UDK 621.7.01:621.937:519.61/.64 ISSN 1580-2949 Original scientific article/Izvirni znanstveni članek MTAEC9, 49(1)139(2015) OPTIMIZATION OF THE PROCESS PARAMETERS FOR SURFACE ROUGHNESS AND TOOL LIFE IN FACE MILLING USING THE TAGUCHI ANALYSIS OPTIMIZACIJA PROCESNIH PARAMETROV GLEDE NA HRAPAVOST POVRŠINE IN TRAJNOSTNO DOBO ORODJA PRI ČELNEM REZKANJU Z UPORABO TAGUCHIJEVE ANALIZE Murat Sarikaya1, Hakan Dilipak2, Akin Gezgin3 1Department of Mechanical Engineering, Sinop University, 57030 Sinop, Turkey 2Manufacturing Department, Technology Faculty, Gazi University, 06500 Ankara, Turkey 3Mechanical Education, Institute of Science and Technology, Gazi University, 06500 Ankara, Turkey msarikaya@sinop.edu.tr Prejem rokopisa - received: 2014-02-13; sprejem za objavo - accepted for publication: 2014-03-12 In this study, the Taguchi method, which is a powerful tool to design quality optimization, is used to find the optimum surface roughness and tool life in milling operations. An orthogonal array, a signal-to-noise (S/N) ratio, and an analysis of variance (ANOVA) are employed to investigate the tool life and the surface-roughness characteristics of AISI D3 steel. Accordingly, the lowest surface roughness and the highest tool life were estimated to be 0.436 ^m and 434.1 s, respectively and, finally, the Taguchi method allowed the optimization of the system for the verification of the tests. Further, ANOVA analysis was revealed that the number of cutter insert was the most important parameter influencing the surface roughness with a 75.27 %, and cutting speed was the most important parameter influencing the tool life with a 95 %. Keywords: material machinability, face milling, Taguchi method, optimization, experimental design V tej študiji je bila uporabljena Taguchijeva metoda kot močno orodje za ugotavljanje razmer za doseganje optimalne hrapavosti in trajnostne dobe orodja pri rezkanju. Ortogonalna matrika, signal hrupa (S/N) in analiza variance (ANOVA) so bili uporabljeni za preiskavo zdržljivosti orodja in hrapavosti površine jekla AISI D3. Na podlagi rezultatov je bila ocenjena najmanjša hrapavost površine 0,436 ^m in največja zdržljivost orodja 434,1 s, Taguchijeva metoda pa je omogočila optimizacijo sistema pri verifikaciji preizkusov. Nadalje je ANOVA analiza odkrila, da je število rezalnih vložkov najbolj pomemben parameter, ki s 75,27 % vpliva na h^apa-vost površine, hitrost rezanja pa je najpomembnejši parameter, ki s 95 % vpliva na življenjsko dobo orodja. Ključne besede: obdelovalnost materiala, čelno rezkanje, Taguchijeva metoda, optimizacija, načrtovanje preizkusov 1 INTRODUCTION speed, the feed rate and the number of cutting inserts) on the surface roughness and the tool life at high cutting Basically, milling is one of the most commonly used speeds. The roughness of machined surface is an import- chip removal operations in manufacturing processes and ant quality indicator in machining processes and the machined parts are usually utilized to assembly with various properties of machined parts such as corrosion, other parts in aerospace, die, medical, automotive, de- wear, friction, and heat transmission are also influenced fense industry and machine design as well as in manu- by surface roughness.3,4 Most of the process parameters facturing industries.1 In addition to the cutting insert, the like spindle speed, feed rate, number of insert, depth of tool holder, workpiece material, cutting speed (V), feed cut, tool holder geometry, cutting insert geometry, tool rate (f), depth of cut (a) and number of milling cutting material, cooling condition affect the tool life and inserts are the most important cutting parameters that surface roughness. Thus, it is difficult to define a general highly affect the performance characteristics such as tool model for tool life and surface roughness.5 Some stati-life and surface roughness. stical methods like Taguchi, Response Surface Methodo-Generally, researchers have focused on the tool logy (RSM), desirability functional analysis, ANOVA deformation, the effects of cutting-tool coatings and and Grey Relational Analysis (GRA) have been applied environmental conditions using a single insert. The costs for optimization and analysis of process parameters. The