Rejection Rate Reduction of the Automotive Thermoplastic Parts in Injection Moulding Using Response Surface Methodology

Huei Ruey Ong; Ifwat Mohd Shah; Wan Mohd Eqhwan Iskandar; Md. Maksudur Rahman Khan; Shein Hong; Ridzuan Ramli; Muhammad Khairul Anuar Mohamed

doi:10.4028/www.scientific.net/KEM.841.225

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HomeKey Engineering MaterialsKey Engineering Materials Vol. 841Rejection Rate Reduction of the Automotive...

Rejection Rate Reduction of the Automotive Thermoplastic Parts in Injection Moulding Using Response Surface Methodology

Abstract:

Plastic injection moulding is widely used for manufacturing due to variety of plastic product. In this study, plastic part defects such as air bubble and gas mark defect are commonly occurs in thermoplastic part, specifically acrylonitrile butadiene styrene (ABS). In order to optimize the process parameters of injection moulding, design of experiment (DOE) with Response Surface Methodology (RSM) model was used. Process parameters such as melt temperature, mould temperature and injection pressure were selected for the DOE development. The experiments were conducted with melt temperature range from 200 °C to 240 °C, mould temperature from 60 °C to 80 °C and injection pressure from 90 to 99%. The result indicates that, all the selected parameters were significantly influence the rejection rate of the automotive ABS part. The optimum melt temperature, mould temperature and injection pressure were 220 °C, 70 °C and 98% respectively, in obtaining minimum rejection rate.

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Periodical:

Key Engineering Materials (Volume 841)

Pages:

225-231

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.841.225

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May 2020

Authors:

Huei Ruey Ong*, Ifwat Mohd Shah, Wan Mohd Eqhwan Iskandar, Md. Maksudur Rahman Khan, Shein Hong, Ridzuan Ramli, Muhammad Khairul Anuar Mohamed

Keywords:

Injection Moulding, Injection Pressure, Melt Temperature, Mould Temperature, Response Surface Methodology

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References

[1] C. Shen, L. Wang, Q. Li, Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method, J. Mater. Pro. Tech. 183(2-3) (2007) 412-418.

DOI: 10.1016/j.jmatprotec.2006.10.036

Google Scholar

[2] L. M. Galantucci, R. Spina, Evaluation of filling conditions of injection moulding by integrating numerical simulations and experimental tests, J. Mater. Pro. Tech. 141(2) (2003) 266-275.

DOI: 10.1016/s0924-0136(03)00276-0

Google Scholar

[3] B. Ozcelik, T. Erzurumlu, Determination of effecting dimensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm, Int. Commu. Heat Mass Trans. 32(8) (2005) 1085-1094.

DOI: 10.1016/j.icheatmasstransfer.2004.10.032

Google Scholar

[4] W.-C. Chen, P.-H. Tai, M.-W. Wang, W.-J. Deng, C.-T. Chen, A neural network-based approach for dynamic quality prediction in a plastic injection moulding process, Exp. Sys. App. 35(3) (2008) 843-849.

DOI: 10.1016/j.eswa.2007.07.037

Google Scholar

[5] C. M. Seaman, A. A. Desrochers, G. F. List, Multiobjective optimization of a plastic injection moulding process, IEEE Trans. Cont. Sys. Tech. 2(3) (1994) 157-168.

DOI: 10.1109/87.317974

Google Scholar

[6] M. A. Islam, H. R. Ong, B. Ethiraj, C. K. Cheng, M. M. R. Khan, Optimization of co-culture inoculated microbial fuel cell performance using response surface methodology, J. Environ. Manage. 225 (2018) 242-251.

DOI: 10.1016/j.jenvman.2018.08.002

Google Scholar

[7] H. R. Ong, D. R. Prasad, M. R. Khan, D. S. Rao, J. Nitthiyah, D. K. Raman, Effect of jatropha seed oil meal and rubber seed oil meal as melamine urea formaldehyde adhesive extender on the bonding strength of plywood, J. Appl. Sci. 12(11) (2012) 1148-1153.

DOI: 10.3923/jas.2012.1148.1153

Google Scholar

[8] H. R. Ong, D. M. R. Prasad, M. M. R. Khan, Optimization of preparation conditions for melamine urea formaldehyde based adhesive for plywood application using response surface methodology, Indian J. Chem. Technol. 23(1) (2016) 39-46.

Google Scholar

[9] B. Ozcelik, T. Erzurumlu, Comparison of the warpage optimization in the plastic injection moulding using ANOVA, neural network model and genetic algorithm, J. Mater. Pro. Tech. 171(3) (2006) 437-445.

DOI: 10.1016/j.jmatprotec.2005.04.120

Google Scholar

[10] T. Erzurumlu, B. Ozcelik, Minimization of warpage and sink index in injection-molded thermoplastic parts using Taguchi optimization method, Mater. Design 27 (2006) 853-861.

DOI: 10.1016/j.matdes.2005.03.017

Google Scholar

[11] B. Farshi, S. Gheshmi, E. Miandoabchi, Optimization of injection molding process parameters using sequential simplex algorithm, Mater. Design 32 (2011) 414-423.

DOI: 10.1016/j.matdes.2010.06.043

Google Scholar