International Journal of Advanced Engineering Research and Studies

E-ISSN2249–8974

Research Article

COMPARISION OF SURFACE ROUGHNESS AND TEMPERATURE BETWEEN CUBIC BORON NITRIDE (CBN) AND CERAMIC CUTTING TOOLS WHEN MACHINING AISI52100 STEEL K. SUBRAMANYAM. P. V. RANGARAO. Prof. C. ESWARA REDDY

Address for Correspondence Department of mechanical Engineering, S. V. University, Tirupati, Andhra Pradesh 517562, India ABSTRACT This paper describes a comparison of surface roughness and tool tip temperature between ceramics and cubic boron nitride (CBN) cutting tools when machining AISI 52100 hardened steel using the Taguchi method. An orthogonal design, signal-to-noise ratio (S/N) and analysis of variance (ANOVA) were employed to determine the effective cutting parameters on the surface roughness. The results indicated that in case of surface roughness the feed rate (f) was found to be a dominant factor, followed by the cutting speed (V), lastly the tool hardness (TH) and in case of tool tip temperature speed was found to be dominant factor, followed by the tool hardness (TH), lastly the feed rate (f). The mixed alumina ceramic cutting tool showed the best performance in case of surface roughness and CBN cutting tool showed the best performance in case of tool tip temperature. In addition, optimal testing parameters were also determined. The confirmation of Experiment was conducted to verify the optimal testing parameter. Improvements of the S/N ratio from initial testing parameters to optimal cutting parameters or prediction capability depended on the S/N ratio and ANOVA results. Moreover, the ANOVA indicated that in case of surface roughness the feed rate was higher significant but other parameters were also significant effects at 90% confidence level. The percentage contributions of the feed rate, cutting speed and tool’s hardness were about 50.93, 39.76, and 2.41 on the surface roughness respectively, and in case of tool tip temperature cutting speed was higher significant but other parameters were also significant effects at 90% confidence level. The percentage contributions of the cutting speed, tool’s hardness and feed rate were about 81.05, 13.38 and 3.32 on the tool tip temperature respectively.

KEYWORDS: ANOVA; Surface roughness; Tool tip temperature; Hard turning; Optimization; Signal to noise ratio involving hard cutting tools such as CBN and 1. INTRODUCTION Engineers want materials with long service lives, and ceramics. processes for shaping them into finished products Various studies have been conducted to investigate with tight geometric tolerances and excellent surface the performance of CBN and ceramic tools when finish. Hardened steel is one such material, used machining hard steels or hardened steels. Eras Aslant particularly in the automotive industry for and Kneecap Caucus [1] have optimized the cutting components such as bearings, gears, shafts, and parameters when turning of hardened AISI 4140 steel cams. Soft steel must be hardened to increase the (63 HRC) with a ceramic tool. Combined effects of strength and wear resistance of parts made from this three cutting parameters, namely cutting speed, feed material. Hardened steels are machined by grinding rate, and depth of cut on two performance measures, process in general, but grinding operations are time flank wear(VB) and surface roughness(Ra), were consuming and are limited to the range of geometries investigated employing an orthogonal array and to be produced. The hardened steel surfaces have an analysis of variance. They found that the cutting abrasive effect on the tool material, and the high speed is the only statistically significant factor temperature on the cutting edge causes diffusion influencing the tool wear. Axial depth of cut has also between tool and chip. Therefore, improved physical influence on tool wear. Also the relationship technological processes, optimum tool selection, between the parameters and the performance determination of optimum cutting parameters or tool measures were determined using multiple linear geometry should be considered. The developments of regressions. new cutting tools have led to the use of higher M.L. Penal, M. Arisen [2] investigated cutting speeds compare with conventional experimentally the effect of tool wear on surface machining. High speed cutting reduces machining roughness in hard turning. It has been found that costs by increasing production rate. However, high there is a good replication of tool on the roughness speed cutting leads to the rapid wear of cutting tools, profile. Concretely, the average roughness and which is caused by the high temperatures generated skewness of the profile are sensitive enough to at the cutting zone and as a result tool life decreases. discriminate different tool wear states, as well as to The ability of polycrystalline cubic boron nitride indicate when the tool should be replaced. Therefore, (CBN) cutting tools to maintain a workable cutting cutting edge state might be predicted with reasonable edge at elevated temperature is, to same extent, accuracy through roughness parameters. Their shared with several conventional ceramic tools. strategy allows tool wear estimation by simple These tools are characterized by high hot hardness, roughness measurements using shop floor wear resistance and good chemical stability and low instrument. fracture toughness. CBN and ceramic tools are used Cora Lahiff [3], J.P. Costes [4], and , S.Y. Luo in the manufacturing industry for hard turning conducted studies on tool wear modes and because of its inertness with ferrous materials and its mechanisms in CBN turning of hard materials. They high hardness. Though CBN particles and binder identified the primary wear modes and discussed the phases such as TiN are harder than carbides in steels, many theories proposed to explain the mechanisms it is still possible that the tool will encounter “soft” contributing CBN tool wear and failure. They abrasive wear. The machining of hardened bearing considered the critical factors that influence the steel represents grooving proportion of applications behavior of CBN tools in continuous hard turning IJAERS/Vol. I/ Issue II/January-March, 2012/58-64

