Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method

Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method

Accepted Manuscript Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi m...

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Accepted Manuscript Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method Sujan Debnath, Moola Mohan Reddy, Qua Sok Yi PII: DOI: Reference:

S0263-2241(15)00478-9 http://dx.doi.org/10.1016/j.measurement.2015.09.011 MEASUR 3567

To appear in:

Measurement

Received Date: Revised Date: Accepted Date:

23 December 2014 11 September 2015 15 September 2015

Please cite this article as: S. Debnath, M.M. Reddy, Q.S. Yi, Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method, Measurement (2015), doi: http://dx.doi.org/10.1016/j.measurement.2015.09.011

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Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method

* Sujan Debnath, Moola Mohan Reddy and Qua Sok Yi School of Engineering & Science Curtin University Sarawak CDT 250 Miri, Sarawak, 98009 Malaysia *Email: [email protected]

Abstract: In this experimental work, the effect of various cutting fluid levels and cutting parameters on surface roughness and tool wear was studied. Taguchi orthogonal array was employed to minimize the number of experiments. The experiments were carried out on mild steel bar using a TiCN+Al2O3 +TiN coated carbide tool insert in the CNC turning process. The effect of feed rate was found to be the dominant factor contributing 34.3% to surface roughness of the work-piece. The flow rate of the cutting fluid also showed a significant contribution (33.1%). However, cutting speed and depth of cut showed little contribution to surface roughness. On the other hand, cutting speed (43.1%) and depth of cut (35.8%) were the dominant factors influencing tool wear. However, application of cutting fluid (13.7%) showed a considerable contribution, while the feed rate gave the least contribution to tool wear. The optimum cutting conditions for desired surface roughness and tool wear were at a high level of cutting speed, medium level of depth of cut, low level of feed rate and low-flow high-velocity (LFHV) cutting fluid flow from the selected levels. Keywords: Cutting Fluid, Cutting Parameters, Surface Roughness, Tool Wear, Taguchi Method

1.0 Introduction Lubricants are widely used in all sectors of industry for cooling and lubricating the tool and work-piece interface in order to enhance machinability. Owing to the advantages of cutting fluids, their consumption in the machining industry is increasing rapidly. In 2005, the amount of lubricants used in machining was reported as nearly 38 Mt with an estimated increase of 1.2 % over the next decade. Approximately 85 % of the cutting fluids used around the world are mineral-based. The increased use of mineral and petroleum based oil causes several negative impacts on the environment and poses significant health hazards. It is

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reported that around 80 % of all occupational infections of operators were due to skin contact with cutting fluids (Shashidhara and Jayaram 2010). This is because the complex composition of cutting fluids can lead to irritant or allergenic properties. Because of hazardous substances, toxic and less biodegradable cutting fluids caused many techno-environmental problems and serious health problems, such as: lung cancer, respiratory diseases, dermatological and genetic diseases (Ozcelik et al. 2011). The International Agency for Research on Cancer (IARC) reported that petroleum-based cutting fluids, which contain heterocyclic and polyaromatic rings are carcinogenic and exposure to them could result in occupational skin cancer (Abdalla et al. 2007). Therefore, in recent years, all costs involved with cutting fluids related to purchasing, recycling, and chip drying are increasing due to legislation from national and international authorities for environmental protection. For that reason, industries are emphasizing cleaner production processes in the machining process and product life cycle (Fratila, 2009). Minimum quantity lubrication (MQL), with environmental friendly cutting fluids, has been successfully applied in some of the machining processes. Recently, biodegradable lubricants have been gradually replacing synthetic lubricants. Biodegradable cutting fluids that accomplish the lowest amount of environmental contamination can provide high reliability and satisfactory economic conditions. Additionally, the output of bio-based cutting fluids is cleaner and contributes less mist in the air, subsequently minimizing the occupational health risks (Kuram et al. 2011). Although biobased (vegetation) cutting fluids are not perfect in all aspects, they have the least negative impact on the environment compared to other cutting fluids (Debnath et al. 2014).