of cutting tools are important to manufacturers. The cost optimization using Taguchi method has revealed a factor causes lowering to the minimum level of an unique and powerful optimization tool that differs from implementation with the least number of inserts. In traditional applications.6 For instance, Kivak et al.7 contrast, the increase in the duration of the process is studied the optimization of drilling parameters based on also a well-known factor.2 The aim of this study is to find the Taguchi method to minimize the surface roughness out the effects of the cutting parameters (the cutting (Ra) and thrust force (Ff). Their study showed that the cutting tool was the most significant parameter for the Ra. Moreover, the results of verification test demonstrated the Taguchi method for drilling operations was successful to obtain the better surface quality of the machined parts. Aslan et al.8 evaluated the Ra and cutting-tool wear during the machining of AISI 4140 (63 HRC) steel with an experiment according to the Taguchi's L27 orthogonal array. From the ANOVA table, it was found out that tool wear is affected by cutting speed with 30 %. They suggested a 250 m/min cutting speed, a 0.25 mm depth of cut and a 0.05 mm/r feed rate to minimize the Ra value. Gunay and Yucel9 investigated the Ra during the machining of high-alloy white cast iron with an experiment according to the Taguchi L18 orthogonal array. According to ANOVA data, they explained that the most important parameter was feed rate for Ni-Hard with 62 HRC although the cutting speed was the most important parameter for Ni - Hard with 50 HRC. Neseli et al.10 studied the influence of the tool geometry on the surface finish when turning the AISI 1040 steel with an Al2O3/TiC tool using the response-surface methodology (RSM). Their results indicated that the tool-nose radius was the dominant factor for the surface roughness with a 51.45 % contribution to the total variability of the model. Asilturk and Akkus11 applied the Taguchi method to minimize the surface roughness, which is Ra and Rz, in turning of hardened AISI 4140 steel (51 HRC) using coated carbide tools. In addition, their study explored the effects of the cutting speed, the feed rate and the depth of cut on the responses. From the ANOVA analysis, it was determined that the feed rate was the most significant parameter on results. Further, it was seen that the optimum machining parameters for Ra and Rz were different. Kacal and Gulesin12 optimized the machining parameters for the finish turning of austempered cast iron (GJS-400-15). Their experimental investigation was conducted based on Taguchi's L18 orthogonal array. Statistical analysis, which was ANOVA, demonstrated that feed rate is the most important factor with 69.5 % for surface roughness (Ra). In addition, they identified the best machining parameters for Ra to be: an austempering temperature of 290 °C, a ceramic tool, a cutting speed of 800 m/min and a feed rate of 0.05 mm/r. Sarikaya and Gullu13 studied the Taguchi design and a response-surface-methodology-based analysis of the machining parameters for CNC turning under MQL. It was found that the most effective parameter for the surface roughness is the feed rate. In addition, the cooling conditions also significantly affect the surface roughness. Kadirvel and Hariharan14 investigated the optimization of the die-sinking micro-edm process for multiple performance characteristics using the Taguchi-based grey relational analysis. Their study indicated that, on the basis of a confirmation test, the improvement in the performance characteristics was found to be as follows: MRR 3.86 %, TWR 4.20 % and SR 3.51 %. Kivak15 investigated the Taguchi-method-based optimization of drilling parameters when drilling the AISI 316 steel with a PVD monolayer- and multilayer-coated HSS drills. The analysis results revealed that the feed rate was the dominant factor affecting the surface roughness and the cutting speed was the dominant factor affecting the flank wear. Koklu16 investigated the influence of the process parameters and the mechanical properties of aluminum alloys on the burr height and the surface roughness in dry drilling using the Taguchi method. The analysis of variance and