Determining the influence of cutting fluids on tool wear and surface roughness during turning of AISI 304 austenitic stainless steel

Determining the influence of cutting fluids on tool wear and surface roughness during turning of AISI 304 austenitic stainless steel

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Determining the influence of cutting fluids on tool wear and surface roughness during turning of AISI 304 austenitic stainless steel M. Anthony Xavior ∗ , M. Adithan Mechanical Engineering, VIT University, Vellore 632014, Tamil Nadu, India

a r t i c l e

i n f o

a b s t r a c t

Article history:

Knowledge of the performance of cutting fluids in machining different work materials is

Received 16 January 2007

of critical importance in order to improve the efficiency of any machining process. The

Received in revised form

efficiency can be evaluated based on certain process parameters such as flank wear, surface

23 January 2008

roughness on the work piece, cutting forces developed, temperature developed at the tool

Accepted 27 February 2008

chip interface, etc. The objective of this work is to determine the influence of cutting fluids on tool wear and surface roughness during turning of AISI 304 with carbide tool. Further an attempt has been made to identify the influence of coconut oil in reducing the tool

Keywords:

wear and surface roughness during turning process. The performance of coconut oil is also

Turning

being compared with another two cutting fluids namely an emulsion and a neat cutting oil

Tool wear

(immiscible with water). The results indicated that in general, coconut oil performed better

Surface roughness

than the other two cutting fluids in reducing the tool wear and improving the surface finish.

Coconut oil

Coconut oil has been used as one of the cutting fluids in this work because of its thermal and oxidative stability which is being comparable to other vegetable-based cutting fluids used in the metal cutting industry. © 2008 Elsevier B.V. All rights reserved.

1.

Introduction

AISI 304 steel finds its application in air craft fittings, aerospace components such as bushings, shafts, valves, special screws, cryogenic vessels and components for severe chemical environments. They were also being used for welded construction in aerospace structural components. Most of the components require certain machining in different machines. During machining of AISI 304 the operators encounter certain difficulties such as premature tool failure and poor surface finish due to high temperature at tool–work piece interface. In order to overcome these difficulties, the artisans working in small and tiny industries started using coconut oil as a cutting fluid for machining. It has been found that coconut oil



extended the tool life with a better surface finish for machining at low and medium cutting speed. In this context, this study becomes necessary to understand the theory behind the performance of coconut oil during the machining of AISI 304 material.

1.1.

Machining

Turning is a widely used machining process in which a singlepoint cutting tool removes material from the surface of a rotating cylindrical work piece. The material removed, called chip, slides on the face of tool, known as tool rake face, resulting in high normal and shear stresses and, moreover, to a high coefficient of friction during chip formation. Most of

Corresponding author. Tel.: +91 416 2202228/43091; fax: +91 416 2243092/40411. E-mail address: Xavior [email protected] (M.A. Xavior). 0924-0136/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jmatprotec.2008.02.068

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Table 1 – Typical chemical composition for the AISI 304 C Si Mn Cr Ni Mo Cu Ti V W Co Nb Pb Fe

0.05487 0.64 1.66 18.2 9.11 0.092 0.14 0.006 0.046 0.048 0.40 0.013 0.015 69.7

the mechanical energy used to form the chip becomes heat, which generates high temperatures in the cutting region. Due to the fact that, higher the tool temperature, the faster the wear, the use of cutting fluids in machining processes has, as its main goal, the reduction of the cutting region temperature, either through lubrication and reduction of friction wear, and through a combination of these functions. Among all the types of wear, flank wear affects the work piece dimension, as well as quality of surface finish obtained, to a large extent. Asibu (1985) found that flank wear results in changes in the mechanics of the cutting process, an increased tendency for chatter and changes in the dimension of the product. In practice, the extent of flank wear is used as the criteria in determining the tool life (Byrd and Ferguson, 1978). Flank wear may be due to adhesive wear or abrasive wear caused by the hard second phases in the work material (Ramalingam and Wright, 1981). In machining of parts, surface quality is one of the most specified customer requirements where major indication of surface quality on machined parts is the surface roughness value. Noordin et al. (2001) determined that the surface roughness is dependent on the feed rate whereby the use of lower feed rate produced better surface finish. It was also determined that the surface roughness values obtained increased when the cutting speed was increased. Higher surface roughness values at higher cutting speeds can be explained by the highly ductile nature of austenitic stainless steels, which increases the tendency to form a large and unstable built up edge (BUE). The presence of the large and unstable BUE causes poor surface finish. Wear at the cutting edge directly influences the machined surface roughness since the edge is in direct contact with the newly machined surface (Ezugwu and Kim, 1995).

