Experimental investigation of cutting zone temperature and flank wear correlation in turning AISI...

Indian Journal of Engineering & Materials Sciences

Vol. 21, April 2014, pp. 139-148

Experimental investigation of cutting zone temperature and flank wear correlation

in turning AISI 1045 steel with different tool geometries

N Senthilkumara* & T Tamizharasan

b

aAdhiparasakthi Engineering College, Melmaruvathur 603 319, India bTRP Engineering College, Irungalur, Tiruchirappalli 621 105, India

Received 22 November 2012; accepted 1 November 2013

This paper focuses on the experimental investigation and analysis of temperature generated at the cutting zone during

machining and the wear of cutting insert at the flank face and the relationship between these parameters. Uncoated cemented

carbide inserts of different geometries are used to turn AISI 1045 steel. Three levels of cutting insert geometries cutting

insert shape (included angle), relief angle and nose radius are chosen. Taguchi’s design of experiments (DoE) is used to

design the experiments and using signal-to-noise (S/N) ratio, the control parameters are analyzed, showing a correlation

between the two responses. Analysis of variance is performed to determine the most significant factor over the responses

and empirical models are developed for predicting the responses using multiple linear regression models. Cutting zone

temperature is mostly influenced by nose radius followed by cutting insert shape and for flank wear the significance of

cutting insert shape is more, followed by nose radius. Confirmation experiment performed with optimum cutting insert

geometry shows a reduction in cutting zone temperature and flank wear.

Keywords: Cutting zone temperature, Flank wear, Taguchi’s technique, ANOVA

Machining is the term used to describe removal of

material from the workpiece, and cutting generally

involves single-point or multipoint cutting tools with a

clearly defined tool shape. The cutting tool is set at a

certain depth of cut and travels to the left with a certain

velocity called as feed rate, which is the distance the

tool travels per unit revolution of the workpiece as the

workpiece rotates at certain cutting speed to remove the

material as chip which moves up the face of the cutting

tool1. The schematic diagram of the orthogonal cutting

process is shown in Fig. 1, in which a cutting tool

moves to the left along the workpiece at a constant

velocity V and depth of cut to. A chip is produced ahead

of the tool by deforming and shearing the material

continuously along the shear plane1.

The geometry of cutting tools affects the quality and

productivity of machining operations, chip control,

magnitude, and direction of the cutting force and its

components. For a given combination of the tool and

work materials, there is the cutting temperature,

referred to as the optimal cutting temperature at which

the combination of minimum tool wear rate, minimum

stabilized cutting force, and highest quality of the

machined surface, is achieved2.

The cutting edge is formed by the intersection of the

rake face with the clearance face or flank of the tool.

The tool is so designed and held in such a position that

the clearance face does not rub against the freshly cut

metal surface. The nose of the tool is at the intersection

of all three faces and may be sharp, but more

frequently there is a nose radius between the two

clearance faces3. The various shapes of cutting tool

inserts used for machining AISI 1045 steel in this work

is shown in Fig. 2.

In this study, nine ISO designated uncoated carbide

cutting tool inserts having different geometries4,5

such

as shape of the cutting insert (included angle of the

tool), relief angle and nose radius are chosen for

analysis. The machining parameters cutting speed, feed

—————

*Corresponding author (E-mail: [email protected])

Fig. 1—Schematic illustration of orthogonal cutting

INDIAN J ENG. MATER. SCI., APRIL 2014

140

rate and depth of cut are kept constant. The

correlation between cutting zone temperature and

flank wear are studied to determine the influence of

one response over another response. This analysis is

essential since the output quality characteristics

depends on the geometry of the cutting tool and

machining parameters selected6,7

. The stability of the

cutting tool will be decided by the geometries chosen.

