Original Research Article
The Effect of Parameters and Optimization of Surface Roughness and Cutting Temperature in Turning of AISI 1020 Carbon Steel using Taguchi Technique and ANOVA
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Article Number: DRJEIT7391583602
DOI: https://doi.org/10.26765/DRJEIT7391583602
ISSN: 2354-4155
Vol. 9 (4), Pp. 192-200, May 2022
Author(s) retain the copyright of this article
This article is published under the terms of the
Creative Commons Attribution License 4.0.
Abstract
Quality and productivity play significant role in today’s manufacturing market. In this research work, turning operation is performed on mild steel aimed at evaluating the best process environment which could simultaneously satisfy requirements of both quality and as well as productivity. Here we conducted experiments by taking Cutting Speed, Feed Rate & Depth of cut as process parameters and got the optimized value of Surface Roughness and Cutting temperature. An L9 orthogonal array, the signal-to-noise (S/N) ratio and the analysis of variance (ANOVA) are employed to the study the performance characteristics in the turning on CNC using tungsten coated carbide cutting tool insert with a nose radius of 0.8mm. The analysis of results shows that the combination of process parameters for minimum surface roughness is obtained at 75m/min cutting speed, 0.2 mm/rev feed and 1.0 mm depth of cut for minimum surface roughness. It is observed that feed rate and the speed plays important role in minimizing surface roughness. For maximum material removal rate, the optimum values are cutting speed of 75m/min, feed rate of 0.2 mm/ rev and depth of cut of 0.6 mm. It is also observed that depth of cut and the speed plays important role in minimizing cutting temperature. Finally, the relationship between cutting parameters and responses were developed by using the Minitab 18.1 software and regression equations were developed. For surface roughness and cutting temperature, the values of 2.313 and 65.390C were determined using their respectively objective functions with their optimum parameters (values).
Keywords: Taguchi, ANOVA, signal to noise ratio, Minitab, insert, cutting temperature and surface roughnessReceived: April 19, 2022 Accepted: May 10, 2022 Published: May 18, 2022