Response Surface Method for optimizing the injection moulding process in bracket production

Yusraini Muharni, Evi Febianti, Muhammad Galih Permana, Hartono Hartono

Abstract


Citra Karya Metal is a mould maker and injection service for industries that produce plastic products such as brackets for washing machines. Defective products in the bracket's production are serious problems since the number is large. The existence of this defective product is quite challenging for the company in terms of time and cost. Products that do not comply with the standards will be reworked by flushing them from excessive plastic or destroying them to become plastic pellets for rework. This research aims to provide recommendations for the optimum injection moulding machine settings to reduce defects in bracket production. The Response Surface Method is used to optimize three independent variables: injection speed, pressure, and screw backstop position. The research finds that the most influential factors were injection speed and pressure. The optimum factor level values for injection speed and pressure were 47.5 per cent and 61.5 bar, respectively.


Keywords


Influential factors; injection moulding; optimum setting; response surface method

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References


J. Zhang, Q. Cao, and X. He, “Contract and product quality in platform selling,” European Journal of Operational Research, vol. 272, no. 3, pp. 928–944, Feb. 2019, doi: 10.1016/j.ejor.2018.07.023.

S. Wagner et al., “Operationalised product quality models and assessment: The Quamoco approach,” Information and Software Technology, vol. 62, pp. 101–123, Jun. 2015, doi: 10.1016/j.infsof.2015.02.009.

D. L. Allaix and V. I. Carbone, “An improvement of the response surface method,” Structural Safety, vol. 33, no. 2, pp. 165–172, Mar. 2011, doi: 10.1016/j.strusafe.2011.02.001.

A. K. Abdelmoety, M. Kainat, N. Yoosef-Ghodsi, Y. Li, and S. Adeeb, “Strain-based reliability analysis of dented pipelines using a response surface method,” Journal of Pipeline Science and Engineering, vol. 2, no. 1, pp. 29–38, Mar. 2022, doi: 10.1016/j.jpse.2021.11.002.

F. Terroso-Saenz, A. González-Vidal, A. P. Ramallo-González, and A. F. Skarmeta, “An open IoT platform for the management and analysis of energy data,” Future Generation Computer Systems, vol. 92, pp. 1066–1079, Mar. 2019, doi: 10.1016/j.future.2017.08.046.

H. He, S. Cheng, Y. Chen, and B. Lan, “Compression performance analysis of multi-scale modified concrete based on response surface method,” Case Studies in Construction Materials, vol. 17, p. e01312, Dec. 2022, doi: 10.1016/j.cscm.2022.e01312.

W. C. Lee, S. Yusof, N. S. A. Hamid, and B. S. Baharin, “Optimizing conditions for enzymatic clarification of banana juice using response surface methodology (RSM),” Journal of Food Engineering, vol. 73, no. 1, pp. 55–63, Mar. 2006, doi: 10.1016/j.jfoodeng.2005.01.005.

D. Shim, “Effects of process parameters on additive manufacturing of aluminum porous materials and their optimization using response surface method,” Journal of Materials Research and Technology, vol. 15, pp. 119–134, Nov. 2021, doi: 10.1016/j.jmrt.2021.08.010.

D. C. Montgomery, G. C. Runger, and N. F. Hubele, Engineering Statistics. Wiley, 2010. Accessed: Oct. 29, 2022.

A. Deaconescu and T. Deaconescu, “Response Surface Methods used for optimization of abrasive waterjet machining of the stainless steel X2 CrNiMo 17-12-2,” Materials, vol. 14, no. 10, p. 2475, May 2021, doi: 10.3390/ma14102475.

X. Lu, M. Chiumenti, M. Cervera, H. Tan, X. Lin, and S. Wang, “Warpage analysis and control of thin-walled structures manufactured by laser powder bed fusion,” Metals, vol. 11, no. 5, p. 686, Apr. 2021, doi: 10.3390/met11050686.

C. (Jonathan) Dong, “Development of a process model for the vacuum assisted resin transfer molding simulation by the response surface method,” Composites Part A: Applied Science and Manufacturing, vol. 37, no. 9, pp. 1316–1324, Sep. 2006, doi: 10.1016/j.compositesa.2005.08.012.

L. M. S. Pereira, T. M. Milan, and D. R. Tapia-Blácido, “Using Response Surface Methodology (RSM) to optimize 2G bioethanol production: A review,” Biomass and Bioenergy, vol. 151, p. 106166, Aug. 2021, doi: 10.1016/j.biombioe.2021.106166.

E. Oliaei et al., “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 & Technology, vol. 32, no. 8, pp. 710–720, Aug. 2016, doi: 10.1016/j.jmst.2016.05.010.

M. Colledani and T. Tolio, “Integrated quality, production logistics and maintenance analysis of multi-stage asynchronous manufacturing systems with degrading machines,” CIRP Annals, vol. 61, no. 1, pp. 455–458, 2012, doi: 10.1016/j.cirp.2012.03.072.

I. M. Yusri, A. P. P. Abdul Majeed, R. Mamat, M. F. Ghazali, O. I. Awad, and W. H. Azmi, “A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel,” Renewable and Sustainable Energy Reviews, vol. 90, pp. 665–686, Jul. 2018, doi: 10.1016/j.rser.2018.03.095.

I. M. Yusri, A. P. P. Abdul Majeed, R. Mamat, M. F. Ghazali, O. I. Awad, and W. H. Azmi, “A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel,” Renewable and Sustainable Energy Reviews, vol. 90, pp. 665–686, Jul. 2018, doi: 10.1016/j.rser.2018.03.095.

W. Zhao and Z. Qiu, “An efficient response surface method and its application to structural reliability and reliability-basedoptimization,” Finite Elements in Analysis and Design, vol. 67, pp. 34–42, May 2013, doi: 10.1016/j.finel.2012.12.004.

M. Rostami, V. Farzaneh, A. Boujmehrani, M. Mohammadi, and H. Bakhshabadi, “Optimizing the extraction process of sesame seed’s oil using response surface method on the industrial scale,” Industrial Crops and Products, vol. 58, pp. 160–165, Jul. 2014, doi: 10.1016/j.indcrop.2014.04.015.

R. Hoseinzadeh Hesas, A. Arami-Niya, W. M. A. Wan Daud, and J. N. Sahu, “Preparation of granular activated carbon from oil palm shell by microwave-induced chemical activation: Optimisation using surface response methodology,” Chemical Engineering Research and Design, vol. 91, no. 12, pp. 2447–2456, Dec. 2013, doi: 10.1016/j.cherd.2013.06.004.




DOI: http://dx.doi.org/10.36055/jiss.v8i2.17063

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