation is 95.28%, indicate that the model is able to predict the response with high accuracy. Adjusted R2 is a modified R2 that has been adjusted for the number of terms in the model. If unnecessary terms are included in the model, R2 can be artificially high, but adjusted R2 (90.57 % obtained for above equation) may get smaller. The standard deviation of errors in the modeling, S=0.614239 Comparing the p-value to a commonly used α-level = 0.05, it
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 160 is found that if the p-value is less than or equal to α, it can be concluded that the effect is significant. This clearly indicates that the MT has greatest influence on cycle time, followed by cool time, HP and IP. The p-values for MT, IP, HP and Cool time are 0.002, 0.765, 1.000 and 0.006 respectively. It can be seen that the most influencing parameters to cycle time for polypropylene material is melt temperature and cooling time followed by injection pressure and holding pressure. The same conclusion is carried out for following equations -The regression equation for tensile strength Tensile Strength = - 137 + 0.611 MT (°c) + 0.247 IP(MPa) + 0.0244 HP(MPa) + 0.133 Cool Time(sec) S = 0.424088 R-Sq = 96.84% R-Sq(adj) = 93.67% Graph 1: Residual Plots for cycle time of size L=125mm*B=55mm* H=15mm Graph 2: Residual Plots for tensile strength of size L=125mm*B=55mm* H=15mm 3.2 Mathematical Equations for Size L=100mm* B=50mm* H=10mm -The regression equation for cycle time Cycle Time = - 244 + 1.00 MT + 0.007 IP + 0.439 HP+ 1.88 Cool Time S = 1.41798 R-Sq = 90.95% R-Sq(adj) = 81.90% 1.00.50.0-0.5-1.0 99 90 50 10 1 Residual Percent 42.040.539.037.536.0 0.5 0.0 -0.5 -1.0 Fitted Value Residual 0.500.250.00-0.25-0.50-0.75 3 2 1 0 Residual Frequency 987654321 0.5 0.0 -0.5 -1.0 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Cycle Time(Sec) 0.80.40.0-0.4-0.8 99 90 50 10 1 Residual Percent 2726252423 0.50 0.25 0.00 -0.25 -0.50 Fitted Value Residual 0.60.40.20.0-0.2-0.4 4 3 2 1 0 Residual Frequency 987654321 0.50 0.25 0.00 -0.25 -0.50 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Tensile Strength(MPa)
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 161 -The regression equation for tensile strength Tensile Strength = - 189 + 0.917 MT + 0.133 IP + 0.0193 HP + 0.133 Cool Time S = 0.380516 R-Sq = 97.45% R-Sq(adj) = 94.91% Graph 3: Residual Plots for cycle time of size L=100mm* B=50mm* H=10mm Graph 4: Residual Plots for tensile strength of size L=100mm* B=50mm* H=10mm 3.3 Mathematical Equations of Size L=90mm* B=40mm* H=10mm -The regression equation for cycle time Cycle Time = - 135 + 0.556 MT - 0.111 IP + 0.053 HP + 3.00 Cool Time S = 0.800219 R-Sq = 96.54% R-Sq(adj) = 93.08% -The regression equation for tensile strength Tensile Strength = - 122 + 0.611 MT + 0.183 IP + 0.0193 HP + 0.183 Cool Time S = 0.369031 R-Sq = 97.60% R-Sq(adj) = 95.21% 210-1-2 99 90 50 10 1 Residual Percent 40.037.535.032.530.0 1 0 -1 -2 Fitted Value Residual1.51.00.50.0-0.5-1.0-1.5 3 2 1 0 Residual Frequency 987654321 1 0 -1 -2 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Cycle Time(Sec) 0.500.250.00-0.25-0.50 99 90 50 10 1 Residual Percent 2726252423 0.4 0.2 0.0 -0.2 -0.4 Fitted Value Residual 0.60.40.20.0-0.2-0.4 4 3 2 1 0 Residual Frequency 987654321 0.4 0.2 0.0 -0.2 -0.4 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Tensile Strength(MPa)
