Volume 118 No. 11 2018, 241-250 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu doi: 10.12732/ijpam.v118i11.30 ijpam.eu OPTIMIZATION OF INJECTION MOULDING MOULD FLOW ANALYSIS USING TAGUCHI APPROACH 1 N.Subramani, 2 J.Ganesh Murali, 3 P.Vijaya Rajan, 4 C.Godwin Jose 1 Assistant Professor, 2 Professor, Karpagam College of Engineering, Coimbatore. 3 Assistant Professor, Sri Sairam Engineering College, Chennai. 4 Assistant Professor, PSN College of Engineering and Technology,Tirunelveli. 1 subramanikcemech@gmail.com Abstract: This work is intended to optimize the mould flow of the hopper by using the ANSYS and Taguchi method. Moulding is considered the most prominent process for mass production. There are many kinds of plastic moulding methods are there. In that method here the injection moulding method is selected for the research. Each technique has its own advantages in the manufacturing of specific item. Among these various plastic production technologies, injection moulding takes up approximately 32%, because of its ability in producing complex parts with low cost and high productivity. Here the analysis helps us to optimize the flow in moulding. 1. Introduction Injection moulding has become the most important process for manufacturing plastic parts due to its ability to produce complex shapes with good dimensional accuracy[1].cad/ Cam can help designer to speed up design for the plastic part and mould design process and reduce the long lead time[2] The Taguchi approach is mostly used in the industrial environment, but it can also be used for scientific research. The method is based on balanced orthogonal arrays[3].in this work, the combined effects of multi moulding process parameters are analyzed by the combination of orthogonal experiments and mould flow simulation tests and then the sensible gate location and optimized parameter combination is obtained[4].this study applies of the optimization strategy based on Taguchi s experimental designs [5-6] The introduction of simulation software has made a significant impact in the injection moulding industry. With the increasing use of computers in design engineering, the amount of commercially available software on the market has also increased [7]. Traditional trial runs on the factory floor can be replaced by less costly computer simulations. Now days, research on optimizing the plastic injection moulding process has developed a lot. CAD/CAE tools are used to produce an optimal mould gating design using CATIA and Mould flow applications. The mould flow analysis helps in reducing costs and time and also prevents other defects occurring in the process [8]. Injection moulding is a process of forming a product by forcing molten plastic material under pressure into a mould where it is cooled, solidified and subsequently released by opening the two or three halves of the mould. Bryce.M.D (1996) has stated that the injection moulding is used for the formation of intricate plastic parts with excellent dimensional accuracy. The design of a polymer or plastic injection mould is an integral part of plastic injection moulding as the quantity of the final plastics part is greatly reliant on the injection mould. Figure 2.1 Injection moulding 241
Figure 2.3 Hopper The Mould flow analysis was performed using Autodesk Mould Flow analysis software. 3. Steps involved in Mould Flow Analysis Figure 2.2 Injection moulding cycle 1. Factors Affecting Injection Moulding There are several factors that are critical to the injection moulding process. These include: Plastic Melt Temperatures Barrel Temperatures Nozzle Temperatures Plastic Flow Rates Plastic Pressure or Screw Back Pressure Plastic Cooling Rates 1. Converting the 3D model in STEP OR IGES format. 2. Meshing the model by using dual domain type of mesh. 3. Importing the meshed file to the solver package specifying the boundary condition, loads such as injection pressure, injection time, mould temperature, melt temperature, material properties etc. 4. Building the feed system such as sprue, runner and gate. 5. Mesh the feed system and cooling lines. 6. Run the analysis for different analysis types like fill, flow, war page etc. 7. Study the result, interpret them. 8. Establish the optimized data for runner, gate, sprue dimensions, coolant temperature etc. Based on the analysis the optimal combination of part geometry, material choice, gate location and process parameter to produce quality finish part are determined. 2. Mould Flow Analysis Analysis is essential for designing and mould making through simulation step-up and result interpretation to show how changes to wall thickness, gate location, material and geometry affects manufacturability and also experiments with what-if scenarios before finalizing a design. Figure 2.4 Meshing of Hopper part 4. Parameters for Mould Flow Analysis Specification of the moulding material including grade and color. Moulding machine specification. 242