Tamizhmanii and Hasan8 evaluated the relationship

between flank wear on CBN and PCBN tool and

cutting forces during machining AISI 440C martensitic

stainless steel and observed that the greater the values

of flank wear, the higher friction of tool on the work

material and higher heat generation occurs. Akhyar et

al.9 optimized the cutting parameters in turning Ti-6%

Al-4%V alloy steel with coated and uncoated carbide

tools under dry condition and high cutting speed using

Taguchi method. Motorcu10

optimized machining

parameters and tool’s nose radius using Taguchi

method over surface roughness while turning AISI

8660 steel with ceramic tool and developed a second

order regression model for prediction. Ramanujam et

al.11

investigated the effect of machining parameters in

turning Al-Sic MMC using PCD insert and optimized it

using grey relational analysis over specific power and

surface finish. Kaladhar et al.12

optimized multiple

responses surface roughness and MRR while turning

AISI 202 austenitic stainless steel with coated carbide

tool and further confirmed it by calculating the

confidence interval.

Problem Identification

Cutting tools are subjected to high localized

stresses, high temperatures, sliding of the chip along

the rake face and sliding of the tool along the freshly

cut surface during metal removal process. These

conditions induce tool wear which in turn adversely

affects tool life, quality of the machined surface and

its dimensional accuracy and consequently the

economics of cutting operations. The rate of tool wear

depends on tool and workpiece materials, tool shape,

cutting fluids, machining parameters and machine-

tool characteristics.

The mechanical energy consumed in the cutting area

is converted into heat. The main sources of heat are the

shear zone, the interface between the tool and the chip

where the friction force generates heat; and the lower

portion of the tool tip, which rubs against the machined

surface. The interaction of these heat sources,

combined with the geometry of the cutting area results

in a complex temperature distribution. The temperature

generated in the shear plane is a function of the shear

energy and the specific heat of the material. Increase of

temperature on the tool face depends on the friction

conditions at the interface. Temperature distribution

will be a function of many other factors such as the

thermal conductivities of the workpiece, the tool

materials, the specific heat, cutting speed, depth of cut,

and the use of a cutting fluid. As cutting speed

increases, there is little time for the heat to be dissipated

away from the cutting area and so the proportion of the

heat carried away by the chip increases. If the tool is dull

or worn, heat is also generated when the tool tip rubs

against the machined surface.

Cutting insert shape (included angle) is considered

since the sharpness of the cutting edge has an effect

on flank wear and surface roughness. Relative edge

strength of the cutting inserts increases with increase

in the included angle and the tendency for chipping

and breaking of inserts with various shapes increases

with decrease in included angle of the cutting tool

inserts, which is given in Fig. 3. Strength refers to the

cutting edge shown by the included angle of the

cutting tool inserts1.

Fig. 2—Various shapes of cutting tool inserts for machining

Fig. 3—Edge strength for various cutting inserts

SENTHILKUMAR & TAMIZHARASAN: FLANK WEAR IN TURNING AISI 1045 STEEL

141

Rubbing of flank face of the cutting tool is avoided

by providing relief angle, thereby reducing surface

roughness. It is observed that if the relief angle is

increased, the volume of wear required to reach a

particular width of flank wear land is also increased.

On the other hand, with large relief angles the

mechanical strength of the cutting edge is low and the

tool is more liable to chipping or fracture. The tool

wear is less at the optimum relief angle. Nose radius

of cutting tool is responsible for larger area of contact,

which removes more material from workpiece. Larger

nose radius gives better surface finish. On the other

hand, reducing the nose to a point may make the

surface finish unacceptable. Increasing the nose radius

also decreases the tool wear so that higher cutting

speed can be employed. When the nose radius is

increased excessively, cutting forces and possibility of

chatter increases.

The cause and effect diagram of turning process

involving various input control parameters such as

cutting tool insert shape, relief angle and nose radius

which have an impact on the output responses flank

wear and cutting zone temperature is shown in Fig. 4.

Experimental Procedure

Material selection

The workpiece material chosen for the experimental

investigation of cutting insert geometries is AISI 1045,

medium carbon steel. AISI 1045 is a low cost alloy

with adequate strength and toughness suitable for most

of the engineering and construction applications, whose

Brinell hardness value is 181 BHN. Engineering

applications of AISI 1045 steel includes shafts, pins,

bolts, gears, forgings, cold drawing and extrusion.

Table 1 shows the chemical composition of the chosen

AISI 1045 workpiece material.

