EVALUATION OF MACHINABILITY CHARACTERISTICS OF … · 2.2 Force Measurement The normal grinding force (F n) and the tangential force (F t) were measured using grinding dynamometer

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),

ISSN 0976 – 6359(Online) Volume 2, Issue 2, August- December (2011), © IAEME

126

EVALUATION OF MACHINABILITY CHARACTERISTICS OF

INDUSTRIAL CERAMIC COATINGS USING GENETIC

PROGRAMMING BASED APPROACH

Mohammed Yunus1, Dr. J. Fazlur Rahman

2 and S.Ferozkhan

3

1. Research scholar, Anna University of Technology Coimbatore

Professor, Department of Mechanical Engineering H.K.B.K.C.E.,

Bangalore, India.

[email protected]

Mobile: +919141369124

2. Supervisor, Anna University of Technology Coimbatore

Professor Emeritus, Department of MechanicalEngineering

H.K.B.K.C.E., Bangalore, India.

[email protected]

Mobile: +919845350742

3. Lecturer, Department of Mechanical Engineering,

H.K.B.K.C.E., Bangalore, India.

[email protected]

ABSTRACT

Ceramic coated components have the advantage features of both metal and

ceramics, i.e. good toughness, high hardness and wear resistance. Despite their

outstanding characteristics, ceramic materials are not used in many cases due to

high cost of machining. A major drawback to engineering applications of

ceramic is due to their brittleness and fracture toughness, which makes them

difficult and costly to machine. In order to study the precision machining

processes of ceramics like grinding and lapping, number of trial experiments

were conducted to find out the influence of various cutting parameters on

surface quality production as well as grinding and lapping performances.

Surface grinding of ceramic coating materials was done on samples specimens

coated with Alumina (Al2O3), Alumina-Titania (Al2O3-TiO2) and Partially

stabilized zirconia (PSZ )using diamond and CBN (cubic boron nitride)

INTERNATIONAL JOURNAL OF MECHANICAL

ENGINEERING AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online)

Volume 2, Issue 2, August- December (2011), pp. 126-137

© IAEME: www.iaeme.com/ijmet.html

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127

grinding wheels. Based on the experimental results, the influence of cutting

parameters, namely, cutting force, surface roughness and bearing area

characteristics were evaluated and optimum machining conditions have been

suggested for better performance in precision machining of ceramic coating

materials.

Genetic programming (GP) has proved to be a highly versatile and useful tool

for identifying relationships in data for which a more precise theoretical

construct is unavailable. The prediction of machinability of ceramic oxide

coatings, depending on input parameters (type of grinding wheels, type of

coatings, depth of cut, grinding speed, lapping duration etc.), was made by

means of genetic programming using data values on the outputs ( normal and

tangential forces, surface roughness Ra, Rt etc.) of grinding and lapping

operations of experimental results already made. This paper highlights how one

can use GP technique in the prediction of output parameters. Commercial

Genetic Programming (GP) software-Discipulus is used to derive a

mathematical modelling of relations for various input and output parameters

used in characterisation. A special genetic approach for the modelling of

Machinability characteristics in coated components is proposed on the basis of

a training data set. Different types of genetic models for prediction of different

machinability properties with greater accuracy (less than 1%) were also

proposed by simulated evolution.

Keywords: Evolutionary computation; Genetic programming; Cutting force;

Diamond and CBN grinding wheel; Precision machining; Grinding and

Lapping; Optimizations of Cutting parameters; Precision Machining Processes;

Surface roughness. .

1. INTRODUCTION

With the projected wide spread applications of ceramic coating materials, it

is necessary to develop an appropriate technology for their efficient and cost

effective machining processes [5] & [8-[10 ]. Grinding of ceramics is a difficult

task, as it is generally associated with cracking, splintering and delamination of

surfaces. Conventional processes and tools are not generally suited for the

machining of ceramics. Standard machining tools can be used with

optimization of machining parameters in operating conditions [ 7-10 ]. Various

precision machining techniques that could be adopted for efficient machining

of ceramic materials, namely grinding and lapping processes are studied in

detail for the parametric influence of various cutting parameters of precision

machining of ceramic coating materials [12-13].

Ceramic coated components used in industrial applications, generally

require post treatments like heat treatment and surface finishing by precision

machining [1-5]. Good surface finish and high efficiency in machining to meet

the demands of tight tolerances are generally achieved by grinding, lapping and

polishing like precision machining processes [3].

