Optimization of CNC Turning for EN36 Alloy Steel Using Coated Carbide insert

Optimization of CNC Turning for EN36 Alloy Steel Using Coated Carbide insert

Shalaka Jadhav

P.G. Student

Department of Mechanical Engineering Karmaveer Bhaurao Patil of Engineering Satara, India

Vijay Sabnis

Associate Professor Department of Production Engineering

Karmaveer Bhaurao Patil of Engineering, Satara Satara, India

Abstract: This study applies Taguchis design of experiment methodology for optimization of process parameters in CNC Turning of EN36 Alloy Steel using coated carbide. Experiments have been carried out based on L27 orthogonal array with three process parameters namely cutting speed, feed and depth of cut for surface roughness, material removal rate and cylindricity. Experiments were conducted under dry environment. The optimal parameter combinations for surface roughness, material removal rate and cylindricity were found out. ANOVA was performed to know which input parameter has most significant effect on performance measures.

Keywords: CNC Turning, EN36 Alloy Steel, Taguchi, ANOVE

1. INTRODUCTION

   Turning is basic and most widely used cutting operation in metal cutting industries [1]. Modern industries strive hard to achieve improved quality of product and this can be achieved by selection of proper material and method [2]. S.J. Raykar et.al [3]The study optimized the cutting parameters for high speed turning of Al7075, were grey relational analysis has been used for multi-optimization. The recommended cutting parameters were 200 m/min speed, 0.1 mm/rev feed, and 0.5 mm depth of cut with coated carbide insert in dry condition.

   M. Murali Mohan et.al [4] the optimization of EN36 Alloy steel was performed using RSM. Results showed that temperature is mainly affected by depth of cut while surface roughness by feed. Sayak Mukherjee et.al [5] studied effect of cutting parameters on material removal rate was studied. An optimum combination was obtained and the study was also useful for computer aided process planning. Shreemony kumar Nayak et.al [6] paper aims at investigating influence of different cutting parameters on different performance measures in dry turning of AISI304 using ISO P30 uncoated cemented carbide cutting tool. Optimum parametric combinations were found out for different responses. Even attempt was made to simultaneously optimize machining parameters using grey relational analysis. 88.76% of improvement was found in GRG. Sundrendra Kumar Saini et.al [7] in this research work CNC Turning of Al8011 is performed using carbide inserts. The optimum sets as well as combined effect were estimated using Taguchi-fuzzy

   application. Analysis showed that feed is the most significant process parameter followed by depth of cut and cutting speed.

   B. Singarvel et.al [8] analyzed, optimization is performed using taguchi based utility concept coupled with principal component analysis turning of EN25 steel with PVD and CVD coated tools. Results showed that principle component analysis is successfully employed for estimation of weight factors. The result of ANOVA shows that coated tool is most significant parameter followed by cutting speed. R. Deepak Joel Johnson et.AL [9] an effort has been made to reduce the quantity of cutting fluid used. The optimization of cutting parameters and fluid application parameters was done for turning of OHNS steel with minimal cutting fluid application using taguchi technique. The results clearly indicated that minimal cutting fluid application enhanced the cutting performances by improving surface finis. Harsh Y Valera et.al

   [10] presents experimental study of power consumption and surface roughness for turning of EN31 alloy steel. Result showed that all cutting parameters significantly affect the responses. Ashok Kumar Sahoo et.al [11] studied taguchi DOE and regression analysis for optimization of process parameters in turning AISI 1040 steel using coated carbide insert under dry condition. L9 orthogonal array was used. Optimum parameter combinations were found. Further grey relational analysis combines with taguchi method has been proposed. Results show good agreement between estimated value and experimental value. The improved grey relational grade is found to be 0.284. B. C. Routaru et. al [12] made an attempt has been made to optimize the surface roughness prediction model using genetic algorithm to find optimum cutting parameters [12]

2. MATERIAL

The work piece material selected for this study was EN36 Alloy steel which is widely used in disc wheels, grooved parts, and gears, heavy duty gears for aircrafts, heavy vehicles and automobile parts. Workpiece used was a cylindrical bar having dimensions Ø34 mm x 90 mm. Chemical composition was checked at Material test Laboratory, Mumbai.

Table 1: Chemical composition of EN36 Alloy steel (%)

III METHODOLOGY

Taguchis method is one of the effective experimentation techniques in improving quality and cutting down cost at same time. In Taguchis method, quality is measured by deviation of

2. Higher-the-better

=-10log (2)

a characteristic from its target value. A loss function is developed for this deviation. Since the elimination of noise factors is impractical and often impossible, the taguchi method seeks to minimize the effect of noise and to determine the optimal level of important controllable factors based on concept of robustness. Taguchi method uses a special design of orthogonal array to study the entire parameters space with only a small number of experiments. ANOVA was performed to find the most significant factor that affect response.

