Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis

Plastic injection moulding is one of the important processes to produce the plastic product with complex shape and high accuracy. The quality of the plastic product in plastic injection moulding process is affected by four factors, and that factorsare plastic materials, mould design, machine paramet...

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Main Author:	Mohd Ali, Noorfa Idayu
Format:	Thesis
Language:	English English
Published:	2018
Subjects:	T Technology (General) TJ Mechanical engineering and machinery
Online Access:	http://eprints.utem.edu.my/id/eprint/23325/1/Multi-Response%20Injection%20Moulding%20Process%20Parameters%20Optimization%20Using%20Taguchi%20Method%20With%20Grey%20Relational%20Analysis.pdf http://eprints.utem.edu.my/id/eprint/23325/2/Multi-respone%20injection%20moulding%20process%20parameters%20optimization%20using%20Taguchi%20method%20with%20grey%20relational%20analysis.pdf
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id	my-utem-ep.23325
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Md Ali, Mohd Amran
topic	T Technology (General) TJ Mechanical engineering and machinery
spellingShingle	T Technology (General) TJ Mechanical engineering and machinery Mohd Ali, Noorfa Idayu Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis
description	Plastic injection moulding is one of the important processes to produce the plastic product with complex shape and high accuracy. The quality of the plastic product in plastic injection moulding process is affected by four factors, and that factorsare plastic materials, mould design, machine parameters and production operator. It is well known that in industry practice, machine parameters are changed by the skill of experienced operator using trial and error method. Therefore, process parameters should be optimized using the proper method such as the design of experiment (DOE). The purpose of this study is to examine the process parameters for injection moulding on material characteristics such as part weight, warpage, geometrical shrinkage and mechanical properties.In mechanical properties, ultimate tensile strength, tensile modulus and percentage of elongationwere studied. The parameters involved in this study weremould temperature, melt temperature, injection time and cooling time. Taguchi method was used where L9 with nineruns with three repetitions were conducted. The optimization is carried out in two ways by using single response and multi-response of Taguchi method based grey relational analysis (GRA)for all responses. Hence, the optimum result of the single responsefor part weight is the mould temperature which contributes 58.88%. Meanwhile, for war page, melt temperature contributes 38.96%. For shrinkage, mould temperature contributes 67.76%. For mechanical properties such as ultimate tensile strength and tensile modulus, mould temperature contributes 93.33% and 40.37%, respectively.For the percentage of elongation, melt temperature contribution is 51.41%.Multi-response optimization shows that a set of input parameters for all responses are mould temperature at 560 C,melt temperature at 250oC,injection time at 0.7s and cooling time at 15.4s.ANOVA result shows that cooling time contributes 86.76% for all responses.Therefore, the multi-response optimization can predict the quality of plastic product producedin plastic injection moulding process.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Ali, Noorfa Idayu
author_facet	Mohd Ali, Noorfa Idayu
author_sort	Mohd Ali, Noorfa Idayu
title	Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis
title_short	Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis
title_full	Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis
title_fullStr	Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis
title_full_unstemmed	Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis
title_sort	multi-respone injection moulding process parameters optimization using taguchi method with grey relational analysis
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Manufacturing Engineering