The material for cutting insert used for analysis is

uncoated cemented carbide, WIDIA make – THM

grade of Brinell hardness value of 1433 BHN, whose

chemical composition is given in Table. 2.

The SEM photo-micrograph of the cemented

carbide tool is as shown in Figure 5. The micrograph

shows the particles of predominant Tungsten carbide.

Some voids are present during compacting. The

structure is variable composition of solid solution

phases of WC and TiC. The black areas are voids.

The areas in between the grains are cobalt solid

solution. The marginal dendritic solid solution of

cobalt is seen at the extreme right.

Experimental set-up and methodology

The experiments are conducted on a CNC Turning

center, Lokesh make 2 axis CNC TL-20, swing

Table 1—Chemical composition of AISI 1045 Steel

Sl. No Elements Alloying %

1 Carbon(C) 0.312

2 Silicon(Si) 0.189

3 Manganese(Mn) 0.852

4 Chromium(Cr) 0.025

5 Molybdenum(Mo) 0.033

6 Titanium(Ti) 0.005

7 Vanadium(V) 0.004

8 Tungsten(W) 0.033

9 Phosphorous(P) 0.039

10 Sulphur(S) 0.011

11 Copper(Cu) 0.031

12 Aluminium(Al) 0.037

13 Iron 98.429

Fig. 4—Cause and effect diagram of problem statement

Table 2—Chemical compositions of cutting tool insert

Sl. No Elements/compound Alloying %

1 Tungsten carbide 96.4

2 Titanium carbide 0.5

3 Tantalum carbide 0.8

4 Cobalt 2.19

Fig. 5—SEM image of cemented carbide cutting insert


142

diameter 350 mm, between centre 600 mm, spindle

speed 4500 rpm, main motor power of 11 kW. After

performing the machining process, the flank wear is

measured and recorded by using a Mitutoyo digital

tool makers microscope of specifications, eyepiece

15X, view field diameter 13 mm, objective 2X,

working distance 67 mm, total magnification 30X.

Cutting zone temperature is measured using

METRAVI MT-9 make compact IR thermometer

with dual laser targeting with specifications of IR

temperature range of -50°C to 1000°C, operating

temperature of 0 to 50°C, response time of 150 ms.

Infrared pyrometers allow users to measure

temperature where conventional sensors cannot be

employed in specific applications when dealing with

moving objects or where non-contact measurements

are required. IR thermometer is chosen since it has

quick response, versatility, portability and non-

invasiveness but has the disadvantages of difficulty in

determining the infrared detection area where

accuracy is needed, can only record surface

temperatures and inability to measure very minute

targets13-15

. The experimental setup used to determine

the cutting zone temperature is shown in Fig. 6. The

cutting tool holder with cutting insert is kept upside

down so that the chip generated during the cutting

process will flow downwards thereby exposing the

zone for measuring the temperature at the cutting

zone. The IR thermometer is positioned on the turret

head of the CNC turning center using a stand clamped

to it, thereby continuous measurement of cutting zone

temperature is possible with at most accuracy. The

cutting length of the workpiece, 300 mm is kept

constant throughout the experiment for all conditions,

so that the variation in flank wear can be well

analyzed.

Taguchi technique

Taguchi technique is a powerful tool in quality

optimization16

. Taguchi technique makes use of a special

design of orthogonal array (OA) to examine the quality

characteristics through a minimal number of

experiments. The experimental results based on the

orthogonal array are transformed into S/N ratios to

evaluate the performance characteristics17

. Taguchi’s

DoE is used to design the orthogonal array for three

parameters varied through three levels. The control

parameters and their levels chosen are shown in Table 3.

