Optimisation of machining parameters for turning operations based on response surface methodology

Abstract

Design of experiments has been used to study the effect of the main turning parameters such as feed rate, tool nose radius, cutting speed and depth of cut on the surface roughness of AISI 410 steel. A mathematical prediction model of the surface roughness has been developed in terms of above parameters. The effect of these parameters on the surface roughness has been investigated by using Response Surface Methodology (RSM). Response surface contours were constructed for determining the optimum conditions for a required surface roughness. The developed prediction equation shows that the feed rate is the main factor followed by tool nose radius influences the surface roughness. The surface roughness was found to increase with the increase in the feed and it decreased with increase in the tool nose radius. The verification experiment is carried out to check the validity of the developed model that predicted surface roughness within 6% error.

Introduction

Surface roughness is one of the most important requirements in machining process, as it is considered an index of product quality. It measures the finer irregularities of the surface texture. Achieving the desired surface quality is critical for the functional behaviour of a part. Surface roughness influences the performance of mechanical parts and their production costs because it affects factors, such as friction, ease of holding lubricant, electrical and thermal conductivity, geometric tolerances and more. The ability of a manufacturing operation to produce a desired surface roughness depends on various parameters. The factors that influence surface roughness are machining parameters, tool and work piece material properties and cutting conditions. For example, in turning operation the surface roughness depends on cutting speed, feed rate, depth of cut, tool nose radius, lubrication of the cutting tool, machine vibrations, tool wear and on the mechanical and other properties of the material being machined. Even small changes in any of the mentioned factors may have a significant effect on the produced surface [1].

Therefore, it is important for the researchers to model and quantify the relationship between roughness and the parameters affecting its value. The determination of this relationship remains an open field of research, mainly because of the advances in machining and materials technology and the available modeling techniques. In machinability studies investigations, statistical design of experiments is used quite extensively. Statistical design of experiments refers to the process of planning the experiments so that the appropriate data can be analysed by statistical methods, resulting in valid and objective conclusions [2]. Design methods such as factorial designs, Response Surface Methodology (RSM) and taguchi methods are now widely use in place of one factor at a time experimental approach which is time consuming and exorbitant in cost.

Previously, most published studies show the tendency to seek effect of cutting conditions like cutting speed, feed rate and depth of cut on surface roughness as well as less number of trials (Table 1). Present study seeks to find out the effect of above parameters and cutting geometry such as tool nose radius on the surface roughness value and 81 number of experiments. Thiele and Melkote [3] had used a three-factor complete factorial design to determine the effects of work piece hardness and cutting tool edge geometry on surface roughness and machining forces. These models concluded that the effect of the two-factor interaction of the edge geometry and work piece hardness on the surface roughness is also found to be important. Mital and Mehta [4] have conducted a survey of surface prediction models developed and factors influencing the surface roughness. They have developed the surface finish models for aluminium alloy 390, ductile cast iron, medium carbon leaded steel, medium carbon alloy steel 4130, and inconel 718 for a wide range of machining conditions defined by cutting speed, feed and tool nose radius. They concluded that cutting speed, feed and tool nose radius have a significant effect on the surface roughness. Sundram and Lambert [5], [6] have developed the mathematical models for predicting the surface roughness of AISI 4140 steel during the fine turning operation using both TiC coated and uncoated tungsten carbide throw away tools. Noordin et al. [7] studied the application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. They concluded that feed was the most significant factor that influences the surface roughness, however (SECA)² and (feed × SECA) also provide contribution for the surface roughness. Suresh et al. [8] have developed a surface roughness prediction model for turning mild steel using a response surface methodology to produce the factor effects of the individual process parameters. Surface roughness prediction model has been optimised by using genetic algorithms (GAs). The Taguchi method was used by [9], [16], [17] to find the optimal cutting parameters for turning operations. The study found that feed rate and tool nose radius having highest effect. Choudhury and El-Baradie [10] revealed that cutting speed was the main influencing factor on the tool wear, followed by the feed rate and the depth of cut. Munoz and Cassier [11] developed mathematical model for surface roughness for different types of steel such as AISI 1020, AISI 1045 and AISI 4140. They found that surface finish improves by increasing cutting speed and tool nose radius and by decreasing the feed rate. The depth of cut does not seem to have a significant influence on surface finish. Fang and Wang [12] developed an empirical model for surface roughness using two level fractional factorial design (2⁵⁻¹) with three replicates considering work piece hardness, feed rate, cutting tool point angle, cutting speed and cutting time as independent parameters using non linear analysis. Paulo Davim [13], the cutting speed has greater influence on the roughness followed by the feed and depth of cut has no significant influence on surface roughness found by using the Taguchi method. Lee, Tarng and Jaun [14], [15] have developed a system for measuring surface roughness of turned parts through computer vision system. They extracted the features of the surface image and thus predicted the surface roughness of the turned parts using the image of the turned surface and turning conditions. Petropoulos et al. [18] had used multi regression analysis and ANOVA for statistical study of surface roughness in turning of PEEK composite. The result for all three PEEK’S examined increase in feed causes significant increase in all the surface roughness, increase of cutting speed was favourable, as decreases roughness but only slightly. Nikolaos et al. [19] used 2³ full factorial design for AISI 316L steel with three variables named feed, speed and depth of cut for application of femoral head. The established equation showed that the depth of cut was the main influencing factor on the surface roughness. It increased with increasing the depth of cut and feed rate respectively, but it decreased with increasing the cutting speed. Nikos [20] used Response Surface Methodology (RSM) and fuzzy logic system through the Adaptive Neuro-Fuzzy Inference System (ANFIS) for Ti6Al4 V titanium alloy. The feed rate has been verified as the most important parameter for the surface of Ti6Al4 V. The polynomial models that have been employed to predict the surface roughness produced during the Ti6Al4 V turning, only the 2FI model was successful in R_a prediction. Lalwani et al. [21] used RSM for investigations of cutting parameters influence on cutting forces and surfaces finish in hard turning of MDN250 steel and concluded that good surface roughness can be achieved when cutting speed and depth of cut are set nearer to their high level of the experimental range and feed rate is at low level of the experimental range. Mohamed Dabnum et al. [22] describe the development of surface roughness model for turning glass ceramic (MACOR) utilising design of experiment and response surface methodology and showed that the feed rate was the main influencing factor on the roughness, followed by cutting speed and depth of cut. Choudhury and EL-Baradie [23] developed surface roughness prediction model for turning of EN 24T utilising response surface methodology. The results have revealed that the effect of feed is much more pronounced than the effect of cutting speed and depth of cut on the surface roughness. However, a higher cutting speed improves the surface roughness.

The aim of the present study has been, therefore to develop the surface roughness prediction model of AISI 410 steel with the aid of statistical method under various cutting conditions. By using response surface methodology and (3⁴) full factorial design of experiment, quadratic model has been developed with 95% confidence level.