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

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...

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Bibliographic Details
Published in:Annals of data science 2023-10, Vol.10 (5), p.1405-1419
Main Authors: Rathod, Nikhil J., Chopra, Manoj K., Chaurasiya, Prem Kumar, Vidhate, Umesh S., Dasore, Abhishek
Format: Article
Language:English
Subjects:
Alloys
Artificial Intelligence
Big Data
Business and Management
Carbide tools
Content analysis
Cutting parameters
Cutting speed
Cutting tools
Data science
Decision making
Design optimization
Economics
Feed rate
Finance
Insurance
Lubricants & lubrication
Management
Mechanical engineering
Multiple criterion
Optimization
Orthogonal arrays
Process parameters
Statistics for Business
Surface roughness
Tool life
Turning (machining)
Variables
Variance analysis
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Summary: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.
ISSN:2198-5804
2198-5812
DOI:10.1007/s40745-021-00369-2