EFFECT OF CUTTING SPEED, FEED RATE AND DEPTH OF CUT ON SURFACE ROUGHNESS OF MILD STEEL IN TURNING OPERATION

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1 EFFECT OF CUTTING SPEED, FEED RATE AND DEPTH OF CUT ON SURFACE ROUGHNESS OF MILD STEEL IN TURNING OPERATION Mr. M. G. Rathi1, Ms. Sharda R. Nayse2 1 mgrathi_kumar@yahoo.co.in, 2 nsharda@rediffmail.com 1Assistant Professor, Department of Mechanical Engineering, Government College of Engineering Aurangabad, (MS), India 2Student, Department of Mechanical Engineering, Government College of Engineering Aurangabad, (MS), India Abstract This paper deals with effect of cutting parameters (cutting speed, feed rate and depth of cut) on a surface roughness in turning operation on mild steel of (21% C) by high speed steel cutting tool in dry condition and as a result of that the combination of the optimal levels of the factors was obtained to get lowest surface roughness. Experiments have been conducted using Taguchi s experimental design technique. An orthogonal array, the signal to noise ratio, and the analysis of variance are employed to investigate cutting characteristics of mild steel using high speed steel. Experimental results reveal that among the cutting parameters, the cutting speed is most significant machining parameter for surface roughness followed by feed and depth of cut. Keywords Mild steel, surface roughness, turning operation, Taguchi method, S/N ratio. I. INTRODUCTION Today, the manufacturers facing many challenges to increase the production rate by decreasing operation cost to enhance the quality the of the product to fulfill the customer requirements and satisfaction. Product designers constantly strive to design machinery that can run faster, last longer, and operate more precisely than ever. Modern development of high speed machines has resulted in higher loading and increased speeds of moving parts. Bearings, seals, shafts, machine ways, gears, for example must be accurate both in dimensionally and geometrically. Unfortunately, most manufacturing processes produce parts with surfaces that are unsatisfactory from the standpoint of geometrical perfection or quality of surface texture. Among the several factors machining factors will affect them most. Among these machining parameters, cutting speed, feed rate and depth of cut play a significant role in machining quality that are controlled by the user. Therefore, suitable selection of these parameters is necessary to reach optimal machining conditions to enhance production efficiency. Mild steel has a relatively low tensile strength, but it is cheap and malleable, surface hardness can be increased through carburizing. Carbon content makes mild steel malleable and ductile, but it cannot be hardened by heat treatment. Since the turning is the primary operation in most of the production process in the industry, surface finish of the turned components has greater influence on the quality of the product. Surface finish in turning has been found to be influenced in varying amounts by a number of factors such as feed rate, work hardness, unstable built up edge, speed, depth of cut, cutting time use of cutting fluids etc. These are three primary input control parameters in the basic turning operations. They are cutting speed, feed rate and depth of cut. Cutting speed always refers to the spindle and work piece. Feed is the rate at which the tool advances along its cutting path. Depth of cut is the thickness of the material that is removed by one pass of the cutting tool over the work piece. I. MATERIALS AND METHODS The present research work reflects the usage of L9 Taguchi orthogonal array design as the effect of three different parameters (cutting speed, feed rate and depth of cut) on the surface roughness of the sample of mild steel was aimed after turning operations were done 9 times in Vishwa Tooling at Waluj Aurangabad (M.H.) India followed by measurements of surface roughness around the part with the help of Taylor Hobson surface finish tester in Mikronics lab, Chikhlathana, Aurangabad, India. The total length (50 mm) and diameter (20 mm)of the three samples are same and the surface roughness measurement were taken of each 20 mm around each workpiece. The turning operations were performed by high speed steel cutting tool in dry turning.mild steel with carbon (0.21%), manganese (0.64%) was selected as sample work piece material. The values of three input control parameters for the turning operation are as under: Page 30

2 Table I: Details of turning operations Factors Level 1 Level 2 Level 3 Cutting Speed (rpm) Feed Rate (mm/rev) Depth of Cut (mm) Trial Cutting Speed (rpm) Table II: Assignment of factors in L9 array Feed (mm/rev) Depth of Cut (mm) Surface Roughness (µm) S/N Ratio II. REGRESSION ANALYSIS Mathematical model for cutting speed, feed and depth of cut of mild steel sample work piece are obtained from regression analysis to predict surface roughness. In multiple linear regression analysis, R2 is a value of the correlation coefficient and should be in between 0.85 and 1. In this study, results obtained from surface roughness in good agreement with regression model (R2 > 0.85) i.e. matched very well with the experimental data. So the relation is acceptable. Table III: Regression Statistics Multiple R R Square Adjusted R Square Standard error Observations 9 Page 31

3 III. ANALYSIS OF S/N RATIO The aim of any experiment is always to determine the highest possible S/N ratio for the result. A high value of S/N implies that the signal is much higher than the random effects of the noise factors or minimum variance. As mentioned earlier three quality characteristics, i.e. the lower is better, higher is better and nominal is best. A lower surface is always preferred for long life, with reduced maintenance and man power and hence lower is better. S/N characteristics can be expressed as, Where, n = number of test in a trial, yi = the value for the ith test in that trial, Lj = overall loss function MSD = Lj = ( i2) Signal to noise ratio according to lower is better quality characteristics as follows, S = 10 log(msd) N MSD = mean square derivation for output characteristics. From the S/N ratio analysis, the optimal parameter are variable m/s Cutting speed (Level 3), mm/rev Feed rate (Level 3) and 0.8 mm Depth of cut (Level 3). Main Effects Plot for SN ratios Data Data Means Means SP EED F E E D Mean of SNrati os DEPTH OF CUT Signal- to-noise: Smaller is better Fig 1: Main effect plot for S/N ratio The influence of each control factor (cutting speed, feed and depth of cut) on the surface roughness was analyzed from the S/N ratio response table, which express the S/N ratio at each level of control factor. The control factor influence is determined by its level difference values. A bigger control factor level difference means a greater influence on surface roughness. It has been seen from table VII delta difference between higher and lower value of S/N ratio, is higher for depth of cut factor that is 3.53 then for factor feed is 1.06 and followed by cutting speed factor that is 0.82 so it is concluded that depth of cut factor has greatest influence on surface roughness of sample work piece. MainEffects Plot for Means Data Speed Feed Mean of Means Dept 0 of h cut Fig 2: Main effect plot for means of mean Page 32

