Sakip Koksal Department of Mechanical Engineering Sakarya University, Turkey (SAUT) Sakarya, Turkey

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1 Optimization of Cutting Parameters for Surface Roughness of Stainless Steel in Drilling Process Ferit Ficici Department of Mechanical Education Sakarya University, Turkey (SAUT) Sakarya, Turkey Sakip Koksal Department of Mechanical Engineering Sakarya University, Turkey (SAUT) Sakarya, Turkey M.Cemil Karacadag Department of Information Systems Sakarya University, Turkey (SAUT) Sakarya, Turkey Abstract Stainless steels have the characteristics of high strength, low thermal conductivity, high toughness and high work hardening coefficient which cause great difficulty in drilling processes. This paper searches the optimum cutting parameters when drilling an AISI 304 stainless steel using modified HSS drill tools. Also the application of Taguchi technique and analysis of variance (ANOVA) for minimization of surface roughness (Ra) influenced by drilling cutting parameters has been presented. The optimum drilling cutting parameter combination was obtained by using the analysis of signal-to-noise ratio. It has been concluded that modification of drill and feed rate were the most influential factors on the surface roughness (Ra). The best results of the surface roughness (Ra) were obtained at higher cutting speeds and lower feed rates by using 0.5 µm drill. The correlation between the cutting parameters and the surface roughness was determined using multiple linear regressions. The analysis of the confirmation experiments for the surface roughness has shown that Taguchi parameter design can successfully verify the optimum test. The difference between the value of the minimum predicted surface roughness and the actual surface roughness values from confirmation experiments is 4.34% with 99.5% confidence level. Keywords- Stainless steel; drilling; taguchi technique; anova I. INTRODUCTION Stainless steels resist chemical and electrochemical influences of atmosphere, water, gases, acids and bases. The main alloying elements are chromium and nickel. The first ensures the corrosion resistance, while the second extends the austenitic region into the environment temperatures[1]. These materials play an extremely important role in industry, in addition to their applications in automotive, aircraft, aerospace industries, building, and medical apparatus where high corrosion occurs [2,3]. The machining of stainless steel materials generally gives short tool lives, limited metal removal rate, large cutting forces and power consumption due to their high temperature strength, rapid work-hardening during machining and reaction with most tool materials at high cutting speeds [4]. The twist drill, the cutting geometry of which is much more complex than other cutting tools is most widely used in machining. Drill geometry, the drill and workpiece materials are the most important factors which influence the drill performance and the hole quality [5].Hole quality is very important in the aircraft, building and medical apparatus industries [6]. The hole diameter accuracy and the surface roughness inside the hole are the important quality characteristics of holes [5]. 114

2 Many researchers have concentrated on the determination In this paper the settings of drilling parameters were of the best drilling process. Hence, many numerical and experimental methods have been developed and used in the past decade in order to predict and determine significant parameters which affect the drilling process and hole accuracy. The effects of machining parameters on the hole quality in the drilling process have also been investigated. Adachi et al. [7] investigated the effect of cutting speed of 27 m/min and feed of 0.1 mm/rev on cutting parameters, such as cutting force and tool wear, during the drilling of austenitic stainless steel with TiN-coated drills. Lin [4] conducted experiments using TiN coated WC to drill stainless plates and found that high drilling speed and feed rate would create a large surface roughness and affect tool life simultaneously. Pirtini and Lazoglu [8] developed a new mathematical model for the estimation of cutting forces and surface roughness, based on the mechanics and dynamics of the drilling process. Ihsan et al. [9] carried out turning tests on AISI 304 austenitic stainless steel to determine the optimum machining parameters. Zafer and Sezgin [10] determined the best suitable cutting condition for the machining of AISI 304 stainless steels by considering the acoustic emission during the cutting process. The best cutting speeds and feed rates were determined with respect to flank wear, BUE, chip form, surface roughness of the machined samples and machine tool power consumption. A statistical analysis of hole quality was carried out by Furness et al.[10] They investigated the influence of feed rate and cutting speed on the measured hole quality specifications during dry drilling of hot rolled steel by using a full factorial analysis of variance. With the expectations of hole location error, the hole quality was affected by the cutting parameters. Asilturk and Akkus [11] have studied the optimization of turning parameters based on the Taguchi method to minimize surface roughness. Their study was conducted using the L9 OA in a CNC turning machine. Tests were carried out on hardened AISI 4140 with coated carbide cutting tools. determined by using Taguchi s experimental design method. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) and regression analyses were employed to find the optimal levels and to analyze the effect of the drilling cutting parameters on surface roughness of AISI 304 austenitic stainless steel. II.MATERIAL AND METHOD The cutting experiments were conducted by drilling in dry cutting conditions on a HAAS TM-1 three axis CNC milling machine equipped with a maximum spindle speed of rpm and a 5.6 kwdrive motor. Fig. 1 shows the CNC milling machine where the actual drilling is operated. The CNC part programs were created by employing Catia CAD/CAM software on a personal computer (PC), Intel Pentium IV at 2.8 GHz. Figure1. CNC milling machine and data measurement equipment used in the experimental works The drilling tool for the DIN 338 standard and the code of B that would be utilized in drilling AISI 304 stainless steel material were selected from the Turkish Machine Tool Industry Catalog (see Fig. 2). 115