International Journal of Advanced Engineering Research and Studies and how this knowledge can be applied to optimize tool performance. They showed that the dominant wear mechanisms are abrasion, adhesion and diffusion due to chemical affinity between elements from workpiece and insert. S.Y.Luo [5] conducted experiments to study wear characteristics in turning high hardness alloy steel by ceramic and CBN tools. The experimental results showed that the main wear mechanism for the CBN tools was the abrasion of the binder material by the hard carbide particles of the workpiece. Variations of tool wear with the cutting speed and the hardness of the work material are discussed accordingly. Tool life for CBN and ceramic tools is increased with cutting speed until it reaches a maximum value, thereafter the tool life starts to decrease. They found that flank wear increases gradually with the cutting time. Radu Pavel [6] studied experimentally the effect of tool wear on surface finish for a case of continuous and interrupted hard turning. They presented new findings concerning the evolution of common surface roughness parameters as well as the evolution of surface topography. They found a good correlation between flank wear aspect and machined surface. As cutting time progresses surface roughness increases due to the increase in tool wear. In case of continuous cutting Ra, Rz, Rpk tend to increase significantly with the tool wear. Sullivan. D.O [7] and Bernhard Muller [8] studied temperature measurements in single point turning. They focused on the use of infrared pyrometer for temperature measurements. There has been much research in to the development of infrared thermometry for the measurement of temperature. Pyrometer has been applied in a turning process to show the influence of cutting speed and tool wear on the temperatures. They found that an increase in cutting speed resulted in a decrease in machined surface temperature. This reduction in temperature is due to the higher metal removal rate which resulted in more heat being carried away by the chip and thus less heat being conducted in to the workpiece. There were increase in temperature due to increased tool wear. Because of the increase in tool wear, the amount of heat energy flowing in to the tool at the cutting edge was increased. This was due to the increase in the contact area at the tool-chip and tool-work interface due to the flank and crater wear. N.A.Abukashim [9] investigated heat partition in high speed turning of high strength alloy steel. They found that the main regions where heat is generated during the orthogonal cutting process are three. Firstly heat is generated in the primary deformation zone due to plastic work done at the shear plane. Secondly, heat is generated in the secondary deformation zone due to work done in deforming the chip and in overcoming the sliding friction at the tool-tip interface zone. Finally the heat generated in tertiary deformation zone, at the tool work piece interface, is due to the work done to overcome friction, which occurs at the rubbing contact between IJAERS/Vol. I/ Issue II/January-March, 2012/58-64