In current study Smaller-the- better characteristics is used for Surface roughness and Cylindricity whereas Higher- the- better characteristics is used for Material removal rate.

The equations for calculating S/N ratios for as follows:

1. Lower-the-better

=-10log (1)

IV EXPERIMENTAL SETUP

Figure.1 shows experimental setup for machining EN36 Alloy steel using coated carbide insert. Turning experiment was conducted using Feeler CNC Lathe FTC-20. All the experiments were conducted under dry environment. Coated carbide insert was used as cutting tool with ISO Coding TNMG160408MP and with tool holder PTLNR2020. All experimental runs were carried out under dry environment.

The experiment was carried out with out with controllable 3- level factors and 3 response variable. The present paper deals with 3 process parameters i.e. , Cutting speed, Feed and Depth of cut and 3 response factors, Surface roughness (Ra), Material Removal Rate (MRR) and Cylindricity (Ø).

Based on Taguchis design of experiment and orthogonal array L27, total 27 experiments were carried out. Table 2 shows process parameters and their levels.

Figure.1: Photographic view of experimental setup for machining

.

Table 2: Process Parameters and their levels

V. RESULTS AND DISCUSSION

All twenty-seven experimental runs are tabulated in Table 3 along with input parameters setting and experimental results. Reading for respective performance measure was taken. Mitutoya surface roughness was used for measurement of surface roughness (Ra). Cylindricity was measured using CMM.

Table 4 shows experimental results and S/N ratios for Ra, MRR and Cylindricity

Table 3: Taguchi L27 Orthogonal Array for experimental runs and results

Table 4: Experimental results and S/N ratios for Ra, MRR and Ø

The optimal para,eteric combinations for each performance measure were found by main effect plots for S/N Ratios.

The level of parameter with highest S/N ratio gives the optimal level.

Figure 2 shows main effect plot for Ra. The optimal prameteric combination for Ra is 2f11. Thus, optimum parameter value for minimum surface roughness is 150 m/min cutting speed, feed 0.18 mm/rev and depth of cut is 0.5 mm. Further ANOVA was performed. Table 5 shows ANOVA for Ra.

The experimental results were analyzed using analysis of variance (ANOVA) for identifying the significant factors affecting the performance measures. The results of ANOVA for Ra are shown in Table 7.This analysis was carried out for a significance level of 0.05 (Confidence level of 95 %). The ANOVA result shows that, the F-value for the cutting speed and feed is larger than that of the depth of cut i.e. the largest contribution to the workpiece surface roughness or finish is due to the feed rate. Feed rate (the most significance factor) contributed 55.06 % for Ra.

Figure 2: Main effects plot for S/N ratio of Ra

Table 5: ANOVA for Ra

Figure.3: Main effects plot for S/N ratio of MRR

Figure 3 shows main effect plot for MRR. The optimal prameteric combination for MRR is 3f33. Thus, optimum parameter value for minimum surface roughness is 160 m/min cutting speed, feed 0.22 mm/rev and depth of cut is

1.5 mm. Further ANOVA was performed. Table 6 shows

ANOVA for MRR. Table 6 shows the realized significance levels, associated with the F-tests for each source of variation. The ANOVA result shows that the F-value for the depth of cut and feed is larger than the cutting speed i.e. the largest contribution to the MRR is due the depth of cut. Depth of cut contributes 83.73% for MRR.

Table 6: ANOVA for MRR

Figure 4: Main effect plot for S/N ratios of Cylindricity

Figure 3 shows main effect plot for Cylindricity. The optimal prameteric combination for Cylindricity is 1f13. Thus, optimum parameter value for minimum surface roughness is 140 m/min cutting speed, feed 0.18 mm/rev and depth of cut is 1.5 mm. Further ANOVA was performed. In Table 7, the ANOVA result shows that the F-value for the feed rate and depth of cut is larger than that of the cutting speed. The largest contribution to the cylindricity is of depth of cut i.e. 42.03%. The percent contribution of the second most significance factor i.e. feed was found to be 30.85%.

Table 7: ANOVA for Cylindricity

VI CONCLUSION

In this study, the effects of cutting speed, feed and depth of cut on surface roughness, material removal rate and cylindricity during CNC Turning of EN36 Alloy Steel were investigated using Taguchis experimental design. The final conclusion arrived, at the end of this work are as follows:

• From this analysis, the optimal parametric combinations for Ra, MRR and Cylindricity were found.

• The optimal parametric combinations for Ra, MRR and Cylindricity is 2f11, 2f22, 1f13 respectively.

• ANOVA was performed; Ra is most significantly affected by feed rate whereas MRR and Cylindricity is most significantly affected by Depth of cut.

• Thus Taguchi method is powerful and effective design of experiment technique.

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