30120140501011 2-3

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30120140501011 2-3

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 5, Issue 1, January (2014), pp. 108-115 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET ©IAEME OPTIMIZATION OF CRITICAL PROCESSING PARAMETERS FOR PLASTIC INJECTION MOLDING OF POLYPROPYLENE FOR ENHANCED PRODUCTIVITY AND REDUCED TIME FOR NEW PRODUCT DEVELOPMENT Mr. A.B. Humbe(1), (1) Dr. M.S. Kadam(2) Student, M.E. Manufacturing, Mechanical Engineering Department, J.N.E.C. Aurangabad, Maharashtra, India (2) Professor, Mechanical Engineering Department, J.N.E.C. Aurangabad, Maharashtra, India ABSTRACT Injection molding has been a challenging process for many plastic components manufacturers and researchers to produce plastics products meeting the requirements at very economical cost. Since there is global competition in injection molding industry, sousing trial and error approach to determine process parameters for injection molding is no longer hold good enough. Since plastic is widely used polymer due to its high production rate, low cost and capability to produce intricate parts with high precision. It is much difficult to set optimal process parameter levels which may cause defects in articles, such as shrinkage, war page, line defects. Determining optimal process parameter setting critically influences productivity, quality and cost of production in plastic injection molding (PIM) industry. In this paper optimal injection molding condition for minimum cycle time were determined by the DOE technique of Taguchi methods. The various observation has been taken for material namely Polypropylene (PP).The determination of optimal process parameters were based on S/N ratios. Keywords: Injection Molding, DOE, Taguchi Optimization. PROCEDURE • Set the parameters based on historical data and experience • Fine tune the process parameters for the component which can be considered for evaluation and observe the trend while setting each process. 108
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME • • • • • Document the data for research and analysis further using DOE (Taguchi Method/ Minitab) Derive a standard based on the material and the configuration Optimize the setting time and validate the process Injection Molding Machine Sectional view Document the data for research and analysis further using DOE (Taguchi Method/Minitab) Derive a standard based chart on the material and the configuration. EXPERIMENTATION In the analysis part we have analyzed 4 important parameters like Melting Temperature, Holding Pressure, Cool Time and Injection pressure at three levels, The response for this considered is cycle time. Table No.1 Large Component SR. PART NAME/ MT IP(MPa) HP(MPa) COOL CYCLE NO. PARAMETERS (°c) TIME TIME (sec) (Sec) 1 2 3 Large component with size: 525 X 320 X 80mm Large component with size: 600 X 200 X 195mm Size of Large component is: 500 X 275 X 175mm PSNRA1 PMEAN1 225 225 225 228 228 228 229 229 229 81 84 85 81 84 85 81 84 85 55 52 56 52 56 55 56 55 52 20 22 23 23 20 22 22 23 20 39 38 42 40 39 42 44 48 42 -31.8213 -31.5957 -32.465 -32.0412 -31.8213 -32.465 -32.8691 -33.6248 -32.465 39 38 42 40 39 42 44 48 42 219 219 219 220 220 220 225 225 225 76 71 79 76 71 79 76 71 79 50 48 51 48 51 50 51 50 48 26 25 22 22 26 25 25 22 26 35 34 35 36 39 39 41 34 40 -30.8814 -30.6296 -30.8814 -31.1261 -31.8213 -31.8213 -32.2557 -30.6296 -32.0412 35 34 35 36 39 39 41 34 40 204 204 204 205 205 205 217 217 217 44 50 56 44 50 56 44 50 56 25 30 33 30 33 25 33 25 30 16 23 27 27 16 23 23 27 16 66 67 69 69 70 77 78 79 79 -36.3909 -36.5215 -36.777 -36.777 -36.902 -37.7298 -37.8419 -37.9525 -37.9525 66 67 69 69 70 77 78 79 79 109
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME Analysis of the S/N Ratio SN ratio (Smaller is Better) PATRT-1 PART-2 PART-3 Part -1:-The main effect plot for SN ratio graphs above depicts certain characteristics of each parameter. The Melting Temperature graph indicates the steep slope when compared to other parameter graph curves. This also means that it is holding the first rank in control parameters among the chosen ones. The Melting Temperature curve slope starts very shallow slope from 225 to 228 and after that its slope is very steep till 229. The slope of this graph is very steep, which means that we need to attack or act on this parameter first to reduce our cycle time. Part -2:-The main effect plot for SN ratio graphs