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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME

362

APPLICATION OF NON -TRADITIONAL OPTIMIZATION FOR

QUALITY IMPROVEMENT IN TOOL HOLDERS

K. Saravana kumar

Assistant Professor, Department of Mechanical Engineering, Karpagam University,

Coimbatore, India

Dr.A.K. Shaik Dawood

Professor, Department of Industrial Engineering, King Khalid University, Abha,

SaudiArabia

Email:[email protected]

P.A. Azeem Hafiz

Assistant Professor, Department of Industrial Engineering, King Khalid University,

Abha, SaudiArabia

R. Karthikeyan

Assistant Professor, Department of Management Studies, Karpagam University,

Coimbatore, India

ABSTRACT

In the present scenario, quality has become an important factor, which determines the de-

velopment of a company. Initially the companies were going in for 100%inspection of the com-

ponents for maintaining their quality. Since quality lies in the efficient control of defects, nowa-

days newer statistical quality control techniques are employed. At present all the companies are

moving towards six sigma concept. Even then most of the companies are not able to achieve this

target. This is mainly attributed to the use of machines with poor process capabilities. This

project aims at improving the process capability of machines by optimizing the control parame-

ters thereby reducing the number of defects arising.

This work deals with the problem arising in a special grinding process know as face pro-

file grinding done in compression rings of a piston. The rejection level for this process was very

high as the crowning tolerance values were not within the limits. In order to reduce the number

of defects, initially Taguchi’s Design of Experiments (DOE) is used to find the better set of

process parameters that minimizes the tolerance values. Then Response Surface Methodology

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 3, Issue 3, September - December (2012), pp. 362-377 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2012): 3.8071 (Calculated by GISI) www.jifactor.com

IJMET

© I A E M E



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(RSM) is employed to find out the mathematical model, which relates the control parameters

with the performance measure. This model obtained is used as the objective function for per-

forming minimization of absolute value, in Genetic algorithm (GA). On academic interest, Par-

ticle Swarm Optimization (PAO) is also used for minimization and results obtained by GA and

PSO are compared. Finally confirmation experiments are conducted for the results obtained, with

95% Confidence level. Based on the above observations, suggestions have been made on setting

the parameters to improve the quality.

Key Words: Design of Experiments, Response Surface Methodology, Genetic Algorithm.

I. INTRODUCTION

The goal of any industrial experimentation in manufacturing is to devise the ways of mi-

nimizing the deviation of a quality characteristic from its target value. This can be done only by

identifying factors which impact the quality characteristic in question and by changing the ap-

propriate factor levels so that the deviations are minimized and the quality characteristic is on

target. The classical methods for DOE developed by R.A. Fisher, include a full variety of statis-

tical design techniques based on Latin squares. A major problem with Fisher’s approach in man-

ufacturing industry is the time and cost required to learn and use it. Taguchi’s approach utilizes

Robust design and is applied to a range of problems. The Response Surface Methodology (RSM)

is a collection of mathematical and statistical techniques that are useful for modeling and analy-

sis of problems in which a response of interest is influenced by several variables and the objec-

tive is to optimize this response. Here in this problem, the objective is to find the level if cutting

speed, work head speed and the fine feed rate that minimizes the ovality tolerance values. In

most RSM problems, the form of relationship between the response and the independent va-

riables in unknown. Thus, the first step in RSM is to find a suitable approximation for the true

functional relationship between the response and independent variables. It the response is well

modeled by a linear function of the independent variables, then the approximating function is the

first order model. The regression equation takes the form

y = b0+b1x1+b2x2+……..+bpxp.

Where b0, b1, b2……..bp, called the regression coefficients, are determined from the data.

II. LITERATURE REVIEW

Optimization technique has focused the interest of many researchers during the last 15

years. Following are the overview of the relevant work done earlier related to the problem identi-

fied and the methodology to be adopted to solve the chosen problem for this work. It gives the

description of literature reviewed from the various research papers published in international and

national journals. [1] optimization metal cutting process in manufacturing industries for increas-

ing demand of quality product in the market. In present scenario optimization methods in metal

cutting processes, considered to be a vital tool for continual improvement of output quality in

products and processes include modeling of input–output and in-process parameters relationship



364

and determination of optimal cutting conditions. Authors analyzed several optimization tech-

niques, incorporates the use of one or more of the existing modeling and optimization tech-

niques, making the framework a unified and effective means. [2] A new optimization technique

based on genetic algorithms (GA) for the determination of the cutting parameters in machining

operations. In metal cutting processes, cutting conditions have an influence on reducing the pro-

duction cost and time and deciding the quality of a final product. The authors formed new me-

thodology as the modification of recommended cutting conditions obtained from a machining

data, learning of obtained cutting conditions using neural networks and the substitution of better

cutting conditions for those learned previously by a proposed GA. The authors used several op-

timization technique and they concluded that genetic algorithm-based approach in complex ma-

chining systems and automated process planning system and compared with a number of other

emerging optimization-techniques. [3] a genetic algorithmic approach for optimization of surface

roughness due to use of highly automated machine tools in the industry, manufacturing requires

reliable models and methods for the prediction of output performance of machining processes.