4 From the main effect plot, factor A (Cutting speed) level3, factor B (Feed) level 3 and factor C (Depth of cut) level 3. As per taguchi method of DOE to get a optimal level of a parameter S/N ratio should have higher, means the level where S/N ratio is higher that the value parameter at that level will be optimum, from above graph it can be seen that in all three parameter level 3 has the highest S/N ratio for the cutting speed at level 3 value is 500 rpm, for feed at level 3 value is mm/rev and for depth of cut at level 3 value is 0.6 mm. Table IV: ANOVA Table for Means ANOVA DF SS MS F Significance F Regression Residual Total From table VI, optimal parameters of Turning Operation were A1, B3 & C2. Table VI shows that SN Ratio (SNR) of the surface roughness for each level of the factors. The difference of SNR between level 1 and 3 indicates that Cutting Speed contributes the highest effect ( max-min = 1.2) on the surface roughness followed by Feed Rate ( max-min=0.6) and Depth of Cut ( max-min = 1.01) Table V: ANOVA Table for Signal-to Noise Ratio for the Response Data ANOVA DF SS MS F Significance F Regression Residual Total Therefore the predicted optimum value of surface roughness βp (Surface roughness) = [ ]+[ ]+[ ]=1.06 From table VII, optimal parameters of Turning Operation were A3, B2 and C1. Table VII shows that SN Ratio (SNR) of the surface roughness for each level of the factors. The difference of SNR between level 1and 3 indicates that Depth of Cut contributes the highest effect ( max-min = 3.53) on the surface roughness followed by Feed Rate ( max-min = 1.06) and Cutting Speed ( max-min = 0.82). Therefore the predicted optimum value of surface roughness ηp(surface roughness) = [-6.67-(-7.108)]+[-6.65-(-7.108)]+[-5.45-(-7.108)] = Table VI: Response Table for Average Surface Roughness Level Cutting Speed (A) Feed Rate (B) Depth of Cut (C) Delta ( max-min) Rank Page 33

5 Table VII: Response Table for Signal-to-Noise Ratio of Surface Roughness Level Cutting Speed (A) Feed Rate (B) Depth of Cut (C) Delta( max-min ) Rank Therefore the predicted optimum value of surface roughness ηp(surface roughness) = [-6.67-(-7.108)]+[-6.65-(-7.108)]+[-5.45-(-7.108)] = IV. ANALYSIS OF ANOVA ANOVA was used to determine the significant parameters influencing the surface roughness of the sample work piece. The percent contribution of each factor in the total sum of square can be used to evaluate the importance of the factor change on the performance characteristic. Additionally the F value named after fisher can be used to determine which factor significantly affects the performance characteristic. Larger F value indicates that the variance of the input parameter makes a big change on the performance. According to this analysis, the most effective parameters with respect to surface roughness of sample work piece are cutting speed, feed and depth of cut. Percentage contribution indicates the relative power of factor to reduce variation. For a factor with high percentage contribution, a small variation will have great influence on the performance. According to table depth of cut was found to be major factor affecting the surface roughness, whereas feed found to be second ranking factor, the percentage contribution of depth of cut is much lower than two other parameters. Table VIII: Results of ANOVA Factor Degree of Freedom Sum of Squares Mean square % Contribution F-Ratio Cutting speed Feed Depth of Cut Error Total Page 34

6 V. RESULT AND DISCUSSION Comparing F values of ANOVA Table IV and V of surface roughness with the suitable F values of the Factors and their interactions respectively for 95% confidence level respectively show that the Depth of Cut (F =27.98 and F = 32.55) and was only the significant factor and other two factors feed and cutting speed are the factors found to be insignificant. Main effect plot for means: Fig 1 and Fig 2 show the effect of the each level of the three parameters on surface roughness for the mean values of measured surface roughness at each level for all the 9 trial runs. From Table VI, Table VII and Fig 1, Fig 2, optimal levels of the parameters for minimum Surface Roughness are first level of Depth of Cut (C1) i.e. 0.2 mm, second level of Feed (B2) i.e rev/min and first level of cutting speed (A1) i.e. 190 rpm. APPENDIX β, η = Surface roughness (µm) β p, η p = Predicted surface roughness (µm) DF = Degree of freedom SS = Sum of square F= Ratio of SS and MS R = Regression coefficient REFERENCES [1] [2] [3] [4] Diwakar Reddy. V, ANN Based Prediction on Surface Roughness in Turning, International Conference on Trends in Mechanical Engineering, Bangkok, [5] Mahapatra S.S, Parametric Analysis and Optimization of Cutting Parameters for Turning Operations based on Taguchi Method, Proceedings of the International Conference on Global manufacturing and Innovation, pp , July [6] Raghuwanshi B.S, A Course in Workshop Technology Vol. II (Machine Tools), Dhanpat Rai & Company Pvt. Ltd, [7] C. Vidal, V Infante, P. Pecas, P. Vilaca, Application of Taguchi Method in the Optimization of an aeronautic aluminum alloy, Departmento de engenharia Mecanica, Instituto Superior Tecnico, Av. Rovisco Pais, Lisboa, Portugal. Page 35