Grinding of ceramics is a difficult task, as it is generally associated with



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cracking of surface. In order to study the effect of precision machining

processes[14-15], experiments were conducted to check the machining

parameters like surface quality, grinding forces etc. on ceramic coated

components for different machining conditions.

The main object of this study is to evaluate the behavior of A, AT, PSZ,

Super-Z alloy and ZTA ceramic coating materials subjected to different

grinding conditions. The performance was evaluated by measuring grinding

force [14], surface finish [8-9] and bearing area characteristics and also using oil

film retainability characteristics.

1.2 Genetic Programming (GP)

GP works with a large population of candidate programs and uses the

Darwinian principle of “survival of the fittest” to produce successively better

programs for a given problem and evolves a program that predicts the target

output from a data file of inputs and outputs [17-19]. The programs evolved by

GP software – Discipulus [20], in this case Java, C/C++ and assembly

interpreter programs represents a mapping of input to output data. This is done

by Machine Learning that maps a set of input data to known output data. The

aims of using the machine learning technique on engineering problems are to

determine data mining and knowledge discovery. GP provides a significant

benefit in many areas of science and industry [21-25]. The Discipulus GP [20]

system uses AIM Learning Technology. “AIM” stands for “Automatic

Induction of Machine Code”. AIM Learning and Discipulus deal with the

machine learning speed problem. This speed allows the analyst to able to make

many more runs to investigate relationships between data and output, assess

information content of data streams, uncover bad data or outliers, assess time

lag relationships between inputs and outputs, and the like. The evolved models

have been used to develop process prediction or to control algorithms. Hence

GP technology has been selected for the present work.

GP solutions are computer programs that can be easily inspected,

documented, evaluated, and tested. The GP solutions are easy to understand the

nature of the derived relationship between input and output data and to examine

the uncover relationships that were unknown before. Genetic Programming

evolves both the structure and the constants to the solution simultaneously.

Discipulus GP strongly discriminates between relevant input data and inputs

that have no bearing on a solution [20]. In other words, Discipulus performs

input variable selection as a by-product of its learning algorithm.

The following step by step procedure will be implemented for a steady state

GP algorithm [17-19].

1. Initialization of population: Generate an initial population of random

compositions of the functions and terminals of the problem (computer

programs).

2. Fitness evaluation: Execute each program in the population, randomly it

selects some programs and assign it a fitness value according to how well it



129

solves the problem by mapping input data to output data. Some programs are

selected as best programs (winner programs)

3. Create a new population of computer programs by exchanging parts of the

“best” programs with each other (called crossover).

4. Copy the best existing programs.

5. Create new computer programs by randomly changing each of the

tournament winners to create two new programs mutation.

6. Iterate Until Convergence. Repeat steps two through four until a program is

developed that predicts the behavior sufficiently.

GP has been successfully used to solve problems in a wide range of broad

categories [17-26]:

1. Systems Modelling, Curve Fitting, Data Modelling, and Symbolic

Regression

2. Industrial Process Control

3. Financial Trading, Time Series Prediction and Economic Modelling

4. Optimisation and scheduling

5. Medicine, Biology and Bioinformatics

6. Design

7. Image and Signal processing

8. Entertainment and Computer games

2. EXPERIMENTAL PROCEDURE

Three different commercially available ceramic coating powder materials namely, Alumina (Al2O3), Alumina-Titania (Al2O3-TiO2), Partially Stabilized Zirconia (PSZ) were used for the preparation of coatings [3] &[7]. A 40 KW Sulzer, Metco plasma spray system with 7MB gun is used for this plasma spraying of coatings. Mild steel plates of 50x50x6 mm were used as substrate to spray the ceramic oxides. They were grit blasted, degreased and spray coated with a 50 to 100 microns Ni Cr Alumel bond coat. The above ceramic materials were then plasma sprayed using optimum spray parameters.

2.1 Precision Machining of Ceramic Coating

Grinding: Using diamond and CBN grinding wheels [20] with the surface

grinding trials were conducted with the grinding conditions mentioned below in

table 1. Machining trials were conducted on different ceramic coated

specimens (A, AT and PSZ ceramic coatings).

The main object of this study is to evaluate the behavior of A, AT and PSZ

ceramic coatings subjected to different grinding conditions. The performance

[14] was evaluated by measuring

Table1. Grinding specifications

Type of grinding Surface grinding

Grinding wheel used Diamond of 185 grit size

CBN of 120 grit size

Wheel speed used 5 to 30m/sec

Depth of grinding 10 to 40µm

Work feed 0.5 mm/sec

Table2. Lapping specifications

Type of Machine Flat surface hand lapping

Lapping medium Abrasive and diamond compound paste.