The prediction of optimal machining conditions for good surface finish and dimensional accura-

cy plays a very important role in process planning. In this work deals with the study and devel-

opment of a surface roughness prediction model for machining mild steel, using Response Sur-

face Methodology (RSM) and the experimentation was carried out with TiN-coated tungsten

carbide (CNMG). The authors concluded that genetic algorithm program gives minimum and

maximum values of surface roughness and their respective optimal machining conditions.

[4] a multi-objective genetic algorithm approach for optimization on surface grinding operations

to optimize grinding conditions, viz. wheel speed, workpiece speed, depth of dressing and lead of

dressing, using multi-objective function model with a weighted approach for surface grinding

process. The procedure evaluates the production cost and production rate for the optimum grind-

ing condition, subjected to constraints such as thermal damage, wheel wear parameters, machine

tool stiffness and surface finish. Genetic algorithm optimum results for production cost, surface

finish and material removal rate compared with quadratic programming technique. [5] the ma-

chining process is evaluated in terms of machining rate and surface finish produced. Higher ma-

chining rate and better surface finish are desirable for better performance of any machining

process. Comprehensive qualitative and quantitative analysis of the material removal mechanism

and subsequently the development of analytical model(s) of material removal (MR) are neces-

sary for a better understanding and to achieve the optimum process performance. In use of ad-

vanced machining processes incurs high investment, operating, maintenance, tooling and other

costs. The authors described that in the absence of analytical models, optimum selection of

process parameters requires extensive experimentation, which is time and money consuming. [6]

A new approach for the optimal sub-division of the depth of cut is presented using a genetic al-

gorithm. The total production-cost minimization is achieved by adding the minimum costs of the

individual rough passes and the finish pass. The selection of the depth of cut during optimization

in multi-pass turning is an important activity, along with the selection of the speed and feed. Au-



365

thors proposed GAs always yields production-cost values that are less than, or equal to, the val-

ues obtained using other methods. [7] computer vision techniques to inspect surface roughness of

a workpiece under a variation of turning operations. The authors used digital camera for captur-

ing surface image of the workpiece and then the feature of the surface image is extracted and al-

so authors used method called self-organizing adaptive modeling as polynomial network for con-

structing the relationships between the feature of the surface image and the actual surface rough-

ness under a variation of turning operations. As a result, the surface roughness of the turned part

can be predicted with reasonable accuracy if the image of the turned surface and turning condi-

tions. [13] a real coded genetic algorithm optimization of machining parameters in order to ob-

tain better surface quality. Since, surface quality is one of the important indicators of customer

requirement in machining process. There are various methods available for optimization prob-

lems viz calculus based, dynamic programming, artificial neural network, simulated annealing,

etc. the authors concluded from experimental analysis that surface roughness decreases with in-

crease in cutting speed and decrease in feed rate. [14] a multi-objective optimization technique,

based on genetic algorithms. In any optimization procedure identifying the output parameter is of

chief important. Many of authors have determined the optimization in single objective approach-

es only and it has limited value to fix optimal cutting conditions. The objectives are maximiza-

tion of tool life and maximization of production rate using genetic algorithm method. The pro-

posed genetic algorithm was implemented in C++. By using of Pareto frontier graphics, several

different situations may be considered, facilitating the choice of right parameters for any condi-

tion. The proposed micro-GA has obtain several, uniformly distributed points, in order to arrange

the Pareto front, at a reasonably low computational cost. Cost analysis can complement the Pare-

to front information, and it helps the decision-making process.

III. METHODOLOGY

A. PROCESS FLOW CHART

The flow chart below shows the series of operations done in the solution metho-

dology

Figure 1 Process Flow Chart



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IV. EXPERIMENTAL DATA

A. MACHINE SPECIFICATION

Initially the process is studied before experimentation. The specifications of the Puma

2000Y CNC Turn mill center machine are as follows. The photograph of the machine in which

the experiment is conducted is given below in figure 2

Figure 2 Puma 2000Y CNC Turn mill center

B. QUALITY CHARACTERISTIC

The measurements associated with the ring crowning tolerances are detailed below. The

measurements are taken using AE GOETZE face profile (OD) measuring instrument.

