Since plastic products are with the features as light, anticorrosive and low cost etc., that are generally used in several of tools or components. Consequently, the requirements on the quality and effectiveness in production are increasingly serious. However, there are many factors affecting the yield rate of injection products such as material characteristic, mold design, and manufacturing parameters etc. involved with injection machine and the whole manufacturing process. Traditionally, these factors can only be designed and adjusted by many times of trial-and-error tests. It is not only waste of time and resource, but also lack of methodology for referring. Although there are some methods as Taguchi method or neural network etc. proposed for serving and optimizing this problem, they are still insufficient for the needs. For the reasons, a method for determining the optimal parameters by the inverse model of manufacturing platform is proposed in this paper. Through the integration of inverse model basing on MANFIS and Taguchi method, inversely, the optimal manufacturing parameters can be found by using the product requirements. The effectiveness and feasibility of this proposal is confirmed through numerical studies on a real case example.
KeywordsOptimal Manufacturing ParameterInjection MoldMultiple Adaptive Network Based Fuzzy Inference System (Manfis)Taguchi Method
S. N. Huang, K. K. Tan and T. H. Lee, “Neural-network- based predictive learning control of ram velocity in injection molding,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 34, Issue 3, pp. 363–368, August 2004.
R. C. Luo and J. H. Tzou, “The development of direct metallic rapid tooling system,” IEEE Transactions on HAutomation Science and EngineeringH, Vol. 4, HIssue 1H, pp. 1–10, January 2007.
R. C. Luo, and Y. L. Pan, “Rapid manufacturing of intelligent mold with embedded microsensors,” IEEE/ ASME Transactions on HMechatronicsH, Vol. 12, HIssue 2H , pp. 190–197, April 2007.
Y. K. Shen and H. W. Chien, “Optimization of the micro-injection molding process using grey relational analysis and moldflow analysis,” Journal of Reinforced Plastics Composites, Vol. 23, pp. 1799–1814, 2004.
H. Qiao, “A systematic computer-aided approach to cooling system optimal design in plastic injection molding,” International Journal of Mechanical Science, Vol. 48, pp. 430–439, 2006.
J. M. Castro, M. Cabrera-R′?os, and C. A. Mount-Ca- mpbell, “Modelling and simulation in reactive polymer processing,” Modelling and Simulation in Materials Science and Engineering, Vol. 12, pp. S121–S149, 2004.
H. S. Yan, and D. Xu, “An approach to estimating product design time based on fuzzy ν-support vector machine,” IEEE Transactions on HNeural NetworksH, Vol. 18, HIssue 3H, pp.721–731, May 2007.
B. Ribeiro, “Support vector machines for quality monitoring in a plastic injection molding process,” IEEE Transactions on HSystems, Man, and Cybernetics, Part C: Applications and ReviewsH, Vol. 35, HIssue 3H, pp. 401–410, August 2005.
M. Cabrera-Rios, J. M. Castro, and C. A. Mount-Cam- pbell, “Multiple quality criteria optimization in in-mold coating (IMC) with a data envelopment analysis approach,” Journal of Polymer Engineering, Vol. 22, No. 5, pp. 305–340, 2002.
M. Cabrera-Rios, J. M. Castro, and C. A. Mount-Camp- bell, “Multiple quality criteria optimization in reactive in-mold coating with a data envelopment analysis approach: II. A case with more than three performance measures,” Journal of Polymer Engineering, Vol. 24, No. 4, pp. 435–450, 2004.
C. E. Castro, M. Cabrera-R′?os, B. Lilly, J. M. Castro, and C. A. Mount-Campbell, “Identifying the best compromises between multiple performance measures in injection molding (IM) using data envelopment analysis (DEA),” Journal of Integrated Design and Process Science, Vol. 7, No. 1, pp. 78–86, 2003.
D. E. Smith, D. A. Tortorelli, and C. L. Tucker, “Analysis and sensitivity analysis for polymer injection and compression molding,” Computer Methods in Applied Mechanics and Engineering, Vol. 167, pp. 325–344, 1998.
A. Gokce, K. T. Hsiao, and S. G. Advani, “Branch and bound search optimization injection gate locations in liquid composite molding processes,” Composites A, Vol. 33, pp. 1263–1272, 2002.
D. E. Smith, “Design sensitivity analysis and optimization for polymer sheet extrusion and mold filling processes,” International Journal for Numerical Methods in Engineering, Vol. 57, pp. 1381–1411, 2003.
