Optimisation of Pipe Fitting in the Injection Moulding Process Using the Taguchi Method
DOI:
https://doi.org/10.58915/aset.v1i1.11Abstract
This paper presents the optimisation of the injection moulding process on a pipe fitting using the Taguchi method. This study optimises the injection moulding parameters that yield minimum sink marks and volumetric shrinkage in the injected pipe fitting. Five parameters: cooling time, melting temperature, packing time, packing pressure, and injection pressure, were considered in the simulation. Taguchi method with L27 Orthogonal array was employed to construct the Design of Experiment (DOE) for the simulation analysis. The main responses are sink marks and volumetric shrinkage. The main effect and ANOVA analysis were also studied. The results revealed that melting temperature was the most significant factor influencing sink mark response. However, packing pressure crucially affects the volumetric shrinkage of the injected pipe fitting part. The optimal injection moulding process can be achieved with a cooling time of 26.52 s, a melt temperature of 218 ℃, an injection pressure of 36.23 MPa, a packing pressure of 60 MPa and a packing time of 5 s.
References
Mi, H. Y., Jing, X., Peng, J., Turng, L. S., & Peng, X. F. Influence and prediction of processing parameters on the properties of microcellular injection molded thermoplastic polyurethane based on an orthogonal array test. Journal of Cellular Plastics, vol 49, issue 5 (2013), pp.439-458.
Akovali, G. Plastic materials: polyvinyl chloride (PVC). Toxicity of building materials Woodhead Publishing, (2012) pp.23-53
Gedde, U. W., Hedenqvist, M. S., Hakkarainen, M., Nilsson, F., & Das, O. Processing of Polymeric Materials. In Applied Polymer Science, Springer, Cham, (2021) pp.453-487.
Sadat-Shojai, M., & Bakhshandeh, G. R. Recycling of PVC wastes. Polymer degradation and stability, vol 96, issue 4 (2011), pp.404-415.
Au, K. M., & Yu, K. M. A scaffolding architecture for conformal cooling design in rapid plastic injection moulding. The International Journal of Advanced Manufacturing Technology, vol 34, issue 5 (2007) pp.496-515.
Wilczyński, K., Wilczyński, K. J., & Buziak, K. Modeling and Experimental Studies on Polymer Melting and Flow in Injection Molding. Polymers, vol 14, issue 10 (2022) pp.2106 (19 pages).
Tang, S. H., Tan, Y. J., Sapuan, S. M., Sulaiman, S., Ismail, N., & Samin, R. The use of Taguchi method in the design of plastic injection mould for reducing warpage. Journal of Materials Processing Technology, vol 182, issue 1-3 (2007) pp.418-426.
Azaman, M. D., Sapuan, S. M., Sulaiman, S., Zainudin, E. S., & Khalina, A. Shrinkages and warpage in the processability of wood-filled polypropylene composite thin-walled parts formed by injection molding. Materials & Design (1980-2015), vol 52, (2013) pp.1018- 1026.
Mercado-Colmenero, J. M., Rubio-Paramio, M. A., de Juanes Marquez-Sevillano, J., & Martin- Doñate, C. A new method for the automated design of cooling systems in injection molds. Computer-Aided Design, vol 104, (2018) pp.60-86.
López, A., Aisa, J., Martinez, A., & Mercado, D. Injection moulding parameters influence on weight quality of complex parts by means of DOE application: Case study. Measurement, vol 90, (2016) pp.349-356.
Eng, J. I. T. S. Optimization of injection molding process parameters by response surface methods. Journal of Production Research, vol 42, (2016) pp.2047-2061.
Hentati, F., Hadriche, I., Masmoudi, N., & Bradai, C. Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation. The International Journal of Advanced Manufacturing Technology, vol 104, issue 9 (2019) pp.4353-4363.
Oliaei, E., Heidari, B. S., Davachi, S. M., Bahrami, M., Davoodi, S., Hejazi, I., & Seyfi, J. Warpage and shrinkage optimisation of injection-molded plastic spoon parts for biodegradable polymers using Taguchi, ANOVA and artificial neural network methods. Journal of Materials Science & Technology, vol 32, issue 8 (2016) pp.710-720.
Gu, F., Hall, P., & Miles, N. J. Performance evaluation for composites based on recycled polypropylene using principal component analysis and cluster analysis. Journal of Cleaner Production, vol 115, (2016) pp.343-353.
Ramesh, S., Nirmala, P., Ramkumar, G., Sahoo, S., Anitha, G., Gnanasekar, A. K., & Isaac JoshuaRamesh Lalvani, J. Simulation process of injection molding and optimisation for automobile instrument parameter in embedded system. Advances in Materials Science and Engineering, vol 2021, Article ID 9720297, (2021) (10 pages).
Shelesh-Nezhad, K., & Siores, E. An intelligent system for plastic injection molding process design. Journal of Materials Processing Technology, vol 63, issue 1-3 (1997) pp.458-462.
Fei, N. C., Mehat, N. M., & Kamaruddin, S. Practical applications of Taguchi method for optimisation of processing parameters for plastic injection moulding: a retrospective review. International Scholarly Research Notices, vol 2013, Article ID 462174, (2013) (11 pages)
Altan, M. Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Materials & Design, vol 31, issue 1 (2010) pp.599-604.
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