.

Figure 3 Tool Holder Ring 2D dimensional drawing

The tool holder ring 2D drawing was as shown above. The back of the ring has to be

pushed against the two stops till the end. Once when the end is reached the measurement starts.

The dimension sensor probe travels along the width of the ring. The measurements are taken at

the specified gauging levels (shown in the figure 3) as the probe traverses in the upward direc-

tion. As the probe returns to its original position the readings are listed in a CRT terminal inter-

faced with the instrument. Finish on the periphery should be barrel honed and ground. The

crowning tolerance is 0.002-0.006 mm over a gauge width of 2 mm.



367

C. MATERIAL COMPOSITON OF THE RING

The table 1 shown below is the material Composition of 20MnCr5

Table 1 Material Composition

COMPOSITION FOR 20MnCr5

Description Specification

Cast Iron Major composition.

Carbon 3.7 – 3.8 %

Silicon 2.5 – 2.7 %

Manganese: 0.60%

Sulphur 0.05 – 0.08%

Phosphors 0.35 - 0.45%

Vanadium Less than 0.1%

Chromium 0.2%

Copper 0.1%

D. CONTROL PARAMETERS OF FACTOR DEVELOP

i) CONTR0L PARAMETERS

In this problem, there are three control parameters (factors). These parameters are se-

lected after a detailed study. The control factors are as follows.

Work head speed (m/min)

Depth of cut (mm)

Fine feed rate (mm/rev)

The cutting speed is the major factor in a grinding operation. Secondly the work head

speed is one of the major factors which should not be left behind. Among the rough feed rate and

fine feed rate, the fine feed is a major factor affecting the crowning tolerance. Hence these

process parameters are chosen as the control factors.

ii) FACTOR LEVELS

The next problem is fixing the levels. Currently grinding is done with a cutting

speed of 1500 m/mm, work head speed of 160 rpm and a fine feed rate of 0.7 mm/min. The table

3 shows the various control factors and their levels. The cutting speed is varied between 1400

and 1550 m/min. Since the minimum cutting speed specified in the manual for effective grinding

is 1400 m/min, the cutting speed is decreased only up to 1450 m/min. The depth of cut is varied

between 0.4 to 0.9. The fine feed rate is varied between 0.12 and 0.05 mm/min.



368

Table 2 Control factors and their levels

D. ORTHOGONAL ARRAY

In order to conduct an experiment with three control factors and three levels, an L9 (33)

orthogonal array is formed. The array is called orthogonal because the levels of various factors

are balanced and can be separated from the effects of the factors within the experiment. Here, L

represents Latin square, 9-represent number of experiments,

3 represents number of levels and 3 on the superscript represent number of factors.

E. EXPERIMENTAL DESIGN SETUP

Now the experiment is designed by substituting corresponding values of various factor

levels in the above table. The table 3 gives the experimental design setup for which the experi-

ments are conducted.

Table 3 Experimental design

E. SELECTION OF RESPONSE

From the tabulation, it can be understood that at the gauging level (h25), the values ob-

tained are within the tolerance limits, but at the gauging level (h27), some of the values are not

within the tolerance limits. Hence, the crowning tolerance values at gauging level (h27) are taken

as the response.

CONTROL FACTORS LEVEL 1 LEVEL 2 LEVEL 3

Spindle speed (m/min) 1400 1450 1550

Depth of cut (mm) 0.4 0.5 0.9

Fine feed rate (mm/rev) 0.12 0.08 0.05

Expt

No

CONTROL FACTORS

Cutting Speed

(m/Min)

Work head

speed (rpm)

Fine feed

rate(mm/Min)

1 1400 0.4 0.12

2 1400 0.5 0.9

3 1400 0.9 0.05

4 1450 0.4 0.08

5 1450 0.9 0.12

6 1450 0.5 0.05

7 1550 0.5 0.12

8 1550 0.4 0.05

9 1550 0.9 0.08



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F. NEED FOR DOE

In the CNC Turn Mill center, for each cycle of operation, for rings are fed. Especially, the fol-

lowing problems found while machining the component.

Runout

Faceout

Ovality

At present the component was produced by using the following parameters.

Depth of cut = 0.05mm

Feed rate = 0.12mm/rev

Cutting speed = 1450 rpm.

As the rejection rate was steady and increasing, an attempt to use Taguchi method to find the

best set of combinations for which the values are within the tolerance limits.