Table 2 – Typical physical and thermal properties for the AISI 304 Parameters Density Elastic modulus Poisson’s ratio Coefficient of thermal expansion Thermal conductivity Specific heat capacity

1.2.

Unit

Value

3

8000 193 0.3 17.8 16.2 500

kg/m GPa – Mm m−1 ◦ C−1 W/mk J/kg K

Austenitic stainless steel

Austenitic stainless steels are characterized by a high work hardening rate, low thermal conductivity and resistance to corrosion (Groover, 1996). Stainless steels are known for their resistance to corrosion. But their machinability is more difficult than the other alloy steels due to reasons such as having low heat conductivity, high BUE tendency and high deformation hardening (Kopac and Sali, 2001). Many attempts have been made to improve the machinability of austenitic stainless steels (O’Sullivan and Cotterell, 2002). It was reported that austenitic stainless steels are difficult to machine (Akasawa, 2003). Problems such as poor surface finish and high tool wear are common in machining of austenitic stainless steel (Kosa, 1989). Ihsan et al. (2004) carried out turning tests on AISI 304 austenitic stainless steel to determine the optimum machining parameters. Zafer and Sezgin (2004) determined the best suitable cutting condition for machining of AISI 304 stainless steels by considering the acoustic emission during the cutting process. The best cutting speed and feed rate were determined according to flank wear, BUE, chip form, surface roughness of the machined samples and machine tool power consumption. It was concluded that, the lowest flank wear is observed at a feed rate of 0.25 mm/rev for all the cutting speeds. Tables 1 and 2 show the chemical composition, physical and thermal properties of AISI 304.

1.3.

Cutting fluids

Cutting fluids have been used in the machining process with the purpose to improve the tribological characteristics of the work piece–tool–chip system. It is interesting to note that the use of coolants for machining was first reported by Taylor in 1907, who achieved up to 40% increase in cutting speed when machining steel with high speed steel tools using water as coolant (Taylor, 1907). Cutting fluids improve the efficiency of machining in terms of increased tool life, improved surface finish, improved dimensional accuracy, reduced cutting force and reduced vibrations (De chiffre, 1988). Cutting flu-

Table 3 – Comparison of kinematic viscosity of the three cutting fluids S. no. 1 2 3

Temperature ( ◦ C) 40 50 60

Viscosity (mPa S) of soluble oil 1.63 1.04 0.89

Viscosity (mPa S) of coconut oil 26.8 20.3 15.46

Viscosity (mPa S) of straight cutting oil 45.7 28.2 19.5

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Table 4 – Critical parameters and their levels S. no.