above depicts certain characteristics of each parameter. The Melting Temperature graph indicates the steep slope when compared to other parameter graph curves. This also means that it is holding the first rank in control parameters among the chosen ones. The Melting Temperature curve slope starts very steep slope from 219 to 220 and after that its slope is very flat till 225. This means that the best temperature to work upon is around 220 and not above that, as the slope becomes flat, there is no much effect on cycle time. The slope of this graph is very steep, which means that we need to attack or act on this parameter first to reduce our cycle time. The Cool Time graph is also equally steep in nature so the second closest ranking is this and so we need to attack this after Melt temperature. This graph also depicts that the slope of curve is very flat after 25 sec. So our best cycle time would be achieved below 25 sec. Part -3:-The main effect plot for SN ratio graphs above depict certain characteristics of each parameter. The Melting Temperature graph indicates the very steep slope when compared to other parameter graph curves.. The Melting Temperature curve slope starts very steep slope from 204 to 205 and after that also its slope is very steep till 217. This means that the best temperature to work upon is around or above 217 and not below that, as the slope continues further, as there will be much effect on cycle time. The slope of this graph is very steep, which means that we need to attack or act on this parameter first to reduce our cycle time. 110
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME Table No.2 Small Component SR. PART NAME/ NO. PARAMETERS 4 5 6 Size of Small component is: 80 X 50 X 45 mm Size of Small component is: 70 X 35 X 25 mm Size of Small component is: 35 X 35 X 15 mm MT (°c) IP(MPa) HP(MPa) COOL TIME (sec) CYCLE TIME (Sec) PSNRA1 PMEAN1 201 201 201 199 199 199 204 204 204 58 56 60 58 56 60 58 56 60 24 20 19 20 19 24 19 24 20 15 19 14 14 15 19 19 14 15 23 26 25 26 27 28 27 21 24 -27.2346 -28.2995 -27.9588 -28.2995 -28.6273 -28.9432 -28.6273 -26.4444 -27.6042 23 26 25 26 27 28 27 21 24 200 200 200 196 196 196 207 207 207 196 196 196 192 192 192 198 198 198 49 56 51 49 56 51 49 56 51 45 40 47 45 40 47 45 40 47 21 20 19 20 19 21 19 21 20 30 28 35 28 35 30 35 30 28 21 18 22 22 21 18 18 22 21 14 20 12 12 14 20 20 12 14 29 31 34 33 37 40 39 37 33 24 31 33 36 38 31 37 32 39 -29.248 -29.8272 -30.6296 -30.3703 -31.364 -32.0412 -31.8213 -31.364 -30.3703 29 31 34 33 37 40 39 37 33 -27.6042 -29.8272 -30.3703 -31.1261 -31.5957 -29.8272 -31.3840 -30.1030 -31.8213 24 31 33 36 38 31 37 32 39 Analysis of the S/N Ratio SN ratio (Smaller is Better) PATRT-4 PART-5 111 PART-6
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME Part -4:-The main effect plot for SN ratio graphs above depict certain characteristics of each parameter. The Melting Temperature graph indicates the steep slope when compared to other parameter graph curves. This also means that it is holding the first rank in control parameters among the chosen ones. The Melting Temperature curve slope starts very steep slope from 199 to 201 and after that its slope is little flat till 204. This means that the best temperature to work upon is around 201 and not above that, as the slope becomes little flat, there is no much effect on cycle time. The slope of this graph is very steep, which means that we need to attack or act on this parameter first to reduce our cycle time. The Hold Pressure is also very steep from 19 till 24. So this parameter also play important role as second player. The Cool Time graph is also equally steep in nature so the third closest ranking is this and so we need to attack this after Melt temperature. This graph also depicts that the slope of curve is less steep till 15 sec, after that the steep increases till 19. So our best cycle time would be achieved around 19 sec. Part -5:-The main effect plot for SN ratio graphs above depicts certain characteristics of each parameter. The Melting Temperature graph indicates the steep slope when compared to other parameter graph curves. This also means that it is holding the first rank in control parameters among the chosen ones. The Melting Temperature curve slope starts very steep slope from 196 to 200 and after that its slope is changing direction till 207. This means that the best temperature to work upon is around 200 and not above or below that, as