The machining parameters18

chosen are, cutting

speed as 285 m/min, feed rate as 0.203 mm/rev and

depth of cut as 0.3 mm19

which are kept constant for

all experiments. Figure 7 shows the nomenclature of

Table 3—Control parameters and their levels

Parameter / Level Symbol Level 1 Level 2 Level 3

Cutting insert shape A C (80°) D (55°) S (90°)

Relief angle (°) B 0 3 7

Nose radius (mm) C 0.4 0.8 1.2

Fig. 6—Experimental setup with position of IR thermometer

Fig. 7—Nomenclature of cutting tool inserts used


143

the different cutting tool insert used with various

shapes, relief angles and nose radius. As per ISO

nomenclature, ‘C’ shape inserts are rhombic inserts

which have 80° included angle, whereas ‘D’ shape

inserts have 55° included angle. ‘S’ shape inserts are

square inserts with an included angle of 90°.

The various combinations of cutting insert shapes,

relief angle and nose radius, based on which the

experiments are conducted is presented in Table 4.

For analysis, there are three categories of

performance characteristics, i.e., smaller-the-better

(Eq. 1), larger-the-better (Eq. 2) and nominal-the-

better (Eq. 3). For smaller-the-better category, the

quality characteristics are usually an undesired output

and for larger-the-better category, the quality

characteristics are usually a desired output and for

nominal-the-best category, the quality characteristics

are usually a nominal output.

Smaller-the-better (minimize):

1

1 2/ 10logn

i

S N yin =

= −

∑ … (1)

Larger-the-better (maximize):

1

1 1/ 10log

2

n

i

S Nn y

i=

= −

∑ … (2)

Nominal-the-best:

2/ 10 log

y

yS N

s

=

… (3)

where yi represents the experimentally observed value

of ith experiment and n is the number of replications

of each experiment. To reduce the value of the quality

characteristics to the smallest possible value zero,

which is the ideal or target value, e.g., flank wear,

surface roughness, cutting forces, etc, smaller-the-

better condition is used. When the values of quality

characteristics as much as possible for responses such

as MRR, tool life and productivity are to be increased,

larger-the-better concept is used. When an ideal or

target value is specified to quality characteristics such

as dimensional tolerances, clearance, etc. are needed;

nominal-the-best condition is applied.

Analysis of variance

For analyzing the effect of categorical factors on a

response is determined by performing an analysis of

variance (ANOVA)20,21

. An ANOVA is a statistical

tool, which decomposes the variability in the response

variable amongst the different factors. Depending on

the type of analysis, it may be important to determine

which factors have a significant effect on the

response, and how much of the variability in the

response variable is attributable to each factor. Using

Minitab-16, statistical software ANOVA is

performed.

Multiple linear regression models

Regression is conceptually simple technique for

investigating functional relationship between output

and input decision variables of a process and may be

useful for manufacturing process data description,

parameter estimation and control22

. The criteria for

fitting the best line through the data in simple linear

regression is to minimize the sum of squares of

residuals (Sr) between the measured values of

response and the values of response calculated with

the regression model. The linear fit is expressed as:

0 1y a a x= + … (4)

where y is the value of response and x is the value of

variable. Multiple linear regressions are the useful

extension of the linear regression when the response

is a linear function of two or more independent

variables, which is the case in many practical

applications. In general, the response variable y may

be related to k regressor variables. The model in Eq.

(5) are called a multiple linear regression model with

k regressor variables.

0 1 1 2 2 ...k k

y x x xβ β β β ε= + + + + + … (5)

The parameters βj, j = 0, 1, . , k, are called the

regression coefficients.

Table 4—L9 orthogonal array

Trial

No

Cutting

Insert Shape

Relief

Angle (°)

Nose Radius

(mm)

ISO Insert

Designation

1 C 0 0.4 CNMG 12 04 04

2 C 3 0.8 CAMG 12 04 08

3 C 7 1.2 CCMG 12 04 12

4 D 0 0.8 DNMG 15 04 08

5 D 3 1.2 DAMG 15 04 12

6 D 7 0.4 DCMG 15 04 04

7 S 0 1.2 SNMG 12 04 12

8 S 3 0.4 SAMG 12 04 04

9 S 7 0.8 SCMG 12 04 08


144

Results and Discussion The output quality characteristics cutting zone

temperature23-25

is measured during the process of

turning using IR thermometer and flank wear26,27

is

measured after performing the turning operation using

Tool maker’s microscope. Table 5 shows the

measured output responses and their corresponding

S/N ratio, calculated using smaller-the-better given in

Eq. (1), since the cutting zone temperature and flank

wear has to be minimized28

.

