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Review on the Effects of Process Parameters on Strength, Shrinkage, and Warpage of Injection Molding Plastic Component

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References

  • Todd, R.H.; Allen, D.K, Alting, L. Manufacturing Processes Reference Guide. Industrial Press, Inc.: New York, 1994.
  • Tiusanen, J.; Vlasveld, D.; Vuorinen, J. Review on the effect of injection molding parameters on the electrical resistivity of carbon nanotube filled polymer parts. Compos. Sci. Technol. 2012, 72, 1741–1752.
  • Industrial Centre. Plastics technology practice. Reading Materials for IC Training Modules, The Hong Kong Polytechnic University 2009, 1(1), 1–34.
  • Mohan, M. Mold technology. Available at http://mold-technology4all.blogspot.com/2011/04/injection-molding-process.html. Last accessed 18th March 2015, 2011.
  • Fang, L.; Wei, M.; Shang, Y.; Jimenez, L.; Kazmer, D.; Barry, C.; Mead, J. Surface morphology alignment of block copolymers induced by injection molding. Polymer 2009, 50 (24), 5837–5845.
  • Alig, I.; Lellinger, D.; Engel, M.; Skipa, T.; Pötschke, P. Destruction and formation of a conductive carbon nanotube network in polymer melts: In-line experiments. Polymer 2008, 49(7), 1902–1909.
  • Pan, Y.; Cheng, H.K.F.; Li, L.; Chan, S.; Zhao, J.; Juay, Y.K. Annealing induced electrical conductivity jump of multi-walled carbon nanotube/polypropylene composites and influence of molecular weight of polypropylene. J. Polym. Sci. Part B: Polym. Phys. 2010, 48 (21), 2238–2247.
  • Pantani, R.; Coccorullo, I.; Speranza, V.; Titomanlio, G. Modeling of morphology evolution in the injection molding process of thermoplastic polymers. Prog. Polym. Sci. 2005, 30, 1185–1222.
  • Pantani. R.; Sorrentino, A.; Speranza, V.; Titomanlio, G. Molecular orientation in injection molding: Experiments and analysis. Rheol. Acta 2004, 43 (2), 109–118.
  • Mok, S.L.; Kwong, C.K. Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding. J. Intell. Manuf. 2002, 13 (3), 165–176.
  • Lam, Y.C.; Zhai, L.Y.; Tai, K.; Fok, S.C. An evolutionary approach for cooling system optimization in plastic injection molding. Int. J. Prod. Res. 2004, 42 (10), 2047–2061.
  • Xuan, P.D. General frameworks for optimization of plastic injection molding process parameters. Sim. Modell. Pract. Theory. 2014, 41, 15–27.
  • Shen, C.; Wang, L.; Li, Q. Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. J. Mater. Process. Technol. 2007, 183, 412–418.
  • Simpson, W.T.; Poplinski, J.D.; Koch, P.N.; Allen, J.K. Metamodels for computer-based engineering design: Survey and recommendations. Eng. Comput. 2001, 17, 129–150.
  • Wang, G.G.; Shan, S. Review of metamodeling techniques in support of engineering design optimization. J. Mech. Des. 2007, 129, 370–380.
  • Papalambros, P.Y. The optimization paradigm in engineering design: promises and challenges. Comput. Aided Des. 2002, 34, 939–951.
  • Park, H.-S.; Dang, X.-P. Structural optimization based on CAD–CAE integration and metamodeling techniques. Comput. Aided Design. 2010, 42, 889–902.
  • Kurtaran, H.; Erzurumlu, T. Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm. Int. J. Adv. Manuf. Technol. 2006, 27, 468–472.
  • Chen, C.-C.; Su, P.-L.; Lin, Y.-L. Analysis and modeling of effective parameters for dimension shrinkage variation of injection molded part with thin shell feature using response surface methodology. Int. J. Adv. Manuf. Technol. 2009, 45, pp.1087–1095.
  • Mathivanan, D.; Parthasarathy, N.S. Prediction of sink depths using nonlinear modeling of injection molding variables. Int. J. Adv. Manuf. Technol. 2009, 43, 654–663.