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/23325/1/Multi-Response%20Injection%20Moulding%20Process%20Parameters%20Optimization%20Using%20Taguchi%20Method%20With%20Grey%20Relational%20Analysis.pdf http://eprints.utem.edu.my/id/eprint/23325/2/Multi-respone%20injection%20moulding%20process%20parameters%20optimization%20using%20Taguchi%20method%20with%20grey%20relational%20analysis.pdf
_version_	1747834035158122496
spelling	my-utem-ep.233252022-06-13T12:06:46Z Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis 2018 Mohd Ali, Noorfa Idayu T Technology (General) TJ Mechanical engineering and machinery Plastic injection moulding is one of the important processes to produce the plastic product with complex shape and high accuracy. The quality of the plastic product in plastic injection moulding process is affected by four factors, and that factorsare plastic materials, mould design, machine parameters and production operator. It is well known that in industry practice, machine parameters are changed by the skill of experienced operator using trial and error method. Therefore, process parameters should be optimized using the proper method such as the design of experiment (DOE). The purpose of this study is to examine the process parameters for injection moulding on material characteristics such as part weight, warpage, geometrical shrinkage and mechanical properties.In mechanical properties, ultimate tensile strength, tensile modulus and percentage of elongationwere studied. The parameters involved in this study weremould temperature, melt temperature, injection time and cooling time. Taguchi method was used where L9 with nineruns with three repetitions were conducted. The optimization is carried out in two ways by using single response and multi-response of Taguchi method based grey relational analysis (GRA)for all responses. Hence, the optimum result of the single responsefor part weight is the mould temperature which contributes 58.88%. Meanwhile, for war page, melt temperature contributes 38.96%. For shrinkage, mould temperature contributes 67.76%. For mechanical properties such as ultimate tensile strength and tensile modulus, mould temperature contributes 93.33% and 40.37%, respectively.For the percentage of elongation, melt temperature contribution is 51.41%.Multi-response optimization shows that a set of input parameters for all responses are mould temperature at 560 C,melt temperature at 250oC,injection time at 0.7s and cooling time at 15.4s.ANOVA result shows that cooling time contributes 86.76% for all responses.Therefore, the multi-response optimization can predict the quality of plastic product producedin plastic injection moulding process. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23325/ http://eprints.utem.edu.my/id/eprint/23325/1/Multi-Response%20Injection%20Moulding%20Process%20Parameters%20Optimization%20Using%20Taguchi%20Method%20With%20Grey%20Relational%20Analysis.pdf text en public http://eprints.utem.edu.my/id/eprint/23325/2/Multi-respone%20injection%20moulding%20process%20parameters%20optimization%20using%20Taguchi%20method%20with%20grey%20relational%20analysis.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=112294 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Manufacturing Engineering Md Ali, Mohd Amran 1. Abohashima, H., Aly, M., Mohib, A., and Attia, H., 2015. Minimization of Defects Percentage in Injection Molding Process using Design of Experiment and Taguchi Approach. Industrial Engineering and Management, 4(5), pp. 1–6. 2. Acır, A., Canlı, M. E., Ata, İ., and Çakıroğlu, R., 2017. Parametric Optimization of Energy and Exergy Analyses of A Novel Solar Air Heater With Grey Relational Analysis. Applied Thermal Engineering, 122, pp. 330–338. 3. Amran, M., Salmah, S., Zaki, M., Izamshah, R., Hadzley, M., Subramonian, S., Shahir, M., and Amri, M., 2014. The Effect of Pressure on Warpage of Dumbbell Plastic Part in Injection Moulding Machine. Advanced Materials Research, 903, pp. 61–66. 4. Annicchiarico, D., and Alcock, J. R., 2014. Review of Factors That Affect Shrinkage of Moulded Part in Injection Moulding. Materials and Manufacturing Processes, 29, pp. 662–682. 