CHAPTER 5 SINGLE OBJECTIVE OPTIMIZATION OF SURFACE ROUGHNESS IN TURNING OPERATION OF AISI 1045 STEEL THROUGH TAGUCHI S METHOD

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1 CHAPTER 5 SINGLE OBJECTIVE OPTIMIZATION OF SURFACE ROUGHNESS IN TURNING OPERATION OF AISI 1045 STEEL THROUGH TAGUCHI S METHOD In the present machine edge, surface roughness on the job is one of the primary goals of manufacturing industry especially, where the parts are either subjected to fatigue loading or require precision fits. The purpose of this study is to find out the optimum surface roughness in turning operation of AISI 1045 Steel with Tungsten carbide cutting tool through Taguchi technique. A L9 orthogonal array, S/N ratios and ANOVA are used with cutting speed, feed rate and depth of cut as turning parameters and with surface roughness as response variable. The result of the study show that the feed rate is the most influencing parameter out of the three parameters under study followed by cutting speed and depth of cut has less significant. Finally, the results are further confirmed by confirmation run. [80]

2 5.1 INTRODUCTION Surface roughness is a widely used index of a machined product and in most cases, is a technical requirement for mechanical products since achieving the desired surface quality is of great importance for their functional behaviour. However, the mechanism of surface roughness formation depends on various uncontrollable factors that make its estimation difficult. Much research time and effort has been devoted to studying surface roughness prediction (Alwarsamy T. et al., 2011). To produce any product of desired quality, it is necessary to select the proper machining parameters. Taguchi s approach is an important tool for optimizing a design for performance, quality and cost. Robust design is an engineering methodology for obtaining product and process condition, which are minimally sensitive to the various causes of variation, and which produce high- quality products with low development and manufacturing costs (Park, 1996). Signal to noise ratio and orthogonal array are two major tools used in robust design. Signal to noise ratio, which measures quality with emphasis on variation, and orthogonal arrays, which accommodates many design factors simultaneously (Park, 1996 & Phadke, 1998). In this study, objective is to obtained optimal values of turning process parameters (cutting speed, feed rate, depth of cut), for optimizing the surface roughness while machining AISI 1045 Steel with Tungsten carbide inserts. For this, L9 orthogonal array, S/N ratios and ANOVA are used for analysis. [81]

3 5.2 LITERATURE REVIEW Taguchi s parameter design offers a systematic approach for optimization of various parameters with regard to performance, quality and cost (Phadke, 1989). Taguchi method offers the quality of product is measured by quality characteristics such as: nominal is the best, smaller is better and larger is better (Phadke, 1998 & Ranjit, 2001). Antony et al. (2001) presents a step-by-step approach to the optimization of a production process of retaining a metal ring in a plastic body by a hot forming process through the utilization of Taguchi methods of experimental design. Feng and Wang (2002) develops an empirical model for the prediction of surface roughness in finish turning considering working parameters i.e. material, feed rate, cutting tool point angle, depth of cut, spindle speed and cutting time. Singh and Kumar (2006) uses Taguchi parameter design approach with utility concept for optimizing multi machining characteristics simultaneously. They used a single performance index, utility value as a combined response indicator of several responses. Gusri et al. (2008) applied Taguchi optimization methodology to optimize cutting parameters in turning Ti-6Al-4v ELI with coated and uncoated cemented carbide tools. They show that the cutting speed and type of tool have a very significant effect on the tool life, and the federate and type of tool have also a very significant effect on the surface roughness. Fnides et al. (2008) conducted tests on X38CrMoV5-1 steel treated at 50 HRC, machined by a mixed ceramic tool to study the influence of the following parameters: feed rate, cutting speed, depth of cut and flank wear on cutting forces and on surface roughness. Lan (2009) uses L 9 orthogonal array of Taguchi experiment is selected for optimizing the multi-objective machining for surface roughness, tool wear and material removal [82]