Various analytical methods for predicting surface roughness, tool life and cutting forces have been investigated by researchers. Development of empirical models for machinability parameters in a variety of machining process have been performed based on data mining techniques such as statistical design of experiments (Taguchi method, response surface methodology etc), computational neural networks, and genetic algorithms. All these methods provide the impact of each individual factor as well as the interactions between factors on the functional objective. Taguchi method is a systematic approach to find optimum values of design factors that lead to an economical design with low variability. Nalbant et al. 2007, studied Taguchi parameter design for the purpose of demonstrating a systematic procedure in process control and recognized the optimum surface roughness performance with a more efficient combination of cutting parameters in turning process. 2

Dry machining is applicable for conventional machining on steels, steel alloys and cast irons except for aluminum alloys. Nonetheless, high friction between the tool and workpiece in dry cutting condition significantly increases the temperature resulting in higher level of abrasion, diffusion and oxidation. The workpiece also experiences a large amount of heat and consequently hinders the achievement of close tolerances and metallurgical damage occurs to its superficial layer (Diniz and Micaroni 2002). Diniz and Micaroni 2002 carried out turning experiments with variable cutting speed, feed and tool nose radius, with and without the use of cutting fluid to identify the best condition for dry cutting. They concluded that the use of cutting fluids in wet cooling can improve the tool life. However, dry cutting showed less power consumption and better surface finish. Devillez et al. 2011 investigated the effect of dry machining on surface integrity and cutting forces in turning of Inconel 718. Wet and dry turning tests were performed at various cutting speeds (0.5 mm depth of cut and 0.1 mm/rev feed rate) with coated carbide tool. It was demonstrated that dry machining with the coated carbide tool leads to potentially acceptable surface quality when using the optimized cutting speed value. Yuan et al. 2011 investigated the influence of different cooling methods such as dry, wet, minimum quantity lubrication (MQL) and MQL with cooling air in milling of the Ti–6Al–4V alloy with uncoated cemented carbide inserts. Cutting force, tool wear, surface roughness and chip morphology were experimentally studied to compare the effects of four different cooling air temperatures. Based on the findings, the authors concluded: (1) MQL with cooling air conditions provided lower cutting force, tool wear and surface roughness than those of tests under dry, wet and MQL conditions.

Oktem et al. (2006) predicted the minimum surface roughness in end milling mold parts using Artificial Neural Network (ANN) and Genetic Algorithm (GA) approach. Kaya (2011) developed tool wear prediction using artificial neural networks (ANN). Choudhary and Baradie (1999) developed response model for tool life surface roughness and cutting force with central composite method using RSM. They found it very useful for assessing the maximum tool life and surface finish. Rodriguez and Labarga (2013) developed an analytical model to predict cutting forces for micromilling operations based on the process geometry. The comparison shows a good agreement between predictions and the experimental measurements.

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Kuram et al. (2013) studied the optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments. They concluded that the specific energy is largely related to cutting fluid type. Their experimental work shows that Canola cutting fluid (CCF-II) was the best to minimize the surface roughness and specific energy. Xavior and Adithan 2009 determined the influence of cutting fluids on tool wear and surface roughness during turning of AISI 304 austenitic stainless steel with cemented carbide tool. Analysis of variance (ANOVA) showed that the feed rate is the influential parameter on surface roughness while cutting speed is influential to tool wear. Importantly, the application of cutting fluid effectively reduced the tool wear and consequently improved the surface finish. Cetin et al., 2011 studied the effect of cutting parameters and cutting fluids on surface roughness during turning AISI 304L. They found that the federate is the most influencing factor on surface roughness. García et al. 2012 investigated the effect of cutting parameters and coating of the cutting tool in the surface residual stresses generated by turning AISI 4340 steel. The results indicated that the residual stresses became more tensile due to an increase in cutting temperature, and resulting in detrimental to the surface of the workpiece. They suggested low feed rate, high cutting speed, and non-coated tools with smaller tool nose radius in order to achieve a better surface finish. Malagi et al. 2012 investigated the factors influencing cutting forces in turning and reported that cutting force increases as the feedrate and depth of cut increases. Sahin et al. 2005 investigated the surface roughness model for machining mild steel with TiN-coated carbide tool. The model was developed in terms of cutting speed, feedrate and depth of cut using response surface methodology. From the results obtained, the authors concluded that surface roughness increases with increasing in feedrate. Conversely, surface roughness decreases with increasing in cutting speed and depth of cut. Cetin et al. 2011 studied the effect of cutting parameters and cutting fluids on surface roughness during turning AISI 304L. They found that the contribution of spindle speed, feedrate, depth of cut and cutting fluids on surface roughness are 0.23 %, 97.40 %, 0.84 % and 0.69 % respectively as shown in Table 4. Based on the level of importance of the cutting parameters on surface roughness determined by ANOVA, CCF-II has a larger influence on the surface roughness compared to the other cutting parameters. The results showed that the surface roughness was reduced by the following sequence: CCF-II, SCF-I, CSSCF, SCF-II, CCF-I, and CMCF where SCF-I: Sunflower based cutting fluids with 8 % of EP additive; SCF-II: Sunflower based cutting fluids with 12 % of EP additive; CCF-I: Canola based 4

