Manufacturing Technology 2019, 19(3):469-476 | DOI: 10.21062/ujep/314.2019/a/1213-2489/MT/19/3/469

Minimum Warpage Prediction in Plastic Injection Process using Taguchi Method and Simulation

Sigit Yoewono Martowibowo1, Reaksa Khloeun2
1 Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung. Jalan Ganesa 10. Bandung 40132. Indonesia
2 Department of Industrial and Mechanical Engineering, Institute of Technology of Cambodia, Russian Federation Boulevard, PO Box 86, Phnom Penh, Cambodia

Plastic materials are used in automobile, electrical and electronic applications, agricultural utilization, household and furniture products, and medical equipments. Among various plastic manufacturing process, injection molding is one of the most commonly used and common methods applied for forming plastic products in the industry. The process requires a molten polymer being injected into a cavity of a mold, which is cooled and the product ejected from the mold. During the Plastic Injection Molding (PIM) process, various defects, such as volumetric shrinkage, warpage, weld line and sink mark can occur. This paper presents a method to minimizing warpage defect on PolyPropylene AZ564 via PIM simulation using Moldflow software. The approach was based on Taguchi method.
Through the effectiveness of this proposed method, it is confirmed using simulation by Moldflow software. The effect of the process parameters on the warpage of a motorcycle number plate bracket, is studied using analysis of variance (ANOVA). From the ANOVA, the significant parameters affecting the process are holding time, holding pressure and injection pressure. The result of the Taguchi prediction shown that minimum warpage is 1.078 mm, which is 7.14% and 9.09% different from the simulation result and experiment, respectively.

Keywords: Plastic Injection Molding, Simulation, Warpage, Design of Experiment, Moldflow
Grants and funding:

Ministry of Research, Technology and Higher Education of the Republic of Indonesia.
Politeknik Manufaktur Astra, Indonesia.

Published: June 1, 2019  Show citation

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Martowibowo SY, Khloeun R. Minimum Warpage Prediction in Plastic Injection Process using Taguchi Method and Simulation. Manufacturing Technology. 2019;19(3):469-476. doi: 10.21062/ujep/314.2019/a/1213-2489/MT/19/3/469.
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    References

