Optimization Of Injection Moulding Parameters Using Moldflow Simulation Software Analyze By Response Surface Method

The purpose of this study is to optimize injection moulding parameters moldflow simulation software analyze by Response Surface Method(RSM). The process parameters selected for this study are melting temperature, mold temperature, injection time and number of gate. In this study, the RSM using Box –...

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Main Author:	Sulaiman, Muhammad Hidayat
Format:	Thesis
Language:	English English
Published:	2019
Subjects:	T Technology (General) TP Chemical technology
Online Access:	http://eprints.utem.edu.my/id/eprint/24943/1/Optimization%20Of%20Injection%20Moulding%20Parameters%20Using%20Moldflow%20Simulation%20Software%20Analyze%20By%20Response%20Surface%20Method.pdf http://eprints.utem.edu.my/id/eprint/24943/2/Optimization%20Of%20Injection%20Moulding%20Parameters%20Using%20Moldflow%20Simulation%20Software%20Analyze%20By%20Response%20Surface%20Method.pdf
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id	my-utem-ep.24943
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Md Ali, Mohd Amran
topic	T Technology (General) TP Chemical technology
spellingShingle	T Technology (General) TP Chemical technology Sulaiman, Muhammad Hidayat Optimization Of Injection Moulding Parameters Using Moldflow Simulation Software Analyze By Response Surface Method
description	The purpose of this study is to optimize injection moulding parameters moldflow simulation software analyze by Response Surface Method(RSM). The process parameters selected for this study are melting temperature, mold temperature, injection time and number of gate. In this study, the RSM using Box – Behnken is used to determine the most significant parameters toward the responses and determine the optimum parameters values. Based on the design of experiment, 27 numbers of experimental data are collected and analyse using RSM modelling. The result collected was optimized using RSM meanwhile P-value and R- squared were calculated using analysis of variance (ANOVA). From the result analysis, the injection time is the most significant among the rest of factors toward the fill time response with 99% For volumetric shrinkage and deflection responses, melt temperature and number of gate contribute 58.58% and 60.32% respectively. Then, the interaction between process parameters toward responses are investigated. For response of fill time as the injection time is the only major factor that affect the fill time. As for volumetric shrinkage, the interaction between melt temperature and injection time made a quadratic shape as the increasing in melt temperature increases the shrinkage while the injection time increases the shrinkage up to 2.1s and after that the shrinkage decreases. As for deflection responses, the increasing melt temperature increase the deflection but the interaction from multiple number of gate decreases the deflection Finally, for the multi – response optimization, the optimization are 280oC melt temperature, 120oC mold temperature, 4.0s injection time and one gate. For the desirability of multi – response, it resulted 0.9593 while the predicted value resulted are 0.3593 deflection, 4.2441s fill time and 5.9209 volumetric shrinkage respectively.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Sulaiman, Muhammad Hidayat
author_facet	Sulaiman, Muhammad Hidayat
author_sort	Sulaiman, Muhammad Hidayat
title	Optimization Of Injection Moulding Parameters Using Moldflow Simulation Software Analyze By Response Surface Method
title_short	Optimization Of Injection Moulding Parameters Using Moldflow Simulation Software Analyze By Response Surface Method
title_full	Optimization Of Injection Moulding Parameters Using Moldflow Simulation Software Analyze By Response Surface Method
title_fullStr	Optimization Of Injection Moulding Parameters Using Moldflow Simulation Software Analyze By Response Surface Method
title_full_unstemmed	Optimization Of Injection Moulding Parameters Using Moldflow Simulation Software Analyze By Response Surface Method
title_sort	optimization of injection moulding parameters using moldflow simulation software analyze by response surface method