cutting fluids with 8 % of EP additive; CCF-II: Canola based cutting fluids with 12 % of EP additive; CMCF: Commercial mineral based cutting fluid, and CSSCF: Commercial semisynthetic cutting fluid. In this study, optimization of cutting parameters, including the influence of the quantity and speed of cutting fluids, were investigated in order to produce a desired surface roughness and longer tool life. A Taguchi orthogonal array was employed to minimize the number of experiments. Yushiroken MIC 2500 cutting fluid was used as an environmentallyfriendly cutting fluid. This cutting fluid is a boron free product and it is specially developed as a bacteria-static type of micro-emulsion. Yushiroken MIC 2500 is suitable for grinding and cutting operations on steel. It has a longer coolant life due to its bacteria-static property and less foaming, and it has superior lubrication properties in metal machining, as well as anti-rust properties.

The operating parameters including cutting speed, feed, depth of cut and lubrication conditions were considered as the experimental variables. The experimental work was carried out on mild steel bars using a TiCN+Al2O3 +TiN coated carbide tool insert (with chemical vapor deposition process), operating under a computerized numerical control (CNC) turning machine. 2.0 Materials and Methods

Surface roughness and tool wear were measured to analyze the effect of various cutting parameters. The range of cutting parameters and cutting fluid conditions was selected based on the preliminary tests as well as the recommendation of standard machining data book for turning processes (SANDVIK Coromant, main catalogue, Sweden 2006) as listed in Table 1 and Table 2. A Mitutoyo surface roughness tester was used to measure the roughness at different positions of the specimen. The chips at various levels were collected for further investigation. CVD coated carbide tool insert with composition of micro columnar TiCN+Al2O3+TiN was used in this machining operation. The insert thickness was 3.97 mm with nose radius of 0.2 mm and relief angle of 7°. This insert is suitable for free cutting steel, carbon steel and alloy steel. The cutting insert was observed under the Leica microscope in order to record tool wear. All the experiments were repeated twice and the average readings were considered. The mild steel specimen of hardness 130BHN, Yushiroken MIC 2500 cutting fluid, tool holder, and coated tool insert are shown in Figure 1(a-d).

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Table 1: Selection of ranges of cutting parameters Parameter

Level for each parameter Level 1 Level 2 Level 3

Cutting speed, Vc (m/min)

100

140

180

Feed rate, Fr (mm/rev)

0.05

0.06

0.07

Depth of cut, Doc (mm)

0.5

1.0

1.5

Cutting fluid condition

LFLV

HFLV

LFHV

Table 2: The cutting fluid flow specifications Flow rate, Q (ml/s) 132

Diameter of tube, D (m) 9.8

Velocity of cutting fluid (m/s) 1.75

247

9.8

3.26

168

6

5.94

Cutting fluid condition Low flow rate low velocity (LFLV) High flow rate low velocity (HFLV) Low flow rate high velocity (LFHV)

Table 3: Surface roughness and tool wear obtained from experiments Trial no

Cutting parameter level

Performances

Cutting

Feed

Depth of Cutting fluid

Surface

Flank

speed,

rate,

cut, Doc

roughness, Ra

wear, Vb

Vc

Fr

(µm)

(mm)

7

1

1

1

LFLV

0.79

47.14

5

1

2

2

HFLV

0.55

44.99

3

1

3

3

LFHV

0.67

44.64

1

2

1

2

LFHV

0.35

41.42

8

2

2

3

LFLV

0.96

50.01

6

2

3

1

HFLV

0.83

49.28

4

3

1

3

HFLV

0.30

42.85

2

3

2

1

LFHV

0.53

42.85

9

3

3

2

LFLV

0.66

39.64

condition, Q

6

(a)

(b)

(c)

(d)

Figure 1: (a) mild steel specimen, (b) cutting fluid, (c) tool holder, (d) inserts

2 m long mild steel was cut into smaller specimens of 110 mm length using a metal horizontal band saw. The flow rate of cutting fluid in the CNC lathe machine was measured by using a measuring cup and stopwatch. First, the valve was opened to 45° and the cutting fluid was allowed to flow for 10 seconds. The final volume was recorded and the flow rate was measured as 132 ml/s. Following similar steps, the cutting fluid velocity was measured as 246.7 ml/s for the fully open valve (90°). A small tube with diameter of 6 mm was inserted into the previous 9.8 mm diameter tube and the valve was opened fully. The resulting flow rate was around 168 ml/s. The flow rate of cutting fluid used and its relative velocity are tabulated in Table 3. Yushiroken MIC 2500 cutting fluid was mixed with water in order to prepare an emulsion form cutting fluid. Water was added until the concentration of the emulsion fell within 7–8 Brix % at 20 °C. This was tested by using a refractometer. Concentration is important because low concentrations can lead to poor lubrication, causing broken or prematurely worn tools and poor surface finishes, while high concentrations can lead to skin irritation and residues on the work-piece.

