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Determination of Optimal Process Parameters for Plastic Injection Molding of Polymer Materials Using Multi-Objective Optimization

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Abstract

The present work concentrated on the determination of optimal plastic injection molding process parameters for two polymer materials, i.e. polyethylene and polypropylene, in order to achieve the highest mechanical properties and lowest volumetric shrinkage. The study used the Taguchi design of experiments with five process parameters (melt temperature, injection pressure, packing pressure, packing time, and cooling time) for the manufacture of 27 polymer sample parts via PIM. The flexural strength, flexural modulus, and yield stress were measured using a three-point bending test, whereas the volumetric shrinkage was measured according to the volume. The results were interpreted by variance analysis (ANOVA) and 3D surface plots. A predictive model was developed using stepwise regression analysis. Two multi-objective optimization methodologies were implemented to evaluate the optimal PIM process parameters for both materials: multi-criteria decision making (MCDM) and the multi-objective genetic algorithm (MOGA). The results showed that the melt temperature was the most significant parameter, followed by packing time, injection pressure, cooling time, and packing pressure. The selected model effectively predicted the responses at an error rate of less than 4% for both materials. The MCDMs found that trial Number-9 exhibited the optimal set of process parameters, and compared to the MCDMs, the MOGA results showed improvement of 7-9%. Significant contribution of the present study is to obtain the optimal manufacturing process parameters in injection molding of the polymeric materials considering two outcomes simultaneously, viz. maximize the mechanical properties and minimize the volumetric shrinkage with injection molding parameters by using multi-objective optimization methodologies with ANOVA and Regression analysis.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon suitable request.

References

  1. Ahmet Demirer, Yavuz Soydan and A.O. Kapti, An Experimental Investigation of the Effects of hot Runner System on Injection Moulding Process in Comparison with Conventional Runner System", Mater Des., 2007, 28, p 1467–1476.

    Article  CAS  Google Scholar 

  2. C.-C. Chen, Su. Pao-Lin and Y.-C. Lin, Analysis and Modeling of Effective Parameters for Dimension Shrinkage Variation of Injection Molded Part with Thin Shell Feature Using Response Surface Methodology, Inter J of Adv Manuf act Tech, 2009, 45, p 1087–1095.

    Article  Google Scholar 

  3. 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, Inter J of Adv Manuf act Tech, 2007, 35, p 468–479.

    Article  Google Scholar 

  4. E. Hakimian and A.B. Sulong, Analysis of warpage and shrinkage properties of injection-molded micro gears polymer composites using numerical simulations assisted by the Taguchi method, Mater Des, 2012, 42, p 62–71.

    Article  CAS  Google Scholar 

  5. P. Postawa and Jk. Koszkul, Change in Injection Moulded Parts Shrinkage and Weight as a Function of Processing Conditions, J of Mater Proc Tech, 2005, 162, p 109–115.

    Article  Google Scholar 

  6. H.-C. Kuo and M.-C. Jeng, Effects of Part Geometry and Injection Molding Conditions on the Tensile Properties of Ultra-High Molecular Weight Polyethylene Polymer, Mater Des, 2010, 31, p 884–893.

    Article  CAS  Google Scholar 

  7. Nik Mizamzul Mehat and Shahrul Kamaruddin, Investigating the Effects of Injection Molding Parameters on the Mechanical Properties of Recycled Plastic parts Using the Taguchi Method, Mater and Manufac Proc, 2011, 26, p 202–209.

    Article  Google Scholar 

  8. Nik Mizamzul Mehat and Shahrul Kamaruddin, Optimization of Mechanical Properties of Recycled Plastic Products via Optimal Processing Parameters Using the Taguchi Method, J of Mater Proc Tech, 2011, 211, p 1989–1994.

    Article  Google Scholar 

  9. B. Ozcelik, Optimization of Injection Parameters for Mechanical Properties of Specimens with Weld Line of Polypropylene Using Taguchi Method, Inter Commun in Heat and Mass Trans, 2011, 38, p 1067–1072.

    CAS  Google Scholar 

  10. C.-P. Fung, J.-R. Hwang and C.-C. Hsu, The Effect of Injection Molding Process Parameters on the Tensile Properties of Short Glass Fiber-Reinforced PBT, Poly-Plas Tech and Engin, 2003, 42, p 45–63.