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Manufacturing Engineering
publishDate	2019
url	http://eprints.utem.edu.my/id/eprint/24943/1/Optimization%20Of%20Injection%20Moulding%20Parameters%20Using%20Moldflow%20Simulation%20Software%20Analyze%20By%20Response%20Surface%20Method.pdf http://eprints.utem.edu.my/id/eprint/24943/2/Optimization%20Of%20Injection%20Moulding%20Parameters%20Using%20Moldflow%20Simulation%20Software%20Analyze%20By%20Response%20Surface%20Method.pdf
_version_	1747834102541713408
spelling	my-utem-ep.249432021-09-29T12:15:40Z Optimization Of Injection Moulding Parameters Using Moldflow Simulation Software Analyze By Response Surface Method 2019 Sulaiman, Muhammad Hidayat T Technology (General) TP Chemical technology The purpose of this study is to optimize injection moulding parameters moldflow simulation software analyze by Response Surface Method(RSM). The process parameters selected for this study are melting temperature, mold temperature, injection time and number of gate. In this study, the RSM using Box – Behnken is used to determine the most significant parameters toward the responses and determine the optimum parameters values. Based on the design of experiment, 27 numbers of experimental data are collected and analyse using RSM modelling. The result collected was optimized using RSM meanwhile P-value and R- squared were calculated using analysis of variance (ANOVA). From the result analysis, the injection time is the most significant among the rest of factors toward the fill time response with 99% For volumetric shrinkage and deflection responses, melt temperature and number of gate contribute 58.58% and 60.32% respectively. Then, the interaction between process parameters toward responses are investigated. For response of fill time as the injection time is the only major factor that affect the fill time. As for volumetric shrinkage, the interaction between melt temperature and injection time made a quadratic shape as the increasing in melt temperature increases the shrinkage while the injection time increases the shrinkage up to 2.1s and after that the shrinkage decreases. As for deflection responses, the increasing melt temperature increase the deflection but the interaction from multiple number of gate decreases the deflection Finally, for the multi – response optimization, the optimization are 280oC melt temperature, 120oC mold temperature, 4.0s injection time and one gate. For the desirability of multi – response, it resulted 0.9593 while the predicted value resulted are 0.3593 deflection, 4.2441s fill time and 5.9209 volumetric shrinkage respectively. 2019 Thesis http://eprints.utem.edu.my/id/eprint/24943/ http://eprints.utem.edu.my/id/eprint/24943/1/Optimization%20Of%20Injection%20Moulding%20Parameters%20Using%20Moldflow%20Simulation%20Software%20Analyze%20By%20Response%20Surface%20Method.pdf text en public http://eprints.utem.edu.my/id/eprint/24943/2/Optimization%20Of%20Injection%20Moulding%20Parameters%20Using%20Moldflow%20Simulation%20Software%20Analyze%20By%20Response%20Surface%20Method.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117724 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Manufacturing Engineering Md Ali, Mohd Amran 1. Amran, M., Salmah, S., Faiz, A., Izamshah, R., Hadzley, M., Manshoor, B., & Amri, M. (2014). Effect of Injection Moulding Machine Parameters on the Warpage by Applying Taguchi Method. Applied Mechanics and Materials, 699, 20 – 25. 2. Amran M., Idayu N., Abd Aziz M. S., Hadzley. M., & Sivarao. (2016). Optimisation of process parameters in linear runner family injeciton mold using moldflow simulation software. ARPN Journal of Engineering and Applied Sciences, 11(4). 3. Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., & Escaleira, L. A. (2008). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76(5), 965-977. 4. Chen, W., Nguyen, M., Chiu, W., Chen, T., & Tai, P. (2015). Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO. The International Journal of Advanced Manufacturing Technology, 83(9-12), 1873-1886. 5. Chen, Z., & Turng, L. (2005). A review of current developments in process and quality control for injection molding. Advances in Polymer Technology, 24(3), 165-182. 