2.1 Design of Experiments If a full factorial design is applied, 81 experimental runs must be conducted for this study. However, experimental study is expensive and time consuming. Therefore, Taguchi

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orthogonal array was employed to minimize the number of experiments. Accordingly, only nine experiments are required for four parameters based on Taguchi orthogonal array design. The experimental data for surface roughness and tool wear are recorded in Table 3. 2.2 Signal-to-noise (S/N) ratio The S/N ratio is the ratio of the mean to the standard deviation. It is used to measure the quality characteristic deviates from the desired value (Selvaraj et al. 2014). Taguchi method suggests that signal to noise ratio (S/N) can be used as a quantitative analysis tool. Since smaller surface roughness and tool wear values are desired in this experiment, the signal-to-noise ratio is chosen as

, where n is the number of

experiments, and yi is the measured value. To obtain the optimum process parameters, the larger S/N ratio denotes better performance. Table 4: S/N ratio calculated for surface roughness and tool wear Trial no

Cutting parameter level

Performances

S/N ratio

Vc

Fr

Doc

Q

Ra (µm)

Vb (mm)

ȠRa

ȠVb

7

1

1

1

LFLV

0.79

47.14

2.01

-33.47

4.04

1120.04

5

1

2

2

HFLV

0.55

44.99

5.25

-33.06

27.52

1093.16

3

1

3

3

LFHV

0.67

44.64

3.48

-32.99

12.10

1088.60

1

2

1

2

LFHV

0.35

41.42

9.08

-32.34

82.40

1046.19

8

2

2

3

LFLV

0.96

50.01

0.38

-33.98

0.15

1154.68

6

2

3

1

HFLV

0.83

49.28

1.60

-33.85

2.56

1146.07

4

3

1

3

HFLV

0.30

42.85

10.60

-32.64

112.44

1065.31

2

3

2

1

LFHV

0.53

42.85

5.49

-32.64

30.11

1065.33

9

3

3

2

LFLV

0.66

39.64

3.59

-31.96

12.87

1021.57

5.64

402.82

41.48 4.61

-296.94 -32.99

284.18

9800.94

Total Mean

Based on the experimental results, S/N ratio is obtained as in Table 4 where Vc is the cutting speed, Fr is the feed rate, DOC is the depth of cut and Q is the cutting fluid flow condition. The optimal level of the process parameters can be found by considering the highest S/N ratio value (Gaitonde et al. 2008). ANOVA was carried out to identify the design parameters that significantly affect the response values.

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3.0 Results and Discussion The effects of each factor level on the quality characteristics were analyzed using the Signal to Noise (S/N) ratio. The S/N ratio is plotted against test level for each control parameter as shown in Figure 2 and Figure 3, where Figure 2(a-d) is shown for surface roughness and Figure 3 (a-d) is shown for tool wear. From the S/N ratio analysis in Figure 2 (a-d), the optimal cutting parameters for surface roughness were determined as cutting velocity180 m/s (level 3), feed rate 0.05 mm/rev (level 1), depth of cut 1 mm (level 2), and cutting fluid of LFHV (level 3). From Figure 3 (a-d), the optimal cutting parameters for the tool wear were determined as cutting velocity180 m/s (level 3), feed rate 0.05 mm/rev (level 1), depth of cut 1 mm (level 2), and cutting fluid of LFHV (level 3). Table 5: ANOVA for surface roughness Analysis of Variance (ANOVA) for surface roughness Parameter

Degree of freedom

Sum of squares

Mean square

Contribution (%)

Vc

2

17.14

8.57

18.43

Fr

2

31.94

15.97

34.33

Doc

2

13.14

6.57

14.13

Q

2

30.81

15.41

33.12

Total

8

93.04

11.63

100.00

Table 6: ANOVA for tool wear Analysis of Variance (ANOVA) for tool wear Parameter Vc Fr Doc Q Total