    Article  CAS  Google Scholar 

  11. H. Li, Z. Guo and D. Li, Reducing the Effects of Weldlines on Appearance of Plastic Products by Taguchi Experimental Method, Inter J of Adv Manuf act Tech, 2007, 32, p 927–931.

    Article  Google Scholar 

  12. S. Kitayama, K. Tamada, M. Takano and S. Aiba, Numerical Optimization of Process Parameters in Plastic Injection Molding for Minimizing Weldlines and Clamping Force using Conformal Cooling Channel, J of Manufact Proc, 2018, 32, p 782–790.

    Article  Google Scholar 

  13. Y.-M. Deng, D. Zheng, B.-S. Sun and H.-D. Zhong, Injection Molding Optimization for Minimizing the Defects of Weld Lines, Poly-Plas Tech and Engin, 2008, 47, p 943–952.

    Article  CAS  Google Scholar 

  14. Wu. Cheng-Hsien and W.-J. Liang, Effects of Geometry and Injection-Molding Parameters on Weld-Line Strength, Poly Engine and Scie, 2005, 45, p 1021–1030.

    Article  Google Scholar 

  15. R. Wang, J. Zeng, X. Feng and Y. Xia, Evaluation of Effect of Plastic Injection Molding Process Parameters on Shrinkage Based on Neural Network Simulation, J of Macro Scie, Part B, 2013, 52, p 206–221.

    Article  CAS  Google Scholar 

  16. Q. Li, L. Li, X. Si and W. Rongji, Modeling the Effect of Injection Molding Process Parameters on Warpage using Neural Network Theory,", J of Macro Scie, Part B, 2015, 54, p 1066–1080.

    Article  CAS  Google Scholar 

  17. Xu. Yingjie, QingWen Zhang, W. Zhang and P. Zhang, Optimization of Injection Molding Process Parameters to Improve the Mechanical Performance of Polymer Product Against Impact, Inter J of Adv Manuf act Tech, 2015, 76, p 2199–2208.

    Article  Google Scholar 

  18. F. Yin, H. Mao and L. Hua, A Hybrid of Back Propagation Neural Network and Genetic Algorithm for Optimization of Injection Molding Process Parameters, Mater Des, 2011, 32, p 3457–3464.

    Article  CAS  Google Scholar 

  19. C. Shen, L. Wang and Q. Li, Optimization of Injection Molding Process Parameters using Combination of Artificial Neural Network and Genetic Algorithm Method, J of Mater Proc Tech, 2007, 183, p 412–418.

    Article  CAS  Google Scholar 

  20. J. Zhao, G. Cheng, S. Ruan and Z. Li, Multi-Objective Optimization Design of Injection Molding Process Parameters Based on the Improved Efficient Global Optimization algorithm and Non-Dominated Sorting-Based Genetic Algorithm, Inter J of Adv Manuf act Tech, 2015, 78, p 1813–1826.

    Article  Google Scholar 

  21. Federico Urban and Carlo Poloni, "Gas-Assisted Injection Molding Optimization with MOGA," in Optim in Indus .: Springer, 2002, pp. 149–161.

  22. Q.Q. Feng and X. Zhou, Automated and Robust Multi-Objective Optimal Design of Thin-Walled Product Injection Process Based on Hybrid RBF-MOGA, Inter J of Adv Manuf act Tech, 2019, 101, p 2217–2231.

    Article  Google Scholar 

  23. Genichi Taguchi, "Introduction to Quality Engineering, "Asian Productivity Organization” 1990, Tokyo, Japan.

  24. Tim A. Osswald, Erwin Baur, and Natalie Rudolph, Plastics Handbook: The Resource for Plastics Engineers.: 2019, Carl Hanser Verlag GmbH Co KG,. Germany.

  25. E. Alfredo Campo, Selection of polymeric materials: how to select design properties from different standards.: 2008, William Andrew.

  26. Gerd Pötsch and Walter Michaeli, Injection molding: an introduction.: 2008, Carl Hanser Verlag, Germany.

  27. B. Ozcelik and T. Erzurumlu, Comparison of the Warpage Optimization in the Plastic Injection Molding using ANOVA, Neural Network Model and Genetic Algorithm, J of Mater Proc Tech, 2006, 171, p 437–445.