Machining parameter

1 2 3 4

Cutting speed, Vc Depth of cut, d Feed rate, f Type of cutting fluid, D

Unit

Level 1

Level 2

m/min mm mm/rev –

38.95 0.5 0.2 Coconut oil

61.35 1.0 0.25 Soluble oil

ids provide lubrication between the work piece and tool and also remove heat generated during the metal cutting process (De Chiffre et al., 1994). The chemical composition and mechanical properties of the work material, the tool and the cutting fluid are of vital importance in determining process performance and finished surface quality. For applications where a metalworking fluid with better lubricating properties is needed, a non-water-miscible fluid may be recommended. In other cases with high cutting velocities, a water-miscible fluid is often preferred due to its better cooling properties (Kajdas, 1989). But application of conventional cutting fluids creates several techno-environmental problems. Environmental pollution due to chemical dissociation/break-down of the cutting fluid at high cutting temperature, biological (dermatological) problems to operators coming in physical contact with cutting fluid, water pollution and soil contamination during disposal. The use of conventional petroleum-based cutting fluids is potentially dangerous. The effects of a particular cutting fluid on mankind, working environment, the work piece and machine tool as well as generally on living environment as a whole are usually expressed by their ecological parameters. Machine operators are affected by contact with various substances within the cutting fluids (Sokovic and Mijanovic, 2001).

1.4.

Vegetable-based cutting fluids

Cutting fluids based on mineral oils are traditionally used in production shops due to their chemical stability and frequent reuse. However, the present trend towards new types of cutting fluids based on vegetable oils and esters in machining is clearly justified by their higher biodegradability and lower environmental impact. Emulsions of vegetable oils were prepared using ionic and non-ionic surfactants for use as metal working fluids. Over the years, vegetable oils and fats have been used and retained their importance as metalworking lubricants. Most attention has been given to vegetable oil-based emulsions, and few references are available on these emulsions as metalworking fluids. The use of vegetable oil in metalworking applications may alleviate problems faced by workers, such as skin cancer and inhalation of toxic mist in the work environments. Jacob et al. (2004) developed a vegetable-based emulsion that can be used in the metal working industry to replace partially or completely the commonly used petroleumbased emulsions. Vegetable oils have good lubricating ability and have been used for the formulation of metal cutting emulsions (Herdan, 1999). Vegetable oil-based emulsions were also a part of recent research to produce stable emulsions to use as metalworking fluids and in other applications (Alander and Warnheim, 1989). Ioan et al. (2002) presented the first

Level 3 97.38 1.2 0.28 Straight cutting oil

experimental results on lubricating capacity of rape seed oil compared to that obtained for a usual mineral oil. Belluco and De Chiffre (2002) made an investigation on the effect of new formulations of vegetable oils on surface integrity and part accuracy in reaming and tapping operations with AISI 316L stainless steel. Cutting fluid was found to have a significant effect on surface integrity and thickness of the strain hardened layer in the sub-surface, as well as part accuracy. Cutting fluids based on vegetable oils showed better performance than mineral oils. The efficiency of six cutting oils was evaluated in drilling AISI 316L austenitic stainless steel using conventional HSS-Co tools by measurements of tool life, tool wear, cutting forces and chip formation. All vegetable-based oils produced better results than the commercially available mineral oil in terms of tool life improvement and reduction in thrust force.

Table 5 – Experimentation and observations S. no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Vc

d

f

D ()

Vb

Ra

38.95 61.35 97.38 38.95 61.35 97.38 38.95 61.35 97.38 97.38 38.95 61.35 97.38 38.95 61.35 97.38 38.95 61.35 61.35 97.38 38.95 61.35 97.38 38.95 61.35 97.38 38.95

0.5 1.0 1.2 1.0 1.2 0.5 1.2 0.5 1.0 0.5 1.0 1.2 1.0 1.2 0.5 1.2 0.5 1.0 0.5 1.0 1.2 1.0 1.2 0.5 1.2 0.5 1.0

0.2 0.25 0.28 0.25 0.28 0.2 0.28 0.2 0.25 0.25 0.28 0.2 0.28 0.2 0.25 0.2 0.25 0.28 0.28 0.2 0.25 0.2 0.25 0.28 0.25 0.28 0.2

C (26.8) S (1.63) St (45.7) S (1.63) St (45.7) C (26.8) St (45.7) C (26.8) S (1.63) St (45.7) C (26.8) S (1.63) C (26.8) S (1.63) St (45.7) S (1.63) St (45.7) C (26.8) S (1.63) St (45.7) C (26.8) St (45.7) C (26.8) S (1.63) C (26.8) S (1.63) St (45.7)