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 162 Graph 5: Residual Plots for cycle time of Size L=90mm* B=40mm* H=10mm Graph 6: Residual Plots for tensile strength of Size L=90mm* B=40mm* H=10mm 3.4 Mathematical Equations of Size L=70mm* B=30mm* H=15mm -The regression equation for cycle time Cycle Time = - 5.52 + 0.0556 MT + 0.152 IP + 0.0609 HP+ 0.411 Cool Time S = 0.262398 R-Sq = 92.25% R-Sq(adj) = 84.51% -The regression equation is Tensile Strength = - 94.3 + 0.533 MT+ 0.123 IP+ 0.0280 HP+ 0.011 Cool Time S = 0.492526 R-Sq = 94.38% R-Sq(adj) = 88.76% 1.00.50.0-0.5-1.0 99 90 50 10 1 Residual Percent 35.032.530.027.525.0 0.5 0.0 -0.5 -1.0 Fitted Value Residual 0.60.40.20.0-0.2-0.4-0.6-0.8 4 3 2 1 0 Residual Frequency 987654321 0.5 0.0 -0.5 -1.0 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Cycle Time(Sec) 0.500.250.00-0.25-0.50 99 90 50 10 1 Residual Percent 262422 0.6 0.4 0.2 0.0 -0.2 Fitted Value Residual 0.40.20.0-0.2 3 2 1 0 Residual Frequency 987654321 0.6 0.4 0.2 0.0 -0.2 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Tensile Strength(MPa)
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 163 Graph 7: Residual Plots for cycle time of Size L=70mm* B=30mm* H=15mm Graph 8: Residual Plots for tensile strength of Size L=70mm* B=30mm* H=15mm 3.5 Mathematical Equations of Size L=65mm* B=20mm* H=20mm -The regression equation for cycle time Cycle Time = - 67.2 + 0.333 MT + 0.0979 IP - 0.145 HP+ 1.31 Cool Time S = 0.432661 R-Sq = 95.19% R-Sq(adj) = 90.37% -The regression equation for tensile strength Tensile Strength = - 93.7 + 0.567 MT + 0.0797 IP+ 0.0007 HP- 0.0499 Cool Time S = 0.178819 R-Sq = 98.46% R-Sq(adj) = 96.93% 0.500.250.00-0.25-0.50 99 90 50 10 1 Residual Percent 25.024.524.023.523.0 0.4 0.2 0.0 -0.2 Fitted Value Residual 0.40.30.20.10.0-0.1-0.2 3 2 1 0 Residual Frequency 987654321 0.4 0.2 0.0 -0.2 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Cycle Time(Sec) 1.00.50.0-0.5-1.0 99 90 50 10 1 Residual Percent 2625242322 0.5 0.0 -0.5 Fitted Value Residual 0.750.500.250.00-0.25 4.8 3.6 2.4 1.2 0.0 Residual Frequency 987654321 0.5 0.0 -0.5 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Tensile Strength(MPa)
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 164 Graph 9: Residual Plots for cycle time of Size L=65mm* B=20mm* H=20mm Graph 10: Residual Plots for tensile strength of Size L=65mm* B=20mm* H=20mm 3.6 Mathematical Equations of Size L=60mm* B=25mm* H=15mm -The regression equation is Cycle Time = - 23.8 + 0.189 MT + 0.0010 IP - 0.107 HP+ 0.667 Cool Time S = 0.102259 R-Sq = 99.70% R-Sq(adj) = 99.40% -The regression equation is Tensile Strength = - 15.0 + 0.185 MT + 0.0565 IP+ 0.0048 HP + 0.0011 Cool Time S = 0.0882733 R-Sq = 98.91% R-Sq(adj) = 97.83% 0.80.40.0-0.4-0.8 99 90 50 10 1 Residual Percent 2322212019 0.5 0.0 -0.5 Fitted Value Residual 0.750.500.250.00-0.25 6.0 4.5 3.0 1.5 0.0 Residual Frequency 987654321 0.5 0.0 -0.5 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Cycle Time(Sec) 0.300.150.00-0.15-0.30 99 90 50 10 1 Residual Percent 25242322 0.2 0.1 0.0 -0.1 -0.2 Fitted Value Residual 0.20.10.0-0.1-0.2 4.8 3.6 2.4 1.2 0.0 Residual Frequency 987654321 0.2 0.1 0.0 -0.1 -0.2 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Tensile Strength(MPa)
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 165 Graph 4.11: Residual Plots for cycle time of Size L=60mm* B=25mm* H=15mm Graph 12: Residual Plots for tensile strength of Size L=60mm* B=25mm* H=15mm 4. RESULT AND DISCUSSIONS To optimize processing parameters with result in the plastic injection process of polypropylene RSM’s D-Optimal method is used for optimum result. By graph no 13 following observations are made. Melt Temperature (MT):- By increasing melt temperature responses like cycle time and tensile strength increases. Therefore optimal setting is in the middle of the range (222.1818) because goal is to minimize cycle time and maximize tensile