3 Surface roughness Ra was measured using a pocket Mahr Surf PS1. A minimum of 10 measurements in the traverse direction were taken, the highest and lowest values were discarded and the average value was recorded (Fig.3). The measured Ra values were between 0.5 and 2.5 mm. The repeatability of the Figure 2. Drill modification presentation measurements was found to be in the range of 2-5%, which The dimensional and mechanical properties of the drilling tool was considered satisfactory for generating empirical models. are displayed in Table 1. HSS drills with diameters of 10 mm were used. TABLE 1. THE DIMENSIONAL PROPERTIES OF THE DRILLING TOOL Tool diameter Flute Tool material 10 mm 2 flute HSS Point angle Helix angle 35 0 Cutting speed 15, 20, 25 m/min Feed rate 0.08,0.12,0.16 mm/rev During the experiments, AISI 304 stainless steel in the form of a 30mm x 75mm x 15mm block was used as the workpiece material. Tables 2 and 3 provide detailed information on the chemical compositions and mechanical properties of AISI 304 stainless steel material. TABLE2. CHEMICAL COMPOSITION OF AISI 304 STAINLESS STEEL (%WT.) C 0,054 Si 0,640 Cr 18,200 Ni 9,110 Mo 0,009 Cu 0,140 Ti V W Co 0,400 Nb Pb Fe 69,700 TABLE 3. MECHANICAL PROPERTIES OF AISI 304 STAINLESS STEEL Workpiece materials Ultimate Tensile Stress (MPa) Yield Stress (MPa) Density (kg/m 3 ) Elongation (%) AISI Figure 3. Surface roughness measurement equipment A. Plan of experiments As per Taguchi s method the total DOF of the selected OA must be greater than or equal to the total DOF required for the experiment. So, an L27 (3 13 ) OA (a standard three-level orthogonal array) having 26 DOF was selected for the present work. Each selected parameter was analyzed at three levels. The control factors and their values at three levels are given in Table 4. TABLE 4.CONTROL FACTORS AND THEIR LEVELS Factors Process parameters Level1 Level2 Level3 A Drill modification condition B Cutting speed (m/min) C Feed rate (mm/rev) Surface roughness being a smaller the better type of machining quality characteristic, the S/N ratio for this type of response was used and is given below [13, 14]: 116

4 III. DATA ANALYSIS RESULTS AND DISCUSSION S / N ratio -10log (y1 y 2... y n ) n (1) Where y 1, y 2,,y n are the responses of the machining characteristic, for a trial condition repeated n times. The S/N ratios were computed using Eq. (1) for each of the 27 trials and the values are reported in Table5. TABLE 5.EXPERIMENTAL DESIGN USING L 27 ORTHOGONAL ARRAY Drill modification condition Cutting speed (m/min) Feed rate (mm/rev) Ra (µm) S/N ratio The plan of tests was developed with the aim of relating the influence of the cutting speed, feed rate and drill condition, with the surface roughness (Ra). The statistical treatment of the data was made in two phases. The first phase was concerned with the ANOVA and the effect of the factors and interactions. The second phase allowed us to obtain the correlations between the parameters. Afterwards, the results were obtained through confirmation tests. A. ANOVA and effect of factors Analysis of the influence of each control factor (A, B and C) on the surface roughness was performed with a so-called S/N response table, using a Minitab 16.1 computer package. The response table of the surface roughness is presented in Table 6 which indicates the S/N ratio at each level of control factor and how it was changed when settings of each control factor were changed from level 1 to level 3. TABLE 6.S/N RESPONSE TABLE FOR SURFACE ROUGHNESS Level A B C Delta Rank The control factor with the strongest influence was determined by differences values. The higher the difference, the more influential was the control factor. It can be seen in Table 6 and Table 7 that the strongest influence was exerted by the drill modification condition (factor A) and the cutting speed (factor B), respectively. 117