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the tool flank face and the newly machined surface of the work piece. They conducted experiments with temperature measurements on the tool rake face during orthogonal cutting at various cutting speeds. They found that tool chip contact area, and hence the proportion of heat conducting in to the tool changes significantly with the cutting speed. That is rake face temperature increases gradually with the cutting speed but not in a strictly linear fashion due to the increase in contact area. X.L. Liu [10] studied experimentally on cutting temperature and tool wear of hard turning of hardened bearing steel. They investigated the performance of CBN tool in finish hard turning with different hardness. They investigated and compared tool life and cutting temperature. They used natural thermometer for the measurement of cutting temperature. They studied the influence of the work piece hardness on changes in cutting temperature and tool wear characteristics. They found that when cutting hardened bearing steel with the increase of the cutting speed, feed and depth of cut, the cutting temperature also increases. D.E. Dimla [11] described an experimental and analytical method for detecting and monitoring the wear on a cutting tool. They developed on-line tool condition monitoring system involving cutting forces and vibration signature measurements. From Their experiments they found that z-axis vibration is most sensitive to the tool wear. They used Kistler mini accelerometer for acceleration signal measurement in three mutually perpendicular directions. Tool wear measurements were made by using tool makers microscope. The obtained data were analyzed in order to investigate effects wear accumulation had on the sensor signals in terms of their sensitivity and repeatability. The ensuing analysis of time and frequency domains showed some components of the measured signals correlate well to the accrued tool wear. C. Scheffer [12] investigated implementation of a monitoring system utilizing simultaneous vibration and strain measurements on the tool tip for the wear of a synthetic diamond tools which are specifically used for the manufacturing of aluminium pistons. They captured vibration signals using an accelerometer coupled to a DSPT siglab analyzer. These included features from the time domain and frequency domains. Each observation consisted of 10 second time signal, sampled at 25.6 kHz. A large number of features indicative of tool wear were automatically extracted from different parts of the original signals. 2. Experimental Details 2.1. Machine and cutting tool specifications Experiments were conducted on a Standard precision lathe with main motor capacity of 15 kW and spindle speeds ranging from 100 – 1600 rpm. Three types of cutting tools were used for the present work. These are mixed alumina ceramic tools, coated ceramic cutting tools and CBN cutting tools. One of the tools was a mixed alumina ceramic with an Al2O3

International Journal of Advanced Engineering Research and Studies (70%):TiC (30%) matrix, which is designated by KY1615. The other insert was coated using a physical vapor deposition (PVD) method. Coating substance takes place on the mixed ceramic substrate and PVD-TiN coated mixed ceramic with a matrix of Al2O3 (70%):TiC (30%) +TiN, which is called as KY4400 grade. The inserts are from Sandvik, reference numbers S-TNGA 160408-KY1615 and STNGA 160408-KY4400. A tool holder PTGNR1616H11 with an approach angle of 910 was used for the experiments. The cutting tool geometry for ceramics as follows Nominal rake angle −6◦, Back rake angle −6◦, Clearance angle 6◦, Approach angle 75o, Major tool cutting angle 60o, Cutting edge length 11mm, Nose radius 0.8mm, Insert thickness 3.18mm. The inserts were rigidly attached to a tool holder of ISO designation of PTGNR1616H11. The last one was a CBN with an Al2O3 +TiC matrix, which is designated by CBN/TiC. The CBN/TiC tools contained CBN (50%), TiC (40%), WC (6%), AlN, and AlB2 (4%). However, the CBN/TiC insert type was CNGA 120408S-L0. The cutting tool geometry as follows Negative rake angle -20o, Side rake angle-6o, Clearance angle 6o, Approach angle 75o, and Major tool cutting angle 80o. Hardness’s of these cutting tools are about 83, 87 and 140HRC, respectively. Details of cutting tools are given in table 1. Table 1: Cutting tool details. TYPE OF CUTTING TOOL (KY1615) (KY4400) (CBN/TiC)

CHEMICAL COMPOSITION OF MATERIAL Al2O3 (70%) +TiC (30%) Al2O3 (70%) +TiC (30%) +TiN CBN (50%) +TiC (40%) + WC (6%) + AlN,AlB2 (4%)

HARDNESS (HRC) 83 87 140

2.2. Workpiece preparation and heat treatment The material used throughout this work was an AISI 52100 (commercially known as EN 31 steel) alloy steel with 60mm diameter and 450mm length with hardness of 45 HRC. The chemical analysis of the material is indicated in table 2. Table 2 chemical analysis of material (% wt) C 0.95-1.20

Si 0.10-3.35

Mn 0.30-0.75

Cr 1.0 – 1.6

Co 0.025

Required hardness is obtained through heat treatment after skin turning. Before hardening annealing was carried out at a temperature of 800 0C and followed by furnace cooling. Hardening is carried out by heating uniformly to 800 - 820°C until heated through. Allow 30 minutes per inch of ruling section and quench immediately in oil and tempering was carried out at a temperature of 3000C for getting hardness of 45 HRC by heating uniformly and thoroughly at the selected tempering temperature and hold for at least one hour per inch of total thickness. 2.3. Surface roughness measurement The surface roughness is measured after end of each test by using Mitutoyo Surface Roughness tester (SJ201 P) stylus type (Mitutoyo Corporation, Japan). In general, experiment was stopped to measure the surface roughness at each 5, 10, 15 and 30 min. IJAERS/Vol. I/ Issue II/January-March, 2012/58-64

