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Simultaneous Optimization of Milling Process Responses for Nano-Finishing of AISI-4340 Steel Through Sustainable Production

  • Muhammed MuazEmail author
  • Sounak Kumar Choudhury
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

A modified Taguchi-grey relational analysis method has been used in this research work to optimize the flat end milling process considering chip compression ratio (Q) and workpiece surface roughness (Ra) simultaneously. Both of the output responses are of equal importance. Therefore, optimization of the process considering both of them at the same time is more practical than optimizing the process considering only a single response at a time. The effectiveness of dry cutting has been experimentally investigated as compared to flooded lubrication condition giving equal weights to the two important process responses simultaneously. From the findings of the analysis and the experimental results, it is recommended to perform flat end milling operation on AISI 4340 steel in dry cutting condition at high speed and low feed rate. Flooded lubrication technique is not feasible for flat end milling of this steel. Performing dry machining on the recommended cutting parameters will lead to cleaner and sustainable production which aims at reducing/omitting waste and making the process environment-friendly. The order of significance of the factors based on the analysis, in sequence, is spindle speed, feed rate, and machining environment. Analysis of variance (ANOVA) performed on grey relational grades confirms the order of significance.

Keywords

Modified Taguchi-grey relational analysis Multi-objective optimization Nano-finishing Sustainable production Flat end-milling 

References

  1. 1.
    Rao, V.R.: Advanced Modeling and Optimization of Manufacturing Processes: International Research and Development. Springer-Verlag, London Limited, London (2011)CrossRefGoogle Scholar
  2. 2.
    Veremchuk, L.V., Tsarouhas, K., Vitkina, T.I., Mineeva, E.E., Gvozdenko, T.A., Antonyuk, M.V., Rakitskii, V.N., Sidletskaya, K.A., Tsatsakis, A.M., Golokhvast, K.S.: Impact evaluation of environmental factors on respiratory function of asthma patients living in urban territory. Environ. Pollut. 235, 489–496 (2018)CrossRefGoogle Scholar
  3. 3.
    Choudhury, S.K., Muaz, M.: Natural oils as green lubricants in machining processes. Ref. Modul. Mater. Sci. Mater. Eng. (2018) (Elsevier)Google Scholar
  4. 4.
    Kalsi, N.S., Sehgal, R., Sharma, V.S.: Multi-objective optimization using grey relational Taguchi analysis in machining. Int. J. Organ. Collect. Intell. 6(4), 45–64 (2016)CrossRefGoogle Scholar
  5. 5.
    Subramaniam, S.T.M., Thangarasu, S.K.: Multi-response milling process optimization using the Taguchi method coupled to grey relational analysis. Mater. Test. 58(5), 462–470 (2016)Google Scholar
  6. 6.
    Balraj, U.S., Krishna, A.G.: Multi-objective optimization of EDM process parameters using Taguchi method, principal component analysis and grey relational analysis. Int. J. Manuf. Mater. Mech. Eng. 4(2), 29–46 (2014)Google Scholar
  7. 7.
    Julong, D.: Introduction to grey system theory. J. Grey Syst. 1, 1–24 (1989)Google Scholar
  8. 8.
    Abhang, L.B., Hameedullah, M.: Determination of optimum parameters for multi-performance characteristics in turning by using grey relational analysis. Int. J. Adv. Manuf. Technol. 63, 13–24 (2012)CrossRefGoogle Scholar
  9. 9.
    Lin, J.L., Lin, C.L.: The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics. Int. J. Mach. Tools Manuf. 42(2), 237–244 (2002)CrossRefGoogle Scholar
  10. 10.
    Lin, C.L.: Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics. Mater. Manuf. Process. 19(2), 209–220 (2004)CrossRefGoogle Scholar
  11. 11.
    Chiang, Y.M., Hsieh, H.H.: The use of the Taguchi method with grey relational analysis to optimize the thin-film sputtering process with multiple quality characteristic in color filter manufacturing. Comput. Ind. Eng. 56(2), 648–661 (2009)CrossRefGoogle Scholar
  12. 12.
    Maiyar, L.M., Ramanujam, R., Venkatesan, K., Jerald, J.: Optimization of machining parameters for end milling of Inconel 718 super alloy using Taguchi based grey relational analysis. Procedia Eng. 64, 1276–1282 (2013)CrossRefGoogle Scholar
  13. 13.
    Sarikaya, M., Yilmaz, V., Dilipak, H.: Modeling and multi-response optimization of milling characteristics based on Taguchi and gray relational analysis. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 230(6), 1049–1065 (2016)Google Scholar
  14. 14.
    Sarikaya, M., Güllü, A.: Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25. J. Clean. Prod. 91, 347–357 (2015)CrossRefGoogle Scholar
  15. 15.
    Kitagawa, T., Kubo, A., Maekawa, K.: Temperature and wear of cutting tools in high-speed machining of Inconel 718 and Ti-6Al-6V-2Sn. Wear 202(2), 142–148 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia

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