Advertisement

Modeling and multiresponse optimization of cutting parameters in SPDT of a rigid contact lens polymer using RSM and desirability function

  • Muhammad Mukhtar LimanEmail author
  • Khaled Abou-El-Hossein
ORIGINAL ARTICLE
  • 8 Downloads

Abstract

Amidst different conventional contact lens manufacturing techniques, single-point diamond turning (SPDT) is one of the recently developed ultra-high precision machining techniques employed in the fabrication of advanced contact lenses due to its capability of producing high optical surfaces of complex shapes and nanometric accuracy. SPDT is regarded as an effective process for the generation of high-quality functional surfaces in optical industries. However, despite advances in the ultra-high precision machining, it is not always easy to achieve a high-quality surface finish with maximum productivity. Machining parameters, namely cutting speed, feed rate, and depth of cut, play the lead role in determining the machine economics and quality of machining. The present study focuses on the determination of the optimum cutting conditions leading to minimum surface roughness as well as electrostatic charge and maximum productivity, in SPDT of the polymethyl methacrylate (PMMA) contact lens polymer using monocrystalline diamond cutting tool. The optimization is based on the response surface methodology (RSM) together with the desirability function approach. In addition, a mathematical model is developed for surface roughness (Ra), electrostatic charge (ESC), and material removal rate (MRR) using RSM regression analysis for a rigid contact lens polymer by the Design-Expert software. RSM allowed the optimization of the cutting conditions for minimal surface roughness, electrostatic charge, and maximal material removal rate which provides an effective knowledge base for process parameters, to make its enhancement of process performance in SPDT of contact lens polymer.

Keywords

PMMA contact lens polymer Electrostatic charge Material removal rate Single-point diamond turning Response surface methodology Surface roughness and optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Funding information

We would like to acknowledge the support of the National Research Foundation (NRF) of South Africa and the Research Capacity Development, Nelson Mandela University for the financial support.