5. Annicchiarico, D., Attia, U. M., and Alcock, J. R., 2013. Part Mass and Shrinkage in Micro Injection Moulding: Statistical Based Optimisation Using Multiple Quality Criteria. Polymer Testing, 32, pp. 1079–1087. 6. Aravind, S., Shunmugesh, K., Biju, J., and Vijayan, J. K., 2017. Optimization of Micro-Drilling Parameters by Taguchi Grey Relational Analysis. Materials Today: Proceedings, 4(2), pp. 4188–4195. 7. Asghar, A., Abdul Raman, A. A., and Daud, W. M. A. W., 2014. A Comparison of Central Composite Design and Taguchi Method for Optimizing Fenton Process. The Scientific World Journal, 2014, pp. 1–14. 8. Azaman, M. D., Sapuan, S. M., Sulaiman, S., Zainudin, E. S., and Khalina, A., 2013. Shrinkages and Warpage in The Processability of Wood-Filled Polypropylene Composite Thin-Walled Parts Formed by Injection Molding. Materials and Design, 52, pp. 1018–1026. 9. Azaman, M. D., Sapuan, S. M., Sulaiman, S., Zainudin, E. S., and Khalina, A., 2014. Numerical Simulation Analysis of The In-Cavity Residual Stress Distribution of Lignocellulosic (Wood) Polymer Composites Used in Shallow Thin-Walled Parts Formed by The Injection Moulding Process. Materials and Design, 55, pp. 381–386. 10. Barghash, M. A, and Alkaabneh, F. A., 2014. Shrinkage and Warpage Detailed Analysis and Optimization for The Injection Molding Process Using Multistage Experimental Design. Quality Engineering, 26(3), pp. 319–334. 11. Bharti, P. K., Khan, M. I., and Singh, H., 2010. Recent Methods for Optimization of Plastic Injection Molding Process – A Retrospective and Literature Review. International Journal of Engineering Science and Technology, 2(9), pp. 4540–4554. 12. Bhattacharya, D., and Bepari, B., 2014. Feasibility Study of Recycled Polypropylene Through Multi Response Optimization of Injection Moulding Parameters Using Grey Relational Analysis. Procedia Engineering, 97, pp. 186–196. 13. Bociaga, E., Jaruga, T., Lubczyńska, K., and Gnatowski, A., 2010. Warpage of Injection Moulded Parts As The Result of Mould Temperature Difference. Archives of Materials Science and Engineering, 44(1), pp. 28–34. 14. Cadena-Perez, A. M., Yanez-Flores, I., Sanchez-Valdes, S., Rodriguez-Fernandez, O. S., Fernandez-Tavizon, S., de Valle, L. F. R., Lozano-Ramirez, T., Guillermo, J. G. and Sanchez-Cuevas, J. L., 2015. Shrinkage Reduction and Morphological Characterization of PP Reinforced With Glass Fiber and Nanoclay Using Functionalized PP As Compatibilizer. International Journal of Material Forming, pp. 1-8. 15. Campo, E. A., 2006. The Complete Part Design Handbook For Injection Molding of Thermoplastics, Germany: Hanser Publishers. 16. Chandramouli, S., and Eswaraiah, K., 2017. Optimization of EDM Process Parameters in Machining of 17-4 PH Steel using Taguchi Method. Materials Today: Proceedings, 4(2), pp. 2040–2047. 17. Chen, S., Li, H., Hsu, P., and Peng, H., 2011. Effects of Processing Parameters on Tensile Strength of Thin-Wall HDPE Parts Injection Molded by Ultra High- Speed Process. Polymer-Plastics Technology and Engineering, 50, pp. 276–281. 18. Chen, W., Huang, C., and Huang, C., 2013. Finding Efficient Frontier of Process Parameters for Plastic Injection Molding. Journal of Industrial Engineering International, 9(25), pp. 1–25. 19. Chen, Z., and Turng, L. S., 2005. A Review of Current Developments in Process and Quality Control for Injection Molding. Advances in Polymer Technology, 24(3), pp. 165–182. 20. Cheng, J., Liu, Z., and Tan, J., 2013. Multiobjective Optimization of Injection Molding Parameters Based on Soft Computing and Variable Complexity Method. International Journal of Advanced Manufacturing Technology, 66(5-8), pp. 907–916. 21. Chiang, Y. C., Cheng, H. C., Huang, C. F., Lee, J. L., Lin, Y., and Shen, Y. K., 2011. Warpage Phenomenon of Thin-Wall Injection Molding. International Journal of Advanced Manufacturing Technology, 55, pp. 517–526. 22. Dang, X. P., 2014. General Frameworks for Optimization of Plastic Injection Molding Process Parameters. Simulation Modelling Practice and Theory, 41, pp. 15–27. 23. Dar, U. A., Xu, Y. J., Zakir, S. M., and Saeed, M. U., 2016. The Effect of Injection Molding Process Parameters on Mechanical and Fracture Behavior of Polycarbonate Polymer. Journal of Applied Polymer Science, pp. 1–9. 