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2.4 Temperature measurement Temperature of tool tip is measured by using infrared pyrometer (tpi 377) with a temperature measurement range of -50 - 10000C and with an adjustable emissivity range of 0.3 – 0.95. The accuracy of instrument at 230C and emissivity 0.95 is +/- 2 % of reading. Response time of pyrometer is 1 second and operating temperature range is 0 – 1500C. The measurement is carried out by lining up the laser with the target (tool tip) and holding the front of the thermometer 20 cm from the tool tip. 2.4. Experimental Design The Taguchi design was selected to find out the relationships between independent variables, surface roughness and tool tip temperature. The independent variables were cutting speed, feed rate, depth of cut and tool’s hardness. The experiments were carried out to analyze the influence of cutting parameters on surface roughness and tool tip temperature for machining hardened AISI 52100 steels. Cutting parameters were selected keeping in mind that the hard turning operation was generally used as a finishing operation as an alternative to grinding. The depth of cut was fixed as 0.2mm in all test conditions. Three feed rates (f) 0.06, 0.08, and 0.11mm/rev were selected. Three cutting speeds (V) were chosen: 580, 800, and 1150 rpm. Details of experimental design, control factors and their levels, and results for surface roughness are shown in Table 3, and the results for tool tip temperature are shown in table 4. These tables show that the experimental plan had three levels. A standard Taguchi experimental plan with notation L9 was chosen. The rows in the L9 orthogonal array used in the experiment corresponded to each trial and the columns contained the factors to be studied. The first column consisted of cutting speed, the second contained the feed rate and the consecutive column consisted of the cutting tool’s hardness. The experiments were conducted twice for each row of the orthogonal array to circumvent the possible errors in the experimental study. In the Taguchi method, the experimental results are transformed into a signal-tonoise (S/N) ratio. This method recommends the use of S/N ratio to measure the quality characteristics deviating from the desired values. To obtain optimal testing parameters, the-smaller-the-better quality characteristic for machining the steels was taken due to measurement of the surface roughness and tool tip temperature. The formula to calculate S/N ratio is given in “Eq.(1)”. The S/N ratio for each level of testing parameters was computed based on the S/N analysis. This design was sufficient to investigate the three main effects. With S/N ratio analysis, the optimal combination of the testing parameters could be determined. S/N=-10 log 10 (∑

)

(1)

Where y is the response and n is the number of responses calculated in a row

International Journal of Advanced Engineering Research and Studies

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Table 3 Experimental design and results for surface roughness and their S/N ratios CONTROL FACTORS TRAIL NO 1 2 3 4 5 6 7 8 9

V(rpm)

f(mm/rev)

580 580 580 800 800 800 1150 1150 1150

0.06 0.08 0.11 0.06 0.08 0.11 0.06 0.08 0.11

EXPERIMENTAL VALUES Measured TH (HRC) surface S/N ratio (db) roughness (µm) 83 1.84 -5.30 87 2.76 -8.82 140 3.85 -11.71 87 2.60 -8.30 140 3.72 -11.41 83 3.14 -9.94 140 2.79 -8.91 83 4.93 -13.86 87 4.78 -13.56 Mean S/N ratio (db) -10.20

Table 4 Experimental design and results for tool tip temperature and their S/N ratios CONTROL FACTORS TRAIL NO 1 2 3 4 5 6 7 8 9

V(rpm)

f(mm/rev)

TH (HRC)

580 580 580 800 800 800 1150 1150 1150

0.06 0.08 0.11 0.06 0.08 0.11 0.06 0.08 0.11

83 87 140 87 140 83 140 83 87

EXPERIMENTAL VALUES Measured S/N ratio (db) Temperature(oc) 48.4 -33.7 52.9 -34.47 49.1 -33.82 64.2 -36.15 61.5 -35.78 59.7 -35.52 57.3 -35.16 63.3 -36.02 67.8 -36.62 Mean S/N ratio (db) -35.24