Diamond size 2-10 µ m

Lapping speed 0.2 m/sec

Lapping pressure 0.05 MPa

Lapping time duration 25 minutes



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1. Grinding force ( both normal and tangential forces)

2. Surface finish produced which also includes the bearing area

characteristics

2.2 Force Measurement

The normal grinding force (Fn) and the tangential force (Ft) were measured

using grinding dynamometer [14].The ground samples were measured for

different surface finish parameters such as Ra, Rt, and tp values using Taylor

Hobson’s stylus tracing profilometer.

2.3 Lapping

A circular disc of 50 mm in diameter made of bright steel was coated with

NiCrAlumel bond coat of thickness 75µm and subsequently coated with

different ceramic coating materials namely, Alumina (A), Alumina-Titania

(AT), Partial Stabilized Zirconia (PSZ), Super-Z alloy and ZTA [19-20].

Samples were initially ground to achiev pre lapping finish and then further

lapped under the conditions mentioned above in table 2. The process variables

chosen were lapping time (ranging 5 to 25 minutes) and size of the diamond

abrasives in lapping [5].

The lapped discs were thoroughly cleaned and measurement in respect of

surface finish was made using Taylor Hobson’s surface finish profilometer.

3. Genetic Programming Methodology: Genetic programming can be the most general approach among evolutionary

computation methods in which the coatings subjected to different precision

machining operations and cutting parameters variation adaptation are those

hierarchically organized computer programs whose size and form dynamically

change during simulated evolution. The initial population in GP is obtained by

the creation of random computer programs consisting of available function

genes from set F and available terminal genes from set T. In Genetic

programming modelling, it is necessary to select suitable terminal from set F

and available terminal genes from set f (0)[17-25]. From these, the

evolutionary process will try to build as fit an organism (i.e. mathematical

model) as possible for machinability characteristics prediction. The organisms

consist of both terminal and function genes and have the nature of computer

programs which differ in form and size.

In our case the set of terminal genes f (0) is: f (0) = {Process inputs}.The

selected set of function genes F is: F = {+, -, *, /}, where +,-,*, / are the

mathematical operation of addition, subtraction, multiplication and division.

The quality of the individual organism (i.e. prediction) is found out using

fitness function. In our case, four different functions are used.

Process Inputs: Depth of cut (µm),

Hardness of a wheel,

Hardness of coatings (HV),



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Grinding speed (m/sec),

Toughness (MPa√m)

Thermal conductivity (W/m K)

Thermal Diffusivity (10-7

m2/sec)

Measured Process Outputs f(0) Normal Grinding force Fn (N)

Tangential Grinding force Ft (N)

Surface roughness Ra (µm) Table 3. Experimental Results of evaluating the Surface Roughness Ra during surface grinding for different coatings

Table 4. Experimental Results of evaluating Normal force Fn during surface grinding for different coatings

Experimental results GP Output

Depth

of cut

in µm (V1)

Grinding

speed

(V0)

Ra in µm

for PSZ

ground with Diamond

wheel

Ra in µm for

AT ground

with Diamond

wheel

Ra in µm for

AT ground

with CBN wheel

Ra in µm for A

ground with

Diamond wheel

Ra in µm for

A ground

with CBN wheel

Ra in µm

for PSZ

ground with CBN

wheel

PSZ using

CBN wheel

10 5 1 0.82 1.9 0.85 1 1.5 1.620467

10 10 0.9 0.82 1.7 0.8 0.8 1.4 1.449796

10 15 0.85 0.95 1.65 0.82 0.65 1.25 1.360538

10 30 0.8 0.9 1.7 0.81 0.64 1.15 1.253909

20 5 1.3 0.9 1.6 1.1 1.4 1.5 1.357232

20 10 1.1 0.84 1.6 1 1.3 1.35 1.348581

20 15 0.95 0.8 1.5 0.95 1.2 1.05 1.032365

20 30 0.7 0.85 1.4 0.79 1.1 1.2 1.209249

30 5 1.1 0.95 1.8 1.2 1.5 1.4 1.291292

30 10 0.98 0.77 1.5 1.1 1.4 1.2 1.209249

30 15 1 0.78 1.4 1 1.2 1.1 1.130757

30 30 0.62 0.73 1.3 0.71 1.15 1.25 1.360538

40 5 0.89 1 1.1 1.1 1.7 1.1 1.003651

40 10 0.75 0.75 0.9 1 1.6 1 1.000821

40 15 0.68 0.9 0.82 0.8 1.5 0.98 0.971932

40 30 0.63 0.6 0.73 0.7 1.4 0.93 0.891324


Depth of

cut in µm

(V1)