24. Deng, Y. M., Zhang, Y., and Lam, Y. C., 2010 . A Hybrid of Mode-Pursuing Sampling Method and Genetic Algorithm for Minimization of Injection Molding Warpage. Materials and Design, 31(4), pp. 2118–2123. 25. Erzurumlu, T., and Ozcelik, B., 2006. Minimization of Warpage and Sink Index in Injection-Molded Thermoplastic Parts Using Taguchi Optimization Method. Materials and Design, 27(10), pp. 853–861. 26. Fangcheng, X., Lei, C., and Xiao, W., 2013. Calculation of Shrinkage Rate for Injection Molding Based on Moldflow. Proceedings of the FISITA 2012 World Automotive Congress , pp. 1501–1510. 27. Farshi, B., Gheshmi, S., and Miandoabchi, E., 2011. Optimization of Injection Molding Process Parameters Using Sequential Simplex Algorithm. Materials and Design, 32(1), pp. 414–423. 28. Fei, N. C., Mehat, N. M., and Kamaruddin, S., 2013. Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review. ISRN Industrial Engineering, 2013, pp. 1–11. 29. Fu, J., and Ma, Y., 2016. Mold Modification Methods to Fix Warpage Problems for Plastic Molding Products, Computer-aided Design and Applications. 13(1), pp. 138-151. 30. Gheorghe, O. C., Florin, T. D., Vlad, G. T., and Gabriel, D. T., 2014. Optimization of Micro Injection Molding of Polymeric Medical Devices Using Software Tools. Procedia Engineering, 69, pp. 340–346. 31. Gobinath, S., Elangovan, D., Sivakumar, E., and Devadasan, S. R., 2016. Viability Study of Use of Reprocessed High Density Polyethylene for Manufacturing Products and Multi Response Optimization of Injection Moulding Parameters Using Grey Relational Analysis. Polymers and Polymer Composites, 24(7), pp. 547–555. 32. Goodship, V., 2004. Practical Guide to Injection Moulding. Rapra Technology Limited and ARBURG Limited. 33. Guo, W., Hua, L., Mao, H., and Meng, Z., 2012. Prediction of Warpage in Plastic Injection Molding Based on Design of Experiments. Journal of Mechanical Science and Technology, 26(4), pp. 1133–1139. 34. Hassan, H., 2013. An Experimental Work on The Effect of Injection Molding Parameters on The Cavity Pressure and Product Weight. International Journal of Advanced Manufacturing Technology, 67, pp. 675–686. 35. Heidari, B. S., Oliaei, E., Shayesteh, H., Davachi, S. M., Hejazi, I., Seyfi, J., Bahrami, M. and Rashedi, H., 2017. Simulation of Mechanical Behavior and Optimization of Simulated Injection Molding Process For PLA Based Antibacterial Composite and Nanocomposite Bone Screws Using Central Composite Design. Journal of the Mechanical Behavior of Biomedical Materials, 65, pp. 160–176. 36. Hussin, R., Mustafa, M., Annuar, A., Azmi, H., Zakaria, M., and Khalil, A. N., 2015. An Optimization of Shrinkage in Injection Molding Parts by Using Taguchi Method. Journal of Advanced Research in Applied Mechanics, 10, pp. 1–8. 37. Huszar, M., Belblidia, F., Davies, H. M., Arnold, C., Bould, D., and Sienz, J., 2015. Sustainable Injection Moulding: The Impact of Materials Selection and Gate Location on Part Warpage and Injection Pressure. Sustainable Materials and Technologies, 5, pp. 1–8. 38. Jamsheed, M., Rahman, M. A., Moyeed, M. A., and Ahmed, G. M. S., 2015. Design and Analysis of Plastic Injection Mould for CAM BUSH with Submarine Gate. Materials Today: Proceedings, 2, pp. 2083–2093. 39. Jou, Y., Lin, W., Lee, W., and Yeh, T., 2014. Integrating the Taguchi Method and Response Surface Methodology for Process Parameter Optimization of the Injection Molding, 1285(3), pp. 1277–1285. 40. Kai, Y., and Basem, S. E. H., 2009. Taguchi’s Orthogonal Array Experiment. In Design for Six Sigma: A Roadmap for Product Development, 2nd Edition., New York: McGraw-Hill Professional. 41. Kamaruddin, S., Khan, Z. A., and Foong, S. H., 2010. Application of Taguchi Method in the Optimization of Injection Moulding Parameters for Manufacturing Products from Plastic Blend. International Journal of Engineering and Technology, 2(6), pp. 574–580. 42. Karasu, M. K., Cakmakci, M., Cakiroglu, M. B., Ayva, E., and Demirel-Ortabas, N., 2014). Improvement of Changeover Times Via Taguchi Empowered SMED / Case Study on Injection Molding Production. Measurement, 47, pp. 741–748. 