strength. Vertical faint line in the second column of graph represents optimal setting of melt temperature. Injection Pressure (IP):- Increasing IP also increases both the responses like cycle time and tensile strength also, but effect on cycle time is minimum as compare to tensile strength. Therefore composite desirability decreases by increasing pulse on time. Hence optimal setting is in the lower of the range (81.9293) because goal is to maximize tensile strength and minimize cycle time. Vertical faint line in the third column of graph represents optimal setting of IP. Holding Pressure (HP):- Increasing holding pressure tensile strength and cycle time decreases, but goal is to increase tensile strength and decrease cycle time .Therefore composite desirability increases by increasing holding pressure. Hence optimal setting is in the higher of the range (56) 0.20.10.0-0.1-0.2 99 90 50 10 1 Residual Percent 2221201918 0.10 0.05 0.00 -0.05 -0.10 Fitted Value Residual 0.100.050.00-0.05-0.10-0.15 3 2 1 0 Residual Frequency 987654321 0.10 0.05 0.00 -0.05 -0.10 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Cycle Time (sec) 0.10.0-0.1 99 90 50 10 1 Residual Percent 24.524.023.523.0 0.10 0.05 0.00 -0.05 -0.10 Fitted Value Residual 0.100.050.00-0.05-0.10 2.0 1.5 1.0 0.5 0.0 Residual Frequency 987654321 0.10 0.05 0.00 -0.05 -0.10 Observation Order Residual Normal Probability Plot Versus Fits Histogram Versus Order Residual Plots for Tensile Strength(MPa)
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 166 because goal is to minimize cycle time and maximize tensile strength. Vertical faint line in the fourth column of graph represents optimal setting of holding pressure. Cooling Time (Cool Time):- Increasing cooling time cycle time and tensile strength increases, but goal is to decreases cycle time. Therefore composite desirability decreases slightly by decreasing. Hence optimal setting is in the lower of the range (20) because goal is to maximize tensile strength and minimize cycle time. Vertical faint line in the fifth column of graph represents optimal setting of cool time. By the similar way observations are made for graph no. 14 to graph no.18 of different size. Graph 13: Optimization Plot for cycle time and tensile strength of size L=125mm*B=55mm* H=15mm Graph 14: Optimization Plot for cycle time and tensile strength of size L=100mm* B=50mm* H=10mm Graph 15: Optimization Plot for cycle time and tensile strength Size of component L=90mm* B=40mm* H=10mm Cur High Low0.98923 D Optimal d = 0.97858 Minimum Cycle Ti y = 35.1285 d = 1.0000 Maximum Tensile y = 23.0043 0.98923 Desirability Composite 20.0 23.0 52.0 56.0 81.0 85.0 222.0 228.0 IP(MPa) HP(MPa) Cool TimMT (°c) [222.1818] [81.9293] [56.0] [20.0] Cur High Low0.90220 D Optimal d = 0.81435 Minimum Cycle Ti y = 33.8565 d = 0.99953 Maximum Tensile y = 26.9982 0.90220 Desirability Composite 19.0 22.0 48.0 53.0 71.0 79.0 217.0 221.0 IP(MPa) HP(MPa) Cool TimMT (°c) [220.8788] [79.0] [48.0] [19.0] Cur High Low0.90102 D Optimal d = 0.81387 Minimum Cycle Ti y = 28.6752 d = 0.99751 Maximum Tensile y = 26.7883 0.90102 Desirability Composite 17.0 19.0 38.0 43.0 68.0 74.0 209.0 215.0 IP(MPa) HP(MPa) Cool TimMT (°c) [214.6364] [74.0] [38.0] [17.0]
