Multi-Parametric Optimization in CNC Dry Turning of ... · picked exclusively based on handbook esteems/maker ... and utility concept has been used for optimization of the ... the

Multi-Parametric Optimization in CNC Dry Turning of Chromoly Steel

using Taguchi coupled Desirability Function Analysis and Utility concept

Dilip Kumar Bagal*

Department of Mechanical Engineering,

Government College of Engineering, Kalahandi, Bhawanipatna, Odisha, India.

Biswajit Parida, Abhishek Barua, Siddharth Jeet


Centre for Advanced Post Graduate Studies, BPUT, Rourkela, Odisha, India.

Bibhuti Bhusan Sahoo


Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India.

Abstract Numerous materials such as alloys or composites find broader

range of applications on the basis of many aspects. Among

them, High Tensile Steel or Chromoly steel also finds a wider

range of application due to its good mechanical properties,

machinability, cost and availability. This study focuses on

optimizing turning parameters by to minimize surface

roughness, chip length and tool wear rate. Experiments have

been conducted using the L9 orthogonal array in a CNC

turning machine. Dry turning tests are carried out on AISI

4140 Chromoly Steel with CNMG 120408 Carbide insert and

PCLNR 2525 M11 hardened steel tool. A comparative attempt

was made to determine optimal turning condition using

Desirability Function Analysis and Utility concept based multi

objective optimization techniques. The statistical techniques

are used to explore the effects of cutting speed, feed rate and

depth of cut on surface roughness, chip length and tool wear

rate. In order to determine the optimum cutting parameters for

minimum surface roughness, chip length and tool wear rate

the model developed from this experiment can be used in

metal turning in different industries.

Keywords: Chromoly Steel, CNC turning, Desirability

Function Analysis and Utility concept.

Introduction Machining has become crucial to the modern man. It is used

unswervingly or indirectly in the production of nearly all the

goods and services being created all over the world. [1]. The

traditional machining processes include many operations

namely turning, boring, shaping, drilling, reaming, milling,

etc. [1]. Out of these machining processes, turning still

remains as the most important operation used to shape metal,

because the condition of operation is wide-ranging. Increasing

yield and reducing manufacturing cost has always been the

prime objective of any organization. Since material removal is

a workshop associated activity where very strong economic or

production rate constraints pertain, it should be noted that the

choice of tools, fluids, operating conditions etc. are of

supreme importance. The definitive objective of the science of

metal cutting is to resolve practical problems associated with

efficient material removal in the metal cutting process. [2-6]].

Turning can be defined as the removal of metal from the outer

diameter of a revolving cylindrical work piece. It is used to

decrease the diameter of the work piece, usually to a

quantified dimension, and to produce a smooth surface on the

metal. Turning is the essential activity in the greater part of

the generation forms that creates the segments, which have

basic highlights requiring particular surface finish. In

manufacturing industry, the cutting conditions keep on being

picked exclusively based on handbook esteems/maker

proposals/administrators involvement with a specific end goal

to accomplish the most ideal surface wrap up. Because of a

lacking information of the many-sided quality and parameters

influencing the surface complete in turning task, an ill-advised

choice may cause high assembling expenses and low item

quality. Further, the tool wear rate (TWR) assumes an

imperative job in turning activity and low TWR is constantly

favored. Thus, the best possible choice of cutting apparatuses

and process parameters is a critical basis for accomplishing

low surface roughness and low TWR in the machining

procedure [3-10].

The optimization of process parameters requires an efficient

methodological methodology by using experimental methods

and mathematical/statistical models. Taguchi method is used

to optimize the performance characteristics of process

parameters, which is proved to be a powerful tool that differs

from traditional practices. This approach can economically

satisfy the needs of problem solving and design optimization

with less number of experiments without the need for process

model developments. Thus, it is possible to reduce time and

cost for the experimental investigations. The original Taguchi

method is designed to optimize a single performance

characteristic. However, most of the products have multiple

performance characteristics, and hence, there is a need to

obtain a single optimal process parameters setting, which can

be used to produce products with optimum or near optimum

characteristics as a whole. Many researchers have suggested

different modifications to the original Taguchi method for

multi-response optimization.

The main objective of this study is to achieve optimum

turning parameters (cutting speed, feed rate and depth of cut)

for minimum on surface roughness, chip length and tool wear

rate while turning hardened AISI 4140 chromoly steel

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 13, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com

Page 21 of 26

(41HRC) with CNMG 120408 insert which is made of carbide

used with PCLNR 2525M11 tool made of hardened steel.