43. Kashyap, S., and Datta, D., 2015. Process Parameter Optimization of Plastic Injection Molding: A Review. International Journal of Plastics Technology, 19(1), pp. 1–18. 44. Kavade, M. V, and Kadam, S. D., 2012. Parameter Optimization of Injection Molding of Polypropylene by using Taguchi Methodology. IOSR Journal of Mechanical and Civil Engineering, 4(4), pp. 49–58. 45. Kc, B., Faruk, O., Agnelli, J. A M., Leao, A. L., Tjong, J., and Sain, M., 2016. Sisal-glass Fiber Hybrid Biocomposite: Optimization of Injection Molding Parameters Using Taguchi Method For Reducing Shrinkage. Composites Part A: Applied Science and Manufacturing, 83, pp. 152–159. 46. Khan, M., Afaq, S. K., Khan, N. U., and Ahmad, S., 2014. Cycle Time Reduction in Injection Molding Process by Selection of Robust Cooling Channel Design. ISRN Mechanical Engineering, 2014, pp. 1-8. 47. Khan, Z. A., Kamaruddin, S., and Siddiquee, A. N., 2010. Feasibility Study of Use of Recycled High Density Polyethylene and Multi Response Optimization of Injection Moulding Parameters Using Combined Grey Relational and Principal Component Analyses. Materials and Design, 31, pp. 2925–2931. 48. Kim, B., and Min, J., 2017. Residual Stress Distributions and Their Influence on Post-Manufacturing Deformation of Injection-Molded Plastic Parts. Journal of Materials Processing Technology, 245, pp. 215–226. 49. Kim, J., Ahn, S., Atre, S. V., Park, S. J., Kang, T. G., and German, R. M., 2014. Imbalance Filling of Multi-Cavity Tooling During Powder Injection Molding. Powder Technology, 257, pp. 124–131. 50. Kitayama, S., and Natsume, S., 2014. Multi-objective Optimization of Volume Shrinkage and Clamping Force For Plastic Injection Molding Via Sequential Approximate Optimization. Simulation Modelling Practice and Theory, 48, pp. 35–44. 51. Kitayama, S., Onuki, R., and Yamazaki, K., 2014. Warpage Reduction With Variable Pressure Profile in Plastic Injection Molding Via Sequential Approximate Optimization. International Journal of Advanced Manufacturing Technology, 72, pp. 827–838. 52. Kuo, C., and Liao, H., 2015. Dimensional Accuracy Optimization of the Micro-plastic Injection Molding Process Using the Taguchi Design Method, 21(2), pp. 224-248. 53. Kuo, H. C., and Jeng, M. C., 2010. Effects of Part Geometry and Injection Molding Conditions on The Tensile Properties of Ultra-High Molecular Weight Polyethylene Polymer. Materials and Design, 31(2), pp. 884–893. 54. Kuram, E., Tasci, E., Ihsan, A., Metin, M., Yilmaz, F., and Ozcelik, B., 2013. Investigating The Effects of Recycling Number and Injection Parameters on The Mechanical Properties of Glass-Fibre Reinforced Nylon 6 Using Taguchi Method. Materials and Design, 49, pp. 139–150. 55. Kurt, M., Kaynak, Y., Kamber, O. S., Mutlu, B., Bakir, B., and Koklu, U., 2010. Influence of Molding Conditions on The Shrinkage and Roundness of Injection Molded Parts. International Journal of Advanced Manufacturing Technology, 46, pp. 571–578. 56. Kurtaran, H., and Erzurumlu, T., 2006. Efficient Warpage Optimization of Thin Shell Plastic Parts Using Response Surface Methodology and Genetic Algorithm. International Journal of Advanced Manufacturing Technology, 27, pp. 468–472. 57. Kurtaran, H., Ozcelik, B., and Erzurumlu, T., 2005. Warpage Optimization of A Bus Ceiling Lamp Base Using Neural Network Model and Genetic Algorithm. Journal of Materials Processing Technology, 169(2), pp. 314–319. 58. Kusić, D., Kek, T., Slabe, J. M., Svečko, R., and Grum, J., 2013. The Impact of Process Parameters on Test Specimen Deviations and Their Correlation With AE Signals Captured During The Injection Moulding Cycle. Polymer Testing, 32, pp. 583–593. 59. Kwiatkowski, D., Gnatowski, A., and Nabiałek, J., 2011. Numerical Analysis of Residual Stresses and Deformation of Injection Moulded Parts Manufactured From Polymeric Composite With Different Processing Conditions. Kompozyty, 11(4), pp.294–298. 60. Leeba, V., Aravind, S., and Shunmugesh, K., 2017. Multi-Objective Optimization of Machining Parameters During Dry Turning of 11SMn30 Free Cutting Steel Using Grey Relational Analysis. Materials Today: Proceedings, 4, pp. 4196–4203. 