Table 5. S/N response table of surface roughness Symbol

Control factors

f V TH

Feed rate (mm/rev) Cutting speed (rpm) Cutting tool’s hardness (HV)

3. Results and discussion 3.1. Analysis of control factors for surface roughness Analysis of the influence of each control factor (V, f, and TH) on the surface roughness was performed with a so-called signal-to-noise (S/N) response table. The experimental design, results for surface roughness and S/N ratios are shown in Table 3. The control factors and their un-coded values of surface roughness were included in this table. Table 5 shows the S/N response table of surface roughness. It 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 2. The influence of interactions between control factors was neglected here. The control factor with the strongest influence was determined by differences value. The higher the difference, the more influential was the control factor. The control factors were sorted in relation to the difference values. It could be seen in Table 5 that the strongest influence was exerted by feed rate, followed by cutting speed, lastly hardness of cutting tool respectively. Since the first level of the feed rate was about -7.50 dB while the third level of the feed rate was about -11.73 dB the difference being the most highest of 4.23 dB. It is followed by the cutting speed. The difference between the first level of the cutting speed and third level of the cutting speed was found to be about 3.95 dB, which is significant level IJAERS/Vol. I/ Issue II/January-March, 2012/58-64

Level 1 -7.50 -8.16 -9.70

Average S/N ratio (db) Level 2 Level 3 -10.91 -11.73 -9.88 -12.11 -10.22 -10.68

Max-min 4.23 3.95 0.98

again. The tool hardness showed the least effect on the surface roughness since the difference between the first level and third level were about 0.98 dB. 3.2. Main effect on the surface roughness Fig. 1(a) and 1(b) shows the main effect plots for surface roughness of the hardened steel for S/N ratios and mean values, respectively. The greater is the S/N ratio, the smaller is the variance of the surface roughness around the desired value. Optimal testing conditions of these control factors could be very easily determined from the response graph. The best surface roughness value was at the higher S/N value in the response graph. For main control factors, Fig. 1a indicates the optimum condition for the tested samples (f1, V1, and TH1).

Fig. 1(a) – Main effect plots for surface roughness: S/N ratio (db)

International Journal of Advanced Engineering Research and Studies

Fig. 1(b) – Main effect plots for surface roughness: mean (µm). Thus, it could be concluded that the minimum surface roughness of the hardened steel can be achieved and their optimal setting of control factors for tested samples are shown in Table 6. Table-6: Optimum level of control factors for surface roughness Main control factors Cutting speed Feed rate Hardness of cutting tool

V f

Optimum level 1 1

TH

1

Symbol

Optimum value 580 rpm 0.06 mm/rev Mixed Ceramic tool (KY 1615)

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3.3. Analysis of variance The ANOVA was used to investigate which design parameters significantly affect the quality characteristics of the surface roughness for the turning process. Examination of the calculated value of variance ratio(F), which is the variance of the factor divided by the error variance for all control factors. The results of the ANOVA of surface roughness in machining hardened steels are shown in Table 7. In addition to degrees of freedom, mean of squares (MS), sum of squares (SS), F-ratio and Pvalues associated with each factor level were presented. This analysis was performed for a confidence level of 90%. The F value for each design parameters was calculated. The calculated value of the F showed a high influence of the feed rate (f) on the surface roughness since F-calculation was equal to 7.39 while F-table was about 9.0, but the cutting speed (V), and hardness of the tool (TH) had also significant effects on the surface roughness since Ftest was equal to 5.77, 0.35, respectively. The last column of table 7 indicates the percentage of each factor contribution (P) on the total variation, thus exhibiting the degree of influence on the result. It was important to observe the P-values in the table. From the analysis of Table 7, the feed rate (P≈50.93%) showed a high significant effect. It was followed by cutting speed (P≈39.76%), and cutting tool’s hardness (P≈2.41%) as well. 3.4. Analysis of control factors for tool tip temperature Analysis of the influence of each control factor (V, f, and TH) on the tool tip temperature was performed with a so-called signal-to-noise (S/N) response table. The experimental design, results for temperature and S/N ratios are shown in Table 4. The control factors and their un-coded values of tool tip temperature were included in this table. Table 8 shows the S/N response table of tool tip temperature. It 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 2. The influence of interactions between control factors was neglected here. The control factor with the strongest influence was determined by differences value. The higher the difference, the more influential was the control factor. The control factors were sorted in relation to the difference values.