  11. 11. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 167 Graph 16: Optimization Plot for cycle time and tensile strength Size of component: L=70mm* B=30mm* H=15mm Graph 17: Optimization Plot for cycle time and tensile strength Size of component: L=65mm* B=20mm* H=20mm Graph 18: Optimization Plot for cycle time and tensile strength Size of component: L=60mm* B=25mm* H=15mm To overcome the problem of conflicting responses of single response optimization, multi response optimization was carried out using desirability function in conjunction with response surface methodology. Various multi-characteristic models have been developed. Goals and limits were established for each response in order to accurately determine their impact on overall desirability. A maximum or minimum level is provided for all response characteristics which are to be optimized. The ranges and goals of input parameters viz. melt temperature, injection pressure, holding pressure and cooling time and the response characteristics cycle time and tensile strength are given. The goal of optimization is to find a good set of conditions that will meet all the goals. It is not necessary that the desirability value is 1.0 as the value is completely dependent on how closely Cur High Low0.93538 D Optimal d = 0.99814 Minimum Cycle Ti y = 23.0037 d = 0.87657 Maximum Tensile y = 25.2309 0.93538 Desirability Composite 14.0 17.0 32.0 38.0 61.0 67.0 202.0 208.0 IP(MPa) HP(MPa) Cool TimMT (°c) [208.0] [61.0606] [32.0] [14.0] Cur High Low0.86638 D Optimal d = 0.78870 Minimum Cycle Ti y = 19.8452 d = 0.95172 Maximum Tensile y = 24.8696 0.86638 Desirability Composite 14.0 16.0 26.0 31.0 55.0 62.0 198.0 202.0 IP(MPa) HP(MPa) Cool TimMT (°c) [202.0] [59.8081] [31.0] [14.0] Cur High Low0.90227 D Optimal d = 0.84829 Minimum Cycle Ti y = 18.6068 d = 0.95967 Maximum Tensile y = 24.6274 0.90227 Desirability Composite 12.0 16.0 24.0 30.0 40.0 48.0 192.0 199.0 IP(MPa) HP(MPa) Cool TimMT (°c) [199.0] [48.0] [30.0] [12.0]
  12. 12. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 168 the lower and upper limits are set relative to the actual optimum. A set of 9 optimal solutions is derived along with one global solution for the specified design space constraints. 5. CONCLUSION In present work, experimental investigation has been reported on plastic injection molding process of Polypropylene material part. Response surface methodology (RSM) has been utilized to investigate the influence of four important parameters of PIM – melt temperature, injection pressure, holding pressure and cooling time on two responses namely cycle time and tensile strength. Taguchi design was employed to conduct the experiments and to develop a correlation between the PIM parameters and each response. The analysis of experimental work is performed using MINITAB 16 statistical software. The important conclusions found that most significant factors for cycle time are melt temperature and cooling time least significant factors are injection pressure and holding pressure. For tensile strength most significant factors are melt temperature and injection pressure and least significant factors are holding pressure and cooling time. 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 to enhance productivity and reduce time for new product development. Table 3: Optimal Setting with responses for large type component using RSM’s D-Optimal Method SR. NO. PART NAME/ OPTIMIZE SETING PARAMETERS AND RESPONSE MT (°c) IP (MPa) HP (MPa) COOL TIME(Sec) CYCLE TIME(Sec) TENSILE STRENGTH (MPa) 1 Part Size L=125mm*B=55mm*H=15mm Thickness MIN=1mm; MAX=5mm 222.1 81.9 56 20 35.1285 23.0043 2 Part Size L=100mm* B=50mm* H=10mm Thickness MIN=1mm; MAX=2mm 220.8 79 48 19 33.8565 26.9982 3 Part Size L=90mm* B=40mm* H=10mm Thickness MIN=0.5mm;MAX=2mm 214.6 74 38 17 28.6752 26.7883 Table 4: Optimal Setting with responses for small type component using RSM’s D-Optimal Method SR. NO. PART NAME/ OPTIMIZE SETING PARAMETERS AND RESPONSE MT (°c) IP (MPa) HP (MPa) COOL TIME(Sec) CYCLE TIME(Sec) TENSILE STRENGTH (MPa) 4 Part Size L=70mm* B=30mm* H=15mm Thickness MIN=2mm;MAX=4mm 208 61.06 32 14 23.0037 25.2309 5 Part Size L=65mm* B=20mm* H=20mm Thickness MIN=1mm;MAX=3mm 202 59.808 31 14 19.8452 24.8696 6 Part Size L=60mm* B=25mm* H=15mm Thickness MIN=1mm;MAX=3mm 199 48 30 12 18.6068 24.6274