Grinding

speed in

m/sec (V0)

Fn in N for

PSZ using

diamond wheel

Fn in N for

AT using

diamond wheel

Fn in N for

AT using

CBN wheel

Fn in N for

A using

diamond wheel

Fn in N for

A using

CBN wheel

Fn in N for

PSZ using

CBN wheel

PSZ using

CBN wheel

10 5 0.5 2.5 4 2.8 4.2 3.9 4.074322

20 10 1.7 0.42 5.2 0.5 5.2 5.2 5.414451

30 15 0.7 0.55 5 0.6 5.5 5.5 5.40726

40 30 1.1 0.7 4.8 0.65 5.4 5.4 5.333083

10 5 1.25 2.5 4.1 2.7 4.6 4.1 3.996253

20 10 2 2.1 5.4 2.2 5.9 5.5 5.40726

30 15 1.3 0.6 5.8 0.7 6.5 6 5.923153

40 30 1.8 0.75 5.2 0.72 6.4 5.6 5.607811

10 5 1.3 3 4.8 3.3 5.3 4.7 4.746202

20 10 2.2 2.8 5.6 3.1 6 5.9 5.73641

30 15 1.5 0.7 6.2 0.78 6.6 6.4 6.407451

40 30 1.8 0.81 5.2 0.825 6.4 5.6 5.670387

10 5 1.7 3.8 5 3.9 5.6 4.9 4.962764

20 10 3 3.1 5.7 3.5 6.2 6.1 5.923153

30 15 1.8 0.9 6.5 1.1 7 6.75 6.853525

40 30 2 1.3 6.2 1.2 6.8 6.4 6.407451



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Table 5. Experimental Results of evaluating Tangential force Ft during surface grinding for different coatings

Experimental results

Depth

of cut

in µm ( V1)

Grinding

speed in

m/sec (V0)

Ft in N for

PSZ using

diamond wheel

Ft in N for

PSZ using

CBN wheel

Ft in N for

AT using

diamond wheel

Ft in N for

AT using

CBN wheel

Ft in N for

A using

diamond wheel

Ft in N for A

using CBN

wheel

10 5 0.2 0.3 0.18 0.4 0.2 0.6

20 10 0.215 0.5 0.25 0.62 0.35 0.8

30 15 0.24 0.6 0.14 0.8 0.3 0.9

40 30 0.29 0.55 0.255 0.75 0.21 0.85

10 5 0.24 0.4 0.145 0.6 0.3 0.42

20 10 0.29 0.65 0.33 0.85 0.4 0.65

30 15 0.35 0.71 0.31 1 0.35 0.75

40 30 0.4 0.69 0.41 0.95 0.24 0.7

10 5 0.35 0.56 0.21 0.8 0.44 0.95

20 10 0.38 0.8 0.34 1.2 0.47 1.5

30 15 0.54 1 0.21 1.4 0.45 1.8

40 30 0.5 0.98 0.46 1.3 0.4 1.6

10 5 0.45 0.75 0.22 1 0.53 0.4

20 10 0.5 1 0.52 1.5 0.57 0.62

30 15 0.67 1.4 0.23 1.8 0.53 0.8

40 30 0.62 1.2 0.55 1.6 0.68 0.75

Table 6. Experimental Results of evaluating surface roughness Ra after lapping for different coatings

4. GENETIC MODELS – RESULTS AND DISCUSSION

Using GP simulation, During Grinding operation, the Normal force Fn is given

by,

Where V2=

Hardness of coatings, V3=Toughness of coatings and V4=Hardness of Grinding

wheels (refer table 4.)


Depth

of cut

in µm

( V4)

Lapping

time in

minutes

( V1)