5 TABLE 7.MEANS RESPONSE TABLE FOR SURFACE ROUGHNESS Level A B C Delta Rank The main effects for S/N ratios are shown in Figure1. Optimal testing conditions of these control factors can be very easily determined from this graph. A response graph showed the change of the S/N ratio when the setting of the control factor was changed from one level to the other. The best wear rate was at the higher S/N values in the response graphs. It could be seen in Figure 1 that the initial optimum condition for the tested samples becomes A 3 B 3 C 1 for the main control factors. It is evident that the drill modification condition had the greatest effect on the optimal testing condition. Surface roughness values (Ra) were found to decrease with increasing cutting speed. In addition, surface roughness decrease was obtained with decreasing feed rate [2,15]. b) Figure 4. Main effects plots for surface roughness of stainless steel: (a) S/N ratio and (b) mean. It has been shown that decreasing the feed-rate and increasing the cutting speed is the best way to obtain a smaller surface roughness generated in the drilling of stainless steels. An ANOVA of the data was done with the surface roughness (Ra), with the objective of analyzing the drill tool modification of cutting speed and feed rate on the total variance of the results. Table 8.ANOVA Table for the surface roughness a) Source DF SDQ Variance F test F table P a (%) A b B b C b 6.25 AB c 4.50 AC d 0.29 BC pooled pooled pooled Error Total SDQ: sum of squares; DF: degrees of freedom; P: percentage of contribution a percentage of contribution. b,c 99.5% confidence level. d 90% confidence level. 118

6 Table 8 shows the results of the ANOVA with the surface approach. The optimal test conditions were set for the roughness (Ra). This analysis was undertaken for a level of significance of 0.5% and 10%, that is, for a level of confidence of 99.5% and 90%. The last columns of the previous tables indicate the percentage of contribution (P) of each factor on the total variation indicating then, the degree of influence on the result. From Table 8, one can observe that the drill modification condition (P 75%),cutting speed (P 14%) and feed rate (P 7%) have great influence on the surface roughness obtained, especially the tool modification. The interaction tool modification/cutting speed (P 5%) and tool modification/feed rate (P 0.5%) present lower percentages of significance of contribution on the surface roughness. However the interaction cutting speed/feed rate has no influence on the surface roughness. Thus, this interaction was pooled. B. Regression analysis significant factors and a selected number of trials were run under specified cutting conditions. The average of the results from the confirmation tests is compared with the predicted average based on the parameters and levels tested. The S/N ratios for the surface roughness can be estimated with the help of following prediction equations: ˆ surface roughness T ( A T) ( B T) ( C T) where ˆ is the predicted average, T the overall experimental average, and A 3,B 3, and C 1 is the mean response for the factors. By combining like terms, the equation reduces to ˆ 2 (3) surface roughness A3 B3 C1 T A new arbitrary combination of factor levels A 3, B 3, and C 1 is used to predict surface roughness by prediction equation and the S/N ratio is found to be ˆ db (Table 9.). (2) Mathematical models based on cutting parameters, such as drill modification condition, cutting speed and feed rate were obtained from regression analysis using MINITAB 16.1 statistical software to predict surface roughness. The model equation is as follows. An experiment is conducted under factor combination of A 3, B 3, C 1, and the result is compared with value obtained from the predictive equation as shown in Table 6. The resulting model seems to be capable of predicting surface with a reasonable accuracy. Surfaceroughness Cutting speed x Feed rate 2 R x Drill modification x In multiple linear regression analysis, R 2 is the value of the correlation coefficient and should be between 0.8 and 1. The regression model obtained from the surface roughness for drilling process of AISI 304 stainless steel matched very well with the experimental data (R 2 > 0.8). TABLE 9. RESULTS OF THE CONFIRMATION EXPERIMENTS FOR THE SURFACE ROUGHNESS Optimal control parameters Prediction Experimental Level A 3B 3C 1 A 3B 3C 1 S/N ratio for surface roughness (db) A confidence interval for the predicted mean of the confirmation run can be calculated using the following equation [13,16,17]: C. Confirmation test The experimental confirmation test is the final step in verifying the results drawn based on Taguchi s design CI F (1,n 2)xV e Ne 0.5 (4) 119