From the results obtained from the experiments, it

is observed that when the cutting insert shape is

changed from ‘C’ to ‘D’, a decrease in flank wear by

61.38% and cutting zone temperature by 22.71% is

noticed, which is due to the reduction in included

angle of the cutting tool insert. Changing the insert

shape from ‘D’ to ‘S’ increases the flank wear and

cutting zone temperature by 183.61% and 14.60% due

to the higher contact area of tool due to higher

included angle.

A reduction in flank wear by 11.19% and cutting

zone temperature by 10.61% is observed when a relief

angle of 3° is provided to the cutting insert from

neutral relief which is because of the elimination of

cutting inserts rubbing the workpiece surface. With

further increase in relief angle from 3° to 7°, flank

wear increases by 166.93% and cutting zone

temperature increases by 14.75% due to the sharp

cutting edge provided by higher relief angle.

Flank wear and cutting zone temperature increases

with increase in nose radius. 134.62% and 34.65%

increase in flank wear and cutting zone temperature is

observed when nose radius is changed from 0.4 to 0.8

mm. When nose radius is further changed to 1.2 mm,

flank wear and cutting zone temperature increase by

7.38% and 9.67%, respectively. Increasing the nose

radius increases the contact area of the cutting tool

insert with the workpiece thereby increasing flank

wear and cutting zone temperature.

Analysis of cutting zone temperature

The cutting zone temperature at the tool-workpiece

interface has to be lower during the machining process.

Hence, the smaller-the-better condition is chosen as

given in Eq. (1). The best level of input control

parameters are identified by calculating the average

values of S/N ratio, as given in Table 6.

From the response table of cutting zone temperature,

the optimal parameter levels identified are, cutting

insert shape of ‘D’, relief angle of 3° and nose radius of

0.4 mm. Hence, the optimum condition is represented

as A2B2C1. Figure 8 shows the main effects plot for

cutting zone temperature.

Interaction plot is an effective method to study the

effect of one control parameter over another parameter

over a particular output response29,30

. Figure 9 shows

Table 5—Output quality characteristics and corresponding

S/N ratio

Cutting zone temperature Flank wear Trial

No Experimental

value (°C)

S/N ratio Experimenta

l value (mm)

S/N ratio

1 83.7 -38.4545 0.143 16.893

2 108 -40.6685 0.112 19.016

3 154 -43.7504 0.483 6.321

4 114.7 -41.1913 0.118 18.562

5 84.6 -38.5474 0.134 17.458

6 67.9 -36.6374 0.033 29.630

7 116.4 -41.3191 0.168 15.494

8 88.8 -38.9683 0.136 17.329

9 101 -40.0864 0.502 5.986

Table 6—Response table for cutting zone temperature

Level / Parameter Insert shape Relief angle Nose radius

Level 1 -40.96 -40.32 -38.02

Level 2 -38.79 -39.39 -40.65

Level 3 -40.12 -40.16 -41.21

Max – Min 2.17 0.93 3.19

Fig. 8—Main effects plot for cutting zone temperature

Fig. 9—Interaction plot for cutting zone temperature


145

the interaction effect of input control parameters on

cutting zone temperature. No significant interaction is

observed between cutting insert shape and relief angle

except for ‘C’ shape. The cutting zone temperature is

lower for ‘D’ shape. Significant effect is observed

between cutting insert shape and nose radius for all

the three insert shapes. In between relief angle and

nose radius, no significant effect is seen except for 7°

relief angle. Higher cutting zone temperature is

noticed for higher relief angle, due to the lower

contact area of cutting edge.

To determine the control parameter that has a

significant effect on cutting zone temperature,

ANOVA is performed and the results are given in

Table 7. From the ANOVA table it is evident that the

nose radius of the cutting insert is the most critical

control parameter contributing significantly by

50.29%, followed by cutting insert shape by 20.74%

and whereas the contribution of relief angle is

negligible.