From the results of control factors, minimum surface roughness was obtained under cutting conditions of V= 580 rpm, f = 0.06mm/rev when machining AISI 52100 workpiece by Mixed aluminaceramic (KY 1615) cutting tool. The experimental work was carried out on the same steel using the determined optimal control factors. The surface roughness was found to be about 1.17µm. Then this value was transferred to the S/N ratio (dB), average value of S/N ratio was calculated and it was about -1.36 dB. Moreover, the mean surface roughness was shown in Fig. 1b. It was evident that the feed rate had the greatest effect on the optimal testing conditions. It is followed by the cutting speed. The cutting tool’s hardness was also effective on the surface roughness of the hardened steel. Effects, however, were lower compared to those of feed rates. An orthogonal design, S/N ratio and ANOVA were employed to determine the effective cutting parameters such as V, f and tool’s hardness on the surface roughness. It was concluded that the feed rate was found to be the most important parameters on the surface roughness among control parameters. Table 7: Results of ANOVA for surface roughness in machining hardened steels Symbol v f TH Error Total

Degree of freedom (d.f.) 2 2 2 2 8

Sum of squares (SS) 23.73 30.40 1.44 4.11 59.68

Mean of squares (MS) 11.86 15.20 0.72 2.05 7.46

F-Calculation

F-Table

5.77 7.39 0.35 -

9.00 9.00 9.00 -

Contribution,P (%) 39.76 50.93 2.41 6.90 100

Table 8. S/N response table of surface roughness Symbol

Control factors

V TH f

Cutting speed (rpm) Cutting tool’s hardness (HV) Feed rate (mm/rev)

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Level 1 -34.0 -35.08 -35.0

Average S/N ratio (db) Level 2 Level 3 -35.82 -35.93 -35.75 -34.92 -35.42 -35.32

Max-min 2.53 0.83 0.42

International Journal of Advanced Engineering Research and Studies It could be seen in Table 8 that the strongest influence was exerted by cutting speed, followed by the tool hardness, lastly feed rate respectively. Since the first level of the cutting speed was about -34.0 dB while the third level of the cutting speed was about 35.93 dB the difference being the most highest of 2.53 dB. It is followed by the tool hardness. The difference between the second level of the tool hardness and third level of the tool hardness was found to be about 0.83 dB, which is significant level again. The feed rate showed the least effect on the tool tip temperature since the difference between the second level and third level were about 0.42 dB. 3.5. Main effect on the tool tip temperature

Fig. 2(a) – Main effect plots for tool tip temperature: S/N ratio (db)

Fig. 2(b) – Main effect plots for tool tip temperature: mean (oC). Fig. 2(a) and 2(b) shows the main effect plots for tool tip temperature of the hardened steel for S/N ratios and mean values, respectively. The greater is the S/N ratio, the smaller is the variance of the tool tip temperature around the desired value. Optimal testing conditions of these control factors could be very easily determined from the response graph. The less tool tip temperature value was at the higher S/N value in the response graph. For main control factors, Fig. 2a indicates the optimum condition for the tested samples (V1, f1 and TH3). Thus, it could be concluded that the minimum tool tip temperature of the hardened steel can be achieved and their optimal setting of control factors for tested samples are shown in Table 9. IJAERS/Vol. I/ Issue II/January-March, 2012/58-64

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Table-9: Optimum level of control factors for tool tip temperature V