0.045 0.096 0.134 0.075 0.107 0.071 0.097 0.055 0.126 0.104 0.081 0.085 0.106 0.068 0.095 0.105 0.098 0.095 0.094 0.10 0.077 0.069 0.105 0.076 0.088 0.10 0.060

1.91 2.49 3.16 2.30 3.29 2.11 3.01 2.06 2.46 2.43 2.47 2.59 2.65 2.32 2.59 2.51 2.25 2.61 2.92 2.35 2.33 2.46 2.51 2.68 2.46 2.92 2.14

Vc : cutting speed in m/min; d: depth of cut in mm; f: feed rate in mm/rev; D: type of cutting fluid; Vb : flank wear in mm; Ra : average surface roughness in ␮m; C: coconut oil; S: soluble oil; St: straight cutting oil; : viscosity in mPa S.

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Table 6 – ANOVA for surface roughness S. no. 1 2 3 4 5 6

1.5.

Factor Cutting speed, Vc Depth of cut, d Feed rate, f Type of cutting fluid Total Error

Degree of freedom

Sum of squares

2 2 2 2 8 18

0.09 0.13 0.56 0.13 0.91 1.56

Coconut oil

0.05 0.07 0.28 0.07 – 0.087

Variance 0.575 0.805 3.218 0.805 –

% contribution 9.89 14.29 61.54 14.29 –

2525 M12. After the machining process, the insert was removed and its flank wear was measured using Mitutoyo’s Tool Maker’s microscope.

Coconut oil belongs to unique group of vegetable oils called lauric oils. Chemical composition of coconut oil includes lauric acid (51%), myristic acid (18.5%), caprilic acid (9.5%), palmitic acid (7.5%), olcic acid (5%), capric acid (4.5%), stearic acid (3%) and linoleic acid (1%). Coconut oil is one of the vegetable oils, which remains as a white crystalline solid at temperature below 20 ◦ C. More than 90% of fatty acids of coconut oil are saturated. The iodine value of coconut which is a measure of un-saturation in coconut oil is 7–12. The saturated character of the oil imparts a strong resistance to oxidative stability. The specific density of coconut oil is 0.93 g/cm3 and the Cetane number is 37. The flash point and viscosity index of coconut oil is 294 and −130, respectively. Jayadas and Prabhakaran (2006) analyzed and compared the cooling behavior, thermal and oxidative stabilities of coconut oil with sesame oil, sunflower oil and a mineral oil (Grade 2T oil). The thermal and oxidative stabilities were determined from the onset temperature of decomposition. Onset temperature of thermal degradation of coconut oil is lower compared to sunflower oil and sesame oil whereas the onset temperatures of oxidative degradation are comparable. It had been concluded that coconut oil shows better oxidative stability in comparison to other vegetable oils with high percentage of unsaturated fatty acid content. Coconut oil showed comparatively lesser weight gain under oxidative environment among the vegetable oils considered. Coconut oil has very high pour point (23–25) because of the predominantly saturated nature of its fatty acid constituents precluding its use as base oil for lubricant in temperate and cold climatic conditions.

2.

Mean squares

Experimental procedure

A Centre Lathe (Kirloskar make Turn Master 40) was used for conducting the experiments. AISI 304 was used as the work material and Sandvik’s carbide CNMG 12 04 08 insert was used as the cutting tool. The inserts were clamped mechanically on a rigid tool holder DCLNR