Taguchi techniques were applied in order to determine the effects of the drilling parameters. The literature survey demonstrates that traditional experimental design procedures are too complicated and not easy to use. A large number of experimental tasks have to be performed when the number of process parameters increases. The Taguchi method uses a special design of orthogonal arrays to study the entire parameter space with only a small number of experiments for solving this problem.11 The purpose of this study is to obtain the optimum milling parameters (the cutting speed, the feed rate, the number of cutting inserts) for the minimum surface roughness and the maximum tool life during the milling of AISI D3 steel. The Taguchi parameter-design approach was employed to achieve these goals. Moreover, a statistical analysis (ANOVA) was carried out to see which machining parameter is statistically significant. Finally, confirmation tests were conducted using the optimum cutting conditions determined with the Taguchi optimization method. 2 EXPERIMENTAL PROCEDURE 2.1 Milling experiments In this study, AISI D3 cold-work tool steel was used as the workpiece material. This steel type is used in many manufacturing industries such as the ones manufacturing cold-extrusion and drilling moulds, mould plates, powder-metallurgy kits, ceramic shaping moulds and cold punches. The dimensions of the workpiece were 100 mm X 48 mm x 300 mm. The chemical composition of the work material is as follows: 1.938 % C; 0.37 % Si; 0.22 % Mn; 10.66 % Cr; 0.22 % Ni; 0.135 % V; 0.062 % W; 0.07 % Cu; 85.82 % Fe. The milling tests were performed using a Johnford VMC 550 model, a three-axis CNC vertical machine centre, equipped with the maximum spindle speed of 8000 r/min and a 10 kW drive motor. The values of process parameters were selected from the manufacturer's handbook recommended and the preliminary experiments for the tested material. Process parameters and their levels are shown in Table 1. In all the experiments, the depth of cut was determined as 1 mm. The experiments were carried out under dry cutting conditions. To protect these experiment conditions for each test, a new cutting insert was used for each experi- Symbol Cutting parameter Levels for surface roughness Levels for tool life 1 2 3 1 2 3 A Cutting speed, y/(m/min) 80 120 180 416 500 600 B Feed rate, //(mm/r) 0.08 0.12 0.18 0.08 0.1 0.125 C Number of cutting inserts, z 1 3 6 1 3 6 Table 2: Standard tool holder and cutting insert Tabela 2: Standardno držalo orodja in rezalnega vložka Standard tool holder Standard cutting insert ASX445-080A06R SEMT13T3AGSN-JM D/mm d/mm L/mm Z ßa/mm D1/mm) ^1/mm) F1/mm) Re/mm 80 27 50 6 6 13.4 3.97 1.9 1.5 Levels for surface roughness Levels for tool life Cutting speed, V/(m/min) 80 120 180 416 500 600 Feed rate, //(mm/r) 0.08 0.12 0.18 0.08 0.1 0.125 C Number of cutting inserts, z 1 Table 2: Standard tool holder and cutting insert Tabela 2: Standardno držalo orodja in rezalnega vložka Standard tool holder Standard cutting insert ASX445-080A06R SEMT13T3AGSN-JM D/mm d/mm L/mm ßa/mm Di/mm) ^1/mm) F1/mm) ^e/mm 80 27 50 6 6 13.4 3.97 1.9 1.5 ment. The flowchart for the optimization of the milling parameters is shown in Figure 1. In the milling experi- Figure 1: Flowchart for optimization of milling parameters Slika 1: Diagram poteka optimizacije parametrov rezkanja ments, the coated carbide inserts manufactured by Mitsubishi Carbide were used. Based on the ISO description, they create a SEMT 13T3AGSN tool geometry and a JM-chip-breaker form. In the experiments, the ASX445-080A06R model of the tool holder was used. The geometry of the cutting tool is shown in Figure 2. The geometric features of the tools are listed Table 2. 