    1. ZHENG, R., TANNER, R.I., FAN, X.J. (2011). Injection molding: integration of theory and modeling methods. Springer-Verlag, Berlin. Go to original source...
    2. HUANG, M.C., TAI, C.C. (2001). The effective factors in the warpage problem of an injection-molded part with a thin shell feature. In: Journal of Materials Processing Technology, Vol. 110, No. 1, pp. 1-9. Elsevier BV, Netherlands. Go to original source...
    3. CHEN, W.S., YU, F.J., WU, S.H. (2011). A robust design for plastic injection molding applying Taguchi method and PCA. In: Journal of Science and Engineering Technology, Vol. 7, No. 2, pp.1-8. Taiwan.
    4. ANDRISANO, A.O., GHERARDINI, F., LEALI, F., PELLICCIARI, M., VERGNANO, A. (2011). Design of simulation experiments method for injection molding process optimization. In: Proceeding of the International Conference on Innovative Methods in Product Design, 15-17 June 2011, pp. 476-486, Venice, Italy.
    5. OKTEM, H., ERZURUMLU, T., UZMAN, I. (2007). Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin shell part. In: Materials and Design, Vol. 28, No. 4, pp. 1271-1278. Elsevier BV, Netherlands. Go to original source...
    6. ZHENG, G., GUO, W., WANG, Q., GUO, X. (2015). Influence of processing parameters on warpage according to the Taguchi experiment. In: Journal of Mechanical Science and Technology, Vol. 29, No. 10, pp. 4153-4158. Springer-Verlag, Germany. Go to original source...
    7. KURTARAN, H., OZCELIK, B., ERZURUMLU, T. (2005). Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm. In: Journal of Materials Processing Technology, Vol. 169, No. 2, pp. 314-319. Elsevier BV, Netherlands. Go to original source...
    8. GUO, W., HUA, L., MAO, H. (2014). Minimization of sink mark depth in injection-molded thermoplastic through design of experiments and genetic algorithm. In: The International Journal of Advanced Manufacturing Technology, Vol. 72, No. 1-4, pp. 365-375. Springer-Verlag, Germany. Go to original source...
    9. SHI, H., XIE, S., WANG, X. (2013). A warpage optimization method for injection molding using artificial neural network with parametric sampling evaluation strategy. In: The International Journal of Advanced Manufacturing Technology, Vol. 65, No. 1-4, pp. 343-353. Springer-Verlag, Germany. Go to original source...
    10. FARSHI, B., GHESHMI, S., MIANDOABCHI, E. (2011). Optimization of injection molding process parameters using sequential simplex algorithm. In: Materials and Design, Vol. 32, No. 1, pp. 414-423. Elsevier BV, Netherlands. Go to original source...
    11. MONKA, P., HLOCH, S., ANDREJ, A., SOMSAK, M., MURGAS, F. (2016). Simulation tools used at the injection mould design. In: Manufacturing Technology, Vol. 16, No. 3, pp. 561-569. Institute of Technology and Production Management University of J.E. Purkyne, Czech Republic. Go to original source...
    12. MARTOWIBOWO, S.Y., KASWADI, A. (2017). Optimization and Simulation of Plastic Injection Process using Genetic Algorithm and Moldflow. In: Chinese Journal of Mechanical Engineering, Vol. 30, No. 2, pp. 398-406. Springer-Verlag, Germany. Go to original source...
    13. MARTOWIBOWO, S.Y., KASWADI, A., LUBIS, G.S. (2017). Application of Taguchi method-Moldflow-particle swarm optimization for plastic injection process parameters optimization. In: Manufacturing Technology, Vol. 17, No. 4, pp. 512-519. Institute of Technology and Production Management University of J.E. Purkyne. Czech Republic. Go to original source...
    14. Hwa Chin Plastic Injection Machine. Retrieved February 12, 2016, from http://www.hwa-chin.com.
    15. OZCELIK, B., ERZURUMLU, T. (2006). Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. In: Journal of Materials Processing Technology, Vol. 171, No. 3, pp. 437-445. Elsevier BV, Netherlands. Go to original source...
    16. JOSE, S., FANGUEIRO, J.F., SMITHA, J., CINU, T.A., CHACKO, A.J., PREMALETHA, K., SOUTO, E.B. (2012). Cross-linked chitosan microspheres for oral delivery of insulin: Taguchi design and in vivo testing. In: Colloids and Surfaces B: Biointerfaces, Vol. 92, pp. 175-179. Elsevier BV, Netherlands. Go to original source...
    17. TAGUCHI, G. (1990). Introduction to quality engineering. Asian Productivity Organization, Tokyo.
    18. TSAI, K.M., HSIEH, C.Y., LO, W.C. (2009). A study of the effects of process parameters for injection molding on surface quality of optical lenses. In: Journal of Materials Processing Technology, Vol. 209, No. 7, pp. 3469-3477. Elsevier BV, Netherlands. Go to original source...
    19. GOODSHIP, V. (Ed.) (2004). Arburg-Practical guide to injection moulding. Smithers Rapra Press, Shawbury.
    20. CHEN, W.C., KURNIAWAN, D. (2014). Process parameters optimization for multiple quality characteristics in plastic injection molding using Taguchi method, BPNN, GA, and hybrid PSO-GA. In: International Journal of Precision Engineering and Manufacturing, Vol. 15, No. 8, pp. 1583-1593. Go to original source...
    21. SUDSAWAT, S., SRISEUBSAI, W. (2018). Warpage reduction through optimized process parameters and annealed process of injection-molded plastic parts. In: Journal of Mechanical Science and Technology, Vol. 32, No. 10, pp. 4787-4799. Go to original source...

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