Degree of freedom 2 2 2 2 8

Sum of squares 1.59 0.27 1.32 0.51 3.68

Mean square 0.79 0.13 0.66 0.25 1.84

Contribution (%) 43.12 7.28 35.83 13.77 100.00

Table 5 and Table 6 show the ANOVA results of surface roughness and tool wear to determine the percentage contribution of each parameter on surface roughness and tool wear. It was observed that the feed rate and the cutting fluid condition are the more significant cutting parameters affecting the surface roughness. ANOVA results showed that feed rate, 9

cutting fluid condition, cutting speed, and depth of cut are affecting the surface roughness by approximately 34%, 33%, 18%, and 14% respectively. It was observed that the cutting speed and the depth of cut are the most significant cutting parameters affecting tool wear. ANOVA results showed that cutting speed, depth of cut, cutting fluid condition, and feed rate are affecting tool wear by approximately 43%, 36%, 14%, and 7% respectively.

3.1 Comparison of predicted and experimental results at the optimal cutting conditions The results at optimum cutting condition were predicted using the estimated signal to noise ratio equation discussed by Yang and Tang, 2011. For surface roughness, according to Figure 2(a-d), the optimal cutting parameters are A3B1C2D3 where A is cutting speed, B is feed rate, C is depth of cut, and D is cutting fluid condition. Therefore, the prediction of the S/N ratio is calculated as 11.9, as shown in Table 7. The conformation experimental result at A3B1C2D3 level shows the S/N value as 13.4 which is close to the predicted value. For tool wear, according to Figure 3(a-d), the optimal cutting parameters are A3B1C2D3 where A, B, C, and D have the same meaning as in the paragraph above. Therefore, the prediction of the S/N ratio is calculated as -31.3, as shown in Table 8. The conformation experimental result at A3B1C2D3 level shows the S/N value as -31.8 which is also close to the predicted value.

Figure 2 (a): Influence of cutting velocity to surface roughness

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Figure 2 (b): Influence of feed rate to surface roughness

Figure 2 (c): Influence of depth of cut to surface roughness

Figure 3 (a): Influence of cutting velocity to tool wear

Figure 2 (d): Influence of cutting fluid to surface roughness

Figure 3 (b): Influence of feed rate to tool wear

Figure 3 (c): Influence of depth of cut to tool wear

Figure 3 (d): Influence of cutting fluid to tool wear

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Table 7: Confirmation test for surface roughness Confirmation test for surface roughness Parameter Level

Initial

Prediction

Experiment

A2B2C3D2

A3B1C2D3

A3B1C2D3 0.21

Surface roughness (µm) 4.21

S/N ratio

11.95

13.42

9.21

Improvement

Table 8: Confirmation test for tool wear Confirmation test for tool wear Parameter Level

Initial

Prediction

Experiment

A1B3C3D1

A3B1C2D3

A3B1C2D3 38.92

Surface roughness (µm) S/N ratio

-33.47

-31.37

-31.80

1.67

Improvement

3.2 Discussion based on results of ANOVA for surface roughness Theoretical arithmetic mean value of surface roughness is expressed as where f is the feed and r is the tool nose radius. From this expression, the surface roughness is proportional to the square of feed if the same tool nose radius is used,

.

Based on the results obtained from ANOVA, feed rate had the highest contribution (34.3%) on surface roughness (refer to Table 5). Flow rate and speed of cutting fluid also showed a significant influence (33.1%). Cutting speed (18.4%) and depth of cut (14.1%) showed minimal effect on surface roughness. Based on the experimental finding (as shown in Figure 2), the lowest feed rate (0.05 mm/rev) produced the highest S/N ratio (7.2). Therefore, the surface roughness decreased with the decrease of feed rate. This result is in line with the theory of influence of feed rate on surface roughness and also similar to the results obtained by researches in this area (Reddy et al. 2013) Cutting fluid made a significant contribution (33.1%) to the surface roughness of the work-piece. It acted as a lubricant to reduce the friction at the tool and work-piece interface, and as a coolant to reduce the temperature in the cutting zone. Cutting speed and depth of cut had a little effect on surface roughness of the work-piece in this case. 12

From the confirmation test, the optimal cutting condition occurred at a high level of cutting speed, medium level of depth of cut, low level of feed rate and low-flow high-velocity (LFHV) cutting fluid. This result is in line with the outcome of Lalwani et al. 2008. Since mild steel is a ductile material, it usually leads to the formation of built-upedges (B.U.E) resulting in poor surface texture, burr formation, as well as short tool life. Therefore, the cutting speed has to be high enough to avoid the formation of B.U.E; simultaneously low feed rate can reduce surface roughness.

