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Optimization of injection moulding parameters on wear properties of ultra-high molecular weight polyethylene

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Abstract

Based on the grey relational analysis, this work proposes an effective approach for optimizing various injection moulding parameters on the wear behaviours of ultra-high molecular weight polyethylene (UHMWPE) with diverse performance characteristics. The injection moulding parameters are melting temperature, injection velocity and compaction time. The experimental data were used to calculate wear parameters, such as coefficient of friction, wear rate and hardness. Thirty runs were carried out using the response surface design to determine the optimal factor level condition. The graph and the response table in each level of the parameters are generated with help of grey relational grade. In addition to that, bovine serum is taken, which acts as a lubricant, and the sample hardness is tested. The results showed that there is an impact on the wear behaviour due to the contact load and melt temperature of UHMWPE. According to the grey relational grade, level 2 of injection moulding parameters has a greater effect than levels 1 and 3. With the help of a scanning electron microscope, the worn-out morphologies of samples were studied. Plastic deformation, ploughing, scratching, ironing and fatigue wear are the major wear processes of our study.

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References

  1. Patel N R and Gohil P 2012 Int. J. Emerg. Technol. Adv. Eng. 2 91

    Google Scholar 

  2. Thomas S, Grohens Y and Ninan N (eds) 2015 Biomaterials: design, development and biomedical applications (UK: William Andrew Publishing)

    Google Scholar 

  3. Nasab M B, Hassan M R and Sahari B B 2010 Trends Biomater. Artif. Organs. 24 69

    Google Scholar 

  4. Pajarinen J, Lin T H, Sato T, Yao Z and Goodman S B 2014 J. Mater. Chem. B 2 7094

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hutley T J and Ouederni M (eds) 2016 Polyolefin compounds and materials: fundamentals and industrial applications (Switzerland: Springer, Cham)

    Google Scholar 

  6. Owonubi S J, Agwuncha S C, Fasiku V O, Mukwevho E, Aderibigbe B A, Sadiku E R et al (eds) 2017 Polyolefin fibres (UK: Woodhead Publishing)

    Google Scholar 

  7. Mihalko W M, Haider H, Kurtz S, Marcolongo M and Urish K 2020 J. Orthop. Res. 38 1436

    Article  PubMed  Google Scholar 

  8. Brockett C L, Carbone S, Fisher J and Jennings L M 2017 Wear 374 86

    Article  PubMed  Google Scholar 

  9. Singh G, Klassen R, Howard J, Naudie D, Teeter M and Lanting B 2018 Hip Int. 28 573

    Article  PubMed  Google Scholar 

  10. Teo A J, Mishra A, Park I, Kim Y J, Park W T and Yoon Y J 2016 ACS Biomater. Sci. Eng. 2 454

    Article  CAS  PubMed  Google Scholar 

  11. Wu S L, Qiao J, Guan J, Chen H M, Wang T, Wang C et al 2023 Eur. Polym. J. 184 111799

  12. Patel K, Chikkali S H and Sivaram S 2020 Prog. Polym. Sci. 109 101290

    Article  CAS  Google Scholar 

  13. Gote R P, Romano D, van der Eem J, Zhao J, Zhou F and Rastogi S 2022 Macromolecules 56 361

    Article  ADS  Google Scholar 

  14. Mecking S and Schnitte M 2020 Acc. Chem. Res. 53 2738

    Article  CAS  PubMed  Google Scholar 

  15. Zhang D, Nadres E T, Brookhart M and Daugulis O 2013 Organometallics 32 5136

    Article  CAS  Google Scholar 

  16. Sánchez-Sánchez X, Hernández-Avila M, Elizalde L E, Martínez O, Ferrer I and Elías-Zuñiga A 2017 Mater. Des. 132 1

    Article  Google Scholar 

  17. Kashyap S and Datta D 2015 Int. J. Plast. Technol. 19 1

    Article  CAS  Google Scholar 

  18. Kuo H C and Jeng M C 2010 Mater. Des. 31 884

    Article  CAS  Google Scholar 

  19. Ge S, Wang S and Huang X 2009 Wear 267 770

    Article  CAS  Google Scholar 

  20. Lv H, Chen X, Wang X, Zeng X and Ma Y 2022 Int. J. Heat Mass Transf. 183 122159

    Article  CAS  Google Scholar 

  21. Jorner K, Brinck T, Norrby P O and Buttar D 2021 Chem. Sci. 12 1163

    Article  CAS  PubMed  Google Scholar 

  22. Sarker I H 2021 SN Comput. Sci. 2 420

    Article  PubMed  PubMed Central  Google Scholar 

  23. Gori Y, Verma R P, Patil P P and Taluja R 2021 Webology 18 3

    Google Scholar 

  24. Mukhametzyanov I 2021 Decis. Mak. Appl. Manag. Eng. 4 76

    Article  Google Scholar 

  25. Wang G, Zhao B, Wu B, Zhang C and Liu W 2023 Int. J. Min. Sci. Technol. 33 47

    Article  Google Scholar 

  26. Fu J, Zhang Y, Wang Y, Zhang H, Liu J, Tang J et al 2022 Nat. Protoc. 17 129

    Article  CAS  PubMed  Google Scholar 

  27. Yang Y K 2006 Polym. Plast. Technol. Eng. 45 769

    Article  CAS  Google Scholar 

  28. Yang Y K, Shie J R and Huang C H 2006 Mater. Manuf. Process. 21 832

    Article  CAS  Google Scholar 

  29. Tretinnikov O N, Ogata S and Ikada Y 1998 Polymer 39 6115

    Article  CAS  Google Scholar 

  30. Xue Y, Wu W, Jacobs O and Schädel B 2006 Polym. Test. 25 221

    Article  CAS  Google Scholar 

  31. Unal H and Mimaroglu A 2003 Mater. Des. 24 183

    Article  CAS  Google Scholar 

  32. Jeng M C, Fung C P and Li T C 2002 Wear 252 934

    Article  CAS  Google Scholar 

  33. Chand N, Dwivedi U K and Sharma M K 2007 Wear 262 184

    Article  CAS  Google Scholar 

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Acknowledgements

We would like to thank Mr N Basheer Ahamed, Managing Partner, M/s Metro consulting, Chennai, India, for his critical suggestions. The help rendered by Prof Dr C Muralidharan and Dr C Senthil Kumar (Assistant Professor), Mr N Raja, A Raja Narayanan, R Raj (UG Students) and Mr A Balaguru, Department of Manufacturing Engineering, Annamalai University, Mr R Madura, MR Engineering, Ekkatuthangal, Chennai, in the conduct of the experiments, is also acknowledged.

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Correspondence to N Mohamad Raffi.

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Mohamad Raffi, N., Vijayanand, M. & Sivamani, S. Optimization of injection moulding parameters on wear properties of ultra-high molecular weight polyethylene. Bull Mater Sci 47, 56 (2024). https://doi.org/10.1007/s12034-023-03116-w

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