the slope changes direction, and there is no much effect on cycle time. The slope of this graph is very steep, which means that we need to attack or act on this parameter first to reduce our cycle time. The Hold Pressure is also very steep from 19 till 20 and after that it is changing its direction of slope till 21. So this parameter also play important role as second player. The best cycle time will be achieved if this parameter is kept around 20. The Cool Time graph is also equally steep in nature so the third closest ranking is this and so we need to attack this after Melt temperature. This graph also depicts that the slope of curve is changing at 21. So our best cycle time would be achieved around 21 sec. Part 6:-The main effect plot for SN ratio graphs above depict certain characteristics of each parameter. The Hold Pressure graph indicates the steep slope when compared to other parameter graph curves. This also means that it is holding the first rank in control parameters among the chosen ones. The Melting Temperature curve slope starts very steep slope from 28 to 30 and after that its slope is changing direction till 35. This means that the best pressure to work upon is around 30 and not above or below that, as the slope changes direction, and there is no much effect on cycle time. The slope of this graph is very steep, which means that we need to attack or act on this parameter first to reduce our cycle time. The Melting Temperature is also very steep from 192 till 196 and after that it is changing its direction of slope till 198. So this parameter also play important role as second player. The best cycle time will be achieved if this parameter is kept around 196. The Cool Time and Injection pressure graph does not play a major role in the Cycle time. CONCLUSION Taguchi method stresses the importance of studying the response variation using the signalto-noise (S/N) ratio, resulting in minimization of quality characteristics variation due to uncontrollable parameter. The procurement process was considered as the quality characteristics with the objective of minimizing the Costs involved in the process of procurement. 112
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME 3 different experimentations carried out to study the interaction of each parameter on the other. Many times it becomes very difficult to separate out the contribution of each factor as whole on to the final response. The 3 experiments conducted were for Large size components. Out of the 3 components, all the components exhibit same with respect to its top ranking parameter, that is Melt Temperature. It is can unanimously said that in large size parts the Melt temperature play in important role in deciding the cycle time. There is no pattern seen in other parameters behavior in the experimentation The 3 experiments conducted for small size components reveal a varied ranking. For 2 parts Melt temp was important and last part it was not. When we closed analyzed the reason for such behavior and found out the complexity of part, in term of intricate shape, fine features was driving this change. Design of Experiments is a statistical method and Taguchi method is very proven and robust so we have used this technique. It is important to remember that statistical methods are based on assumptions and iterations, which means the results obtained are to some percentage level of confidence and not 100%. Taguchi claims that his technique is 90% confident on the results. Also, once we act upon / attach on one parameter of any experimentation, we need to run DOE to see the results and find out the ranking of the parameter again. Because when the readings / observations change, the ranking may change depending on its portion of influence on the response parameter. If the ranking remains same, you can carry out next set of experiments by only changing the parameter ranked first. If the ranking changes then our experimentation should attached the next parameter which is ranked. So like this, we can continue to go on, till we receive a satisfactory level of outcome. DOE also gives use some empirical relationships in the form of equation that can be used as a quick reference or guideline while we need to take major decisions like deciding cycle time quickly when a new customer is asking for basic quotation OR while making major decision like buying of additional Injection molding machine, which costs in cores of rupees, etc. We had carried out 3 large and 3 small components experimentations with different parameters. Out of the 9 runs per component, we have chosen the top 2 best SN ratio values from each Large size components and small size component and carried out a regression analysis. With the available parameters, we have a regression equation for all the 6 experiments. By properly substituting the values of the parameters, we can take decisions for future projects. 113