0.70 surface roughness (µm)

0.60 0.50 0.40 high level of cutting speed

0.30 0.20 0.10 0.00 0

1

2 level of feedrate

3

4

Figure 4: Effect of feed rate on surface roughness at the high level of cutting speed The feed rate was selected at a low level because more heat is generated with a high feed rate due to the large material removal rate. At high level of cutting speed, the surface roughness was highly sensitive to feed rate as shown in Figure 4. Reduction in feed rate sharply reduced the surface roughness. The result was similar to the outcome of Thomas and Beauchamp 2003. By applying cutting fluid with LFHV condition, the volume of cutting fluid applied was reduced, while flow speed was increased, showing a better or similar result to the HFLV. At high cutting speeds the conditions at the interfaces were not favorable to the penetration of cutting fluid. This was because the chip-tool interface was mostly plastic in nature. As a result, the effectiveness of coolant when applying HFLV and LFLV were limited in the operations, but LFHV allowed better penetration of the cutting fluid into the interfaces. Thus, the cutting fluid in LFHV aided in reducing the temperature gradient as well as offering adequate lubrication at the interfaces, with significant reduction in friction. Moreover, it

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tended to shorten the length of contact between the tool and work-piece, and consequently reduced the cutting and feed forces. As a result, surface roughness was minimized. Since emulsion-cutting fluid is water-miscible and has better cooling properties, it is more efficient in high cutting velocity operations (Sharma et al. 2009). 3.3 Discussion on flank wear of the inserts Generally, flank wear has been emphasized more than crater wear for analyzing tool wear, because flank wear and the resulting recession of the cutting edge has more direct influence on work-piece dimensions (Sikdar and Chen 2002). The contact stress development between tool and work-piece is the main factor for producing flank wear (Zhao et al. 2002).

Based on the results obtained from ANOVA, cutting speed had the highest contribution (43.1%) on tool wear, followed by depth of cut (35.8%) and cutting fluid condition (13.7%). However, effect of feed rate was minimal on tool wear (7.2%). This is quite similar to the results obtained by Xavior and Adithan 2009 and Mehrban et al. 2008. Cutting speed has the highest contribution to tool wear because it increased the cutting temperature at the cutting edge of the tools. The higher temperature generated in the cutting zone caused the tool to lose its strength and, thus, plastic deformation took place. However, in this case the optimum combination for the best tool life was obtained at the high level of cutting speed (Figure 3a). This may be because of the formation of small size chips at high speed as shown in Figure 5. It was reported by Bhuiyan et al. 2012 that tool wear can be decreased with chip breakage even at higher cutting speeds.

(a)

(b)

Figure 5: (a) chip formation obtained (at 100 m/min, feed rate 0.07 mm/rev, depth of cut 1.5 mm) (b) chip formation obtained (at 180 m/min, feed rate 0.07 mm/rev, depth of cut 1 mm)

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Depth of cut also made a significant contribution to tool wear. The increase in depth of cut increased the contact area between tool and work-piece, thus resulting in high friction plastic deformation, which leads to high tool wear. Further to this, at LQHV condition, the cutting fluid is able to cool the region close to the wear land, thereby decreasing the cutting edge temperature through heat conduction. As a result, it leads to reduction in tool wear. This was also in agreement with the experimental results obtained by Lin et al. 2008.

3.4 Discussion on chip formation Chip formation, which has a remarkable effect on tool life, was affected mostly by cutting speed, followed by depth of cut and feed rate. Radius of curvature of the chip formation increased with cutting speed as shown in Figure 6. This may be due to the changes in inertia of material between the shear planes during the cutting process. By increasing the cutting speed, cutting force increases, consequently increasing the bending moment on the chips.

(a)

(b)

Figure 6: (a) Arc chip formation (at 140 m/min, feed rate 0.05 mm/rev, depth of cut 1 mm) (b) Washer-type helical chip formation (at 180 m/min, feed rate 0.06 mm/rev, depth of cut 0.5 mm)

From the experiment (Figure 6), arc chips were usually formed when the depth of cut is more than 1.0 mm. Formation of arc shape chips decreased plastic deformation and tool wear. When the depth of cut was below 0.5 mm, the short washer-type helical chips with sawtooth shape were formed. This type of chip may damage the cutting tool more because of the generation of heat.

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4.0 Conclusions In this research work, the effect of various cutting fluid levels and cutting parameters on surface roughness and tool wear was studied using Taguchi orthogonal array. Conclusions drawn from this experimental work are as follows: 

Feed rate is the most influential factor (34.3%) on surface roughness, while cutting speed contributed the most (43.1%) to tool wear. Cutting fluid also showed a significant contribution to surface roughness (33.1%) as well as to tool wear (13.7%).