7 be observed that the difference between the value of where F (1, n 2 ) = The F value from the F table at a required the minimum predicted surface roughness and the confidence level at DOF 1 and error DOF n 2 actual surface roughness values from confirmation V e = Variance of error term (from ANOVA) experiments is 4.34%. N e =Effective number of replications REFERENCES S Total number of results (or number of ratios) N N e DOF of mean (=1 always)+dof of all factors included in the estimated of the mean The calculated confidence level is: CI IV.CONCLUSION 1.38 db In this study the optimal cutting condition for drilling operation of stainless steel was determined by varying cutting parameters through the Taguchi optimization technique. The selected L27 (3 13 ) orthogonal array with a total of 27 experimental tests, covered three control factors (tool type, cutting speed and feed rate). A few conclusions can be made from this study: Statistical results (at a 99.5% confidence level) show that the drill modification condition (A), cutting speed (B) and feed rate (C) influence the surface roughness in the drilling process by 74.25%, 13.72%, and 6.25%, respectively. The interaction of AxB had a much higher significant effect at 4.50% while interactions of AxC and BxC had no significant effect on the surface roughness. Deviations between actual and predicted S/N ratios for the surface roughness are negligibly small with 99.5% and 90% confidence levels respectively. The analysis of the confirmation experiments for the surface roughness has shown that Taguchi parameter design can successfully verify the optimum test parameters (A 3 B 3 C 1 ), where the drill modification condition = 0.5 µm (A 3 ), the cutting speed = 25 m/min (B 3 ) and the feed rate = 0.08 m/rev (C 1 ). It can [1] Groover MP, Fundamentals of modern manufacturing: materials, processes, and systems. Upper Saddle River, NJ: Prentice-Hall.3rd Ed 2007, ISBN: [2] Korkut I, Kasap M, Ciftci I, Seker U, Determination of optimum cutting parameters during machining of AISI 304 austenitic stainless steel. Mater Design 2004; 25: [3] Soy U, Numerical Modelling of Stress and Deflection Behaviour for Welded Steel Beam-Column. Steel and Composite Structures 2012; 12(3): [4] Lin TR, Cutting behavior of a TiN-coated carbide drill with curved cutting edges during the high-speed machining of stainless steel. J Mater Proces Tech 2002; 127:8-16. [5] Kurt Y, Bagci E, Kaynak Y, Application of Taguchi methods in the optimization of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes. Int J of Adv Manuf Tech 2009; 40: [6] Basile SA, Modeling transverse motions of a drill bit for process understanding. Precis Eng 1993; 15: [7] Adachi K, Arai N, Okita K, Ogawa K, Niba R.(1990), A study on drilling of SUS304 by TiN-coated drills. Jpn Society Precis Eng 1990; 24(3): [8] Pirtini M, Lazoglu, I, Forces and hole quality in drilling. International J Mach Tools and Manuf 2005; 45: [9] Zafer T, Sezgin Y, Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel. Mater Design 2004; 25: [10] Furness RJ, Wu CL, Ulsoy AG, Statistical analysis of the effects of feed, speed, and wear on hole quality in drilling. J Manuf Sci Eng 1996; 118: [11] Asilturk I, Akkus H, Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement 2011; 44:

8 [12] Ross PJ, In: Taguchi techniques for quality engineering, loss function, orthogonal experiments, parameter and tolerance design.1988, New York: McGraw-Hill Inc. [13] Kapsiz M, Durat M, Ficici F, Friction and wear studies between cylinder liner and piston ring pair using Taguchi design method. Adv Eng Softw 2011; 42: [14] Zhang JZ, Chen JC, Surface Roughness Optimization in a Drilling Operation Using the Taguchi Design Method. Mater and Manuf Processes 2009; 24: [15] Ficici F, Kapsiz M, Durat M, Applications of taguchi design method to study wear behaviour of boronized AISI 1040 steel. I J Phy Sci 2011; 6(2): [16] Soy U, Ficici F, Demir A, Evaluation of the Taguchi Method for Wear Behaviour of Al/SiC/B 4 C Composites. J Compos Mater 2012; 46(7):