Advertisement

Evaluation and Optimization of Surface Roughness and Metal Removal Rate Through RSM, GRA, and TOPSIS Techniques in Turning PTFE Polymers

  • N. Sathiya NarayananEmail author
  • N. Baskar
  • M. Ganesan
  • M. P. Jenarthanan
  • S. Praveen
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

The performance of any component depends on the surface finish which helps in proper working of the assembly. To achieve the higher machining rate which gives the adequate surface finish is crucial. This research work is concentrated to find the optimum turning parameters which end up with higher metal removal rate and better surface finish for –polytetrafluoroethylene (PTFE) polymers which have broad applications in the field of petrochemical, electrical components, food and beverages, bearing pads and laboratory components. The Central composite response surface methodology based 31 number of pilot experiments conducted by considering the cutting speed (v), feed rate (f), depth of cut (ap), and coolant flow rate (Cf) as input machining parameters. The analysis of variance is used to prove the adequacy of the quadratic regression model of output responses. Gray relational analysis (GRA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm are used to get the optimum parameters, and the results are compared.

Keywords

Response surface methodology Desirability function GRA TOPSIS 

References

  1. 1.
    Raja SB, Narayanan NS, Pramad CVS (2012) Optimization of constrained machining parameters in turning operation using firefly algorithm. J Appl Sci 12:1038–1042Google Scholar
  2. 2.
    Raja SB, Baskar N (2012) Application of particle swarm optimization technique for achieving desired milled surface roughness in minimum machining time. Expert Syst Appl 39:5982–5989Google Scholar
  3. 3.
    Raja SB, Baskar N (2010) Investigation of optimal machining parameters for turning operation using intelligent techniques. Int J Mach Mater 8:146–166Google Scholar
  4. 4.
    Sanci Me, Halis S, Kaplan Y (2017) Optimization of machining parameters to minimize surface roughness in the turning of carbon-filled and glass fiber-filled polytetrafluoroethylene. Mater Des Appl 65:295–305Google Scholar
  5. 5.
    Tushar UJ, Hemant AM (2015) Machining of plastics: a review. Int J Eng Gen Sci 3:577–581Google Scholar
  6. 6.
    Xiao KO, Zhang LC (2002) The role of viscous deformation in the machining of polymers. Int J Mech Sci 44:2317–2336CrossRefGoogle Scholar
  7. 7.
    Davim JP, Mata F (2007) A comparative evaluation of the turning of reinforced and unreinforced polyamide. Int J Adv Manuf Technol 33:911–914Google Scholar
  8. 8.
    Fetecau C, Stan F (2012) Study of cutting force and surface roughness in the turning of polytetrafluoroethylene composites with a polycrystalline diamond tool. Measurement 45:1367–1379CrossRefGoogle Scholar
  9. 9.
    Oktem H, Erzurumlu T, Erzincanli F (2006) Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm. Mater Des 27:735–744CrossRefGoogle Scholar
  10. 10.
    Ansari MS, Sharma D, Nikam S (2014) Study of cutting forces and surface roughness in turning of bronze filled polytetrafluoroethylene. Int J Adv Mech Eng 4(2):151–160Google Scholar
  11. 11.
    Sanjeev kumar M, Kaviarasan V, Venkatesan R (2012) Machining parameter optimization of PTFE using genetic algorithm. Int J Mod Eng Res 2(1):143–149Google Scholar
  12. 12.
    Yadav RN (2017) A hybrid approach of Taguchi-response surface methodology for modeling and optimization of duplex turning process. Measurement 100:131–138Google Scholar
  13. 13.
    Salmasnia A, Kazemzadeh RB, Tabrizi MM (2012) A novel approach for optimization of correlated multiple responses based on desirability function and fuzzy logics. Neurocomputing 91:56–66Google Scholar
  14. 14.
    Mukherjee I, Ray PK (2008) Optimal process design of two-stage multiple responses grinding processes using desirability functions and metaheuristic technique. Appl Soft Comput 8:402–421Google Scholar
  15. 15.
    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 95:99–115Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • N. Sathiya Narayanan
    • 1
    Email author
  • N. Baskar
    • 1
  • M. Ganesan
    • 1
  • M. P. Jenarthanan
    • 2
  • S. Praveen
    • 2
  1. 1.Department of Mechanical EngineeringSaranathan College of EngineeringTrichyIndia
  2. 2.School of Mechanical EngineeringSASTRA Deemed UniversityTanjoreIndia

Personalised recommendations