LFHV was the most effective flow condition from selected levels in reducing surface roughness and tool wear.



From the confirmation test, the optimal parameters for both surface roughness and tool wear were obtained at high cutting speed (180 m/min), medium depth of cut (1 mm), low feed rate (0.05 mm/rev) and LFHV cutting fluid applied from the selected levels.



Experimental tests gave good agreement between the predicted and experimental values for both surface roughness and tool wear.

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Diniz, A. E., and Micaroni R. 2002. "Cutting Conditions for Finish Turning Process Aiming: The Use of Dry Cutting" International Journal of Machine Tools and Manufacture 42 "8": 899-904. Fratila, D. 2009. "Evaluation of near-Dry Machining Effects on Gear Milling Process Efficiency" Journal of Cleaner Production 17 (9): 839-845. Gaitonde, V. N., Karnik S. R., and Davim J. P. 2008. "Selection of Optimal Mql and Cutting Conditions for Enhancing Machinability in Turning of Brass" Journal of Materials Processing Technology 204 (1–3): 459-64. Garcia, N. V., Gonzalo O., and Bengoetxea I. 2012. "Effect of cutting parameters in the surface residual stresses generated by turning in AISI 4340 steel" International Journal of Machine Tools and Manufacture 61: pp. 48-57. Ghani J.A., Choudhury L.A., and Hassan H.H. 2004. “Application of Taguchi method in the Optimization of end milling Parameters” Journal of Materials Processing Technology 145 (1): 84-92. Kaya, B., Oysu C., and Ertunc H.M. 2011. "Force-torque based on-line tool wear Estimation system for CNC milling of inconel 718 using neural networks" Advances in Engineering Software 42: 76–84. Kuram, E., Ozcelik B., Demirbas E., Şik E., and Tansel I. N. 2011. "Evaluation of New VegetableBased Cutting Fluids on Thrust Force and Surface Roughness in Drilling of Aisi 304 Using Taguchi Method" Materials and Manufacturing Processes 26 (9): 1136-1146. Kuram, E., Ozcelik B., Bayramoglu M., Demirbas E., and Tolga Simsek B. 2013. "Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments" Journal of Cleaner Production. 42: 159-166. Lalwani, D. I., Mehta N. K., and Jain P. K. 2008. "Experimental Investigations of Cutting Parameters Influence on Cutting Forces and Surface Roughness in Finish Hard Turning of Mdn250 Steel" Journal of Materials Processing Technology 206 (1–3): 167-179. Lin, Y. J., Agrawal A., and Fang Y. 2008. "Wear Progressions and Tool Life Enhancement with Alcrn Coated Inserts in High-Speed Dry and Wet Steel Lathing" Wear 264 (3–4): 226-34. Mehrban, M., Naderi D, Panahizadeh V, and Moslemi N. H., 2008. "Modelling of Tool Life in Turning Process Using Experimental Method" International Journal of Material Forming 1 (1): 559-562. Nalbant, M., Gokkaya H., and Sur G. 2007. "Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning" Materials & Design 28 (4): pp. 13791385. Oktem, H., Tuncay E., and Fehmi E. 2006. "Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm" Materials and Design 27: 735–744.

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(a)

(b)

(c)

(d)

Figure 1: (a) mild steel specimen, (b) cutting fluid, (c) tool holder, (d) inserts

Figure 2 (a): Influence of cutting velocity to surface roughness

Figure 2 (c): Influence of depth of cut to surface roughness

Figure 2 (b): Influence of feed rate to surface roughness

Figure 2 (d): Influence of cutting fluid to surface roughness

Figure 3 (a): Influence of cutting velocity to tool wear

Figure 3 (c): Influence of depth of cut to tool wear

Figure 3 (b): Influence of feed rate to tool wear

Figure 3 (d): Influence of cutting fluid to tool wear

surface roughness (µm)

Interaction of cutting speed and feed rate on surface roughness 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

high level of cutting speed

0

1

2 level of feedrate

3

4

Figure 4: Effect of feed rate on surface roughness at the high level of cutting speed

(a)

(b)

Figure 5: (a) chip formation obtained (at 100 m/min, feed rate 0.07 mm/rev, depth of cut 1.5 mm) (b) chip formation obtained (at 180 m/min, feed rate 0.07 mm/rev, depth of cut 1 mm)

(a)

(b)