  • Ozcelik, B.; Erzurumlu, T. Determination of effecting dimensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm. Int. Commun. Heat Mass Transfer 2005, 32, 1085–1094.
  • Park, G.J. Analytic Methods for Design Practice, Springer: London, 2007.
  • Chen, C.-P.; Chuang, M.-T.; Hsiao, Y.-H.; Yang, Y.-K.; Tsai, C.-H. Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis. Expert Syst. Appl. 2009, 36, 10752–10759.
  • Chen, X.; Lam, Y.C.; Li, D.Q. Analysis of thermal residual stress in plastic injection molding. J. Mater. Process. Technol. 2000, 101, 275–280.
  • Chiang, K.T.; Chang, F.P. Analysis of shrinkage and warpage in an injection-molded part with a thin shell feature using the response surface methodology. Int. J. Adv. Manuf. Technol. 2007, 35, 468–479.
  • Zou, Q.; Ari, G.; Hess, R. Using DOE techniques on molding simulation to improve injection molded part quality. Proceedings of the 54th Annual Technical Conference ANTEC, 1996.
  • Kwak, T.S.; Suzuki, T.; Bae, W.B.; Uehara, Y.; Ohmori, H. Application of neural network and computer simulation to improve surface profile of injection molding optic lens. J. Mater. Process. Technol. 2005, 170, 24–31.
  • Yarlagadda, P.K.D.V.; Khong, C.A.T. Development of a hybrid neural network system for prediction of process parameters in injection molding. J. Mater. Process. Technol. 2001, 118, 109–115.
  • Yarlagadda, P.K.D.V. Development of an integrated neural network system for prediction of process parameters in metal injection molding. J. Mater. Process. Technol. 2002, 130–131, 315–320.
  • Kenig, S.; Ben-David, A.; Omer, M.; Sadeh, A. Control of properties in injection molding by neural networks. Eng. Appl. Artif. Intell. 2001, 14, 819–823.
  • Chen, W.-C.; Tai, P.-H.; Wang, M.-W.; Deng, W.-J; Chen, C.-T. A neural network-based approach for dynamic quality prediction in a plastic injection molding process. Expert Syst. Appl. 2008, 35, 843–849.
  • Altan, M. Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Mater. Des. 2010, 31, 599–604.
  • Shen, C.; Wang, L.; Li, Q. Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. J. Mater. Process. Technol. 2007, 183, 412–418.
  • Chen, W.-C.; Fu, G.L.; Tai, P.H.; Deng, W.J. Process parameter optimization for MIMO plastic injection molding via soft computing. Expert Syst. Appl. 2009, 36, 1114–1122.
  • Chen, W.-C.; Wang, M.-W.; Chen, C.T.; Fu, G.L. An integrated parameter optimization system for MISO plastic injection molding. Int. J. Adv. Manuf. Technol. 2009, 44, 501–511.
  • Ozcelik, B.; Erzurumlu, T. Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. J. Mater. Process. Technol. 2006, 171, 437–445.
  • Kurtaran, H.; Ozcelik, B.; Erzurumlu, T. Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm. J. Mater. Process. Technol. 2005, 169, 314–319.
  • Tzeng, C.-J.; Yang, Y.K.; Lin, Y.H.; Tsai, C.H. A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology. Int. J. Adv. Manuf. Tech. 2012, 63, 1–14.
  • Yin, F.; Mao, H.; Hua, L. A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters. Mater. Des. 2011, 32, 3457–3464.
  • Yin, F.; Mao, H.; Hua, L.; Guo, W.; Shu, M. Back propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding. Mater. Des. 2011, 32, 1844–1850.
  • Mok, S.L.; Kwong, C.K.; Lau, W.S. An intelligent hybrid system for initial process parameter setting of injection molding. Int. J. Prod. Res. 2000, 38, 4565–4576.
  • Shi, F.; Lou, Z.L.; Zhang, Y.Q.; Lu, J.G. Optimisation of plastic injection molding process with soft computing. Int. J. Adv. Manuf. Tech. 2003, 21, 656–661.
  • Antony, J.; Kaye, M. Experimental Quality—A Strategic Approach to Achieve and Improve Quality. Kluwer Academic Publishers: Norwell, MA, 1999.
