Prediction of Surface Roughness in Boring Operation on Mild Steel using Taguchi Approach

Abstract

Surface quality is one of the specified customer and technical requirements for machined parts. There are many cutting parameters that have an effect on surface roughness, but those are difficult to quantify adequately. In a finished boring operation, many parameters such as cutting speed, feed and depth of cut are known to have a large impact on surface quality. In this paper, experimental work on a Jig boring machine has been conducted to study the effect of major cutting parameters on surface roughness. During this experimental work, the surface roughness is measured at different levels with a set of machining parameters. These results are used for optimizing cutting parameters for low-surface roughness or fine-finished surface. This project report discusses the use of Taguchi methodology, an orthogonal array, S/N ratio and analysis of variance (ANOVA) in order to optimize the selected cutting parameters for minimizing the surface roughness in machining mild steel work piece with carbide tip cutting tool on a Jig boring machine tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. The experimental results reveal that the most significant machining parameter is cutting speed that affects the surface roughness of the part in a finished boring operation. The predicted values and experimentally measured values are fairly close, and they are confirmed by using validation experiments.