References

  1. 1.
    Jiang Q, Zhang L, Pittolo M (2000) The dependence of surface finish of a spectacle polymer upon machining conditions, Progress of Machining Technology. Aviation Industry Press, Beijing, pp 7–12Google Scholar
  2. 2.
    Dusunceli N, Colak OU (2008) The effects of manufacturing techniques on viscoelastic and viscoplastic behavior of high density polyethylene (HDPE). Mater Des 29:1117–1124CrossRefGoogle Scholar
  3. 3.
    Liman MM (2017) Diamond turning of contact lens polymers. Masters of Engineering, Mechatronics Engineering, Nelson Mandela Metropolitan University, Port ElizabethGoogle Scholar
  4. 4.
    Olufayo OA (2014) Ultra-high precision manufacturing of contact lens polymer. PhD, Mechatronics Engineering, Nelson Mandela Metropolitan University, Port ElizabethGoogle Scholar
  5. 5.
    Liman M M, Abou-El-Hossein K, Jumare A I, Odedeyi P B, Lukman A N (2017) Modelling of surface roughness in ultra-high precision turning of an RGP contact lens polymer, in Key engineering materials, pp 183–187Google Scholar
  6. 6.
    Kwok T-C (2011) An investigation of factors affecting surface generation in ultra-precision machining with fast tool servo. The Hong Kong Polytechnic University, Hong KongGoogle Scholar
  7. 7.
    Reddy BS, Kumar JS, Reddy KVK (2009) Prediction of surface roughness in turning using adaptive neuro-fuzzy inference system. Jordan J Mech Indust Eng 3:252–259MathSciNetGoogle Scholar
  8. 8.
    Amran M, Salmah S, Hussein N, Izamshah R, Hadzley M, Kasim M et al (2013) Effects of machine parameters on surface roughness using response surface method in drilling process. Procedia Eng 68:24–29CrossRefGoogle Scholar
  9. 9.
    Palanikumar K (2007) Modeling and analysis for surface roughness in machining glass fibre reinforced plastics using response surface methodology. Mater Des 28:2611–2618CrossRefGoogle Scholar
  10. 10.
    Rajasekaran T, Palanikumar K, Vinayagam B (2011) Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool. Prod Eng 5:191–199CrossRefGoogle Scholar
  11. 11.
    Lou MS, Chen JC, Li CM (1998) Surface roughness prediction technique for CNC end-milling. J Ind Technol 15:1–6Google Scholar
  12. 12.
    Olufayo O, Abou-El-Hossein K, Kadernani M (2014) Tribo-electric charging in the ultra-high precision machining of contact lens polymers. Procedia Mater Sci 6:194–201CrossRefGoogle Scholar
  13. 13.
    Ficker T, Kapička V, Macur J, Slavíček P, Benešovský P (2004) Fractality of electrostatic microdischarges on the surface of polymers. Acta Polytechnica. J Adv Eng 44Google Scholar
  14. 14.
    Gubbels GPH (2006) Diamond turning of glassy polymers, vol 68, CiteseerGoogle Scholar
  15. 15.
    Gubbels G, Van Der Beek G, Hoep A, Delbressine F, Van Halewijn H (2004) Diamond tool wear when cutting amorphous polymers. CIRP Ann Manuf Technol 53:447–450CrossRefGoogle Scholar
  16. 16.
    Hossain SJ, Ahmad N (2012) Adaptive neuro-fuzzy inference system (ANFIS) based surface roughness prediction model for ball end milling operation. J Mech Eng Res 4:112–129Google Scholar
  17. 17.
    Cus F, Zuperl U (2009) Particle swarm intelligence based optimisation of high speed end-milling. Archives of Computational Materials Science and Surface Engineering 1:148–154Google Scholar
  18. 18.
    Aykut Ş (2011) Surface roughness prediction in machining castamide material using ANN. Acta Polytechnica Hungarica. J Appl Sci 8:21–32Google Scholar
  19. 19.
    Suresh P, Rao PV, Deshmukh S (2002) A genetic algorithmic approach for optimization of surface roughness prediction model. Int J Mach Tools Manuf 42:675–680CrossRefGoogle Scholar
  20. 20.
    Van Luttervelt C, Childs T, Jawahir I, Klocke F, Venuvinod P, Altintas Y et al (1998) Present situation and future trends in modelling of machining operations progress report of the CIRP working group ‘modelling of machining operations’. CIRP Ann 47:587–626CrossRefGoogle Scholar
  21. 21.
    Chabbi A, Yallese MA, Meddour I, Nouioua M, Mabrouki T, Girardin F (2017) Predictive modeling and multi-response optimization of technological parameters in turning of polyoxymethylene polymer (POM C) using RSM and desirability function. Measurement 95:99–115CrossRefGoogle Scholar
  22. 22.
    Panda M, Biswal S, Sharma Y (2016) Experimental analysis on the effect of process parameters during CNC turning on nylon-6/6 using tungsten carbide tool. Int J Eng Sci Res Technol 5:2277–9655Google Scholar
  23. 23.
    Lazarević D, Madić M, Janković P, Lazarević A (2012) Surface roughness minimization of polyamide PA-6 turning by Taguchi method. J Prod Eng 15:29Google Scholar
  24. 24.
    Gaitonde V, Karnik S, Mata F, Davim JP (2010) Modeling and analysis of machinability characteristics in PA6 and PA66 GF30 polyamides through artificial neural network. J Thermoplast Compos Mater 23:313–336CrossRefGoogle Scholar
  25. 25.
