Abstract
There has recently been a growing amount of interest in developing process control methods to maintain consistent part quality during injection molding. Some commercially available control methods are believed to offer improved machine and process capability in order to achieve this goal. Regardless of these advantages, however, these methods typically require considerable experimentation before an optimal process setting can be obtained for a particular case. Therefore, a more systematic scheme that does not require extensive experimentation, which in turn costs time and money, is needed. Computer-aided engineering (CAE) is considered to be a good tool for predicting the flow behavior in the cavity during the filling and post-filling stages. If CAE software can accurately predict a part quality indicator, such as one concerned with the dimensional stability, under a given process condition, time-consuming experimentation would no longer be necessary to determine the relationship between the process conditions and part quality. However, the relationship between the process conditions and part quality indicators, such as the dimensional stability, is highly nonlinear and takes no explicit form. The goal of this study is to use CAE tools to establish such a relationship and generate part quality data based on predictions. A second-order regression model is established in order to correlate the part quality with four key process variables using an optimization technique. Finally, we obtain the reference values under these process conditions. A case study was conducted using both a simulation and experimentation for a plaque-shaped piece of polypropylene. The results indicate reasonable agreement between the simulation and experimentation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
L. S. Shah, Adaptive on–line process control of injection molding using an empirical model, M.S. Thesis, Cornell University, Ithaca (1987).
K. Mimura, A case study: Warpage prediction and control of an injection molded part using an empirical model, M.S.Thesis, Cornell University, Ithaca (1992).
P. J. Wang, Adaptive on–line process control of injection molding using pressure–volume–temperature model, Ph.D. Thesis, Cornell University, Ithaca (1991).
Moldflow Pty. Ltd., Prospectus of intelligent process control program (1995).
P. Mapleston, Injection molding: Real–time process control is said to provide perfect shots, Modern Plastics, 29 (1999) 29–30.
D. S. Choi and Y. T. Im, Prediction of shrinkage and warpage in consideration of residual stress in integrated simulation of injection molding, Composite Structures, 47 (1999) 655–665.
F. Yin, H. Mao, L. Hua, W. Guo and M. Shu, Back propagation neural network modeling for warpage prediction and optimi–zation of plastic products during injection molding, Materials & Design, 32 (2011) 1844–1850.
T. Erzurumlu and B. Ozcelik, Minimization of warpage and sink index in injection–molded thermoplastic parts using Taguchi optimization method, Materials & Design, 27 (2006) 853–861.
B. Ozcelik and T. Erzurumlu, Comparison of the warpage opti–mization in the plastic injection molding using ANOVA, neural network model and genetic algorithm, Journal of Materials Processing Technology, 171 (2006) 437–445.
M. C. Huang and C. C. Tai, The effective factors in the warp–age problem of an injection–molded part with a thin shell feature, Journal of Materials Processing Technology, 110 (2001) 1–9.
B. Ozcelik and I. Sonat, Warpage and structural analysis of thin shell plastic in the plastic injection molding, Materials & Design, 30 (2009) 367–375.
H. Kurtaran, B. Ozcelik and T. Erzurumlu, Warpage optimiza–tion of a bus ceiling lamp base using neural network model and genetic algorithm, Journal of Materials Processing Technology, 169 (2005) 314–319.
B. Ozcelik and T. Erzurumlu, Determination of effecting di–mensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm, International Communications in Heat and Mass Transfer, 32 (2005) 1085–1094.
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, 35 (2007) 468–479.
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, 27 (2006) 468–472.
B. H. Lee and B. H. Kim, Optimization of part wall thicknesses to reduce warpage of injection–molded parts based on the modified complex method, Polymer–Plastics Technology and Engineering, 34 (1995) 793–811.
K. T. Chiang and F. P. Chang, Application of grey–fuzzy logic on the optimal process design of an injection–molded part with a thin shell feature, International Communications in Heat and Mass Transfer, 33 (2006) 94–101.
K. T. Chiang, The optimal process conditions of an injection–molded thermoplastic part with a thin shell feature using grey–fuzzy logic: A case study on machining the PC/ABS cell phone shell, Materials & Design, 28 (2007) 1851–1860.
Y. Zhang, Y. M. Deng and B. S. Sun, Injection molding warpage optimization based on a mode–pursuing sampling method, Polymer–Plastics Technology and Engineering, 48 (2009) 767–774.
J. Zhou, L. S. Turng and A. Kramschuster, Single and multi–objective optimization for injection molding using numerical simulation with surrogate models and genetic algorithm, International Polymer Processing, 21 (2006) 509–520.
J. Zhou and L. S. Turng, Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach, Polymer Engineering and Science, 47 (2007) 684–694.
H. Ye, Y. Wu and K. K. Wang, An optimization scheme for part quality in injection molding, Proceedings of Internation–al Mechanical Engineering Congress and Exposition 1997, ASME, Dallas, Texas, U.S.A. (1997) 139–149.
W. Guo, L. Hua, H. Mao and Z. Meng, Prediction of warpage in plastic injection molding based on design of experiments, Journal of Mechanical Science and Technology, 26 (4) (2012) 1133–1139.
Y. S. Kim, E. S. Jeon and D. R. Kim, Optimization of process variables for improvement of seat–backboard peel strength using response surface design method, Journal of Mechanical Science and Technology, 31 (12) (2017) 5915–5920.
J. S. Moon and J. M. Lee, Shear viscosity measurement of highly filled polycarbonate melts using a slit–die rheometer, Korea–Australia Rheology Journal, 25 (2013) 129–135.
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by Associate Editor Sang-Hee Yoon
Jong-Sin Moon is a Principal Researcher at LG Electronics in Inchon, Korea. He received his Ph.D. degree in Engineering Mechanics from Seoul National University, Korea. He was a Postdoctoral researcher in Sibley School of Mechanical and Aerospace Engineering at Cornell University, U.S.A.
Byoung-Ho Choi is a Professor at the School of Mechanical Engineering at Korea University. He received Ph.D. degree in Applied Mechanics from Korea University. He was a Postdoctoral researcher in Department of Civil and Materials Engineering at University of Illinois at Chicago, U.S.A, and he also worked for the Dow Chemical Company, U.S.A. as a Research Specialist.
Rights and permissions
About this article
Cite this article
Moon, JS., Choi, BH. Modeling and validation of the effects of processing parameters on the dimensional stability of an injection-molded polypropylene plate. J Mech Sci Technol 32, 5623–5630 (2018). https://doi.org/10.1007/s12206-018-1108-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-018-1108-6