Optimization of Tool Life, Surface Roughness and Production Time in CNC Turning Process Using Taguchi Method and ANOVA

N. J. Rathod, M. K. Chopra, Prem Kumar Chaurasiya () and U. S. Vidhate
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N. J. Rathod: Sarvepalli Radhakrishnan University
M. K. Chopra: Sarvepalli Radhakrishnan University
Prem Kumar Chaurasiya: Bansal Institute of Science and Technology
U. S. Vidhate: SMP Engineers and Electricals PVT. LTD

Annals of Data Science, 2023, vol. 10, issue 5, No 2, 1179-1197

Abstract: Abstract This research uses Taguchi technique for process parameter optimization for AISI 304 Stainless Steel material so as to enhance tool life and decrease production time and reduce surface roughness. For the Taguchi process, ANOVA, Machining criterion include feed rate, cutting speed and depth of cut that are utilized when take into account tool life, surface roughness, and production time (ANOVA). Taguchi and Surface Response Methodology are used to prepare the statistical aspects of the experiment. The results of the experiments that were used to calculate S/N ratios, which were then utilized, maximize tool life, Surface Roughness, and production time parameters. Confirmation tests are then used to predict overall tool life, minimum production time, and surface roughness. Cutting speed is a significant achieves on tool life, cutting speed is a significant achieve on development time, and depth of cut is a significant achieve on surface roughness, according to the findings. The feed rate of 0.10 mm/rev, depth of cut of 0.30 mm, and cutting speed of 550 m/min was found to be the finest parameters for maximizing tool life. Cutting speed of 550 m/min, feed rate of 0.14 mm/rev, and depth of cut of 0.40 mm are the best conditions for minimizing production time. Cutting speed of 550 m/min, feed rate of 0.14 mm/rev, and depth of cut of 0.40 mm are the best conditions for achieving the lowest possible surface roughness.

Keywords: SS 304; Tool Life; Production time; Surface roughness; Taguchi; RSM; ANOVA (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s40745-022-00423-7

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