    Read ML (2010) The impact of material surface characteristics on the wetting properties of silicone hydrogel contact lenses. PhD Thesis, The University of ManchesterGoogle Scholar
  26. 26.
    Goel B, Singh S, Sarepaka RGV (2016) Precision deterministic machining of polymethyl methacrylate by single-point diamond turning. Mater Manuf Process 31:1917–1926CrossRefGoogle Scholar
  27. 27.
    Yu N, Fang F, Wu B, Zeng L, Cheng Y (2018) State of the art of intraocular lens manufacturing. Int J Adv Manuf Technol 98(1–28):1103–1130CrossRefGoogle Scholar
  28. 28.
    Lagado C. (2018). Hard lens materials, PMMA (polymethyl methacrylate). Available: https://www.lagadocorp.co/en/products/hard-lens-materials/. Accessed Mar 30 2018
  29. 29.
    Otieno T (2018) The machinability of rapidly solidified aluminium alloy for optical mould inserts. PhD, Mechatronics Engineering, Nelson Mandela University, Port ElizabethGoogle Scholar
  30. 30.
    Bombay I I o T (2017) Machine operation calculations, Indian Institute of Technology. Available: http://www.d.umn.edu/~rlindek1/ie1225/MACHCALC_1_doc.doc
  31. 31.
    Montana (2018) Turning equations. Available: http://www.montana.edu/jdavis/met314/documents/homework/Turning%20Examples.pdf. Accessed Jul 23 2018
  32. 32.
    Dasarathi (2017) CNC: material removal rate (MRR)—what is it? Available: https://www.cadem.com/single-post/cnc-milling-turning-material-removal-rate. Accessed Jan 05 2017
  33. 33.
    Bouzid L, Boutabba S, Yallese MA, Belhadi S, Girardin F (2014) Simultaneous optimization of surface roughness and material removal rate for turning of X20Cr13 stainless steel. Int J Adv Manuf Technol 74:879–891CrossRefGoogle Scholar
  34. 34.
    Kolahan F, Khajavi A (2010) A statistical approach for predicting and optimizing depth of cut in AWJ machining for 6063-T6 Al alloy. Int J Mech Syst Sci Eng 2Google Scholar
  35. 35.
    Aultrin KJ, Anand MD (2016) Experimental investigations and prediction on MRR and SR of some non ferrous alloys in AWJM using ANFIS. Indian J Sci Technol 9:13Google Scholar
  36. 36.
    Bouzid L, Yallese MA, Chaoui K, Mabrouki T, Boulanouar L (2015) Mathematical modeling for turning on AISI 420 stainless steel using surface response methodology. Proc Inst Mech Eng B J Eng Manuf 229:45–61CrossRefGoogle Scholar
  37. 37.
    Lakshminarayanan A, Balasubramanian V (2009) Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints. Trans Nonferrous Metals Soc China 19:9–18CrossRefGoogle Scholar
  38. 38.
    Ramesh S, Karunamoorthy L, Palanikumar K (2008) Surface roughness analysis in machining of titanium alloy. Mater Manuf Process 23:174–181CrossRefGoogle Scholar
  39. 39.
    Palanikumar K, Karthikeyan R (2006) Optimal machining conditions for turning of particulate metal matrix composites using Taguchi and response surface methodologies. Mach Sci Technol 10:417–433CrossRefGoogle Scholar
  40. 40.
    Neşeli S, Yaldız S, Türkeş E (2011) Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement 44:580–587CrossRefGoogle Scholar
  41. 41.
    Otieno T, Abou-El-Hossein K (2016) Effect of cutting parameters on tool wear in diamond turning of new optical aluminium grade. Chinese Optics, 2016, 9(5): 579–587.Google Scholar
  42. 42.
    Jumare AI, Abou-El-Hossein K, Goosen WE, Cheng Y-C, Abdulkadir LN, Odedeyi PB et al (2018) Prediction model for single-point diamond tool-tip wear during machining of optical grade silicon. Int J Adv Manuf Technol 98(1–11):2519–2529CrossRefGoogle Scholar
  43. 43.
    Otieno T, Abou-El-Hossein K, Hsu W, Cheng Y, Mkoko Z (2015) Surface roughness when diamond turning RSA 905 optical aluminium. Optical Manufacturing and Testing XI Proc SPIE:957509Google Scholar
  44. 44.
    Benardos P, Vosniakos G-C (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43:833–844CrossRefGoogle Scholar
  45. 45.
    Alao A (2007) Precision micro-scaled partial ductile mode machining of silicon. MSc thesis, International Islamic University, MalaysiaGoogle Scholar
  46. 46.
    Alao A, Konneh M (2009) A response surface methodology based approach to machining processes: modelling and quality of the models. Int J Exp Des and Process Optim 1:240–261Google Scholar
  47. 47.
    Stat-Ease I M, USA (2008) Design-Expert ® Version 7.1.6 software, edGoogle Scholar
  48. 48.
    Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments. WileyGoogle Scholar
  49. 49.
    Saedon J, Jaafar N, Jaafar R, Saad NH, Kasim MS (2014) Modeling and multi-response optimization on WEDM Ti6Al4V. In: Applied mechanics and materials, pp 123–129Google Scholar
  50. 50.
    Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Technol 12:214–219CrossRefGoogle Scholar
  51. 51.
    Harrington EC (1965) The desirability function. Ind Qual Control 21:494–498Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Mechatronics Engineering DepartmentNelson Mandela UniversityPort ElizabethSouth Africa

Personalised recommendations