Multiple linear regression models are developed in

order to predict the output responses for a given set of

input control parameters. Minitab-16, statistical

software is used for developing the linear regression

models. The advantage of developing these models is

to determine the value of the output responses for a

given set of input parameters without performing the

experiments. The developed empirical model to

predict the temperature that prevails at the cutting

zone is given in Eqs (6)-(8) for different insert shapes.

For ‘C’ Shape:

Cutting Zone Temperature = 75.194 + 0.551802*

Relief angle

+ 47.75* Nose radius … (6)

For ‘D’ Shape:


Relief angle

+ 47.75* Nose radius … (7)

For ‘S’ Shape:


Relief angle

+ 47.75* Nose radius … (8)

The residual plots of cutting zone temperature

obtained during generating the empirical model using

regression analysis for geometrical parameters of

cutting tool inserts chosen is shown in Fig. 10. The

normal probability plot of the residuals follows a

straight line in which all the points are very nearer to

the straight line and points are distributed evenly on

both sides of the straight line. In residual versus fits

plot, the residuals appear to be randomly scattered

around zero. No evidence of non-constant variance,

missing terms, outliers, or influential points exists.

The histogram of the residuals shows the distribution

of the residuals for all observations of cutting zone

temperature, which is skewed towards left side.

Residuals versus order graph plot shows that the

residual is higher for observation order 4 and for

majority of the observations, the residual values are

below the zero line.

Analysis of flank wear

For a given value of input control parameters, the

flank wear at the cutting edge of the insert has to be

lower. Hence, the smaller-the-better condition is

chosen as given in Eq. (1). The best level of various

parameters are identified by calculating the average

values of S/N ratio, corresponding to all level of

parameters and are given in Table 8.

Table 7—ANOVA table for cutting zone temperature

Source DOF Seq SS Adj MS F P % Contribution

Insert shape 2 7.161 3.5803 0.84 0.544 20.74

Relief angle 2 1.469 0.7343 0.17 0.853 4.25

Nose radius 2 17.368 8.6840 2.03 0.330 50.29

Residual

error

2 8.536 4.2681 24.72

Total 8 34.533

Fig. 10—Residual plot of cutting zone temperature during

regression modeling

Table 8—Response table for flank wear

Level / Parameter Insert shape Relief angle Nose radius

Level 1 14.08 16.98 21.28

Level 2 21.88 17.93 14.52

Level 3 12.94 13.98 13.09

Max – Min 8.95 3.96 8.19


146

From the response table of flank wear, the optimal

parameter level are identified as, cutting insert shape

of ‘D’, relief angle of 3° and nose radius of 0.4 mm.

Hence, the optimum condition is represented as

A2B2C1. From the response table of flank wear, the

main effects plot is drawn as shown in Fig. 11.

The interaction effect of input parameters over

flank wear is shown in Fig. 12. There is no significant

interaction effect between insert shape and relief

angle except for ‘D’ shape insert. The interaction

effect is larger between insert shape and nose radius,

and for higher included angle of insert, the flank wear

is more, which is due to the large area of contact. In

between relief angle and nose radius, there is no

significant interaction effect except for 7° relief angle,

due to higher flank wear.

From the ANOVA results shown in Table 9, it is

evident that the cutting insert shape is the critical

control parameter, which contributes 35.61%,

followed by nose radius by 28.76%, whereas the

contribution of relief angle is negligible.

The empirical model developed to predict the flank

wear at the cutting edge of the carbide cutting tool

inserts is given in Eqs (9)(-(11) for different inserts.

For ‘C’ Shape:

-Flank Wear = 0.00965465 0.0293964*+

Re lief angle

0.197083* Nose radius+ … (9)

For ‘D’ Shape:

Flank Wear = - 0.160655 0.0293964*+

Re lief angle

0.197083* Nose radius+ … (10)

For ‘S’ Shape:

0.013012 0.0293964*Flank Wear = +

Re lief angle

0.197083* Nose radius+ … (11)

The residual plot of flank wear obtained during

regression analysis when geometrical parameters

alone are considered is shown in Fig. 13. The normal

probability plot of the residuals follows a straight line

with the residuals situated nearer to the straight line.