To understand more about the tool wear the microscopic picture of inserts were observed using Carl Zeiss optical microscope, having magnification range of 500×. The average surface roughness on the work piece was measured using Mitutoyo’s Surftest surface finish measuring instrument. The experimentation for this work was based on Taguchi’s design of experiments (DOE) and orthogonal array. A large number of experiments have to be carried out when the number of the process parameters increases. To solve this task, the Taguchi method uses a special design of orthogonal arrays to study the entire parameter space with a small number of experiments only. In this work, three cutting parameters namely, cutting speed, depth of cut and feed rate were considered for experimentation. Along with this, the type of cutting fluid used, is also considered as one of the critical input parameters while designing the experiments. Table 3 shows the kinematic viscosity of the three cutting fluids considered in this work at various temperature. Accordingly there are four input parameters and for each parameters three levels were assumed. For a four factors, three level experiment, Taguchi had specified L27 (3)4 orthogonal array for experimentation. The response obtained from the trials conducted as per L27 array experimentation was recorded and further analyzed. Table 4 shows the parameters and their levels considered for the experiments. Cutting fluid is one of the parameters that does not have any quantitative levels but each oil is being considered as one level for experimentation. Table 5 shows the actual cutting parameters used for each trial of experiment and the corresponding values of observed Vb (flank wear) and Ra (average roughness value of surface finish) obtained.

3.

Analysis of variance (ANOVA)

The observed values of tool flank wear (Vb , mm) and surface roughness (Ra , ␮m) were used for determining the significant factors influencing the machining process. The significant parameters influencing the surface roughness and tool wear were found using the ANOVA procedure. Tables 6 and 7 show the ANOVA for surface roughness and tool wear, respectively. From the calculations it is being inferred that feed has more influence on surface roughness and cutting speed has more

Table 7 – ANOVA for tool wear S. no. 1 2 3 4 5 6

Factor Cutting speed, Vc Depth of cut, d Feed rate, f Type of cutting fluid Total Error

Degree of freedom 2 2 2 2 8 18

Sum of squares 0.00139 0.00030 0.00116 0.00014 0.00299 0.00801

Mean squares

Variance

0.000695 0.000150 0.000580 0.000070 – 0.000445

1.562 0.337 1.303 0.157 – –

% contribution 46.49 10.03 38.73 4.65 – –

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Fig. 1 – Feed rate vs. surface roughness. (1) Coconut oil, (2) soluble oil and (3) straight cutting oil.

influence on tool wear. Further it is also being inferred that cutting fluid has considerable influence on both the process parameters, i.e. on Vb and Ra . Model calculation for determining the percentage influence of each cutting parameters on surface roughness is being presented in Section 3.1.

measured during the trials. The subsequent steps were selfexplanatory

3.1.

grand total sum of squares =

Model calculation of ANOVA for surface roughness

A model calculation for determining the percentage contribution of one cutting parameter on surface roughness is being presented here. In the first step, the overall mean was calculated which was the average of the surface roughness

overall mean (m) :

1 27



i =

1 67.98 = 2.52 27

 ´2

i = 173.93

sum of squares due to mean = number of experiments × m2 = 171.46

Fig. 2 – Feed rate vs. surface roughness. (1) Coconut oil, (2) soluble oil, (3) straight cutting oil; depth of cut (d): 0.5 mm [constant]; cutting speed (Vc ): 38.95 m/min, 61.35 m/min and 97.38 m/min at the three points a, b and c, respectively.

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905

Fig. 3 – Cutting speed vs. tool wear. (1) Coconut oil, (2) soluble oil, (3) straight cutting oil; depth of cut (d): 0.5 mm [constant]; feed rate (f): 0.2 mm/rev, 0.25 mm/rev, 0.28 mm/rev at the three points a, b and c, respectively.

total sum of squares = grand total sum of squares −sum of squares due to mean = 2.47 sum of squares due to cutting speed 2

2

Similarly, the percentage contribution of the other three cutting parameters, viz. depth of cut, feed rate and cutting fluid on surface roughness was evaluated. The results of the ANOVA for surface roughness were summarized in Table 6.