2.2 Measurements One of the most important quality indicators of the machined parts is surface roughness or surface quality. According to the standard, the average surface roughness is defined as R^,. In present work, Ra was determined by using a Mahr Perthometer M1 with a cut-off length of 0.8 mm and a sampling length of 5 mm based on ISO 4287 standard. It was considered by measuring the mean of the three roughness results performed from different locations on the workpiece. Generally, the cutting tool-wear occurs in combination with the predominant wear mode, dependent upon the cutting parameters, cooling/lubrication conditions, workpiece material, cutting tool material and the tool insert geometry. The forms of the cutting-tool wear, Figure 2: Cutting tool employed in the experiments Slika 2: Rezalno orodje, uporabljeno pri preizkusih Flank face Figure 3: Conventional features of tool-wear measurements17 Slika 3: Običajne meritve obrabe orodja17 A B often expressed as the principal types of the tool wear are nose, flank, notch and crater wear types, and Figure 3 shows how these wear features are usually measured.17 In the present work, tool deformations were measured and investigated using a Mitutoyo light microscope with a 0.001 mm sensitivity and a capability of magnifying 5 to 10 fold. The experiments revealed that the notch wear was observed during the machining at high cutting speeds. The determination of the machining time was based on ISO 2688-1. When determining the tool life, VBmax was taken as 1 mm. The flank wear on the cutting tools was periodically measured and recorded to determine the tool life. control factors and three levels, the DFt is given as twenty-six. In order to make the performance comparisons between control factors and its different combinations, an OA having at least nine or twenty-seven test trials (DFt + 1) shoud be selected. Therefore, the standard L27 (33) OA is selected for the study and the surface-roughness and tool-life values are measured via the experimental design for each combination of the control factors. Determination of the quality characteristics of the measured control factors is provided by the S/N ratios. 3 RESULTS AND DISCUSSIONS 2.3 Control factors and orthogonal array The cutting speed V (m/min), feed rate f (mm/r) and number of cutting inserts (z) were selected as the control factors for the surface-roughness and tool-life values, and their levels were determined as shown in Table 1. The orthogonal array (OA) enables an effective tool to conduct the test with the small number of studies in the Taguchi experimental method. The total degree-of-free-dom (DFt) is the basis of orthogonal array for experimental design.18 In present work, because there are three 3.1 Analysis of the signal-to-noise (S/N) ratio With the Taguchi method we used the signal-to-noise (S/N) ratio as the quality characteristic of choice. In milling operations, a lower surface roughness and larger tool life are indications of a better performance. Therefore, in order to obtain the optimum machining performance, the smaller-the-better (Equation 1) and larger-the-better (Equation 2) ratios were selected for the minimum surface roughness and maximum tool life, respectively: Table 3: Experimental results, means and corresponding S/N ratios Tabela 3: Eksperimentalni rezultati, povpre~ja in ustrezna razmerja S/N Cutting-parameter level Measured Exp. No. A B C surface Calculated Measured tool Calculated, Cutting speed, V/(m/min) Feed rate, f/(mm/r) Number of inserts, z roughness, (S/N)/dB life, T/s (S/N)/dB 1 1 1 1 0.160 15.9176 435 52.7698 2 1 1 2 0.582 4.7015 426 52.5882 3 1 1 3 1.079 -0.6604 403 52.1061 4 1 2 1 0.383 8.3360 401 52.0629 5 1 2 2 0.784 2.1137 365 51.2459 6 1 2 3 1.194 -1.5401 338 50.5783 7 1 3 1 0.586 4.6420 369 51.3405 8 1 3 2 1.109 -0.8986 354 50.9801 9 1 3 3 1.400 -2.9226 328 50.3175 10 2 1 1 0.206 13.7227 266 48.4976 11 2 1 2 0.723 2.8172 236 47.4582 12 2 1 3 1.084 -0.7006 233 47.3471 13 2 2 1 0.456 6.8207 231 47.2722 14 2 2 2 0.869 1.2196 222 46.9271 15 2 2 3 1.261 -2.0143 193 45.7111 16 2 3 1 0.724 2.8052 216 46.6891 17 2 3 2 1.160 -1.2892 210 46.4444 18 2 3 3 1.409 -2.9782 167 44.4543 19 3 1 1 0.303 10.3711 118 41.4376 20 3 1 2 0.708 2.9993 110 40.8279 21 3 1 3 1.104 -0.8594 100 40.0000 22 3 2 1 0.552 5.1612 102 40.1720 23 3 2 2 0.945 0.4914 97 39.7354 24 3 2 3 1.290 -2.2118 86 38.6900 25 3 3 1 0.761 2.3723 97 39.7354 26 3 3 2 1.211 -1.6629 91 39.1808 27 3 3 3 1.449 -3.2214 82 38.2763 Smaller-the-better: — =-101og1Q Larger-the-better: ^ =-101ogio 1 n 1 n i=1 Ji (1) (2) Here, ji is the ith measure of the experimental results in a run/row and n gives the number of measurements in each trial/row test. The S/N ratios for the surface roughness and tool life were calculated