6. Elsheikhi, S. A., & Benyounis, K. Y. (2017). Mathematical Modeling and Optimization of Injection Molding of Plastics. Reference Module in Materials Science and Materials Engineering, 1 – 16. 7. Francis, L. F. (2016). Melt Processes. Materials Processing, 105–249. 8. Goodship, V. (2016). Injection Molding of Thermoplastics. Design and Manufacture of Plastic Components for Multifunctionality, 103–170 9. Gurjeet Singh, M. K. Pradhan, & Ajay Verma. (2015). Effect of Injection Moulding Process Parameter on Tensile Strength Using Taguch Method. International Journal of Industrial and Manufacturing Engineering, 2015, 1844 – 1849 10. Jacyna, J., Kordalewska, M., & Markuszewski, M. J. (2018). Design of Experiments in metabolomics-related studies: An overview. Journal of Pharmaceutical and Biomedical Analysis, 598 – 606. 11. Khan, M., Afaq, S. K., Khan, N. U., & Ahmad, S. (2014). Cycle Time Reduction in Injection Molding Process by Selection of Robust Cooling Channel Design. ISRN Mechanical Engineering, 1 – 8. 12. Kitayama, S., Yokoyama, M., Takano, M., & Aiba, S. (2017). Multi-objective optimization of variable packing pressure profile and process parameters in plastic injection molding for minimizing warpage and cycle time. The International Journal of Advanced Manufacturing Technology, 92(9-12), 3991-3999. 13. Kim, J. K., & Jeon, E. S. (2017). Optimization of Injection Molding Process Parameters to Improve Mechanical Strength of LFT Specimen. International Journal of Applied Engineering Research, 12(13), 3671-3676. 14. Li, K., Yan, S., Zhong, Y., Pan, W., & Zhao, G. (2019). Multi-objective optimization of the fiber-reinforced composite injection molding process using Taguchi method, RSM, and NSGA-II. Simulation Modelling Practice and Theory, 91, 69-82. 15. Mohd, A., Roslan, A. A., & Baba, N. B. (2016). Effect of Injection Molding Parameters on Recycled ABS (r-ABS) Mechanical Properties. Indian Journal of Science and Technology,9(9), 1 – 6. 16. Morelli, C. L., Pouzada, A. S., & Sousa, J. A. (2009). Influence of hybridization of glass fiber and talc on the mechanical performance of polypropylene composites. Journal of Applied Polymer Science, 114(6), 3592-3601. 17. Nagahanumaiah, & Ravi, B. (2009). Effects of injection molding parameters on shrinkage and weight of plastic part produced by DMLS mold. Rapid Prototyping Journal, 15(3), 179-186. 18. Nasir, S., Ismail, K. A., Shayfull, Z., & Shuaib, N. A. (2013). Comparison between Single and Multi Gates for Minimization of Warpage Using Taguchi Method in Injection Molding Process for ABS Material. Key Engineering Materials, 594-595, 842–851. 19. Rajib, P., Samrat, P., & Dipayan, D. (2012). Optimization of micro-injection molding process with respect to tensile properties of polypropylene. Indian Journal of Fibre and Textile Research, 37, 11-15 20. Riduan, B. Z., Haris, M. H. M., & Hamzah, Z.(2017) Optimization of plastic injection molding process parameters using Taguchi method for warpage defect. National Innovation and Invention Competition Through Exhibition, 1 – 9. 21. Rosato, D., Rosato, D. V., & Rosato, D. V. (1995). Injection molding handbook. New York: Champman & Hall. 22. Shen, C., Wang, L., & Li, Q. (2007). Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. Journal of Materials Processing Technology, 183(2-3), 412-418. 23. Soy, U., Findik, F., Yetgin, S. H., Gokkurt, T., & Yıldırım, F. (2017). Fabrication and Mechanical Properties of Glass Fiber/Talc/CaCO3 Filled Recycled PP Composites. American Journal of Applied Sciences,14(9), 878 – 885. 24. Tutak, P. (2017). Application of Moldflow Simulation in Injection Molding of Plastic Tank. Journal of Applied Computer Science Methods, 9(1), 79–88. 25. Uy, M., & Telford, J. K. (2009). Optimization by Design of Experiment techniques. 2009 IEEE Aerospace Conference, 1 – 10. 26. Wang, Q., Zhen, M., Wu, Z., & Cai, Y. (2017). Effect of process parameters on cavity pressure in injection molding. Advances in Materials, Machinery, Electronics I, 1 – 9. 27. Zhang, S. (2013). Optimizing the Filling Time and Gate of the Injection Mold on Plastic Air Intake Manifold of Engines. Information Technology Journal, 12(13), 2473–2480.

Optimization Of Injection Moulding Parameters Using Moldflow Simulation Software Analyze By Response Surface Method

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