Number of impression. Shrinkage of the material. Type of mould Type of runner or gate Parting line of mould Types of ejection system. Type of cooling system. Injection pressure. Shot weight. Distance between the tie bars. Shut height of mould. Shut height of machine. Clamping force. A fully detailed component drawing. 5. Air trap analysis Figure 5.1 Air Trap Analysis at 50cm3 / sec Figure 5.4 Air Trap Analysis at 100cm3/ sec The ANSYS Mould flow analysis of the occurrence of air traps in the hopper component is performed. Further the analysis for different situations is considered based on the variance of mould flow. In the above results, the pink colour indicates the presence of air traps. For the first case air traps occur throughout the mould. The number of air traps is more due to the slow mould flow mould flow rate. In the last case also the number of air traps is more. This is due to the high mould flow mould flow rate. So with slower as well as higher mould flow s we can see there is a high occurrence of air traps. In the case three, there is a mode mould flow rate number of air traps due to the mode mould flow rate mould flow. 5.1 Bulk Temperature Analysis Figure 5.2 Air Trap Analysis at 60cm3/ sec Figure 5.5 Bulk Temperature Analysis at 50cm3/ sec Figure 5.3 Air Trap Analysis at 75cm3/ sec Figure 5.6 Bulk Temperature Analysis at 60cm3/ sec 243
Figure 5.7 Bulk Temperature Analysis at 75cm3/ sec Figure 5.11 Freeze Time Analysis at 75cm3/ sec Figure 5.8 Bulk Temperature Analysis at 100cm3/ sec From the above cases we have concluded that, when the temperature with in the mould is high, freeze time will increase. Mould temperature is directly proportional to the freeze time. The mould temperature will also affect the cycle time of the process. In Fig 5.8 Bulk Temperature is low, so there is a decrease in freeze time.likewise in Fig 5.12 the Bulk temperature increases and the freeze time also increases. 5.2 Freeze Time Analysis Figure 5.12 Freeze Time Analysis at 100cm3/ sec There is a balance always struck between freeze times and feeding system. The freeze time depends up on the wall thickness.from the above results we come to conclude that the freeze time depends up on the temperature of the mould and also heat transfer to the walls. In Fig 5.18 The maximum time taken for solidification is about 273.5 seconds and minimum time for solidification is 266.7 seconds. 5.3 Fill Time Analysis Figure 5.9 Freeze Time Analysis at 50cm3/ sec Figure 5.13 Fill Time Analysis at 50cm 3 / sec Figure 5.10 Freeze Time Analysis at 60cm3/ sec Figure 5.14 Fill Time Analysis at 60cm3/ sec 244
Fig 5.15 Fill Time Analysis at 75cm3/ sec Figure 5.18 Filling Pressure Analysis at 60cm3/ sec Figure 5.16 Fill Time Analysis at 100cm3/ sec Fill time analysis is used to evaluate the time for filling the mould cavity with molten metal. If the mould flow rate of flow of molten metal is high, then the fill time is low. Thus the of flow of molten metal is inversely proportional to the fill time. In Fig 5.19, the mould flow is low and so the fill time is increased as 6.882 seconds. In Fig 5.17, the mould flow is 100cm 3 / second and the fill time reduces to 1.666 seconds. If mould flow mould flow rate increases the air trap increases for the optimal process the correct should be selected. Figure 5.19 Filling Pressure Analysis at 75cm3/ sec 5.4 Filling Pressure Analysis Figure 5.20 Filling Pressure Analysis at 100cm 3 / sec Figure 5.17 Filling Pressure Analysis at 50cm 3 / sec From the results shows that the pressure is still nonexistence at the initial stages and increases somewhat due to the filling of small amount of feedstock in to the mould. Filling pressure is inversely proportional to the filling time and directly proportional to. Increase in pressure leads to slight increase in temperature. 5.5 Weld Line Analysis 245
of different mould flow are studied and the defects are highlighted in different colour. The fig 5.25 shows that there was an maximum defect in the corners. Therefore increasing rate of mould flow results in formation of weld line. 6. Optimisation Using Taguchi Figure 5.21 Weld Line Analysis at 50cm3/ sec Figure 5.22 Weld Line Analysis at 60cm3/ sec Figure 5.23 Weld Line Analysis at 75cm3/ sec There are a number of parameters that have influences on an injection moulding process, which are types of material used, types of mould base material, types of cavity insert material, types of machine, the profile of the parts, selection of coolant runners as well as selection of the coolant liquid. However in this study, only a few major factors are taken into considerations as to make sure the result can be achieved. Assumptions to be made Gate dimension factor is neglected because of its design is not identical for every part. The temperature of the environment is assumed constant. The coolant is assumed as pure water. The effects of other minor factors (Other than melting temperature, mould temperature, filling and packing processes) are not to be under the topic of discussion. The layout of the cooling channels is assumed to maintain a constant temperature. The effects due to the shape and size of the mould and product are neglected due to various shapes of product. The plastic material used in all of the simulations is amorphous thermoplastic PC/ABS blend, Cycoloy C2950HF from GE. Its viscosity is between 102 and 104poise Where the shear rate is in 102-103 s-1 range. The range of melt temperature is between 220 O C and 400 O C approximately. Table 6.1 Physical and Mechanical Properties Figure 5.24 Weld Line Analysis at 100cm 3 / sec Weld lines are also a mouldings defects, Weld lines are generally formed when two melt fronts come in contact with each other so that they do not bond perfectly. weld lines are also formed due to presences of pins,cores and multiple gates are one of the most significant defects from both performance and appearances point of u This can cause a weak area in the part which can cause breakage when the part is under stress. In the results we conclude that weld line Specific heat, Cp (J/kg o C) 1871 Glass t ransit ion temperature, Tg ( o C) Thermal expansion coefficient, α (mm/m o C) 112 74 Elastic modulus, E (MPa) 2.63 x 10 3 Poisson's ratio, Ʋ 0.23 Thermal conductivity, K (w/m o C) 0.27 The length of weld line X of the hopper obtained from the experiment is used to calculate the signal-tonoise (S/N) ratio to obtain the best parameter setting 246
