INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET)

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1 INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN (Print) ISSN (Online) Volume 3, Issue, January- April (202), pp IAEME: Journal Impact Factor (20):.2083 (Calculated by GISI) IJMET I A E M E OPTIMIZATION OF SURFACE ROUGHNESS IN TURNING HIGH CARBON HIGH CHROMIUM STEEL BY USING TAGUCHI METHOD R. R. Deshmukh, V. R. Kagade 2 Department of Mechanical Engineering, Jawaharlal Nehru Engineering College, Aurangabad Department of Mechanical Engineering, Shree Tuljabhavani College of Engineering,Tuljapur ABSTRACT vijaykagade@gmail.com The surface quality of the machined parts is one of the most important product quality characteristics and one of the most frequent customer requirements. In this study the Taguchi robust parameter design for modelling and optimization of surface roughness in dry single-point turning of high carbon high chromium steel using TiCN,Al 2 O 3,TiN coated carbide inserts was presented. Three cutting parameters, the RPM speed (753, 984, 225 rpm), the feed rate (0.07, , 0.04 mm/rev), and the depth of cut (0.7,.2,.7 mm), were used in the experiment. Each of the other parameters was taken as constant. The average surface roughness (Ra) was chosen as a measure of surface quality. The experiment was designed and carried out on the basis of standard L 9 Taguchi orthogonal array. The data set from the experiment was employed for conducting the optimization procedures, according to the principles of the Taguchi method. The results of calculations were in good agreement with the experimental data. A certain discrepancy between the experimental results and calculations could be interpreted as the presence of measurement errors, many irregularities and deficiencies in the turning process, as well as environmental effects. The results presented in this work confirm the effectiveness of Taguchi s technique in optimization of cutting processes. Key words: turning process, surface roughness, Taguchi method, regression analysis, ANOVA. 32

2 . INTRODUCTION Among various cutting processes, turning process is one of the most fundamental and most applied metal removal operations in a real manufacturing environment. The surface roughness of the machined parts is one of the most significant product quality characteristics. This characteristic refers to the deviation from the nominal surface of the third up to sixth order. The actual surface profile is the superposition of error of the form, waviness and roughness. The order of deviation is defined in international standards. The surface roughness greatly affects the functional performance of mechanical parts such as wear resistance, fatigue strength, ability of distributing and holding a lubricant, heat generation and transmission, corrosion resistance, etc. The perfect surface quality in turning would not be achieved even in the absence of irregularities and deficiencies of the cutting process, as well as environmental effects. There are various parameters used to evaluate the surface roughness. In the present research, the average surface roughness (Ra) was selected as a characteristic of surface finish in turning operations. It is the most used standard parameter of surface roughness. In a machining process, there are two sharp and often conflicting requirements. The first is high-quality surfaces and the second is high production rate. An extremely high quality surface can produce higher production costs and time consumption. Therefore, the machine tool operators would not push the machine tool and/or cutting tool to its limit, rather using less risky process factors for that reason, which neither guarantees the achievement of the desired surface quality nor attains maximum production rate or minimum production cost [6]. Hence, it is of great importance to exactly quantify the relationship between surface roughness and cutting conditions. The materials having high-tensile strength and resistance to wear and impact, which are frequently used for drawing dies, blanking dies, forming dies, bushings, gauges, etc., are generally difficult to machine[5]. HCHCr-High carbon high chromium steel is one of these materials. For the machining of High carbon high chromium steels, the cutting tool materials must be harder than the work piece materials. These types of materials can be machined with carbide tools []. Among the cutting parameters affecting machining variables for steel, speed has maximum effect & depth of cut has minimum effect. Tool tip temperature increases with increase in cutting speed. At high speeds, surface finish is least affected. Surface finish deteriorates at high feed rates; hence to obtain good surface finish, feed rate may be kept low. At low speeds cutting force are high & tendency of work material to form a built up edge is also stronger. At lower speeds, surface roughness increases with increasing feed but at higher speeds surface roughness is less dependent on feed [2]. The Taguchi parameter design method is an efficient experimental method in which a response variable can be optimized, given various control and noise factors, and using fewer experimental runs than a factorial design [3]. This paper demonstrates the application of the Taguchi method for identifying the optimal cutting parameters for surface roughness in dry turning of high carbon high chromium steel. 2. LITERATURE REVIEW Suresh Dhiman, et al [2008] studied effect of cutting parameters (feed, speed, depth of cut) of AISI 08 steel on various factors (tool tip temperature, surface roughness, and cutting forces) that account for machining costs. A cylindrical bar of AISI 08 steel (length 25 mm, diameter 25 mm) was used to carry out experiments on lathe by HSS single point cutting tool without using any coolant. Among the cutting 322

