Investigations on hard turning using SS304 sheet metal component grey based Taguchi and regression methodology

In the current study, tool life and production time during harsh machining of SS304 using coated carbide inserts are examined. The experiment was conducted in a dry condition. Using a Grey-based Taguchi technique, the process parameters were optimized. Regression analysis has also been used to build...

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Bibliographic Details
Published in:International journal on interactive design and manufacturing 2024-07, Vol.18 (5), p.2653-2664
Main Authors: Rathod, Nikhil J., Chopra, Manoj Kumar, Shelke, Santosh N., Chaurasiya, Prem Kumar, Kumar, Rajan, Saxena, Kuldeep Kumar, Prakash, Chander
Format: Article
Language:English
Subjects:
CAE) and Design
Carbide tools
Computer-Aided Engineering (CAD
Content analysis
Correlation coefficients
Cutting speed
Design of experiments
Electronics and Microelectronics
Engineering
Engineering Design
Feed rate
Industrial Design
Inserts
Instrumentation
Mechanical Engineering
Metal sheets
Original Paper
Parameter estimation
Prediction models
Process parameters
Regression analysis
Steel
Taguchi methods
Tool life
Turning (machining)
Variables
Variance analysis
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Summary:In the current study, tool life and production time during harsh machining of SS304 using coated carbide inserts are examined. The experiment was conducted in a dry condition. Using a Grey-based Taguchi technique, the process parameters were optimized. Regression analysis has also been used to build prediction models for tool life and production time, and their suitability has been assessed. Hard turning operations result in good quality tool life of around 136 and production time of approximately 78. The ideal parametric combination for quality characteristics has been revealed to be speed of 580 m/min, Feed Rate of 0.13 mm/rev, and Depth of Cut of 0.25 mm, respectively, using the grey-based Taguchi technique. Cutting Speed is regarded as most significant aspect in determining tool life and production time. The models for prediction have a good correlation coefficient. This clearly demonstrates improved model fit and was determined to be highly significant.
ISSN:1955-2513
1955-2505
DOI:10.1007/s12008-023-01244-5