    Article  CAS  Google Scholar 

  28. R. Manivannan and M. Pradeep Kumar, Multi-Attribute Decision-Making of Cryogenically Cooled Micro-EDM Drilling Process Parameters using TOPSIS Method, Mater Manufact Proc, 2017, 32, p 209–215.

    Article  CAS  Google Scholar 

  29. N. Yuvaraj and M. Pradeep Kumar, Multiresponse Optimization of Abrasive Water Jet Cutting Process Parameters using TOPSIS Approach, Mater Manufact Proc, 2015, 30, p 882–889.

    Article  CAS  Google Scholar 

  30. A. Shukla, P. Agarwal, R.S. Rana and R. Purohit, “Applications of TOPSIS Algorithm on Various Manufacturing Processes: A Review,” Mater Today: Proc, 2017, 4, p 5320–5329.

    Google Scholar 

  31. C. Prasenjit, B. Arnab, M. Supraksh, B. Soumava and C. Shankar, Development of a Hybrid Meta-Model for Material Selection using Design of Experiments and EDAS Method, Engine Transac, 2018, 66, p 187–207.

    Google Scholar 

  32. Partha Protim Das and Shankar Chakraborty, "Application of Grey Correlation-Based EDAS Method for Parametric Optimization of Non-Traditional Machining PROCESSES," Scie Irani, 2020.

  33. Q.Q. Feng, L. Liu and X. Zhou, Automated Multi-Objective Optimization for Thin-Walled Plastic Products using Taguchi, ANOVA, and Hybrid ANN-MOGA, Inter J of Adv Manuf act Tech, 2020, 106, p 559–575.

    Article  Google Scholar 

  34. V.S. Thangarasu, G. Devaraj and R. Sivasubramanian, High Speed CNC Machining of AISI 304 Stainless Steel; Optimization of Process Parameters by MOGA, Int J Eng Sci Technol, 2012, 4, p 66–77.

    Google Scholar 

  35. Nor Atiqah Zolpakar, Swati Singh Lodhi, Sunil Pathak, and Mohita Anand Sharma, "Application of Multi-objective Genetic Algorithm (MOGA) Optimization in Machining Processes," in Opt of Manufact Proc.: Springer, 2020, pp. 185–199.

  36. Minitab User Manual, "Making data analysis easier," MINITAB Inc., USA, 2001

  37. K. Kalita, I. Shivakoti and R.K. Ghadai, Optimizing Process Parameters for Laser Beam Micro-Marking Using Genetic Algorithm and Particle Swarm Optimization, Mater Manufact Proc, 2017, 32, p 1101–1108.

    Article  CAS  Google Scholar 

  38. H. Oktem, T. Erzurumlu and I. Uzman, Application of Taguchi Optimization Technique in Determining Plastic Injection Molding Process Parameters for a thin-shell part, Mater Des, 2007, 28, p 1271–1278.

    Article  CAS  Google Scholar 

  39. Hasan Oktem, Durmus Kır, Mustafa Cöl, Ece Simooğlu, “Determination of the Most Appropriate Injection Process Parameters Affecting the Mechanical Properties of Plastic Products Using Taguchi Method”, J . Mac Des Manufact, 7, 2, 19-27, MATİM, 2016.

  40. B. Ozcelik, A. Ozbay and E. Demirbas, Influence of Injection Parameters and Mold Materials on Mechanical Properties of ABS in Plastic Injection Molding, Int Communin Heat and Mass Transfer, 2010, 37, p 1359–1365.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Turkish Standard Institute and Lecturer Ece Simooğlu Sarı, and Dr. Durmuş Kır at Kocaeli University. This study was supported by the Scientific Research Project Unit of Kocaeli University (KOU-BAP-2013/68).

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Authors and Affiliations

  1. Division of Machine and Metal Technologies, Hereke Asım Kocabıyık, Kocaeli University, Kocaeli, Turkey

    Hasan Öktem

  2. Research and Developement Department (Gear Division), Elecon Engineering Company Limited, V.V Nagar, Gujarat, India

    Dinesh Shinde

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  1. Hasan Öktem
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Öktem, H., Shinde, D. Determination of Optimal Process Parameters for Plastic Injection Molding of Polymer Materials Using Multi-Objective Optimization. J. of Materi Eng and Perform 30, 8616–8632 (2021). https://doi.org/10.1007/s11665-021-06029-z

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