2

= 3[(A1 − m) + (A2 − m) + (A3 − m) ] = 0.0906

4. where A1 is the average surface roughness value observed when the first level of cutting speed was used for machining. Similarly A2 and A3 are the average surface roughness values observed when the second and third level of cutting speed was used for machining. The sum of squares due to each of the remaining three factors are calculated using similar relationships and found to be 0.13, 0.56 and 0.13 for the factors depth of cut, feed rate and the type of cutting fluid, respectively. degree of freedom for the error = degree of freedom for the total sum of squares

Mathematical modeling

Multiple linear regression models were developed for flank wear and surface roughness using Minitab-15 software. The response variable is the flank wear and the surface roughness, whereas the predictors are cutting speed, feed rate, depth of cut and the viscosity of the cutting fluids. The viscosity of each cutting fluid at 40 ◦ C was considered for the mathematical modeling. Accordingly the equations of the fitted model for flank wear and surface roughness is given below. Vb = 0.00052Vc + 0.0194d + 0.336 f + 0.000069 − 0.0459

−sum of degrees of freedom for various factors

Ra = 0.00280Vc + 0.299d + 6.87f + 0.00067 + 0.376

= 26 − 8 = 18

where Vb is the flank wear in mm, Vc is the cutting speed in m/min, d is the depth of cut in mm, f is the feed rate in mm/rev, Ra is the surface finish in ␮m and  is the viscosity in mPa S.

mean squares =

sum of squares due to each factor degrees of freedom for each factor

5. mean squares due to the factor variance ratio = mean squares error

percentage of contribution sum of squares for each factor × 100 = total sum of squares =

0.09 × 100 = 9.89 for cutting speed. 0.91

Results and discussions

5.1. Performance of coconut oil with respect to surface roughness and tool wear The technological tests to assess the performance of cutting fluids were carried out on a turning process with recording of the important observations such as, cutting forces and wear of tools, temperature of work piece and tool insert, chip shape and color of chip, surface quality obtained and vibrations of

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Fig. 4 – Microphotographs of tool wear. Machining condition: Vc , 38.95 m/min; d, 0.5 mm and f, 0.25 mm/rev.

machine tool, cutting tool and work piece. In this work, only two parameters namely tool wear and surface roughness was considered to understand the performance of coconut oil as a metal working fluid when machining Stainless steel AISI 304. From the ANOVA table for surface roughness, it was found that feed rate (61.54%) is the most significant parameter, which affects the surface roughness of AISI 304 material while turning. The surface roughness variation at different feed rates was compared for various cutting oils. Experiments were conducted by varying the feed rate, keeping the other parameters namely cutting speed and depth of cut constant at 90 m/min and 1 mm, respectively for each oil individually and graph was plotted between feed rate and surface roughness. Fig. 1 shows the plot between the feed rate and surface roughness obtained during the turning process in the presence of each cutting fluid. It was observed that the surface roughness increases as

the feed rate increases and the surface roughness on the work piece is less in the case of coconut oil at all the feed rates. As the feed rate is increased from 0.1 mm/rev to 0.355 mm/rev, it is observed that soluble oil starts off with a lower surface roughness almost equivalent to that of coconut oil. But as the feed rate increases, the increase in surface roughness value is high in the case of soluble oil and straight cutting oil. Coconut oil gives better surface finish at every feed rate and the surface roughness obtained with coconut oil is much lower than that obtained with other cutting fluids. Further experiments were carried out by varying all the three cutting parameters for each cutting fluids and the process parameter values (surface roughness and tool wear) were recorded. From the recorded values Figs. 2 and 3 were plotted between surface roughness Vs feed rate and tool wear Vs cutting speed. From the graphs it is being inferred that for any combination of cutting param-

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907

Figs. 5–10 – Surface plots, Ra : surface roughness, Vb : flank wear, d: depth of cut, Vc : cutting speed and f: feed rate.

eters coconut oil always outperform the other two cutting fluids.