61. Leyva-Porras, C., Esneider-Alcalá, M. A., Toxqui-Terán, A., Márquez-Lucero, A., and Aguilar-Martínez, J. A., 2013. Effect of Molding Parameters on Young’s Modulus of An Injection Molded Low-Density Polyethylene (LDPE). Industrial and Engineering Chemistry Research, 52, pp. 5666–5671. 62. López, A., Aisa, J., Martinez, A., and Mercado, D., 2016. Injection Moulding Parameters Influence on Weight Quality of Complex Parts By Means of DOE Application: Case Study. Measurement, 90, pp. 349–356. 63. Maddah, H. A., 2016. Polypropylene as A Promising Plastic: A Review. American Journal of Polymer Science, 6(1), pp. 1–11. 64. Mehat, N., Kamaruddin, S., and Othman, A., 2012. Reducing the Shrinkage in Plastic Injection Moulded Gear via Grey-Based-Taguchi Optimization Method. Proceedings of the World Congress on Engineering 2012, 3, pp. 1–4. 65. Mehat, N. M., Kamaruddin, S., and Othman, A. R., 2013. Investigating the Effects of Injection Moulding Process Parameters on Multiple Tensile Characteristics of Plastic Spur Gear via Experimental Approach. Advanced Materials Research, 748, pp. 544–548. 66. Mehat, N. M., Kamaruddin, S., Othman, A. R., Mehat, N. M., Kamaruddin, S., and Othman, A. R., 2014. Hybrid Integration of Taguchi Parametric Design, Grey Relational Analysis, and Principal Component Analysis Optimization for Plastic Gear Production. Chinese Journal of Engineering, 2014, pp. 1–11. 67. Mirvar, S. E., Kaleybar, R. M., and Afsari, A., 2011. Optimization of Injection Molding Process Parameters to Increase the Tensile Strength in Polyamide-Specimen Using the Taguchi Method. Advanced Materials Research, 341-342, pp. 395–399. 68. Mohammad, F., A., Bassam M. E., A., and James G. S., 2005. Handbook of Industrial Chemistry: Organic Chemicals. New York : McGraw-Hill Education. 69. Mukras, S. M. S., and Al-Mufadi, F. A., 2015. Simulation of HDPE Mold Filling in The Injection Molding Process with Comparison to Experiments. Arabian Journal for Science and Engineering, pp. 1-10. 70. Muthuramalingam, T., and Mohan, B., 2014. Application of Taguchi-grey Multi Responses Optimization on Process Parameters in Electro Erosion. Measurement: Journal of the International Measurement Confederation, 58, pp. 495–502. 71. Nagahanumaiah, and Ravi, B., 2009. Effects of Injection Molding Parameters on Shrinkage and Weight of Plastic Part Produced by DMLS Mold. Rapid Prototyping Journal, 15(3), pp. 179–186. 72. Nandagopal, K., and Kailasanathan, C., 2016. Analysis of Mechanical Properties and Optimization of Gas Tungsten Arc Welding (GTAW) Parameters on Dissimilar Metal Titanium (6Al-4V) and Aluminium 7075 by Taguchi and ANOVA Techniques. Journal of Alloys and Compounds, 682, pp. 503–516. 73. Nelabhotla, D. M., Jayaraman, T. V., Asghar, K., and Das, D., 2016. The Optimization of Chemical Mechanical Planarization Process-Parameters of C-Plane Gallium-Nitride Using Taguchi Method and Grey Relational Analysis. Materials and Design, 104, pp. 392–403. 74. Nian, S.C., Wu, C.Y., and Huang, M.S., 2015. Warpage Control of Thin-Walled Injection Molding Using Local Mold Temperatures. International Communications in Heat and Mass Transfer, 61, pp. 102–110. 75. Öktem, H., 2012. Optimum Process Conditions on Shrinkage of An Injected-Molded Part Of DVD-ROM Cover Using Taguchi Robust Method. International Journal of Advanced Manufacturing Technology, 61, pp. 519–528. 76. Oliaei, E., Heidari, B. S., Davachi, S. M., Bahrami, M., Davoodi, S., Hejazi, I., and Seyfi, J., 2016. Warpage and Shrinkage Optimization of Injection-Molded Plastic Spoon Parts for Biodegradable Polymers Using Taguchi, ANOVA and Artificial Neural Network Methods. Journal of Materials Science and Technology, 32(8), pp. 1–11. 77. Ozcelik, B., Ozbay, A., and Demirbas, E., 2010. Influence of Injection Parameters and Mold Materials on Mechanical Properties of ABS in Plastic Injection Molding. International Communications in Heat and Mass Transfer, 37(9), pp. 1359–1365. 78. Park, H. S., and Nguyen, T. T., 2014. Optimization of Injection Molding Process for Car Fender in Consideration of Energy Efficiency And Product Quality. Journal of Computational Design and Engineering, 1(4), pp. 256–265. 79. Patil, A., Patel, A., and Purohit, R., 2017. An Overview of Polymeric Materials for Automotive Applications. Materials Today: Proceedings, 4(2), pp. 3807–3815. 