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Recent Research Reviews Journal

Optimization of Hard Turning Parameters of AISI 410 Steel Using Taguchi and Regression Analysis

Open Access
Published: 04 July, 2026
Pages: 187-200
  • Nandagopal Kaliappan , Nandagopal Kaliappan
    Professor, Department of Mechanical Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Professor, Department of Mechanical Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
  • Sudharsan M. , Sudharsan M.
    Student, Department of Mechanical Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Student, Department of Mechanical Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
  • Deevan R. , Deevan R.
    Student, Department of Mechanical Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Student, Department of Mechanical Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
  • Abishek Daniel A. Abishek Daniel A.
    Student, Department of Mechanical Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Student, Department of Mechanical Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
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How to Cite

Kaliappan, Nandagopal, Sudharsan M., Deevan R., and Abishek Daniel A. 2026. “Optimization of Hard Turning Parameters of AISI 410 Steel Using Taguchi and Regression Analysis”. Recent Research Reviews Journal 5 (1): 187-200. https://doi.org/10.36548/rrrj.2026.1.012.
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Keywords

AISI 410 Stainless Steel
Hard Turning
Taguchi Method
Regression Analysis
Surface Roughness (Ra)
ANOVA
Tool Wear

Abstract

The appropriate selection of machining parameters is essential for achieving high surface quality during the hard turning of hardened martensitic stainless steel. In this research, effects of the key parameters on the surface roughness during the process of hard turning of hardened AISI 410 stainless steel will be examined. Taguchi L9 orthogonal array has been used to design the experiments and measure surface roughness values after each experiment with the help of Mitutoyo SJ-210 surface tester. Mean response analysis and Analysis of Variance (ANOVA) has been used to analyze the experimental data and determine the significance of machining parameters, and multiple linear regression models have been developed to predict the surface roughness. The Taguchi method has been used to optimize the machining parameters for minimum surface roughness. Feed rate is identified as the most significant machining parameter, while depth of cut and cutting speed are the two important parameters. Surface roughness values in wet turning operation is always smaller than values of dry turning operation, providing an average improvement of 13–25%. The multiple linear regression models have provided the prediction of surface roughness with error less than 9%.

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