using Equations 1 and 2, as shown in Table 3. The milling parameters were divided by considering different levels and possible effects, according to the selected orthogonal array. According to the experimental results, the mean of the surface roughness was 0.8701 ^m and its mean S/N ratio was 2.353 dB. The mean value of tool life and its mean S/N ratio were also calculated as 232.4 s, and 46.031 dB, respectively. Further, the effects of input parameters on the responses can be analyzed with help of S/N ratios. These effects are defined and evaluated according to the total mean values of the experimental-trial results or S/N ratios. The minimum surface-roughness and the maximum tool-life values can be calculated from the total mean values of the experimental-trial results for the surface roughness and tool life. An important requirement when calculating the optimum points is to identify the optimum levels of machining parameters. They were defined by assessing different levels of the input parameters, based on the results from combinations produced by the OA. The levels of the control factors were determined for both the surface roughness and tool life, presented in Table 4, and S/N graphics of these levels were used for the evaluation (Figures 4 and 5). As shown in Figure 4, the surface roughness is found to be minimal at low cutting speed and feed rate and the minimum number of cutting inserts. The surface roughness increases with a rise in the cutting speed for a given value of the feed rate. Since the temperature increases between the tool material and the workpiece material through the friction under higher cutting speed, the cut chips by the cutting tool stick over the cutting insert with help of the high temperature.19 Therefore, the surface roughness increases during the experiments at higher cutting speeds. The increased feed 1.31.21.11.00.90.80.70.60.5- v (m/min) f (mm/rev) z(pes) :.......y /■ 0.8701 pm ID 6- •a & £ s 80 120 180 0.08 0.12 0.18 1 3 6 Levels of the control factor V (m/min) f (mm/rev) z (PCS) N : \ 2.35 dB 80 120 180 0.08 0.12 0.18 1 3 6 Levels of the control factor Figure 4: Main effects plot of the factors and S/N graph for the surface roughness Slika 4: Prikaz glavnih faktorjev in odvisnost S/N od hrapavosti površine rate and number of cutting inserts lead to vibration, generating more heat and, thereby, a higher surface roughness occurs.20 The tool life based on the wear value of VBmax = 1 mm for all the experiments is drawn. According to Figure 5, the longest tool life was obtained at the lowest values of the cutting speed, feed rate and number of cutters. When Figure 5 is evaluated, it is possible to observe that an increased number of cutting inserts leads to a decreased tool life. This can be explained with the cutting temperature and the vibrations generated at the tool/chip contact when the number of cutting inserts is increased. It is known that a high cutting temperature occurs in the primary deformation zone Table 4: Response table for S/N ratios (dB) and means Tabela 4: Tabela odzivov (dB) za različna razmerja S/N in za različna sredstva Control Surface roughness (Ra) Tool life (T) factors Level 1 Level 2 Level 3 Delta Level 1 Level 2 Level 3 Delta S/N ratios A 3.30 2.26 1.49 1.80 51.55 46.76 39.78 11.77 B 5.37 2.04 -0.35 5.72 47.00 45.82 45.27 1.73 C 7.79 1.16 -1.90 9.69 46.66 46.15 45.28 1.39 Means A 0.81 0.88 0.92 0.12 379.89 219.33 98.11 281.78 B 0.66 0.86 1.09 0.43 258.56 226.11 212.67 45.89 C 0.46 0.90 1.25 0.79 248.33 234.56 214.44 33.89 V (m/min) f (mm/rev) z(pes) \; ; \ 1 \ 416 500 600 0.08 0.1 0.125 1 3 6 Levels of the control factor S 48 O s (A •2 44 V (m/min) f (mm/rev) z(pes) 416 500 600 0.08 0.1 0.125 1 3 6 Levels of the control factor Figure 5: Main effects plot of the factors and S/N graph for the tool life Slika 5: Prikaz glavnih faktorjev in odvisnost S/N od zdržljivosti orodja with the increasing cutting speed. Thus, the wear mechanisms are accelerated and so the tool life is decreased. The S/N ratios of the surface-roughness data obtained from the experimental work, later used to determine the optimum level of each variable, were calculated in Table 3. Figure 4 illustrates the graphs of the S/N ratios that were calculated for the surface roughness. As mentioned above, the maximum value of the S/N ratios gives the optimum cutting conditions. Thus, the optimum combination for the Ra was determined as AiBiCi (Ai = 80 m/min, Bi = 0.08 mm/r, Ci = 1 cutting insert). The S/N ratios for the tool-life data obtained from the experimental results were calculated in Table 3. Figure 5 illustrates the graphs of the S/N ratios that were calculated for the tool life. The optimum combination for the tool life was determined as AiBiCi (Ai = 416 m/min, Bi = 0.08 mm/r, Ci = i cutting insert). 