arrangement. From this technique, the percentage of contribution is calculated in determining which of the factor has significant effect on part s war page. Taguchi method is again applied where there are three factors identified to be controlled; Pressure (A), Temperature (B),Fill time(c). Each factor is downsized to five levels where an orthogonal array of L9 is chosen and all parameters have been identified. Table 6.2 The Three Level of Effective Factor For Experiment Variance Factors Levels 1 2 3 Pressure, A (MPa) 6.2 7.1 7.8 Bulk temperature,b ( o c) 230.0 230.5 231.06 Filling time,c(s) 6.8 3.4 2.2 Table 6.3 L9 Orthogonal Array Variance Trail Control factor A B C 1 1 1 1 2 1 2 2 3 1 3 3 4 2 1 2 5 2 2 3 6 2 3 1 7 3 1 3 8 3 2 1 9 3 3 2 Table 6.4 The Combination Parameters for the Control Factors CONTROL FACTOR TRAIL A B C 1 6.2 230.0 6.8 2 6.2 230.5 3.4 3 6.2 231.1 2.2 4 7.1 230.0 3.4 5 7.1 230.5 2.2 6 7.1 231.1 6.8 7 7.8 230.0 2.2 8 7.8 230.5 6.8 9 7.8 231.1 3.4 The length of weld lines data obtained from the simulation process are also analysed using Analysis of Variance (ANOVA) and the level of confidence is set at 0.05. The results are then taken and compared with the results obtained from the SN ratio method. The interaction effect of factors is identified and the contribution of each factor towards the total effect is analysed. The percentage contribution calculated determines which of the factors mainly affect the length of weld lines. The length of weld line is then measured and S/N ratios are calculated. In this case, the smaller the better quality equation from Taguchi method is chosen as far as weld line is concerned. able 6.5 Summary of the Results Length of Weld Line CONTROL FACTOR LENGTH OF WELD LINE NO PRESSURE TEPERATURE FILLTIME S/N FOR D A B C D 1 6.2 230 6.8 6.14-15.763 2 6.2 230.5 3.4 6.06-15.649 3 6.2 231.1 2.2 6.42-16.150 4 7.1 230 3.4 5.98-15.534 5 7.1 230.5 2.2 6.21-15.861 6 7.1 231.1 6.8 6.26-15.931 7 7.8 230 2.2 6.34-16.041 8 7.8 230.5 6.8 6.14-15.763 9 7.8 231.1 3.4 6.1-15.706 The data in Table 6.5 also analyzed using Analysis of Variance (ANOVA) where the relative percentage contribution of all factors is determined by comparing the relative variance. The ANOVA then computes the degrees of freedom, variance, F-ratio, sums of squares, pure sum of square and percentage contribution. The examples of calculations are shown below and the results of S/N ratio for length of weld line in thin plate are listed in Table 6. Only weld line at hole X is considered because length of weld line formation at 247
both of holes that shows the similar pattern under different parameter settings. Table 6.6 Response Table of S/N Ratio For Length of Weld Line Level Pressure Temperature Fill Time 1-15.78-15.78-16.02 2-15.78-15.76-15.63 3-15.93-15.93-15.82 From the S/N ratio response in Table VI, the highest value from each factor is considered the best and chosen as the finest grouping of parameters. Table 6.7 Best Setting of Combination Parameters Factors Paramerers Pressure 7.1 Temperature 230.5 Fill Time 3.4 Furthermore the difference between levels in Table 6.6 also shows which factor is more significant that give effects on length of weld line in thin plate. Therefore, it is understood that the most major factor that affects on length of weld line in hopper is Pressure (A), Temperature (B), Fill time(c).the data in Table 6.5 is also analyzed using Analysis of Variance (ANOVA) that computes the sums of squares, degrees of freedom, variance and percentage contribution. The examples of calculations for these quantities are shown below and the results lengths of weld line in thin plate are summarized in Table 6.8. Figure 6.1 Main effects plot for SN ratios Table 6.8 Anova Table For Hooper SOURCE DF S V F P Pressure 2 0.01027 0.005137 0.72 0.582 Temperature 2 0.05229 0.026147 3.66 0.215 Fill Time 2 0.22595 0.112977 15.81 0.046 Residual Error 2 0.01430 0.007148 Total 8 0.30282 7. Conclusion There are several factors such as feed systems, cooling channel positions, gate sizes that need to be determined first in order to design a plastic injection mould. Simulation software can help us reducing time taken to test. From the above experiment we conclude that the optimised parameters for injection moulding of hooper is pressure 7.1 MPa, Temperature 230.5 o C,Fill time 3.4s and Mould flow rate of 50cm 3 /s. In this stage the formation of the weld line is low than compared to the other cases where we get optimum weld line this optimisation increase the product life and cycle time. The Ansys mould flow results also shows that air traps are minimum at these optimised condition. 248
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