Here, Taguchi’s parameter design approach has been used to

undertake this objective. Here, Desirability function analysis

and utility concept has been used for optimization of the

responses and to find out an optimum turning parameter.

Additionally, a statistical analysis (ANOVA) is also

performed to see which process parameters are statistically

momentous.

Experimental Methodology In this study, a work piece made of AISI 4140 grade (EN

19/SCM 440) chromoly steel was used. Its sizes were Ø100 x

250 mm. The experimental studies were carried out on ACE

MICROMATIC LT-16 300 LM CNC machine. The

experiments were conducted under dry cutting conditions. The

tool holder used was model PCLNR 2525M11 made of

hardened steel. CNMG 120408 insert which is made of

carbide were used as the cutting tool material. The surface

roughness was measured using a Mitutoyo Surftest SJ-410

surface roughness meter within the sampling length of 2.5

m, chip length was measured using Vernier calliper within

the sampling range of 20 mm and tool wear using

Toolmaker’s microscope. Fig. 1 shows the experimental

arrangement. The level of turning parameter ranges and the

initial parameter values were chosen from the manufacturer’s

handbook recommended for the tested material. These cutting

parameters are shown in Table 1.

Figure 1: Experimental scheme

Table 1: Turning parameters

Factors Symbol Level 1 Level 2 Level 3

Cutting Speed(m/min) A 50 70 90

Feed(mm/rev) B 0.06 0.12 0.18

Depth of Cut(mm) C 0.3 0.5 0.7

Utility concept

Utility can be defined as the convenience of an object in


Page 22 of 26

response to the prospects of the users. It is the measure of the

characteristic of an artefact to meet the users’ requirements.

The overall usefulness of an object can be represented by a

unified index termed as utility which is the sum of the

individual performance characteristics attributes of a

particular product [15-22].

If is the measure of performance of jth attribute (criterion)

and there are n alternatives in the entire selection space, then

the joint utility function can be expressed as [15-22]:

(4)

where is the utility of jth attribute and n is the number

of evaluation criteria. The overall utility function is the sum of

individual utilities if the attributes are autonomous, and is

given as follows:

(5)

The overall utility function after assigning weights to the

attributes can be expressed as:

(6)

A preference scale for each attribute is constructed for

defining its utility value. The predilection numbers vary from

0 to 9 (denoting the lowest and highest performance value).

Thus, a preference number of 0 represents the just suitable

attribute and the best value of the attribute is denoted by

preference number 9 [15-22]. The preference number (Pj) for

jth attribute can be stated on a logarithmic scale as follows:

(7)

where and are the observed and the just acceptable

values, and Aj is a constant for jth attribute. For , the

minimum value is taken for beneficial criteria, while the

maximum value is considered for non-beneficial criteria. The

value of Aj can be found by the condition that if yj= (where

is the best value for jth attribute), then Pj = 9 [15-22].

Therefore,

(8)

The overall utility value (U) can now be computed as follows:

(9)

subject to the constraint that

Results and Discussion

The following segments describe the results of the present

study and also present a discussion on the results in light of

the available works.

Best experimental run

Samples are prepared by using Taguchi’s experimental design

which is shown in Table 2. As per design of experiment, 9

experimental runs are carried out in CNC turning machine.

The experimental results for the surface roughness, chip

length and tool wear are listed in Table 2 along with the L9

orthogonal array of input parameters. Typically, small values

of surface roughness, chip length and tool wear are required

for good quality and accuracy in the machining process.

Table 2: Orthogonal array L27 of the experimental runs and

responses

Expt.

No. A B C

Surface

Roughness

(m)

Chip

Length

(mm)

Tool

Wear

(m)

1. 50 0.06 0.3 1.706 13.533 0.590

2. 50 0.12 0.5 2.196 11.533 0.795

3. 50 0.18 0.7 2.199 13.067 0.526

4. 70 0.06 0.5 2.179 11.767 0.729

5. 70 0.12 0.7 1.846 11.600 0.440

6. 70 0.18 0.3 1.056 11.033 0.239

7. 90 0.06 0.7 2.366 13.600 0.805

8. 90 0.12 0.3 1.239 11.267 0.581

9. 90 0.18 0.5 2.066 10.900 0.802

Optimization using Desirability function analysis

The smaller-the-better characteristic is applied to determine

the individual desirability values (di) for surface roughness,

chip length and tool wear using equation (1) since all are to be

minimized. After calculating individual desirability, the

composite desirability (d0) is calculated using equation (3).