3.2 Analysis of variance (ANOVA) The Taguchi method was used for determining the optimum cutting conditions according to the S/N ratio, while the control-factor correlation with the experimental results was determined with the help of an analysis of variance (ANOVA). The analysis of variance (ANOVA) was employed through the Minitab i6.0 Program. Table 5 shows the results of ANOVA for the surface roughness and tool life. In addition to the degree of freedom, the mean of squares (MS), the sum of squares (SS), the F-ratio, P-values and the contribution (PCR) associated with each factor were presented. This analysis was performed for a confidence level of 95 %. The importance of the input parameters in ANOVA analysis was identified by comparing the F-values of each input parameters. The F-value determined in the ANOVA table was compared with the value according to standard F-tables for a given statistical level of importance.21 According to the ANOVA table, the P-value is effective for all three levels at the reliability level of 95 %, because the results for the surface roughness and tool life are lower than 0.05. As the results of the evaluation of the surface roughness, the percentage contributions of input parameters for A, B and C were determined as: (i.63, 2i.96 and 75.27) %, respectively, and the error was i.i4 % (Table 5). Thus, it was found that the number of cutting inserts and the feed rate vary significantly more than the cutting speed regarding the surface roughness in milling the AISI D3 steel. The ANOVA table indicates that the variable most significantly affecting the surface-roughness value is the number of cutting inserts with 75.27 % of PCR. This result clearly shows the effects of the Table 5: Results of ANOVA for responses Tabela 5: Rezultati ANOVA-odzivov Variation of source Degree of freedom (DF) Sum of squares (SS) Mean of squares (MS) F-ratio P-value Contribution (%) Surface roughness (Ra) A 2 0.06i4i 0.0307i i4.26 0.000 i.63 B 2 0.8293i 0.4i466 i92.53 0.000 2i.96 C 2 2.84270 i.42i35 659.97 0.000 75.27 Error (e) 20 0.04307 0.002i5 i.i4 Total 26 3.77650 i00 Tool life (T) A 2 3596i5 i79807 975.62 0.000 95.00 B 2 i00i8 5009 27.i8 0.000 2.65 C 2 5228 26i4 i4.i8 0.000 i.38 Error (e) 20 3686 i84 0.97 Total 26 378547 i00 number of cutting inserts on the surface roughness in milling the AISI D3 steel whose vibration generated a higher frequency between the tool and the workpiece with the increasing number of cutting inserts. The other variable that has an effect on the Ra is the feed rate with 21.96 % of PCR. It is known that an increasing feed rate increases the chip volume removed per unit time.7 Accordingly, Table 5 shows that the percentage contributions of factors A, B, and C for the tool life are (95.00, 2.65 and 1.38) %, respectively, and the error is 0.97 %. Thus, Table 5 indicates that the most effective variable for the tool life is the cutting speed (95.00 %). The feed rate and number of cutting inserts do not affect the tool life. In milling operations, the cutting speed is the most effective cutting parameter, reducing the tool life, because an increasing cutting speed increases the cutting temperature in the primary deformation zone. As a result of this situation, the wear mechanisms are accelerated. 