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Top1. Introduction
The commonly used cutting tool material in conventional machine tools is high-speed steel (HSS). In modern industry the goal is to manufacture low cost, high quality products in short time. Turning is the first most common method for cutting and especially for the finishing machine parts. In a turning operation, it is important task to select cutting parameters for achieving high cutting performance. Usually, the desired cutting parameters are determined based on experience or by use of a handbook. Cutting parameters are reflected on surface roughness, surface texture and dimensional deviations of the product. Surface roughness, which is used to determine and to evaluate the quality of a product, is one of the major quality attribute of a turning product.
Surface roughness is a measure of the technological quality of a product and a factor that greatly influences manufacturing cost. It describes the geometry of the machined surfaces and combined with surface texture. The mechanism behind the formation of surface roughness is very complicated and process dependent. To select the cutting parameters properly, several mathematical models (Taguchi, 1990; Ross, 1988; Phedke, 1989; Disney & Pridmore, 1989; Youg & Tarng, 1998) based on statistical regression or neural network techniques have been constructed to establish the relationship between the cutting performance and cutting parameters. Then, an objective function with constraints is formulated to solve the optimal cutting parameters using optimization techniques. Therefore considerable knowledge and experience is required for this approach. In this study, an alternative approach based on the Taguchi method (Abuelnaga & EI-Dardiry, 1984; Chryssolouris & Guillot, 1990; Chua, Rahman, Wong, & Loh, 1993) is used to determine the desired cutting parameters more efficiently. Mohan Lal, Renganarayanan, and Kalanidhi (2001) studied the improvement in wear resistance and the significance of treatment parameters in different tool and die materials. The results indicated that cryogenic treatment imparts nearly 110% improvement in tool life. Nalbant, Gökkaya, and Sur (2007) stated that the Taguchi method is used to find optimal cutting parameters for surface roughness in turning. The orthogonal array, the signal-to-noise ratio, and analysis of variance are employed to study the performance characteristics in turning operations of AISI 1030 steel bars using TiN coated tools. Three cutting parameters namely, insert radius, feed rate, and depth of cut, are optimized with considerations of surface roughness.
Singh Hari (2008) performed experimentation to obtain an optimal setting of turning process parameters –cutting speed, feed and depth of cut, which may result in optimizing tool life of TiC coated carbide inserts while turning En24 steel (0.4% C). The effects of the selected process parameters on the tool life and the subsequent optimal settings of the parameters have been accomplished using Taguchi’s r design of experiments approach. The results indicate that the selected process parameters significantly affect the mean and variance of the tool life of the carbide inserts. The percent contributions of parameters as quantified in the S/N pooled ANOVA envisage that the relative power of feed (8.78%) in controlling variation and mean tool life is significantly smaller than that of the cutting speed (34.89%) and depth of cut (25.80%). The predicted optimum tool life is 20.19 min.