Equal no. of weights has been assigned to each individual

desirability whose sum is equal to 1.

Table 3: Evaluated Individual Desirability and Composite

Desirability

Sl.

No.

Individual Desirability (di) Composite

Desirability

(d0) Surface

Roughness

Chip

Length

Tool

Wear

1. 0.5038 0.0247 0.3788 0.1690

2. 0.1298 0.7654 0.0171 0.1171

3. 0.1272 0.1975 0.4920 0.2330

4. 0.1425 0.6790 0.1350 0.2342

5. 0.3969 0.7407 0.6440 0.5749

6. 1.0000 0.9506 1.0000 0.9834

7. 0.0000 0.0000 0.0000 0.0000

8. 0.8601 0.8362 0.3958 0.5635

9. 0.2290 1.0000 0.0412 0.2041


Page 23 of 26

Figure 2: SN-ratio graph with factors and their levels

Table 4: The Response Table for Composite Desirability

Level A B C

1 15.575 14.025 6.857

2 5.854 9.473 15.013

3 9.393 8.867 8.731

Delta 9.721 5.158 8.157

Rank 1 3 2

Optimization using Utility concept

Second, Utility concept was applied for optimization of

turning parameters. Using equations (7) and (8), the

preference scales for surface roughness, chip length and tool

wear were constructed.

For surface roughness,

X*: Optimum value of surface roughness 2.518m

X′: Minimum acceptable value of surface roughness 1.056m

(assumed, as all the observed values of surface roughness in

Table 2 are in between 1.056 and 2.199).

Using these values and equations (7) and (8), the preference

scale for surface roughness is,

PSR = 2.82 log(XSR/1.056) (10)

For construction of preference scale for chip length

X*: Optimum value of chip length 14.366m

X′: Minimum acceptable value of chip length 10.9mm

(assumed, as all the observed values of chip length in Table 2

are in between 10.9 and 13.6).


scale for chip length is,

PCL = 75.04 log(XCL/10.9) (11)

For construction of preference scale for tool wear

X*: Optimum value of tool wear 0.988m

X′: Minimum acceptable value of tool wear 0.239m

(assumed, as all the observed values of tool wear in Table 2

are in between 0.239 and 0.805).


scale for tool wear is,

PTW = 14.57 log(XTW/10.9) (12)

The weights to the selected quality characteristics have been

assigned as

WSR: Weight assigned to surface roughness (0.33)

WCL: Weight assigned to chip length (0.33)

WTW: Weight assigned to tool wear (0.34).

The value of the overall utility value has been calculated using

the equation (9)

Table 3: Utility data based on multiple responses

Sl.

No.

Preference number (Pj) Utility Value

(U) Surface

Roughness

Chip

Length

Tool

Wear

1. 4.9651 7.0525 5.7366 5.9162

2. 7.5783 1.8407 7.6223 5.6999

3. 7.5940 5.9088 5.0090 6.1590

4. 7.4995 2.4935 7.0674 5.7006

5. 5.7814 2.0285 3.8780 3.8958

6. 0.0000 5.9831 0.0000 1.9751

7. 8.3501 7.2126 7.6992 7.7534

8. 1.6571 1.0783 5.6323 2.8177

9. 6.9468 0.0000 7.6739 5.9466

Figure 3: SN-ratio graph with factors and their levels

Table 4: The Response Table for Composite Desirability

Level A B C

1 -15.45 -16.12 -10.12

2 -10.95 -15.24 -11.98

3 -14.09 -12.40 -15.13

Delta 4.50 4.14 5.12

Rank 2 3 1

Most Influential Factor

Table 5 and 6 gives the results of the analysis of variance

(ANOVA) for the calculated values of Composite Desirability

and Utility factor respectively. According to Table 5, factor A,

cutting speed with contribution of 50.54% is the most

significant controlled parameters for turning of AISI 4140


Page 24 of 26

steel followed by factor C, depth of cut with 35.74% and

factor B, feed with 7.87% of contribution if the minimization

of surface roughness, chip length and tool wear are

simultaneously considered. Similarly, from Table 6, factor C,

depth of cut with contribution of 43.82% is the most

significant controlled parameters for turning of AISI 4140

steel followed by factor A, cutting speed with 27.28% and

factor B, feed with 26.57% of contribution if the minimization

of surface roughness, chip length and tool wear are

simultaneously considered. According to both the cases feed

is the least significant parameter.