3.3 Verification experiments In the last step of the Taguchi approach, the optimization was confirmed via verification tests then the determination of the parameter levels giving the optimum results. The verification-test results were obtained for the optimum parameter levels (A1B1C1) of the surface roughness. Later, the calculation of the predicted minimum surface roughness Rap from Equation 3, taking into consideration individual effects of factors A, B, C and their levels, is as follows: Rap = ^Re +(A, - ^Re ) + (B1 - TRe) +(C, - TRe ) (3) where A, is the mean (0.81 pm) of the experimental trials at the first level of factor A. B1 is the mean (0.66 pm) of the experimental trials at the first level of factor B. Ci is the mean (0.46 pm) of the experimental trials at the first level of factor C. With Equation 3, the minimum surface roughness was calculated as 0.147 pm. As determined in Figure 5, A, B, C and their levels were used for the calculation of the predicted optimum tool life. The equation for the predicted optimum tool life is as follows: Tp = Tt + (Aj - TT )+(Bj - TT )+(C1 - TT ) (4) where A, B and C are the means (379.89, 258.56 and 248.33) s of the experimental trials at the optimum levels of these factors. With Equation 4, the maximum tool life was calculated as 441.67 s. The confidence interval (CI) was conducted to confirm the output parameters of the verification test. The CI for the estimated optimum results was calculated using the following equation .22 CI = . F„ ■ K, 1 1 Vn eff (5) confidence level and Ve is the degree of freedom of the pooled error variance. Vep is the pooled error variance, r is the number of repeated trials (r^), N is the total number of experimental trials, «eff is the number of effective measured results defined as22: N «eff 1+b (6) where, with respect to Fa;1;Ve, F is the ratio of significant level a, a is the significance level, 1 - a is the where b is total degress of freedom associated with items used in estimate. In the present investigation, three verification tests (r = 3) were made to assess the performance of the experimental study used for the surface roughness at optimum parameters (A1B1C1). The value of Fa;1;Ve = 4.30 which has a 95 % confidence level for the surface roughness, was found with respect to the values of a = 0.05 and Ve = 20, based on the look-up table. According to Equations 5 and 6, the confidence interval (CI) is calculated as 0.07. The result value of the confirmation test performed for the surface roughness is expected to be in the confidence interval of (0.147 ± 0.07) or (0.077 - 0.217) with a 95 % confidence level. In the present work, the values of the surface roughness from the three confirmation tests performed with regard to the optimum levels (A1B1C1) were measured as (0.159, 0.162 and 0.168) pm. As shown in Table 6, the mean of the measurements was 0.163 pm. The mean result of the confirmation tests in the optimum conditions is within the confidence interval (0.077 < 0.163 < 0.217). As the mean result falls within this limit, the experiment is considered satisfactory. The optimization of process parameters was achieved using the Taguchi method for the surface roughness at the confidence level of 95 %. Three confirmation experiments were carried out under the optimum conditions (A1B1C1) for the tool life. The value of Fa;1;ve = 4.30 which has a 95 % confidence level for the tool life, was found with respect to the values of a = 0.05, and Ve = 20, based on the look-up table. According to Equations 5 and 6, the confidence interval (CI) is calculated as 21.4 s. The result value of the confirmation test performed for the tool life is expected to be in the confidence interval of (441.7 ± 21.4) or (420.3 - 463.1) with a 95 % confidence level. In this study, the values of the tool life from the three confirmation tests performed with regard to the optimum levels (A1B1C1) were measured as (440.2, 436.4 and 425.7) s. As shown in Table 7, the mean of the measurements was 434.1 s. The mean result of the confirmation tests in the optimum condition is within the confidence interval (420.3 < 434.1 < 463.1). As the mean result falls within this limit, the experiment is considered satisfactory. The optimization of the process parameters was achieved using the Taguchi method for the tool life at the confidence level of 95 %. According to the optimum test and the predicted combination, the comparisons of the surface roughness and tool life, and the combinations Table 6: Comparisons of surface roughness and tool life Tabela 6: Primerjave hrapavosti površine in zdržljivosti orodja Comparison Surface roughness Tool life Level Ra/yjm (S/N)/dB Level T/s (S/N)/dB Initial