3 parameters affecting machining variables for AISI 08 steel, speed has maximum effect & depth of cut has minimum effect. Tool tip temperature increases with increase in cutting speed. At high speeds, surface finish is least affected. Surface finish deteriorates at high feed rates; hence to obtain good surface finish, feed rate may be kept low. Annealing & normalising AISI 08 steel would improve machinability by coarsening pearlite. At low speeds cutting force are high & tendency of work material to form a built up edge is also stronger. At lower speeds, surface roughness increases with increasing feed but at higher speeds surface roughness is less dependent on feed [2]. E. Daniel Kirby, et al [2006] presented an application of the Taguchi parameter design method to optimizing the surface finish in a turning operation. The Taguchi parameter design method is an efficient experimental method in which a response variable can be optimized, given various control and noise factors, and using fewer experimental runs than a factorial design. The control parameters for this operation included: spindle speed, feed rate, depth of cut, and tool nose radius. Noise factors included varying room temperature, as well as the use of more than one insert of the same specification, which introduced tool dimension variability. A total of 36 experimental runs were conducted using an orthogonal array, and the ideal combination of control factor levels was determined for the optimal surface roughness and signal to noise ratio. A confirmation run was used to verify the results, which indicated that this method was both efficient and effective in determining the best turning parameters for the optimal surface roughness [3]. 3. EXPERIMENTAL DETAILS This experiment was conducted using the hardware listed in Table on CNC lathe machine as shown in the figure. Table : Hardware list Sr. No. Item CNC lathe 2 Surface roughness measurement device 3 Cutting tool inserts Specifications Lakshmi Machine Works (LMW), Model- SMARTURN Spindle: Max. Speed rpm; Max. Power Rating 7.5/5.5 KW; Spindle Nose- Flat (Dia. 40); Spindle Bore Dia.-53 mm Travels & Feed rates: X-axis- 05 mm; Z-axis- 320 mm; Rapids on X-axis- 20 m/min; Rapids on Z axis- 20 m/min General: Weight kg; Power requirement- 5 KVA ; 3-phase : 45 V; wire-4; frequency- 50 Hz Model: Surf test SJ20 P (Mitutoyo) Cut-off Values (lc): 0.8mm; Evaluation Length(es): 2.5mm; Drive speed:measuring:0.25mm/s,0.5mm/s),returning:0.8mm/s; Power Supply:00-240V AC, 50-60Hz,Via AC adapter/built-in rechargeable battery; Roughness parameter: Ra in µm; Stylus tip radius: 5 µm; Measuring force: 4 mn. ISO Grade-TNMG MC;Grade:TT500;Composition:TiCN,Al 2 O 3,TiN; Coat:MTCVD 323

4 The cutting parameters (design factors) considered in the present paper were RPM speed (N), feed rate (f), and depth of cut (t). Other parameters were kept constant for the scope of this research. The average surface roughness (Ra) was chosen as the target function (response, output). Since it was obvious that the effects of factors on the selected function were nonlinear, the experiment was set up with factors at three levels (Table 2). The factor ranges were chosen with different criteria for each factor, in order to use the widest possible ranges of values. Also, the possibility of mechanical system and manufacturer's recommendation were taken into account. Values in coded form Figure : CNC Lathe Table 2: Process variables and their limits Spindle Speed (N) (RPM) Process variables 324 Feed ( f ) (mm/rev) Depth of cut (t) (mm) Based on the selected factors and factor levels, a design matrix was constructed (Table 3) in accordance with the standard L 9 Taguchi orthogonal array (OA). The three levels of each factor were denoted by -, 0 and. This design provided uniform distribution of experimental points within the selected experimental hyper-space and the experiment with high resolution. Table 3: Taguchi s L9 Orthogonal Array Factorial combination Depth Spindle Sr. Feed ( f ) of cut Speed (N) No. (mm/rev) (t) (RPM) (mm)