G.NEED FOR PARAMETER DESIGN

For the problem as stated above, system design cannot be applied. Tolerance design also

becomes costlier. Hence Parameter design is adopted to find a solution for a problem of the

above type. The necessary output required is between 0.005µm at the gauging level (h27), and

some of the values obtained are out of this range. Thus the objective is minimize the mean ob-

tained to a nominal of 0.01

H. EXPERIMENT SETUP USING TAGUCHI DESIGN

i) Rejection chart while using DOE. Table no 5 shown below observation Data chart.

Table 5 Observation Data Chart

Figure no 4 Observation Chart



370

I. SIGNAL TO NOISE RATIO

The S/N ratio is an objective performance measure. The S/N ratio is an evaluation of sta-

bility of performance of an output characteristic. The S/N ratio measures a level of performance

and the effect of noise factors on performance.

S/N ratio, ŋ = 10 log10 [µ2 / σ2]

Mean response is given by, µ = 1/n *

Sensitivity to noise is given by σ2 = 1/n *

By substituting the values obtained from experimentation in the above formulae, the following

table 6 is arrived

Table No 6 Experimental values and parameters

i) S/N RATIO MEAN LEVEL

The response table 7 gives the ranking of the importance of the factors on the response variable,

but it does not indicate the relative magnitude of importance. To fine the magnitude of impor-

tance of various factors.

Table 7 S/N Ratio mean level



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ii) RESPONSE GRAPHS

Figure No 5 S/N Chart for cutting speed

Figure No 6 S/N Chart for Fine feed rat

J. INTERPERTATIONS

From the above graphs, the factor level with maximum S/N ratio is chosen as the

optimum combination for obtaining the required quality characteristic [7]. The optimum set of

control factors found out by employing Taguchi method is listed below.

Work head speed = 1450 rpm

Depth of cut = 0.4mm

Fine feed rate = 0.12 mm/rev

K. COMMENTS ON RESULTS

The values of the control factors arrived as a result of Taguchi’s DOE are not satisfacto-

ry, since they did not produce the desired response. The best combination arrived as a result of

Taguchi method is already present in the experimental design setup as experiment number 1. For

the purpose of confirmation, once again an experiment was conducted with the above combina-



372

tion, but he result obtained was the same as that of the results obtained during the initial experi-

mentation. As stated earlier, the desired response is nominal value of 10.5 µm

M. NEED FOR NON-TRADITIONAL OPTMIZATION

It was decided to apply optimization techniques, in order to find out the possible ways of

minimizing the response by finding better combination, if any. After a detailed study, it was de-

cided that application of non-traditional optimization algorithms was one of the probable ways of

finding a solution for a problem of the above kind. In order to apply optimization algorithms, a

mathematical model of the process is required. Usually Central Composite Design (CCD) of ex-

periments is recommended for obtaining accurate results with RSM. But in this work, CCD is

not employed as it is mostly applied for processes with wide variations in their response [3]. For

a process with minimal variations in its response, the results obtained by modeling with Taguchi

DOE values are quite acceptable. The mathematical model is arrived from the values of DOE

using a technique called Response Surface Methodology (RSM).

V. REGRESSION COEFFICIENT EVALUATION

A. INTERPRETATION OF COEFFICIENTS

The Estimated regression coefficients in uncoded ubuts guveb avive are tge coefficients

of the various factors in the equation that relates the control factors and the response. The equa-

tion should be interpreted as shown below.

Ct = 0.1633x1 – 0.978167x2 + 54.0917x3 – 8.4*10-5

x1 2 – 13.5625x3

2

The equation shown above is the mathematical model of the process obtained by using

RSM. The values of R-sq and R-sq (adj) for this model are given below.

r-sq = 99.8% and R-sq(adj) = 98.1%

where, Ct = Crowning tolerance (µm)

x1 = Cutting speed(m/min)

x2 = Depth of cut (mm)

x3 – Fine feed rate (mm/min)

VI. RESULTS AND DISCUSSION

A.GA result

The following is the automatically generated GA output file for the above problem ob-

tained by using ‘Export to workspace’ command in GA tool box and by typing ‘gaprob-

lem’,’gaoptions’ and ‘garesults’ in command window of MATLAB 7.0. The GA fitness distribu-

tion plot is given in figure no 7

Gaproblem =

Fitnessfcn: @algo

Nvars:3

Options : [1x1 strut]

>>gaoptions

Gaoptions =



373

PopulationType: ‘doubleVector’

PopInitRange: [2x3 doub;e]

populationSize: 12

Elitecount: 2

CrossoverFraction: 0.8000

MigrationDirection: ‘forward’

MigrationInterval: 20

MigrationFraction: 0.2000

Generations: 300

TimeLimit: Inf

FitnessLimit: -Inf

StallGenLimit: 50

StallTimeLimit: 20

InitialPopulation: [ ]

InitialScoress: [ ]

PlotInterval: 1

CreationFcn: @gacreationuniform

FitnessScaligFcn: @fitscalingrank

SelectionFcn: @selectionroulette

CrossoverFcn: @crossoverscattered

MutatuibFcn: { [1x1 function_handle] [0.5000] }

HybridFcn: [ ]

Display: ‘off’

PlotFcns: { [ 1x1 function_handle] }

OutputFcns: [ ]

Vectorized: ‘off’

>> garesults

garesults =

X: [1.4501e +003 169.9772 0.5002]

Fval: 3.5294

exitmessage: ‘Optimization terminated: stall generations limit exceeded.’