4 rate (MRR). By using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the multiple objectives can additionally be integrated and introduced as the signal to noise ratio in the Taguchi experiment. Gopalsamy et al. (2009) used L 18 array, S/N ratios and ANOVA to study the performance characteristics of machining parameters with consideration of surface finish and tool life. The parameters selected by them are cutting speed, feed, depth of cut and width of cut. Results of the study obtained by Taguchi method match closely with ANOVA and cutting speed is the most influencing parameter. Shinde et al. (2011) focuses on the effect of machining parameters on surface finish during turning operation. They considers speed, feed and depth of cut as machining parameters. Kaladhar et al. (2012) applied Taguchi method to determine the optimum process parameters for turning of AISI 304 austenitic steel on CNC lathe. They conducted tests at four levels of cutting speed, feed and depth of cut. The influence of these parameters are investigated on the surface roughness and material removal rate (MRR). Rodrigues et al. (2012) proposes a study for the effect of speed, feed and depth of cut on surface roughness and cutting force in turning mild steel using high speed steel cutting tool. Experiments were conducted on a precision centre lathe and the influence of cutting parameters was studied using analysis of variance (ANOVA) based on adjusted approach. [83]

5 5.3 OBJECTIVE OF THE STUDY The objective of the study is to optimize the surface roughness in turning operation of AISI 1045 steel using Tungsten carbide inserts the help of Taguchi s L9 orthogonal array, S/N ratios and ANOVA and also to find out the optimal levels of each cutting parameters (cutting speed, feed rate and depth of cut) with their percentage contributions. [84]

6 5.4 DESIGN OF EXPERIMENT AND EXPERIMENTAL DETAILS From the literature review it is cleared that cutting speed, feed rate and depth of cut are the three machining parameters that largely affect the surface roughness. Therefore these parameters are selected each at three levels (Table 5.1). An AISI 1045 steel rod of 80mm diameter with 400mm length was turned on Engine lathe of HMT using Tungsten Carbide inserts in dry condition. All the three edges of Tungsten Carbide positive rake triangular inserts were used for each trial condition. Thus 27 cutting edges of carbide inserts were used according to the trial condition specified by orthogonal array. Surface roughness was measured by Mitutoyo portable Surface Roughness tester. Since in this study it was assumed that no interaction exists between the machining parameters. Therefore, a three level orthogonal array with atleast 6 degree of freedom was to be selected which is L 9. The experimental layout using L 9 orthogonal array with responses values of surface finish, their mean values and corresponding signal to noise ratios are given in table 5.2. [85]

7 Table 5.1 Machining Parameters for Surface Roughness and their Levels Factors Factors Levels A. Cutting Speed(m/min) B. Feed Rate(mm/rev) C. Depth of Cut(mm) [86]

8 Table 5.2 L9 Orthogonal Array with Responses, Mean Surface Roughness and S/N ratios Run Cutting Feed Depth Surface Roughness Mean of S/N Speed Rate of Cut (µm) Surface Ratio(dB) (m/min) (mm /rev) (mm) Trial 1 Trial 2 Trial 3 Roughnes s [87]

9 Since, surface roughness is a lower the better type of quality characteristic (because objective is to minimize surface roughness), therefore, the S/N ratio for lower the better type of response was used which is given by the following equation: S/N = -10log.(1) Here, n represents the trial conditions (here it is three) and Y 1, Y 2,Y 3,...Y n represents the values of responses for quality characteristics.the signal to noise ratios were calculated using equation (1) for each of the nine trails and their values are also given in table 2. [88]

10 5.5 RESULTS AND DISCUSSION The mean value of the surface roughness for each machining parameters at different levels were calculated. These average values of surface roughness for each machining parameters at levels 1, 2, 3 are given in table 5.3 and Fig.5.1 Similarly, the average values of S/N ratios of all the three parameters at three different levels were calculated and are shown in table 5.4 and Fig Table 5.3 Response table for Mean Surface Roughness Level Cutting Speed Feed Rate Depth of Cut Delta Rank [89]

11 Table 5.4 Response table for S/N Ratios of Surface Roughness Level Cutting Speed Feed Rate Depth of Cut Delta Rank [90]

12 Mean of Means Main Effects Plot for Means Data Means Cutting Speed Feed Rate Depth of Cut Fig. 5.1 Main effects plot for Mean Surface Roughness [91]

13 Mean of SN ratios Main Effects Plot for SN ratios Data Means Cutting Speed Feed Rate Depth of Cut Signal-to-noise: Smaller is better Fig. 5.2 Main effects plot for S/N ratios of Surface Roughness [92]