5 The cutting parameters in experiment were changed according to different cutting conditions for each trial. All of the trials were conducted on the same machine tool, with the same tool type and the same other cutting conditions. Longitudinal dry turning of steel bars was performed on a CNC lathe. The cylindrical bars, with a diameter of 34.5 mm and length of 00 mm, were fixed in the lathe with a three-jaw chuck. The workpiece material used in the experiment was high carbon high chromium steel (C-.5% or more than 2%, Cr-2%, Mo-% & some traits of W & V; hardness HRC) [4]. TiCN, Al 2 O 3, TiN coated carbide inserts, type TNMG (Taegu Tec) of TT500 grade, were used for turning. The average surface roughness (Ra) of machined workpieces was measured using Surf test SJ20 P (Mitutoyo) profilometer (Figure 2). Figure 2: Equipment for surface roughness measurement The average surface roughness values shown in Table 4 are the arithmetical mean of three measurements. Table 4: Experimental data of surface roughness Sr. No. Speed in RPM Feed in mm/rev 325 Depth of cut in mm Surface Roughness, Ra in µm

6 RESULTS & DISCUSSIONS From Table 4 which shows Experimental Data Related to Surface roughness Design of Experiment [DOE] using Taguchi s Analysis, Regression Analysis & ANOVA for Main effects plot has been done using MINITAB 6. application software. Results of the same with their respective graphs & interpretations are mentioned below in the sequential order. 4. Taguchi Design Taguchi Orthogonal Array Design L 9 (3**3) Factors: 3 Runs: 9 Columns of L 9 (3**4) 4.. Taguchi Analysis: Ra in µm versus Speed in RPM, Feed in mm/rev, Depth of cut in mm Table 5: Response Table for Signal to Noise Ratios Smaller is better Level N f t Delta Rank

7 Main Effects Plot for SN ratios Data Means 0 N f 5 0 Mean of SN ratios t Signal-to-noise: Smaller is better Graph :- Main Effects Plot for SN ratios [Ra] Interpretation: The response table for the surface roughness data show that: For the S/N ratios, RPM speed is ranked, depth of cut 2 & feed 3 which are justified using main effects plot for SN ratios as shown in the above graph. 4.2 Regression Analysis 4.2. Regression Analysis: Ra in µm versus Speed in RPM, Feed in mm/rev, Depth of cut in mm Box-Cox transformation of the response with specified lambda = 0 Regression Equation, ln (Ra) = N f t (i) Table 6: Coefficients Term Coef SE Coef T P Const N f t Summary of Model S = R-Sq = 88.49% R-Sq(adj) = 8.58% PRESS = R-Sq(pred) = 6.9% Table 7: Analysis of Variance 327

8 Source DF Seq SS Adj SS Adj MS F P Regression N f t Error Total Percent Residual Plots for Ra Normal Probability Plot Residual Versus Fits Residual Fitted Value Histogram Versus Order Frequency 2 Residual Residual Observation Order Graph 2: Residual Plots for Ra in µm 8 9 Interpretation of Graph 2: From Histogram: - Histogram is skewed. From Normal probability plot: - For the Ra data, the residuals appear to follow a straight line although the negative tail falls slightly away from the line. No evidence of nonnormality, skewness, outliers, or unidentified variables exists From Residual versus Fits: - The residuals are scattered randomly about zero From Residual versus Order: - For the Ra data, the residuals appear to be randomly scattered about zero. 328