80. Postawa, P., and Koszkul, J., 2005. Change in Injection Moulded Parts Shrinkage and Weight as A Function of Processing Conditions. Journal of Materials Processing Technology, 162-163, pp. 109–115. 81. Prasad, K. S. H., Rao, C. S., and Rao, D. N., 2012. Application of Design of Experiments to Plasma Arc Welding Process : A Review. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 34(1), pp. 75–81. 82. Rahimi, M., Esfahanian, M., and Moradi, M., 2014. Effect of Reprocessing on Shrinkage and Mechanical Properties of ABS and Investigating the Proper Blend of Virgin and Recycled ABS in Injection Molding. Journal of Materials Processing Technology, 214, pp. 2359–2365. 83. Rathi, M. G., and Salunke, M. D., 2012. Analysis of Injection Moulding Process Parameters, International Journal of Engineering Research and Technology, 1(8), pp. 1–5. 84. Razak, M. A., Abdul-Rani, A. M., Rao, T. V. V. L. N., Pedapati, S. R., and Kamal, S., 2016. Electrical Discharge Machining on Biodegradable AZ31 Magnesium Alloy Using Taguchi Method. Procedia Engineering, 148, pp. 916–922. 85. Revilla-Díaz, R., Sánchez-Valdés, S., López-Campos, F., Medellín-Rodríguez, F. J., and López-Ouintanilla, M. L., 2007. Comparative Characterization of PP Nano and Microcomposites by In-Mold Shrinkage Measurements and Structural Characteristics. Macromolecular Materials and Engineering, 292(6), pp. 762–768. 86. Rosato, D. V., Rosato, D. V., and Rosato, M. G., 2000. Injection Molding Machines. Injection Molding Handbook. USA: Springer. 87. Santos, J. D., Fajardo, J. I., Cuji, A. R., García, J. A., Garzón, L. E., and López, L. M., 2015. Experimental Evaluation and Simulation of Volumetric Shrinkage and Warpage on Polymeric Composite Reinforced with Short Natural Fibers. Frontiers of Mechanical Engineering, 10(3), pp. 287–293. 88. Seenuvasaperumal, P., and Elayaperumal, A., 2017. Effect of Calcium Hexaboride Particles on Predicting the Dry Sliding Tribological Process Parameter of Magnesium Composite Using Grey Relational Analysis. Materials Today: Proceedings, 4(2), pp. 557–566. 89. Senthilkumar, N., Tamizharasan, T., and Anandakrishnan, V., 2014. Experimental Investigation and Performance Analysis of Cemented Carbide Inserts of Different Geometries Using Taguchi Based Grey Relational Analysis. Measurement, 58, pp. 520–536. 90. Shi, H., Xie, S., and Wang, X., 2013. A Warpage Optimization Method For Injection Molding Using Artificial Neural Network with Parametric Sampling Evaluation Strategy. International Journal of Advanced Manufacturing Technology, 65, pp. 343–353. 91. Shin, H., and Park, E., 2013. Analysis of Incomplete Filling Defect for Injection-Molded Air Cleaner Cover Using Moldflow Simulation. Journal of Polymers, 2013, pp. 1-13. 92. Shinde, A. B., and Pawar, P. M., 2017. Multi-objective Optimization of Surface Textured Journal Bearing by Taguchi based Grey Relational Analysis. Tribology International, 114, pp. 349–357. 93. Speranza, V., Vietri, U., and Pantani, R., 2011. Monitoring of Injection Molding of Thermoplastics: Average Solidification Pressure as A Key Parameter for Quality Control. Macromolecular Research, 19(6), pp. 542–554. 94. Spina, R., Walach, P., Schild, J., and Hopmann, C., 2012. Analysis of Lens Manufacturing with Injection Molding. International Journal of Precision Engineering and Manufacturing, 13(11), pp. 2087–2095. 95. Tang, S. H., Tan, Y. J., Sapuan, S. M., Sulaiman, S., Ismail, N., and Samin, R., 2007. The use of Taguchi Method in The Design of Plastic Injection Mould for Reducing Warpage. Journal of Materials Processing Technology, 182, pp. 418–426. 96. Tian, M., Gong, X., Yin, L., Li, H., Ming, W., Zhang, Z., and Chen, J., 2016. Multi-objective Optimization of Injection Molding Process Parameters in Two Stages for Multiple Quality Characteristics and Energy Efficiency Using Taguchi Method and NSGA-II. The International Journal of Advanced Manufacturing Technology, pp. 1-12. 97. Tzeng, C. J., Yang, Y. K., Lin, Y. H., and Tsai, C. H., 2012. A Study of Optimization of Injection Molding Process Parameters for SGF and PTFE Reinforced PC Composites Using Neural Network and Response Surface Methodology. International Journal of Advanced Manufacturing Technology, 63, pp. 691–704. 