  13. 13. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME 169 REFERENCES [1] “Optimization of Weld Line Quality in Injection Molding Using an Experimental Design Approach” Tao C. Chang and Ernest Faison, Journal of Injection Moulding Technology, JUNE 1999, Vol. 3, No. 2 PP 61-66. [2] “Setting the Processing Parameters in Injection Molding Through Multiple-Criteria Optimization: A Case Study” Velia Garc´ıa Loera, José M. Castro, Jesus Mireles Diaz, O´ scar L. Chaco´n Mondragon, , IEEE 2008 PP 710-715. [3] “Processing Parameter Optimization For Injection Moulding Products In Agricultural Equipment Based On Orthogonal Experiment And Analysis” Yanwei1 Huyong IEEE 2011 PP 560-564. [4] “Warpage Factors Effectiveness of a Thin Shallow Injection-Molded Part using Taguchi Method” N.A.Shuaib, M.F. Ghazali, Z. Shay full, M.Z.M. Zain, S.M. Nasir. International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 01 PP 182-187. [5] “Optimization of Plastics Injection Molding Processing Parameters Based on the Minimization of Sink Marks” Zhao Longzhi1, Chen Binghui1, Li Jianyun, Zhang Shangbing IEEE 2010 PP 1-3 [6] “Optimization of Injection Molding Process Parameters in the Moulding of MISO” Wen-Chinchen, Xian G, Pu HT, Yi XS, Pan Y J Cell Plast 45:197. [7] “Application of Taguchi Method in the Optimization of Injection Moulding Parameters for Manufacturing Products from Plastic Blends” Kamaruddin , Zahid A. Khan and S. H. Foong. IACSIT International Journal of Engineering and Technology, Vol.2, No.6, December 2010 PP 574-580. [8] “Injection molding parameter optimization using the taguchi method for highest green strength for bimodal powder mixture with SS316L in peg and pmma” K. R. Jamaludina, N. Muhamad, M. N. Ab. Rahman, S. Y. M. Amin, Murtadhahadi, M. H. Ismail, IEEE 2006, PP 1-8. [9] “ANN and GA-based process parameter optimization for MIMO plastic injection molding” Wen-Chin Chen, Gong-Loung Fu, Pei-Hao Tai, Wei-Jaw deng, Yang-chih IEEE 2007, PP 1909-1917. [10] “Optimization of Warpage Defect in Injection Moulding Process using ABS Materia” A. H. Ahmad, Z. Leman, M. A. Azmir, K. F. Muhamad, W.S.W. Harun, A. Juliawati, A.B.S. Alias IEEE 2009 PP 470-474. [11] “Reducing Shrinkage in Plastic Injection Moulding using Taguchi Method in Tata Magic Head Light” Mohd. Muktar Alam, Deepak Kumar, International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064. [12] “Optimization of Critical Processing Parameters for Plastic Injection Molding of Polypropylene for Enhanced Productivity and Reduced Time for New Product Development”, A.B. Humbe and Dr. M.S. Kadam, International Journal of Mechanical Engineering & Technology (IJMET), Volume 5, Issue 1, 2014, pp. 108 - 115, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [13] “Reduction of Short Shots by Optimizing Injection Molding Process Parameters”, M.G. Rathi and Manoj D. Salunke,International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 3, 2012, pp. 285 - 293, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [14] “Optimization of Critical Processing Parameters for Plastic Injection Molding for Enhanced Productivity and Reduced Time for Development”, Anandrao B. Humbe and Dr. M.S. Kadam, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 6, 2013, pp. 223 - 226, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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