14 It is clear from table 5.3 and Fig. 5.1 that surface roughness is minimum at 3 rd level of parameter A (cutting speed), 1 st level of parameter B (feed rate) and also at 3 rd level of parameter C (depth of cut). The S/N ratio analysis from table 5.4 and Fig. 5.2 also shows that the same results that surface roughness is minimum at A3, B1 and C3. The present study used ANOVA to determine the percentage contribution and optimum combination of process parameters more accurately by investigating the relative importance machining parameters. The results of ANOVA of the raw data or mean of response of surface roughness is given in table 5.5 and the results of ANOVA of S/N ratios is given table 5.6. It is evident from these tables that the cutting speed, feed rate and depth of cut significantly affect the value of surface finish. The percentage contributions all the machining parameters are quantified under the last column of both the tables. Both the tables suggests that the influence of feed rate (B) on surface roughness is significantly larger than the influence of cutting speed (A) and depth of cut (C). Effect of machining parameters on surface roughness are as follows: as the cutting speed increases surface roughness decreases, as the feed rate increases surface roughness also increases and as the depth of cut increases surface decreases. These effects of machining parameters are also inferred from Fig. 5.3, Fig.5.4 and Fig.5.5. The diagnostic checking has been performed through normal probability plot for the present study which is shown in Fig The residuals are generally fall on a straight line shows that errors are distributed normally. From the Fig.5.6 it can be concluded that all the values are within the confidence interval of 95%, therefore these values gives better results in future prediction within the limits. [93]

15 Table 5.5.Analysis of Variance for Mean Surface Roughness Source DOF Sum of Mean F-Ratio % Contribution Squares Square Cutting Speed Feed Rate Depth of Cut Error Total [94]

16 Table 5.6.Analysis of Variance for S/N Ratios for Surface Roughness Source DOF Sum of Mean F-Ratio % Contribution Squares Square Cutting Speed Feed Rate Depth of Cut Error Total [95]

17 Surface Plot of Mean vs Cutting Speed, Feed Rate 1.75 MEAN Feed Rate 150 Cutting Speed Fig. 5.3 Surface Plot of Mean Surface Roughness vs Cutting Speed, Feed Rate [96]

18 Surface Plot of Mean vs Cutting Speed & Depth of Cut 1.75 MEAN Depth of Cut 150 Cutting Speed Fig. 5.4 Surface Plot of Mean Surface Roughness vs Cutting Speed & Depth of Cut [97]

19 Surface Plot of MEAN vs Feed Rate, Depth of Cut 1.75 MEAN Depth of Cut Feed Rate Fig. 5.5 Surface plot of Mean Surface Roughness vs Feed Rate, Depth of Cut [98]

20 Percent Normal Probability Plot (response is Means) Residual Fig. 5.6 Normal Probability plot for Mean Surface Roughness [99]

21 5.6 PREDICTION OF OPTIMUM PERFORMANCE AND CONFIRMATION RUN The optimal value of surface roughness is predicted at the selected levels of machining parameters which are A3, B1 and C3.The estimated mean of surface roughness at optimal condition can be calculated by using the following formula: U SR = A3+B1+C3-2T SR Where, U SR = Predicted mean response of surface roughness T SR = Overall mean of tool flank wear width Therefore, U SR = µm Three confirmation runs were conducted at the selected optimal settings of turning process parameters. In this confirmation experiments the average surface roughness was found to be 0.92 µm which is very much close to the prediction. These results show that there is 30.76% decrease in surface roughness when working at optimal condition. [100]

22 5.7 CONCLUSIONS The experimental investigation was conducted to turn AISI 1045 steel bars using Tungsten carbide inserts at three levels of cutting parameters by using Taguchi technique to determine the optimum level of machining parameters. The response table, ANOVA and F-test revealed that the feed rate is most dominant parameter followed by cutting speed and depth of cut. The optimal combination of machining parameters for minimum surface roughness is obtained at 200 m/min cutting speed, 0.15 mm/rev feed rate and 0.2 mm depth of cut. With the experimental results it is found that surface roughness was decreased by 30.76%. In this study, the Taguchi method proves to be an effective technique in order to find out the optimum level of machining parameters. While the results declared here may be generalized to a considerable extent while working on AISI 1045 steel using Tungsten carbide tool, the study is limited to the extreme range of machining parameters used in the study. Any further extrapolation may be confirmed by again conducting the experiment. [101]