Output:[1x1struct]



374

Figure No 7 Fitness value distribution chart

Thus the parameters obtained by Taguchi DOE are fine tuned to obtain still more opti-

mized parameters with best results within the range. The results of GA are as follows. The

crowning tolerance value is given by the sum of the minimal absolute value obtained above and

the nominal value

Ovality tolerance, Ct = 14.0294 µm

Cutting speed, x1 = 1450.1183 mm/min

Depth of cut, x2 = 0.95241mm

Fine feed rate, x3 = 0.5002 mm/rev

B.RESULTS FOR 95 % CONFIDENCE LEVEL

The MINITAB 15.0 output for the 95 % confidence level is given below.

Predicted Response for New Design Points Using Model for r

Table 8 Results for 95% confidence level

Point Fit SE fit 95% CI

1 14.0293 0.0219493 13.9595 14.0992

2 14.0249 0.0220154 13.9548 14.0950

C.CONFIDENCE INTERVAL

i) FOR GENETIC ALGORITHM

Confidence interval, Cl = 0.0698

µconfirmation = 14.0249

Confidence level: 13.9595 ≤ 14.0293 ≤ 14.0992

ii) CONFIRMATION TEST

With the values obtained by optimization, a confirmation test is performed whenever De-

sign of Experiments (DOE) is carried out, confirmation tests should be performed to check the

correctness and reproducibility of the predicted mean and factor levels in the experimental de-



375

sign setup. Since the best combination arrived by Taguchi DOE is already present in the one of

the experiments, the confirmation tests are done only for the values arrived by GA

While giving input values for the process parameters in the machine, decimal values of

cutting and work head speeds cannot be given as input so they are rounded off to the nearest in-

teger. Similarly, fine feed rate values are accepted only till a single decimal point. Since the val-

ues given by GA and PSO are too close, only one confirmation test is done for the values given

below

Cutting speed = 1450 m/min.

Depth of cut = 0.89 mm.

Fine feed rate = 0.5 mm/rev.

D.VERIFICATION OF 95% CONFIDENCE LEVEL

The results obtained are compared with the 95% Confidence Interval obtained earlier.

The value obtained is within the 95% confidence level. This proves the correctness of the Expe-

rimental Design, Modeling and Optimization done earlier.

GA: 13.9595 ≤ 14.0 ≤ 14.0992

Table No: 9 Final validation Data

Figure 8 Final validations Chart



376

E. IMPLEMENTATION

Based on the above observations, suggestions have been made on setting the parameters

to improve the quality. The confirmed factor levels for the optimum response are then imple-

mented in the company for production. Currently, the machining is done only according to this

set of process parameters. The results obtained are good. The amount of rejections has also re-

duced drastically. The process is further studied for the feasibility of extrapolation of the results

obtained, but the probability of getting a good response prediction is low for the present model.

Hence the machining is carried out with the above set of process parameters itself. The actual

inputs and response are given below.

Cutting speed = 1450 m/min.

Depth of cut = 0.89 mm.

Fine feed rate = 0.5 mm/rev.

Ovality tolerance = 0.005 µm

VII. CONCLUSION

The problem related to tolerance arising in a special turning process known as CNC turn-

ing done in compression rings of a tool holder is identified. Initially Taguchi’s Design of Expe-

riments (DOE) has been used to find the better set of process parameters that minimizes the to-

lerance values. In order to find better combination, Response surface Methodology (RSM) is

employed. The mathematical model is obtained by using regression analysis in MINITAB 15.0

which serves as the objective function for optimization with Genetic Algorithm (GA). Finally

confirmation experiments are conducted for the results obtained, with reasonable confidence lev-

el. Based on the above observations, the parameter settings as suggested by promising. This

leads to quality improvement at no additional cost. The main advantage of this solution metho-

dology is that it can be applied to any process in any branch of engineering. Thus it can be as-

sured that by following this method, the terms defects and inspection can be eradicated from the

dictionary of manufacturing.

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