9 4.3 ANOVA-Main Effects Plot Main Effects Plot for Ra Data Means N f 3 2 Mean t Graph 3: Main Effects Plot for Ra in µm 4.3. Interpretation of Main Effects Plot for Ra in µm Speed has the most dominant effect on the observed surface roughness, followed by depth of cut and feed rate, whose influences on surface roughness are smaller. The surface roughness is continuously improved with the increase in speed, but increase in feed rate and depth of cut causes a significant deterioration of surface finish. 4.4 Comparison of Experimental values with Regression Analysis equation values: Table 8: Comparison of Experimental values with Regression Analysis equation values Experimental Values Regression Values Exp. No. Ra in µm Ra in µm

10 From the above table 8 of comparison of Experimental values with Regression Analysis equation values obtained from regression equation (i) it is clear that regression equation (i) perfectly fits for experimental values shown in table 4. Table 9: Optimization from S/N ratio for Ra N F t Since the category smaller-the-better is adopted, it is evident from Graph that the optimal combination of factor levels, which gives the lowest value of the average surface roughness (taking higher values of S/N ratio for each predictor), is as shown in table 9 above & the same is checked from regression equation (i) as shown in table 0 below. Table 0: Check from Regression Analysis equation N F t Ra CONCLUSION & FUTURE SCOPE Among the cutting parameters affecting the surface roughness for HCHCr steel, speed has maximum effect & feed has minimum effect. At high speeds, surface finish is least affected. At low speeds surface roughness increases with increasing feed but at higher speeds surface roughness is less dependent on feed. Also from results of ANOVA main effects plots it is clear that these results fully support the conclusions derived earlier. The future scope for the further development of this experimental investigation is recommended as follows:. Study the effects other predictors such cutting tool geometry, nose radius, etc. on responses which we studied in this experimental investigations and other responses like tool tip temperature, Material removal rate, tool wear, cutting force, etc. 2. Various kinds of other cutting tool materials can be used for the experimental investigation of turning operation on the same workpiece material. NOMENCLATURE List of Abbreviations and Symbols ANOVA Analysis of Variance CNC Computer Numerical Control DOE Design of Experiment TiN Titanium Nitride TiCN Titanium Carbo Nitride Al 2 O Aluminium Oxide MTCVD Medium Temperature Chemical Vapour Deposition 330

11 ACKNOWLEDGMENT I offer my most sincere humble thanks to my esteemed guide Prof.R.R.Deshmukh, Mechanical Engineering Department, Jawaharlal Nehru Engineering College, Aurangabad. Not only for giving his valuable guidance, unflagging encouragement and inspiration, but also for his never-ending willingness to tender generous. It is with humble gratitude & sense of indebtedness; I thank my respected & esteemed Head of Mech. Engg. Department Prof. A.B. Kulkarni & Principal Dr.S.D. Deshmukh for their valuable guidance, suggestion & constant support which lead towards successful completion of this work inspite of their busy departmental & academic schedule. I am also thankful to Mr Gaike Sir & all other staff members at Swagati Engineering Company, W-52, MIDC, Chikalthana, Aurangabad for making available all the facilities for the successful completion of this work. REFERENCES [] Tosun N., Ozler E. L, Optimisation for hot turning operations with multiple performance characteristics, International Journal of Advanced Manufacturing Technology, (2004) 23: DOI 0.007/s [2] Dhiman Suresh, Sehgal Rakesh, Sharma S K & Sharma Vishal S, Machining behaviour of AISI 08 steel during turning, Journal of Scientific & industrial Research; vol.67, May 2008, pp [3] Kirby E. Daniel, Zhang Zhe, Chen Joseph C, Chen Jacob Optimizing surface finish in a turning operationusing the Taguchi parameter design method, International Journal of Advanced Manufacturing Technology (2006) 30: DOI 0.007/s [4] V.D.Kodgire & S.V.Kodgire Material Science & Metallurgy, 6th Edition, 2005, Everest Publishing House, Pune, ISBN No , Pg. No. 438 [5] O.P.Khanna, Material Science & Metallurgy, th Reprint: 2007, Revised Edition: 999, Dhanpat Rai Publicatons (P) Ltd., New Delhi, Pg. No [6] Marinkovic Velibor & Madic Milos, Optimization of surface roughness in turning alloy steel by using Taguchi method, Scientific Research and Essays Vol. 6(6), pp , 9 August, 20; Available online at ISSN Academic Journals 33