The identification of optimal processing parameters is an important practice in the plastic injection moulding industry because of the significant effect of such parameters on plastic part quality and cost. However, the optimization design of injection moulding process parameters can be difficult because more than one quality characteristic is used in the evaluation. This study systematically develops a hybrid optimization method for multiple quality characteristics by integrating the Taguchi parameter design, grey relational analysis, and principal component analysis. A plastic gear is used to demonstrate the efficiency and validity of the proposed hybrid optimization method in controlling all influential injection moulding processing parameters during plastic gear manufacturing. To minimize the shrinkage behaviour in tooth thickness, addendum circle, and dedendum circle of moulded gear, the optimal combination of different process parameters is determined. The case study demonstrates that the proposed optimization method can produce plastic-moulded gear with minimum shrinkage behaviour of 1.8%, 1.53%, and 2.42% in tooth thickness, addendum circle, and dedendum circle, respectively; these values are less than the values in the main experiment. Therefore, shrinkage-related defects that lead to severe failure in plastic gears can be effectively minimized while satisfying the demand of the global plastic gear industry. 1. Introduction Injection moulding is a complex process because of its requirements for numerous delicate adjustments. The quality of an injection-moulded part significantly depends on the selection of appropriate materials, parts, mould designs, and processing parameters. A review of previous works shows that the setting of processing parameters significantly affects the quality of plastic parts [1, 2]. The selection of injection moulding process parameters previously involves a trial-and-error method [3]. However, obtaining an optimal parameter setting for complex-manufacturing processes is difficult because the trial-and-error method is a “one change at a time” test [4]. This tuning exercise is repeated until the quality of the moulded part is found satisfactory, thus incurring high production costs and long setup times [5]. Moreover, the adjustments and modifications of processing parameters rely heavily on the experience and intuition of the moulding personnel [6]. Nevertheless, the growing demand in the industry for expert moulding personnel exceeds the supply; moreover, amateur moulding personnel require more than 10 years of
References
[1]
H. Kurtaran and T. Erzurumlu, “Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm,” International Journal of Advanced Manufacturing Technology, vol. 27, no. 5-6, pp. 468–472, 2006.
[2]
K.-T. Chiang and F.-P. Chang, “Analysis of shrinkage and warpage in an injection-molded part with a thin shell feature using the response surface methodology,” International Journal of Advanced Manufacturing Technology, vol. 35, no. 5-6, pp. 468–479, 2007.
[3]
F. Shi, Z. L. Lou, J. G. Lu, and Y. Q. Zhang, “Optimisation of plastic injection moulding process with soft computing,” International Journal of Advanced Manufacturing Technology, vol. 21, no. 9, pp. 656–661, 2003.
[4]
J.-R. Shie, “Optimization of injection-molding process for mechanical properties of polypropylene components via a generalized regression neural network,” Polymers for Advanced Technologies, vol. 19, no. 1, pp. 73–83, 2008.
[5]
Y. C. Lam, L. Y. Zhai, K. Tai, and S. C. Fok, “An evolutionary approach for cooling system optimization in plastic injection moulding,” International Journal of Production Research, vol. 42, no. 10, pp. 2047–2061, 2004.
[6]
W.-C. Chen, G.-L. Fu, P.-H. Tai, and W.-J. Deng, “Process parameter optimization for MIMO plastic injection molding via soft computing,” Expert Systems with Applications, vol. 36, no. 2, pp. 1114–1122, 2009.
[7]
S. L. Mok, C. K. Kwong, and W. S. Lau, “Review of research in the determination of process parameters for plastic injection molding,” Advances in Polymer Technology, vol. 18, no. 3, pp. 225–236, 1999.
[8]
A. Mahfouz, S. A. Hassan, and A. Arisha, “Practical simulation application: evaluation of process control parameters in Twisted-Pair Cables manufacturing system,” Simulation Modelling Practice and Theory, vol. 18, no. 5, pp. 471–482, 2010.
[9]
H.-J. Shim and J.-K. Kim, “Cause of failure and optimization of a V-belt pulley considering fatigue life uncertainty in automotive applications,” Engineering Failure Analysis, vol. 16, no. 6, pp. 1955–1963, 2009.
[10]
N. M. Mehat and S. Kamaruddin, “Investigating the effects of injection molding parameters on the mechanical properties of recycled plastic parts using the Taguchi method,” Materials and Manufacturing Processes, vol. 26, no. 2, pp. 202–209, 2011.
