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Annals of Data Science
Erschienen in:

09.01.2022

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

verfasst von: Nikhil J. Rathod, Manoj K. Chopra, Prem Kumar Chaurasiya, Umesh S. Vidhate, Abhishek Dasore

Erschienen in: Annals of Data Science | Ausgabe 5/2023

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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.
Vorheriger Artikel Competing Risk Analysis in Constant Stress Partially Accelerated Life Tests Under Censored Information
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Metadaten
Titel
Optimization on the Turning Process Parameters of SS 304 Using Taguchi and TOPSIS
verfasst von
Nikhil J. Rathod
Manoj K. Chopra
Prem Kumar Chaurasiya
Umesh S. Vidhate
Abhishek Dasore
Publikationsdatum
09.01.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 5/2023
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-021-00369-2

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