98. Wang, G., Zhao, G., and Wang, X., 2013. Effects of Cavity Surface Temperature on Mechanical Properties of Specimens with and without A Weld Line in Rapid Heat Cycle Molding. Materials and Design, 46, pp. 457–472. 99. Wang, X., Zhao, G., and Wang, G., 2013. Research on the Reduction of Sink Mark and Warpage of the Molded Part in Rapid Heat Cycle Molding Process. Materials and Design, 47, pp. 779–792. 100. Wang, Y., Kim, J., and Song, J., 2014. Optimization of Plastic Injection Molding Process Parameters for Manufacturing A Brake Booster Valve Body. Materials and Design, 56, pp. 313–317. 101. Wen, T., Xia, C., Yang, C., and Liu, L., 2014. Optimization of Processing Parameters for Minimizing Warpage of Large Thin-Walled Parts in Whole Stages of Injection Molding. Chinese Journal of Polymer Science, 32(11), pp. 1535–1543. 102. Wu, C. Y., Ku, C. C., and Pai, H. Y., 2011. Injection Molding Optimization with Weld Line Design Constraint Using Distributed Multi-Population Genetic Algorithm. International Journal of Advanced Manufacturing Technology, 52, pp. 131–141. 103. Xin, L. L., Sharif, S., and Saad, R. M., 2015. Gas-assisted Injection Molding: Case Study on Process Optimization of CD-Rom Tray Manufacturing. Procedia Manufacturing, 2, pp. 538–542. 104. Xu, G., and Yang, Z., 2015. Multiobjective Optimization of Process Parameters for Plastic Injection Molding Via Soft Computing and Grey Correlation Analysis. International Journal of Advance Manufacturing Technology, 78, pp. 525–536. 105. Xu, G., Yang, Z. T., and Long, G. D., 2012. Multi-objective Optimization of MIMO Plastic Injection Molding Process Conditions Based on Particle Swarm Optimization. International Journal of Advanced Manufacturing Technology, 58, pp. 521–531. 106. Xu, Y., Zhang, Q. W., Zhang, W., and Zhang, P., 2014. Optimization of Injection Molding Process Parameters To Improve the Mechanical Performance of Polymer Product Against Impact. International Journal of Advanced Manufacturing Technology, 76, pp. 2199–2208. 107. Xu, Y. J., Yang, W., Xie, B. H., Liu, Z. Y., and Yang, M. B., 2009. Effect of Injection Parameters and Addition of Nanoscale Materials on the Shrinkage of Polypropylene Copolymer. Journal of Macromolecular Science, Part B, 48(3), pp. 573–586. 108. Yang, Y., Yang, B., Zhu, S., and Chen, X., 2015. Online Quality Optimization of the Injection Molding Process Via Digital Image Processing and Model-Free Optimization. Journal of Materials Processing Technology, 226, pp. 85–98. 109. Yin, F., Mao, H., and Hua, L., 2011a. A Hybrid of Back Propagation Neural Network and Genetic Algorithm for Optimization of Injection Molding Process Parameters. Materials and Design, 32(6), pp. 3457–3464. 110. Yin, F., Mao, H., Hua, L., Guo, W., and Shu, M., 2011b. Back Propagation Neural Network Modeling for Warpage Prediction and Optimization of Plastic Products During Injection Molding. Materials and Design, 32(4), pp. 1844–1850. 111. Zhang, J., Wang, J., Lin, J., Guo, Q., Chen, K., and Ma, L., 2016. Multiobjective Optimization of Injection Molding Process Parameters Based on Opt LHD, EBFNN, and MOPSO. International Journal of Advanced Manufacturing Technology, 85, pp. 2857–2872. 112. Zhang, Y., Mao, T., Huang, Z., Gao, H., and Li, D., 2015. A Statistical Quality Monitoring Method for Plastic Injection Molding Using Machine Built-In Sensors. International Journal of Advanced Manufacturing Technology, pp. 1-12. 113. Zhao, J., Cheng, G., Ruan, S., and Li, Z., 2015. Multi-objective Optimization Design of Injection Molding Process Parameters Based on the Improved Efficient Global Optimization Algorithm and Non-Dominated Sorting-Based Genetic Algorithm. International Journal of Advanced Manufacturing Technology, pp.1813–1826. 114. Zhao, P., Zhou, H., Li, Y., and Li, D., 2010. Process Parameters Optimization of Injection Molding Using A Fast Strip Analysis As A Surrogate Model. International Journal of Advanced Manufacturing Technology, 49, pp. 949–959.

Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis

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