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Optimization on the Turning Process Parameters of SS 304 Using Taguchi and TOPSIS

Author

Listed:
  • Nikhil J. Rathod

    (Sarvepalli Radhakrishnan University)

  • Manoj K. Chopra

    (Sarvepalli Radhakrishnan University)

  • Prem Kumar Chaurasiya

    (Bansal Institute of Science and Technology)

  • Umesh S. Vidhate

    (SMP Engineers and Electricals PVT. LTD)

  • Abhishek Dasore

    (Rajeev Gandhi Memorial College of Engineering and Technology)

Abstract

Turning is a basic machining technique where parameters may be optimised to improve machining performance. The Taguchi and TOPSIS methods were used to find the parameters of optimum process in turning SS 304 using coated carbide tools. Cutting speed, feed rate, and depth of cut are all considered in the operation. This improves tool life while lowering production time and surface roughness. TOPSI and an orthogonal array are used to investigate the effects of input parameters on output parameters. In this work, S/N ratios are utilized to create a decision matrix, which is then utilized to convert a problem with multiple criteria for solving into a single-criteria issue using the TOPSIS approach. The results demonstrated that the strategy proposed is suitable for resolving multi-criteria process parameter enhancements. The best combination of process specifics was found to be 350 m/min cutting speed, 0.12 mm/rev feed rate, and 0.40 mm cut depth.

Suggested Citation

  • Nikhil J. Rathod & Manoj K. Chopra & Prem Kumar Chaurasiya & Umesh S. Vidhate & Abhishek Dasore, 2023. "Optimization on the Turning Process Parameters of SS 304 Using Taguchi and TOPSIS," Annals of Data Science, Springer, vol. 10(5), pages 1405-1419, October.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:5:d:10.1007_s40745-021-00369-2
    DOI: 10.1007/s40745-021-00369-2
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    References listed on IDEAS

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    1. Abdul Majeed, 2019. "Improving Time Complexity and Accuracy of the Machine Learning Algorithms Through Selection of Highly Weighted Top k Features from Complex Datasets," Annals of Data Science, Springer, vol. 6(4), pages 599-621, December.
    2. Ashoke Kumar Bera & Dipak Kumar Jana & Debamalya Banerjee & Titas Nandy, 2021. "A Two-Phase Multi-criteria Fuzzy Group Decision Making Approach for Supplier Evaluation and Order Allocation Considering Multi-objective, Multi-product and Multi-period," Annals of Data Science, Springer, vol. 8(3), pages 577-601, September.
    3. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    4. André G. C. Pacheco & Renato A. Krohling, 2018. "Ranking of Classification Algorithms in Terms of Mean–Standard Deviation Using A-TOPSIS," Annals of Data Science, Springer, vol. 5(1), pages 93-110, March.
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