S = 1.556, R-Sq = 99.1%, R-Sq(adj) = 94.0%

Table 5: ANOVA Result for Composite Desirability

Source DF Adj SS Adj

MS

F-

Value

P-

Value %

A 2 141.74 70.86 29.29 0.130 50.54

B 2 22.06 11.03 4.56 0.314 7.87

C 2 100.23 50.11 20.71 0.154 35.74

Error 1 2.42 2.42 2.420 0.86

Total 7 280.45

S = 1.168, R-Sq = 97.7%, R-Sq(adj) = 90.7%

Table 6: ANOVA Result for Utility factor

Source DF Adj SS Adj

MS

F-

Value

P-

Value %

A 2 31.997 15.99 11.72 0.079 27.28

B 2 31.164 15.58 11.42 0.081 26.57

C 2 51.401 25.70 18.84 0.050 43.82

Error 2 2.729 1.36 2.33

Total 8 117.29

Confirmation Experiment

The confirmation experiments were conducted using the

optimum combination of the turning parameters obtained from

Taguchi analysis. These confirmation experiments were used

to predict and validate the improvement in the quality

characteristics for turning. The optimal conditions using

Desirability function analysis is A2 B3 C1 and for Utility

concept is A1 B1 C2. The final phase is to verify the predicted

results by conducting the confirmation test. The estimated

composite desirability and utility factor can be determined by

using the optimum parameters as

(13)

where a2m and b1m are the individual mean values for both the

composite desirability and utility factor with optimum level

values of each parameters and μmean is the overall mean for

composite desirability and utility factor respectively. The

predicted mean (μpredicted) at optimal setting composite

desirability and utility factor is found to be 0.9834 and 7.9720

respectively

Table 7: Confirmatory test results

Optimization

technique

Optimal

setting

Predicted

Optimal

S/N ratio

Experimental

Optimal

S/N ratio

Desirability

Function

Analysis

A2 B3 C1 0.9834 0.9038

Utility Concept A1 B1 C2 7.9720 7.4620

From the confirmation experiment performed with the same

experimental setup, it may be noted that there is good

agreement between the estimated value and the experimental

value for Desirability Function Analysis and Utility concept

approach. Hence, the obtained parameter setting of turning

process can be treated as optimal.

Conclusion The research work was carried out to study the effect of

cutting parameters on the surface roughness, chip length and

tool wear for AISI 4140 steel. A simplified model based on

Taguchi’s approach with desirability function analysis and

utility concept has been used to determine the optimal settings

of the process parameters for a multi-characteristic problem.

Based on the experiments performed on CNC turning machine

the following conclusions have been drawn:

1. The optimal setting of input parameters for turning using

desirability function analysis and utility concept is as

follows:

Table 8: Optimal Parameters Using Two Optimization

Methods

Optimization

technique

Optimal

setting

Cutting

Speed Feed

Depth of

Cut

Desirability

Function

Analysis

A2 B3 C1 70 m/min 0.18 mm/rev 0.3 mm

Utility

Concept A1 B1 C2 50 m/min 0.06 mm/rev 0.5 mm

2. The percentage contributions of each parameter for

turning process using desirability function analysis and

utility concept is as follows:

Table 8: Parameters and their % contribution

Desirability Function Analysis Utility Concept

A 50.54% 27.28%

B 7.87% 26.57%

C 35.74% 43.82%

3. The ANOVA results shows the both cutting speed and

depth of cut are the most influential parameters respective

to the optimization technique and feed rate is found out to

be the least influential parameter in according to both

optimization techniques.

The unique feature of desirability function analysis and utility

concept is characterization of a single composite optimal

setting of process parameters that in due course leads to

mutual and simultaneous optimization of all the quality


Page 25 of 26

characteristics, leading to establishment of a cost-effective

solution for commercial machining of AISI 4140 steel in CNC

turning.

Acknowledgement This research work is jointly supported by Central Tool Room

and Training Centre (CTTC) Bhubaneswar, MSME, Odisha

(Govt. of India) and Government College of Engineering

Kalahandi, Bhawanipatna, Odisha, India.

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Multi-Parametric Optimization in CNC Dry Turning of ... · picked exclusively based on handbook esteems/maker ... and utility concept has been used for optimization of the ... the