5.2. Microscopic study of tool wear occurring on carbide tool The extent of flank wear is considered a dependable criterion for judging the life of the cutting tool. In case of carbide tools,

through proper alloying of tungsten carbide with titanium and tantalum carbides, sufficient resistance to crater is obtained so that most tools do not fail by cratering, before a reasonable amount of flank wear is obtained on the flank of the tool. The flank wear can be more easily observed and measured than other types of wear and it is relatively easy to predict. The development of flank wear initially involves a high rate followed by a more or less linear trend and finally rises rapidly

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when the amount of wear crosses beyond the critical value. To understand more about the tool wear the microphotograph of inserts were observed using Carl Zeiss optical microscope, having magnification range of 500×. The flank was developed while machining at certain cutting parameters (cutting speed: 38.95 m/min, depth of cut: 0.5 mm and feed rate: 0.25 mm/rev) in the presence of coconut oil is shown in the microphotograph (Fig. 4). And for the same cutting condition, the microphotograph obtained on the insert when the other two cutting fluids were used was also presented. The microphotograph taken at 100× and 200× shows the flank wear caused while machining at lower cutting speed. The figure shows the tool tip where the maximum wearing had occurred. In the case of coconut oil, the tool wear is considerably less when compared to soluble oil and straight cutting oil at lower cutting speed. Moreover, the viscosity of coconut oil is more than that of soluble oil and less than that of straight cutting oil, which favors easy flow of cutting fluid at minimal oil condition. This enables the reduction of friction between the tool and work piece, and easy removal of heat developed at the interface. The heat removal at lower cutting speed gives coconut oil a considerable advantage than that of soluble oil and straight cutting oil. At lower speeds, coconut oil yields lower wear and produces good surface finish when compared to other cutting fluids.

5.3.

Surface plots

A graphical analysis was done on the observed values using Minitab software. The response surface plots obtained for each process parameter with respect to the cutting parameters is being presented. Figs. 5–10 show the estimated response of surface roughness and tool wear for the cutting parameters namely cutting speed, depth of cut and feed rate. Fig. 5 shows the estimated response of surface roughness for the corresponding cutting speed and depth of cut. It is seen that cutting speed has significant effect on surface roughness. As has been previously pointed out, this figure shows cutting speed around 80 m/min gives the lowest surface finish. Ra value is almost constant for lower depth of cut, but the increase is seen for higher values. Fig. 6 shows the estimated response of surface roughness for the corresponding cutting speed and feed rate. From the graph, it is seen that feed rate has the most significant effect on surface roughness and its variation is very high when compared to other parameters. Fig. 7 shows the estimated response of surface roughness for the corresponding feed rate and depth of cut. It is established that feed rate has the highest impact on surface roughness. Fig. 8 shows the estimated response of tool wear for the corresponding cutting speed and feed rate. Initially, the tool wear increases slightly with the increase in cutting speed and it remains constant for cutting speed around 60 m/min. Beyond that, tool wear increases linearly with the increase in cutting speed. Fig. 9 shows the estimated response of tool wear for the corresponding cutting speed and depth of cut. From the graph, it is confirmed that depth of cut has the least significance on tool wear and cutting speed has its domination on tool wear over feed rate and depth of cut. Fig. 10 shows the estimated response of tool wear for the corresponding feed rate and depth of cut. For higher values of feed rate and depth of

cut, the tool wear is considerably high and it is constant for lower values.

6.

Conclusions

Experiments involving cemented carbide tool inserts and AISI 304 stainless steel work material under varying machining parameters and with three different cutting fluids were performed. Cutting fluids were considered as important parameters in the machining process along with cutting speed, feed rate and depth of cut. An analysis of variance (ANOVA) was made and it was found that feed rate has greater influence on surface roughness (61.54% contribution) and cutting speed has greater influence on tool wear (46.49% contribution). Further it was found that cutting fluid has some considerable influence on both surface roughness and tool wear. Effectiveness of the cutting fluids in reducing the tool wear and improving the surface finish was found by comparing the relative performance. In general, coconut oil was found to be a better cutting fluid than the conventional mineral oils in reducing the tool wear and surface roughness. Surface plots were drawn between the various process parameters so as to understand more about their individual relationship and relative contribution to surface roughness and flank wear.

references

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