Optimization of Surface Roughness and Material Removal Rate in Turning of Al-6061 Using Taguchi Methodology, Fuzzy Logic, and Measurement System Analysis

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 4 | 01 | Page :
    By

    Manoj S. Lohate,

  • Sagar B. Kadu,

  • Pravin T. Nitnaware,

  • Vedanti Bhoge,

  • Aniket Shinde,

  • Kaustubh Tattapure,

  • Abhishek Gawali,

  1. Assistant Professor, Department of Mechanical Engg. DYP College of Engineering, Pune, Maharashtra, India
  2. Assistant Professor, Department of Mechanical Engg. DYP College of Engineering, Pune, Maharashtra, India
  3. Assistant Professor, Department of Mechanical Engg. DYP College of Engineering, Pune, Maharashtra, India
  4. Student, Department of Mechanical Engg. DYP College of Engineering, Pune, Maharashtra, India
  5. Student, Department of Mechanical Engg. DYP College of Engineering, Pune, Maharashtra, India
  6. Student, Department of Mechanical Engg. DYP College of Engineering, Pune, Maharashtra, India
  7. Student, Department of Mechanical Engg. DYP College of Engineering, Pune, Maharashtra, India

Abstract

The optimization of machining parameters please a crucial role in improving product quality and productivity in manufacturing processes. This study focuses on the turning of aluminium 6061 alloy, aiming to optimize two key performance measures: Surface Roughness and Material Removal Rate. An integrated approach combining to Taguchi methodology, fuzzy logic and measurement system analysis (MSA) is proposed to achieve this objective. Taguchi design of experiments using and L9 orthogonal array has helped to get the effects on process parameters – speed, cutting depth and feed rate– on the selected responses. Fuzzy logic is utilized to change the multi response problem into a one performance index facility simultaneous optimization of Ra and MRR. Additionally, MSA is conducted using gauge repeatability and reproducibility into bracket are and are bracket closed to ensure the reliability of the measured data.  The analysis reveals that the chosen methodology effectively identifies optimal parameter settings resulting in improved surface quality and machining efficiency.

Keywords: Turning process, Ra, optimization, MRR, Taguchi-Fuzzy hybrid approach, Measurement System Analysis (MSA)

How to cite this article:
Manoj S. Lohate, Sagar B. Kadu, Pravin T. Nitnaware, Vedanti Bhoge, Aniket Shinde, Kaustubh Tattapure, Abhishek Gawali. Optimization of Surface Roughness and Material Removal Rate in Turning of Al-6061 Using Taguchi Methodology, Fuzzy Logic, and Measurement System Analysis. International Journal of Machine Systems and Manufacturing Technology. 2026; 04(01):-.
How to cite this URL:
Manoj S. Lohate, Sagar B. Kadu, Pravin T. Nitnaware, Vedanti Bhoge, Aniket Shinde, Kaustubh Tattapure, Abhishek Gawali. Optimization of Surface Roughness and Material Removal Rate in Turning of Al-6061 Using Taguchi Methodology, Fuzzy Logic, and Measurement System Analysis. International Journal of Machine Systems and Manufacturing Technology. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijmsmt/article=2026/view=245881


References

  1. Verma, N.K. & Sikarwar, A.S. (2015). Optimization of same Process Using Taguchi Method for Various parameters. International Research Journal of Engineering and Technology, e-ISSN: p- 2395-0072
  2. Singh, D.P. & Mall, R.N. (2015). Optimization of Surface Roughness for Aluminum, Using Taguchi method and ANOVO via MINITAB-15. Online ISSN: 2347-4718
  3. Krishankant, Taneja, J. Bector, M. & Kumar R. (2012). Applicaton of Taguchi Method for optimization Machining Parameters in Turning processes, ISSN:2249-8958, Vol. 2, October.
  4. Chowdary, M.P., Sohail, S.W., Sajigh, S.K., & Krishna, K.S. (2019). Optimizing of Ra and MRR for Mild Steel Turning Using Minitab-Taguchi Method. ISSN: 2277-9655.
  5. X. (Jack) Feng1 and X. Wang,” Development of Empirical Models for Surface Roughness Prediction in Finish Turning”, International Journal of Advance Manufacturing Technology, 2002, pp348–356
  6. Sunil Kumar Sharma, S. A Rizvi , R. P Kori “Optimization of Process Parameters in Turning of AISI 8620 Steel Using Taguchi and Grey Taguchi Analysis” , Journal of Engineering Research and Applications ISSN : 2248-9622, Vol. 4, Issue 3
  7. Kompan Chomsamutr1 , Somkiat Jongprasithporn “Optimization Parameters of tool life Model Using the Taguchi Approach and Response Surface Methodology” , International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3
  8. Kaladhar, K. V. Subbaiah, Ch. Srinivasa Rao and K. Narayana Rao “ Application of Taguchi approach and Utility Concept in solving the Multi-objective Problem when turning AISI 202 Austenitic Stainless Steel”, Journal of Engineering Science and Technology Review 4 (1) (2011) 55-61
  9. H. Yang, Y.S. Tarng “Design optimization of cutting parameters for turning operations based on the Taguchi method”, Journal of Materials Processing Technology 84 (1998) 122–129
  10. Abhishek Kumar, ShyamLal, Sudhir Kumar, “Fabrication and characterization of A359/Al2O3 metal matrix composite using electromagnetic stir casting method”, j mater res technol. 2013;2(3):250–254.
Ahead of Print Subscription Original Research
Volume 04
01
Received 13/01/2026
Accepted 24/02/2026
Published 11/03/2026
Publication Time 57 Days
29

Login


My IP

PlumX Metrics