Optimum level 1

Feed rate

f

1

Hardness of cutting tool

TH

3

Main control factors Cutting speed

Symbol

Optimum value 580 rpm 0.06 mm/rev CBN/TiC

From the results of control factors, minimum tool tip temperature was obtained under cutting conditions of V= 580 rpm, f = 0.06mm/rev when machining AISI 52100 workpiece by CBN/TiC cutting tool. The experimental work was carried out on the same steel using the determined optimal control factors. The tool tip temperature was found to be about 35.41. Then this value was transferred to the S/N ratio (dB), average value of S/N ratio was calculated and it was about -30.98 dB. Moreover, the mean tool tip temperature was shown in Fig. 2b. It was evident that the cutting speed had the greatest effect on the optimal testing conditions. It is followed by the cutting tool’s hardness. The feed rate was also effective on the tool tip temperature of the hardened steel. Effects, however, were lower compared to those of cutting speed. An orthogonal design, S/N ratio and ANOVA were employed to determine the effective cutting parameters such as V, f and tool’s hardness on the tool tip temperature. It was concluded that the cutting speed was found to be the most important parameters on the tool tip temperature among control parameters. 3.6. Analysis of variance The ANOVA was used to investigate which design parameters significantly affect the quality characteristics of the tool tip temperature for the turning process. Examination of the calculated value of variance ratio (F), which is the variance of the factor divided by the error variance for all control factors. The results of the ANOVA of tool tip temperature in machining hardened steels are shown in Table 10. In addition to degree of freedom, mean of squares (MS), sum of squares (SS), F-ratio and Pvalues associated with each factor level were presented. This analysis was performed for a confidence level of 90%. The F value for each design parameters was calculated. The calculated value of the F showed a high influence of the cutting speed (V) on the tool tip temperature since F-calculation was equal to 36.15 while F-table was about 9.0, but the hardness of the tool (TH) and the feed rate had also significant effects on the tool tip temperature since F-test was equal to 5.97, 1.48 respectively. The last column of table 10 indicates the percentage of each factor contribution (P) on the total variation, thus exhibiting the degree of influence on the result. It was important to observe the P-values in the table. From the analysis of Table 10, the cutting speed (P≈81.05%) showed a high significant effect. It was followed by the cutting tool’s hardness (P≈13.38%), and feed rate (P≈3.32%) as well.

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Table 10: Results of ANOVA for tool tip temperature in machining hardened steels Symbol v TH f Error Total

Degree of freedom (d.f.) 2 2 2 2 8

Sum of squares (SS) 7.050 1.164 0.289 0.195 8.698

Mean of squares (MS) 3.525 0.582 0.144 0.097 1.087

4. CONCLUSIONS • The following conclusions could be drawn from results of surface roughness using different cutting tools when machining hardened steels. The L9 (34) orthogonal arrays were adopted to investigate the effects of cutting speed, feed rate and hardness of cutting tools on the surface roughness. The results showed that the feed rate exerted the greatest effect on the surface roughness, followed by the cutting speed, lastly the hardness of cutting tool. The estimated S/N ratio using the optimal testing parameter for the surface roughness was calculated. Furthermore, mixed alumina ceramic cutting tool (KY 1615) showed the best performance than those of other tools. Moreover, the ANOVA indicated that the feed rate was high significant but other parameters were significant effects on the surface roughness at 90% confidence level. The percentage contributions of feed rate, cutting speed, and tool’s hardness were about 50.93, 39.76, and 2.41 on surface roughness, respectively. • The following conclusions could be drawn from results of tool tip temperature using different cutting tools when machining hardened steels. The L9 orthogonal arrays were adopted to investigate the effects of cutting speed, feed rate and hardness of cutting tools on the tool tip temperature. The results showed that the cutting speed exerted the greatest effect on the tool tip temperature, followed by the cutting tool’s hardness, lastly the feed rate. The estimated S/N ratio using the optimal testing parameter for the tool tip temperature was calculated. Furthermore, CBN/TiC showed the best performance than those of other tools. Moreover, the ANOVA indicated that the cutting speed was high significant but other parameters were significant effects on the tool tip temperature at 90% confidence level. The percentage contributions of cutting speed, tool’s hardness and feed rate were about 81.05, 13.38, and 3.32 on tool tip temperature respectively. 5. FURTHER SCOPE • In the present work, experiments were conducted only by considering the cutting variables like speed, feed and cutting tool’s hardness on tool tip temperature and surface roughness. Further experiments are required to study the influence of other parameters like tool geometry, cutting fluids and different types of hardened steels. • Further studies have to be made to study the influence of cutting parameters on the cutting forces and power consumption. • The accuracy of the developed model can be improved by considering more number of factors IJAERS/Vol. I/ Issue II/January-March, 2012/58-64

F-Calculation

F-Table

36.15 5.97 1.48

9.00 9.00 9.00 -

Contribution, P (%) 81.05 13.38 3.32 2.25 100

and levels. In addition, some other techniques like the neural networks, fuzzy logic and Genetic Algorithm (GA) might also be used to optimize the cutting parameters. REFERENCES 1.

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