combination A2B1C2 0.723 2.82 A2B1C2 236 47.45 Optimum combination (Experiment) AiBiCi 0.163 15.9 A1B1C1 434.1 52.74 Optimum combination (Prediction) A1B1C1 0.147 16.63 A1B1C1 441.67 52.92 Table 7: Comparisons of experimental trials Tabela 7: Primerjave eksperimentalnih poskusov Comparison Surface roughness Tool life Level Ra/^m Quality loss Level T/s Quality loss Initial combination A2B1C2 0.723 - A2B1C2 236 - Optimum combination (Prediction) A1B1C1 0.147±0.07 - A1B1C1 434.1±21.4 Optimum combination (Confirmation) A1B1C1 0.163 4.9 % A1B1C1 441.67 29.5 % (A2B1C2) selected in the twenty-seven initial trials are 4 CONCLUSIONS given in Table 6. According to the comparison table, the surface roughness and tool life are reduced from 0.723 pm to 0.163 pm, and from 236 s to 434.1 s, respectively. The improved efficiency due to the optimum combination was increased for the surface roughness and tool life by up to 77.45 % ((0.723 - 0.163)/0.723) and by up to 45.63 % ((434.1 - 236)/434.1), respectively. The performance comparisons between the initial parameters and the optimum conditions are given in Table 6. In addition to, the quality losses are listed in Table 7. The quality losses between the initial and optimum combinations for both the surface roughness and tool life are calculated as follows18: L op.(y) L,„,(y) ^ (7) where Lopt(y) and Lint(y) are the optimum and initial combinations, respectively. An is the difference between the S/N ratios for the optimal and initial combinations. The differences between S/N ratios that can be used to evaluate the quality loss of the optimum combination for the surface roughness and tool life, were found to be 13.08 (An = 13.08 (= 15.9 - 2.82)) and 5.29 (An = 5.29 (52.74 - 47.45)), respectively. The quality loss was calculated as 0.049 using Equation 7 for the surface roughness. Thus, the quality loss of the surface roughness for the optimum combination is only 4.9 % of the initial combination. Therefore, the quality losses of the surface roughness were reduced to 95.1 % through the Taguchi application. The quality loss for the tool life was calculated as 0.295, using Equation 7. The quality loss of the tool life for the optimum combination is only 29.5 % of the initial combination. Consequently, the quality losses of the tool life were reduced to 70.5 % through the Taguchi method. This study focuses on the Taguchi method used for investigating the influence of the cutting parameters on the surface roughness and tool life when face milling the AISI D3 steel. In the milling experiments, different levels of the cutting speed, the feed rate and the number of cutting inserts as the machining parameters are used under dry cooling conditions. The experimental results were evaluated using the analysis of the signal-to-noise ratio, the main effect graphs of means and ANOVA. The optimum operating parameters are determined using the Taguchi method. The results can be drawn as follows: The optimum levels of the control factors providing a better surface roughness and tool life were: A1 (the cutting speed, 80 m/min), B1 (the feed rate, 0.08 mm/r), and C1 (the number of cutting inserts, 1 insert), and A1 (the cutting speed, 416 m/min), B1 (the feed rate, 0.08 mm/r), C1 (the number of cutting inserts, 1 insert), respectively. The effects of the process parameters on the surface roughness and tool life were detected by ANOVA analysis. It was revealed that the number of cutting inserts the most important parameter influencing the surface roughness with 75.27 %. Further, cutting speed was the most important parameter influencing the tool life with 95 %. Through the confirmation experiment, the surface roughness was obtained, with one of the initial combinations, as 0.723 pm, and the surface roughness was improved to 77.45 %. The tool life was obtained, with one of the initial combinations, as 236 s, and it was improved to 45.63 %. The quality losses for the surface roughness and the tool life determined from optimum points were calculated as 4.9 % and 29.5 %, respectively. The confirmation-experiment results indicated that the observed values were within the calculated confidence interval (CI) for the confidence level of 95 %. 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