J. K. L. Ho, K. F. Chu, and C. K. Mok, “Minimizing manufacturing costs for thin injection molded plastic components,” International Journal of Advanced Manufacturing Technology, Vol. 26, pp. 517–526, 2005.
N. R. Subramanian, L. Tingyu, and Y. A. Seng, “Optimizing warpage analysis for an optical housing,” Mechatronics, Vol. 15, pp. 111–127, 2005.
G. Courbebaisse and D. Garcia, “Shape analysis and injection molding optimization,” Computational Materials Science., Vol. 25, pp. 547–553, 2002.
K. Alam and M. R. Kamal, “A robust optimization of injection molding runner balancing,” Computers and Chemical Engineering, Vol. 29, pp. 1934–1944, 2005.
S. Dowlatshahi, “An application of design of experiments for optimization of plastic injection molding processes,” Journal of Manufacturing Technology Management, Vol. 15, pp. 445–454, 2004.
S. J. Liu and Y. S. Chen, “The manufacturing of thermoplastic composite parts by water-assisted injection-molding technology,” Composites A, Vol. 35, pp. 171–180, 2004.(water-assisted)
C. P. Fung, “Multi-response optimization of impact performances in fiber-reinforced poly (butylene terephthalate),” Journal of Thermoplastic Composite Materials, Vol. 19, pp. 191–205, 2006. (fiber)
C. P. Fung and P. C. Kang, “Multi-response optimization in friction properties of PBT composites using Taguchi method and principal component analysis,” Journal of Materials Processing Technology., Vol. 170, pp. 602–610, 2005.(T)
C. H. Wu and Y. L. Su, “Optimization of wedge-shaped parts for injection molding and injection compression molding,” International Communications in Heat and Mass Transfer, Vol. 30, pp. 215–224, 2003.
S. J. Liu, C. H. Hsu, and C. Y. Chang, “Parametric characterization of the thin-wall injection molding of thermoplastic composites,” Journal of Reinforced Plastics Composites, Vol. 21, pp. 1027–1041, 2002.
H. P. Heim, “The statistical regression calculation in plastics processing—Process analysis, optimization and monitoring,” Macromolecular Materials and Engineering, Vol. 287, pp. 773–783, 2002. (R)
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, pp. 468–472, 2006.
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, pp. 437–445, 2006.(N)
C. P. Fung, “The study on the optimization of injection molding process parameters with gray relational analysis,” Journal of Reinforced Plastics Composites, Vol. 22, pp. 51–66, 2003.(G)
S. H. Chang, J. R. Hwang, and J. L. Doong, “Optimization of the injection molding process of short glass fiber reinforced polycarbonate composites using grey relational analysis,” Journal of Materials Processing Technology, Vol. 97, pp. 186–193, 2000.(G glass fiber)
G. A. Vagelatos, G. G. Regatos, and S. G. Tzafestas, “Incremental fuzzy supervisory controller design for optimizing the injection molding process,” Expert Systems with Applications, Vol. 20, pp. 207–216, 2001.(F)
M. S. Phadke, “Quality Engineering Using Robust Design,” Englewood Cliffs, NJ: Prentice-Hall, 1989.
D. C. Montgomery, “Design and Analysis of Experiments,” New York: Wiley, 1991.
S. H. Park, Robust Design and Analysis for Quality Engineering. London, U.K.: Chapman & Hall, 1996.
R. Jag. , “Neuro-fuzzy modeling architectures, analysis and applications,” PhD Thesis University of California, Bcrkley, July 1992.
Zhang, J.; Shu-Hung Chung, H.; Wai-Lun Lo, “Chaotic time series prediction using a neuro-fuzzy system with time-delay coordinates,” IEEE Transactions on HKnowledge and Data EngineeringH, Vo. 20, HIssue 7H, pp. 956–964, July 2008.
Hinojosa, J.; Domenech-Asensi, G., “Multiple adaptive neuro-fuzzy inference systems for accurate microwave CAD applications,” HCircuit Theory and Design, ECCTD’07. 18th European Conference onH, pp.767–770, August 27th–30th, 2007.
G. Domenech-Asensi, J. Hinojosa, R. Ruiz, J.A, Diaz- Madrid, “Accurate and reusable macromodeling technique using a fuzzy-logic approach,” IEEE International Symposium on HCircuits and Systems, ISCAS’08H, pp.508–511, May 18th–21st, 2008.