  • Foster, W.T. Basic Taguchi design of experiments. National Association of Industrial Technology Conference, Pittsburgh, PA, 2000.
  • Phadke, M.S. Quality Engineering Using Robust Design, Prentice-Hall: Englewood cliffs, NJ, 1989.
  • Fowlkes, W.Y.; Creveling, C.M. Engineering Methods for Robust Product Design-Using Taguchi Methods in Technology and Product Development, Addison Wesley: Reading, MA, 1997.
  • Torng, C.; Chou, C.Y.; Lui, H.R. Applying quality engineering technique to improve wastewater treatment. Natio. J. Ind. Technol. 1998.
  • Guo, W.; Mao, H,; Li, B.; Guo, X. Influence of processing parameters on molding process in microcellular injection molding. Procedia Eng. 2014, 81, 670–675.
  • Choi, D.-S.; Im, Y.-T. Prediction of shrinkage and warpage in consideration of residual stress in integrated simulation of injection molding. Compos. Struct. 1999, 47, 655–665.
  • Isayev, A.I. Orientation, residual stresses, and volumetric effects in injection molding. Injection and Compression Molding Fundamentals, Marcel Dekker, Inc.: New York, 1987, pp. 227–322.
  • Rees, H. Shrinkages. Understanding Product Design for Injection Molding, 2nd ed., Hanser Publications: Munich, Vienna, New York, 1996, pp. 63–64.
  • Rannar, L.-E. Chapter 1: Injection molding. Published Doctoral Dissertation. Department of Engineering Design and Materials, Norweigian University of Science and Technology, Throndheim, Norway, 2008; pp. 5–13.
  • Huang M.-C.; Tai, C.-C. The effective factors in the warpage problem of an injection-molded part with a thin shell feature. J. Mater. Process. Technol. 2001, 110 (1), 1–9.
  • Malloy, R.A. Part thickness: Manufacturing consideration for injection molded parts. Plastic Design for Injection Molding. Hanser Publications: Munich, Vienna, New York. 1994, p. 65.
  • Malloy, R.A. Part thickness: Manufacturing consideration for injection molded parts. Plastic Design for Injection Molding, Hanser Publications: Munich, Vienna, New York, 1994, p. 71.
  • Malloy, R.A. Anisotropic shrinkage and part distortion; Manufacturing consideration for injection molding parts. Plastic Part Design for Injection Molding, Hanser Publications: Munich, Vienna, New York, 1994, p. 75.
  • Huang, M.-S. Cavity pressure based grey prediction of the filling-to-packing switchover point for injection molding. J. Mater. Process. Technol. 2007, 183 (2–3), 419–424.
  • Tripathi, D. Practical Guide to Polypropylene. Rapra Technology Limited: Shrewsbury, 2002.
  • Kusić, D.; Kek, T.; Slabe, J.M.; Svečko, R.; Grum, J. The impact of process parameters on test specimen deviations and their correlation with AE signals captured during the injection molding cycle. Polym. Test. 2013, 32, 583–593.
  • Polyplastics.com. The outline of injection molding. Available at http://www.polyplastics.com/en/support/mold/outline/ (accessed March 18, 2015), 2015.
  • Polyplastics.com. The outline of injection molding. Available at http://www.misumi-techcentral.com/tt/en/mold/2011/11/103-molding-conditions-of-an-injection-molding-machine.html (accessed March 18, 2015), 2015.
  • Injection Molding Technical Tip. Injection molding start-up for polypropylene. Available at http://www.lyondellbasell.com/techlit/techlit/Tech%20Topics/Injection%20Molding/Tech%20Tips/PP%20Start-Up.pdf (accessed March 18, 2015), 2015.
  • INEOS Olefins & Polymers USA. Tips for injection molding INEOS O&P polypropylene resins. Available at http://www.ineos.com/Global/Olefins%20and%20Polymers%20USA/Products/Technical%20information/Tips%20For%20Injection%20Molding%20INEOS%20OP%20Polypropylene% 20Resins.pdf (accessed March 18, 2015), 2015.

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