In residual versus fits plot, the residuals appear to be

randomly scattered around zero and most of the points

are situated at the average fitted value and the

residuals are minimum. The histogram of the

residuals shows the distribution of the residuals for all

observations which are skewed towards left and the

bell shaped curve is not obtained. Residuals versus

order graph plot can be particularly helpful in a

designed experiment in which the runs are not

Fig. 11—Main effects plot for flank wear

Fig. 12—Interaction plot for flank wear

Table 9—ANOVA table for flank wear

Source DOF Seq SS Adj MS F P % Contribution

Insert shape 2 142.29 71.15 1.22 0.451 35.61

Relief angle 2 25.57 12.79 0.22 0.820 6.40

Nose radius 2 114.91 57.45 0.98 0.504 28.76

Residual error 2 116.76 58.38 29.22

Total 8 399.54

Fig. 13—Residual plot of flank wear during regression modeling


147

randomized. The residuals in the plot are scattered

around the center line and for majority of the residuals

lie below the center line.

The comparison of experimental and predicted

values of cutting zone temperature and flank wear is

shown in Fig. 14. It is observed from the results that a

moderate prediction is possible with these models.

Confirmation experiment for optimum cutting insert geometry

With the identified optimum values of input control

parameters determined an experiment is conducted to

confirm and validate the effectiveness of the cutting

tool by using the same experimental setup. The

quality characteristics values obtained from the

confirmation experiment are given in Table 10, which

shows that the values obtained are considerably better

than that of experimental results obtained for the

various combinations of input parameters. It is

observed that a reduction in flank wear by 54.68%

and cutting zone temperature by 23.33% is achieved

during machining AISI 1045 steel with the

determined optimum cutting insert geometry.

Correlation of flank wear and cutting zone temperature

The relationship between the cutting zone

temperature at the workpiece-tool interface and flank

wear at the cutting edge of the cutting insert31

is

determined, based on the experimental results. It is

observed that, when the cutting zone temperature

increases, the flank wear increases comparably.

Figure 15 shows the 3D mesh plot showing the

correlation between the cutting zone temperature and

flank wear. This correlation effect between flank wear

and cutting zone temperature is important since

increase in cutting zone temperature has a direct

effect on the wear at the flank face of the cutting

Fig. 14—Comparison of experimental and predicted responses

Table 10—Results of confirmation experiment

Optimum condition Confirmation results

Cutting

insert

shape

Relief

angle (°)

Nose

radius (mm)

Flank wear

(mm)

Cutting zone

temperature (°C)

D 3 0.4 0.092 78.3

Fig. 15—Correlation plot between cutting zone temperautre and

flank wear


148

insert. The flank wear increases the surface roughness

of the machined surface of the specimen. Hence, by

improving the geometry of the cutting insert, the

cutting zone temperature and the flank wear can be

controlled, producing quality products at lower rates.

Conclusions The following conclusions have been derived by

applying Taguchi technique on turning AISI 1045

steel with different cutting tool insert geometries to

determine the relationship between cutting zone

temperature and flank wear.

(i) The optimum tool geometries for lower cutting

zone temperature and flank wear is cutting insert

shape of ‘D’ (55° included angle), relief angle of

3° and nose radius of 0.4 mm.

(ii) From these optimum conditions obtained it is

observed that a considerable correlation exists

between cutting zone temperature and flank

wear, which is also exposed from the 3D mesh

plot between them.

(iii) Nose radius is the most significant parameter for

cutting zone temperature followed by cutting

insert shape and for flank wear, the contribution

of cutting insert shape is more followed by nose

radius.

(iv) Machining with optimum cutting insert

geometry shows a reduction in flank wear by

54.68% and cutting zone temperature by

23.33%.

(v) An indirect estimate of flank wear is possible

on-line by measuring the cutting zone

temperature during machining, which is used to

control the flank wear of the cutting tool insert.

(vi) From the analysis, it is further observed that by

suitably altering the geometry of the cutting

inserts, the performances can be improved.

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Experimental investigation of cutting zone temperature and flank wear correlation in turning AISI...

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