[11]
A. Akbarzadeh, S. Kouravand, and B. M. Imani, “Robust design of a bimettallic micro thermal sensor using Taguchi method,” Journal of Optimization Theory and Applications, vol. 157, no. 1, pp. 188–198, 2013.
[12]
H. Li, Z. Guo, and D. Li, “Reducing the effects of weldlines on appearance of plastic products by Taguchi experimental method,” International Journal of Advanced Manufacturing Technology, vol. 32, no. 9-10, pp. 927–931, 2007.
[13]
C.-H. Wu and W.-J. Liang, “Effects of geometry and injection-molding parameters on weld-line strength,” Polymer Engineering and Science, vol. 45, no. 7, pp. 1021–1030, 2005.
[14]
M.-C. Huang and C.-C. Tai, “Effective factors in the warpage problem of an injection-molded part with a thin shell feature,” Journal of Materials Processing Technology, vol. 110, no. 1, pp. 1–9, 2001.
[15]
B. Ozcelik and T. Erzurumlu, “Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm,” Journal of Materials Processing Technology, vol. 171, no. 3, pp. 437–445, 2006.
[16]
B. Ozcelik and I. Sonat, “Warpage and structural analysis of thin shell plastic in the plastic injection molding,” Materials and Design, vol. 30, no. 2, pp. 367–375, 2009.
[17]
H. Oktem, T. Erzurumlu, and I. Uzman, “Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part,” Materials and Design, vol. 28, no. 4, pp. 1271–1278, 2007.
[18]
S. J. Liao, D. Y. Chang, H. J. Chen et al., “Optimal process conditions of shrinkage and warpage of thin-wall parts,” Polymer Engineering and Science, vol. 44, no. 5, pp. 917–928, 2004.
[19]
J. Deng, “Introduction to grey system theory,” Journal of Grey System, vol. 1, pp. 1–24, 1989.
[20]
C.-P. Fung, “Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis,” Wear, vol. 254, no. 3-4, pp. 298–306, 2003.
[21]
C.-P. Fung, C.-H. Huang, and J.-L. Doong, “The study on the optimization of injection molding process parameters with gray relational analysis,” Journal of Reinforced Plastics and Composites, vol. 22, no. 1, pp. 51–66, 2003.
[22]
Y.-K. Yang, “Optimization of injection-molding process for mechanical and tribological properties of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites with grey relational analysis: a case study,” Polymer, vol. 45, no. 7, pp. 769–777, 2006.
[23]
B. F. J. Manly, Multivariate Statistical Methods: A Primer, Chapman and Hall/CRC press, Boca Raton, Fla, USA, 3rd edition, 2005.
[24]
H. C. Lio and Y. K. Chen, “Optimizing multi-response problem in the Taguchi method by DEA based ranking method,” International Journal of Quality and Reliability Management, vol. 19, no. 7, pp. 825–837, 2002.
[25]
C.-P. Fung and P.-C. Kang, “Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis,” Journal of Materials Processing Technology, vol. 170, no. 3, pp. 602–610, 2005.
[26]
Y. H. Chen, S. C. Tam, W. L. Chen, and H. Y. Zheng, “Application of the Taguchi method in the optimization of laser micro-engraving of photomasks,” International Journal of Materials and Product Technology, vol. 11, no. 3-4, pp. 333–344, 1996.
[27]
Y.-K. Yang, J.-R. Shie, and C.-H. Huang, “Optimization of dry machining parameters for high-purity graphite in end-milling process,” Materials and Manufacturing Processes, vol. 21, no. 8, pp. 832–837, 2006.
[28]
K. Pearson, “On lines and planes of closest fit to systems of points in space,” Philosophical Magazine, vol. 6, no. 2, pp. 559–572, 1901.
[29]
H. Hotelling, “Analysis of a complex of statistical variables into principal components,” Journal of Educational Psychology, vol. 24, no. 6, pp. 417–441, 1933.
[30]
K. Mao, W. Li, C. J. Hooke, and D. Walton, “Friction and wear behaviour of acetal and nylon gears,” Wear, vol. 267, no. 1–4, pp. 639–645, 2009.
[31]
S. M. Mousavi, S. A. Shojaosadati, J. Golestani, and F. Yazdian, “CFD simulation and optimization of effective parameters for biomass production in a horizontal tubular loop bioreactor,” Chemical Engineering and Processing, vol. 49, no. 12, pp. 1249–1258, 2010.
[32]
R. K. Roy, Design of Experiment Using the Taguchi Approach: 16 Steps to Product and Process Improvement, John Wiley and Sons, New York, NY, USA, 2001.
