Parametric Optimization in Turning Process of Galvanized Iron Metal using Taguchi Based Six Sigma Technique

Jump To References Section

Authors

  • P. Muraleedharan
     Department of Mechanical Engineering, Karpagam College of Engineering, Coimbatore - 641032, Tamil Nadu ,IN
  • V. R. Muruganantham
     Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore - 641049, Tamil Nadu ,IN
  • A. G. Karthikeyan
     Department of Mechatronics Engineering, School of Mechanical Engineering, REVA University, Bengaluru - 560064, Karnataka ,IN
  • P. Muruganandhan
     Department of Mechanical Engineering, Mahendra Institute of Technology, Namakkal - 637503, Tamil Nadu ,IN
  • M. Mani
     Department of Mechanical Engineering, Sri Ranganathar Institute of Engineering and Technology, Coimbatore - 641110, Tamil Nadu ,IN
  • Beporam Iftekhar Hussain
     Associate Professor, Department of Mechanical Engineering, Bapatla Engineering College, Bapatla - 522102, Andhra Pradesh ,IN

DOI:

https://doi.org/10.18311/jmmf/2023/40599

Keywords:

Galvanized Iron, Material Removal Rate, Process Parameters, Six Sigma Method, Signal to Noise Ratio, Taguchi Method.

Abstract

The Six Sigma approach is utilized in this research to improve the quality of process outputs while machining Galvanized Iron in turning process. The main objective of the present work is to improve the output characterization of MRR (Material Removal Rate) by optimizing the turning process parameters. Taguchi's parameter design is a method for optimizing control settings in Design of Experiments (DOE) to achieve the best results. An orthogonal array offers a framework of equal minimal experiments for prediction and diagnosis of optimal outcomes. The Material Removal Rate (MRR) is evaluated for each experiment. The response fluctuation was investigated using the Signal to Noise (S/N) ratio. In Miniutab 19 software, Taguchi technique reduces quality characteristic variation owing to uncontrollable parameters. Furthermore, statistical analysis reveals that the standard deviation and mean value of confirmatory trial results were lower than it was before Taguchi design run data.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Downloads

Published

2023-12-30

How to Cite

Muraleedharan, P., Muruganantham, V. R., Karthikeyan, A. G., Muruganandhan, P., Mani, M., & Hussain, B. I. (2023). Parametric Optimization in Turning Process of Galvanized Iron Metal using Taguchi Based Six Sigma Technique. Journal of Mines, Metals and Fuels, 71(12), 2616–2623. https://doi.org/10.18311/jmmf/2023/40599

Issue

Volume 71, Issue 12, December 2023

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC

 

References

Schwartz M. Boca raton: encyclopedia and handbook of materials, Parts and Finishes. CRC Press, 3rd edition; 2016. https://doi.org/10.1201/b18998 DOI: https://doi.org/10.1201/b18998

Kwak YH, Anbari FT. Benefits, obstacles, and future of six sigma approach. Techno. 2006; 26:708-15. https:// doi.org/10.1016/j.technovation.2004.10.003 DOI: https://doi.org/10.1016/j.technovation.2004.10.003

Sekulić M, Kovac PP, Gostimirović M, Kramar D. Optimization of high-pressure jet assisted turning process by Taguchi method. Adv Prod Eng Manag. 2013; 8:5-12. https://doi.org/10.14743/apem2013.1.148 DOI: https://doi.org/10.14743/apem2013.1.148

Thomas JJ, Vishnu Prasad VN. Multi objective optimization and comparison of process parameters in turning operation. Int J Eng Res Technol. 2017; 6:193-9. https:// doi.org/10.17577/IJERTV6IS060138 DOI: https://doi.org/10.17577/IJERTV6IS060138

Musavi SH, Davoodi B, Eskandari B. Evaluation of surface roughness and optimization of cutting parameters in turning of AA2024 alloy under different coolinglubrication conditions using RSM method. J Cent South Univ. 2020; 27:1714-28. https://doi.org/10.1007/s11771- 020-4402-2 DOI: https://doi.org/10.1007/s11771-020-4402-2

Das PP, Das P, Ghadai RK, Manickam R, Kalita K. Optimization of turning process parameters by taguchi based six sigma. Mech Mech Eng. 2017; 21:649–56.

Muruganantham VR, Muraleedharan P, Devan PD, Eswar RM. Six sigma - DMAIC method for choice of material in natural fibers in polymer composite based wall brick. AIP Conf Proc. 2022; 2446:150002. https:// doi.org/10.1063/5.0108188 DOI: https://doi.org/10.1063/5.0108188

Das MK, Kumar K, Barman TKR, Sahoo P. Optimisation of EDM process parameters using grey-Taguchi technique. Int J Mach Mach Mater. 2014; 15:235–62. https:// doi.org/10.1504/IJMMM.2014.060553 DOI: https://doi.org/10.1504/IJMMM.2014.060553

Suhail AH, Ismail N, Wong SV, Abdul Jalil NA. Optimization of cutting parameters based on surface roughness and assistance of workpiece surface temperature in turning process. Am J Eng Appl Sci. 2010; 3:102-8. https://doi.org/10.3844/ajeassp.2010.102.108 DOI: https://doi.org/10.3844/ajeassp.2010.102.108

Naik GM, Narendranath S. Optimization of wire-ED turning process parameters by taguchi-grey relational analysis. I-man J Mech Eng. 2018; 8:1-8.

Singh BJ, Sodhi HS. Parametric optimisation of CNC turning for Al-7020 with RSM. Int J Oper Res. 2014; 20:180-206. https://doi.org/10.1504/IJOR.2014.061769 DOI: https://doi.org/10.1504/IJOR.2014.061769

Ketabforoush M, Aziz NA. The effect of taguchi‑based six sigma method on variation reduction in a green construction material production process. IJST-T Civ Eng. 2021; 45:879–89. https://doi.org/10.1007/s40996-021- 00583-1 DOI: https://doi.org/10.1007/s40996-021-00583-1

Chen JC, Li Y, Cox RA. Taguchi-based Six Sigma approach to optimize plasma cutting process: an industrial case study. Int J Adv Manuf Technol. 2009; 4:760–9. https://doi.org/10.1007/s00170-008-1526-1 DOI: https://doi.org/10.1007/s00170-008-1526-1

Li N, Chen Y-J, Kong D-D. Multi-response optimization of Ti-6Al-4V turning operations using Taguchi-based grey relational analysis coupled with kernel principal component analysis. Adv Manuf. 2019; 7:142–54. https://doi.org/10.1007/s40436-019-00251-8 DOI: https://doi.org/10.1007/s40436-019-00251-8