Figure 6: (a) Arc chip formation (at 140 m/min, feed rate 0.05 mm/rev, depth of cut 1 mm) (b) Washer-type helical chip formation (at 180 m/min, feed rate 0.06 mm/rev, depth of cut 0.5 mm)

Table 3: Selection of ranges of cutting parameters Parameter

Level for each parameter Level 1 Level 2 Level 3

Cutting speed, Vc (m/min)

100

140

180

Feed rate, Fr (mm/rev)

0.05

0.06

0.07

Depth of cut, Doc (mm)

0.5

1.0

1.5

Cutting fluid condition

LFLV

HFLV

LFHV

Table 4: The cutting fluid flow specifications Flow rate, Q (ml/s) 132

Diameter of tube, D (m) 9.8

Velocity of cutting fluid (m/s) 1.75

247

9.8

3.26

168

6

5.94

Cutting fluid condition Low flow rate low velocity (LFLV) High flow rate low velocity (HFLV) Low flow rate high velocity (LFHV)

Table 3: Surface roughness and tool wear obtained from experiments Trial no

Cutting parameter level

Performances

Cutting

Feed

Depth

Cutting fluid

Surface

Flank

speed,

rate,

of cut,

condition, Q

roughness, Ra

wear, Vb

Vc

Fr

Doc

(µm)

(mm)

7

1

1

1

LFLV

0.79

47.14

5

1

2

2

HFLV

0.55

44.99

3

1

3

3

LFHV

0.67

44.64

1

2

1

2

LFHV

0.35

41.42

8

2

2

3

LFLV

0.96

50.01

6

2

3

1

HFLV

0.83

49.28

4

3

1

3

HFLV

0.30

42.85

2

3

2

1

LFHV

0.53

42.85

9

3

3

2

LFLV

0.66

39.64

23

Table 4: S/N ratio calculated for surface roughness and tool wear Trial no

Cutting parameter level Vc Fr Doc Q

Performances

S/N ratio

Ra (µm)

Vb (mm)

ȠRa

ȠVb

7

1

1

1

LFLV

0.79

47.14

2.01

-33.47

4.04

1120.04

5

1

2

2

HFLV

0.55

44.99

5.25

-33.06

27.52

1093.16

3

1

3

3

LFHV

0.67

44.64

3.48

-32.99

12.10

1088.60

1

2

1

2

LFHV

0.35

41.42

9.08

-32.34

82.40

1046.19

8 6

2 2

2 3

3 1

LFLV HFLV

0.96 0.83

50.01 49.28

0.38 1.60

-33.98 -33.85

0.15 2.56

1154.68 1146.07

4

3

1

3

HFLV

0.30

42.85

10.60

-32.64

112.44

1065.31

2

3

2

1

LFHV

0.53

42.85

5.49

-32.64

30.11

1065.33

9

3

3

2

LFLV

0.66

39.64

3.59

-31.96

12.87

1021.57

5.64

402.82

41.48 4.61

-296.94 -32.99

284.18

9800.94

Total Mean

Table 5: ANOVA for surface roughness Analysis of Variance (ANOVA) for surface roughness Parameter

Degree of freedom

Sum of squares

Mean square

Contribution (%)

Vc

2

17.14

8.57

18.43

Fr

2

31.94

15.97

34.33

Doc

2

13.14

6.57

14.13

Q

2

30.81

15.41

33.12

Total

8

93.04

11.63

100.00

Table 6: ANOVA for tool wear

Parameter Vc Fr Doc Q Total

Analysis of Variance (ANOVA) for tool wear Degree of Sum of Mean freedom squares square 2 1.59 0.79 2 0.27 0.13 2 1.32 0.66 2 0.51 0.25 8 3.68 1.84

24

Contribution (%) 43.12 7.28 35.83 13.77 100.00

Table 7: Confirmation test for surface roughness Confirmation test for surface roughness Parameter Level

Initial

Prediction

Experiment

A2B2C3D2

A3B1C2D3

A3B1C2D3 0.21

Surface roughness (µm) 4.21

S/N ratio

11.95

13.42

9.21

Improvement

Table 8: Confirmation test for tool wear Confirmation test for tool wear Parameter Level

Initial

Prediction

Experiment

A1B3C3D1

A3B1C2D3

A3B1C2D3 38.92

Surface roughness (µm) S/N ratio

-33.47

-31.37 1.67

Improvement

25

-31.80

Highlights for review 

Fluid flow mechanism of cutting fluid in machining process are explained



Effect of fluid condition & cutting parameters on surface roughness & tool-life



Optimization of cutting parameters to achieve good surface roughness & long tool life



Confirmation test with predicted cutting parameters

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