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME REFERENCE CHART Material Polypropylene(PP) Large Sized Volume range-600cm cube to 1200 cm cube Polypropylene(PP) Small Sized Volume range-up to 650cm cube MT (°c) IP( MPa) HP(MPa) CT(sec) 209 52 30 23 202 54 19 20 The reference chart standardized with the values for the process variables will be validated for the upcoming automotive components that are awaiting pilot run of production. The process would be repeated for the variants to ensure consistency in the physical characteristics of the component produced. Validation will be carried out by bring out actual development of two components. Trials and testing would address the phase of validation as the mould would be tried out for checking the nature of the physical components as an outcome of the development process. The study has evolved reference values for the significant factors suitable for each category of the material. Following is the compilation for the conclusions drawn. Material : PP – Small For small Polypropylene parts, cycle time increased with increase in cooling time & also PP melt temperature Coolant flow & temperature could be controlled to reduce cycle time. It was observed that PP melt temperature in the range of 200-206 degcel. & cooling time of 19 to 21 sec produced consistent parts with optimum cycle time. Material : PP Large For large Polypropylene parts, cycle time increased with increase in cooling time & also PP melt temperature. It was observed that PP melt temperature in the range of 203-209 degcel. & cooling time of 23 to 25 sec produced consistent parts with optimum cycle time. REFERENCES 1. 2. 3. 4. Optimization of Weld Line Quality in Injection Molding Using an Experimental Design Approach Tao c. Chang andErnest Faison, Journal of Injection Moulding Technology, JUNE 1999, Vol. 3, No. 2 PP 61-66. Setting the Processing Parameters in Injection Molding Through Multiple-Criteria Optimization: A Case Study Velia Garc´ıaLoera, José M. Castro, Jesus Mireles Diaz, O´ scar L. Chaco´n Mondragon, , IEEE 2008 PP 710-715. Processing Parameter Optimization For Injection Moulding ProductsIn Agricultural Equipment Based On Orthogonal Experiment And Analysis Yanwei1 Huyong IEEE 2011 PP 560-564. 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. 114
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME 5. 6. 7. 8. 9. 10. 11. 12. 13. 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 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. 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. Ann and ga-based process parameter optimization for MIMO plastic injection molding WenChin Chen, Gong-Loung Fu, Pei-Hao Tai, Wei-Jaw deng, Yang-chih IEEE 2007 PP 19091917. Optimization of Warpage Defect in Injection Moulding Process using ABSMaterial A. H. Ahmad1 Z. Leman2, M. A. Azmir1, K. F. Muhamad1, W.S.W. Harun1, A. Juliawati1, A.B.S. AliasIEEE 2009 PP 470-474. A Systematic Optimization Approach in the MISO Plastic Injection Molding Process WenChinChen Tung-Tsan Lai, Gong-Loung Fu IEEE 2008 PP 2741-2746. Ravishankar. R, Dr.K. Chandrashekara and Rudramurthy, “Experimental Investigation and Analysis of Mechanical Properties of Injection Molded Jute and Glass Fibers Reinforced Hybrid Polypropylene Composites”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 4, 2013, pp. 197 - 206, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. M.G. Rathi and Manoj D. Salunke, “Reduction of Short Shots by Optimizing Injection Molding Process Parameters”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 3, 2012, pp. 285 - 293, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. Anandrao B. Humbe and Dr. M.S. Kadam, “Optimization of Critical Processing Parameters for Plastic Injection Molding for Enhanced Productivity and Reduced Time for Development”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 6, 2013, pp. 223 - 226, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 115

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