Ra in µm for

PSZ using

diamond

wheel

Ra in µm

for AT

using

diamond wheel

Ra in µm for

AT using

CBN wheel

Ra in µm

for A using

diamond

wheel

Ra in µm

for A using

CBN wheel

Ra in µm

for PSZ

using CBN

wheel

PSZ using

CBN wheel

0.25 5 0.5 0.38 0.4 0.72 0.72 0.53 0.54021

0.25 10 0.35 0.28 0.31 0.41 0.42 0.38 0.383549

0.25 15 0.28 0.22 0.24 0.33 0.35 0.3 0.293363

0.25 20 0.2 0.18 0.18 0.24 0.27 0.21 0.209842

0.25 25 0.18 0.15 0.16 0.23 0.25 0.18 0.186963

0.25 30 0.175 0.14 0.14 0.2 0.21 0.18 0.183273

8 5 0.75 0.49 0.5 0.8 0.8 0.7 0.681086

8 10 0.47 0.39 0.42 0.52 0.58 0.5 0.482033

8 15 0.33 0.29 0.31 0.44 0.48 0.36 0.36672

8 20 0.24 0.24 0.26 0.34 0.38 0.28 0.27575

8 25 0.21 0.2 0.205 0.31 0.35 0.22 0.224667

8 30 0.2 0.195 0.2 0.295 0.32 0.2 0.209842

14 5 0.82 0.65 0.68 0.905 0.98 0.88 0.847382

14 0 0.55 0.5 0.52 0.82 0.85 0.6 0.601887

14 15 0.45 0.4 0.43 0.62 0.65 0.51 0.507382

14 20 0.4 0.3 0.32 0.52 0.58 0.45 0.456926

14 25 0.33 0.28 0.3 0.46 0.5 0.38 0.383549

14 30 0.28 0.26 0.28 0.38 0.4 0.32 0.310341



133

Using GP simulation, During Grinding operation, the Tangential force Ft is

given by,

Where V2= Hardness of coatings, V3=Toughness of

coatings and V4=Hardness of Grinding wheels (refer table 5.)

Using GP simulation, During Grinding operation, the surface roughness Ra is

given by,

Where V2= Hardness of coatings, V3=Toughness

of coatings and V4=Hardness of Grinding wheels (Refer table3.)

Using GP simulation, During Lapping operation, the surface roughness Ra is

given by

Where V0= Hardness of coatings, V3=Toughness

of coatings and V2=Hardness of Grinding wheels (refer table 6.)

The percentage deviation of GP (expected) and experimental results for

Normal grinding forces simulation of the Grinding Machining process are

shown on Figure 1 and the Tangential grinding forces are presented in Figure 2.

Whereas results of surface roughness Ra during grinding and lapping

operations are shown in figure 3 and figure 4 respectively. The Discipulus GP

technique was able to simulate these output variables within an average of

1.9% of their measured value, with no value exceeding a 5% deviation.



134

Figure 1. Percentage deviation curve between the best models regarding individual generation and experimental results of Fn

Figure 2. Percentage deviation curve between the best models regarding individual generation and experimental results of Ft

Figure 3. Percentage deviation curve between the best models regarding individual generation and experimental results of Ra

value during Grinding



135

Figure 4. Percentage deviation curve between the best models regarding individual generation and experimental results of Ra

value during Lapping

4 CONCLUSION

Oxide ceramics such as Alumina (A), Alumina-Titania (AT) and Partial

stablized zirconia (PSZ), have been widely used for many industrial

applications by deposition of thick film of hard materials on a relatively softer

substrate of the engineering components. Before deciding on the selection of

coatings, it is essential to look into its characteristics which also includes its

machinability for control of size and shape of end products. The above work

was taken up as product development of ceramic coated surfaces are having

immense value for industrial applications.

In this paper, the genetic programming was used for predicting the

machinability of ceramic materials. In the proposed concept the mathematical

models for verifying the machinability are subject to adaptation. After many

trials, with the help of validation and testing data, the fittest model reliability is

98%. Thus, in this case the reliability was almost 100%. Some of genetically

developed models of cutting parameters of precision machining of ceramic

oxide coatings, out of many successful solutions are presented here. The

accuracies of solutions obtained by GP depend on applied evolutionary

parameters and also on the number of measurements and the accuracy of

measurement. In general, more measurements supply more information to

evolution which improves the structures of models and we have provided

enough data.

In this paper, the genetic programming was used for predicting the

Machinabiliy characteristics for verifying the experimental results of Cutting

parameters which are subject to adaptation. Its reliability is approximately 98%

in the prediction of various outputs of results. In the testing phase, the

genetically produced model gives the same result as actually found out during

the experiment, with the reliability of almost cent percent. It is inferred from

our research findings that the genetic programming approach could be well

used for the prediction of Machinability characteristics of ceramic coatings



136

without conducting the experiments. This helps to establish efficient planning

and optimizing of process for the quality production of ceramic coatings

depending upon the functional requirements. Further work is on progress, for

the prediction/optimization of mechanical and tribological characteristics of

Industrial ceramic coatings by developing a mathematical model.

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Documents

EVALUATION OF MACHINABILITY CHARACTERISTICS OF … · 2.2 Force Measurement The normal grinding force (F n) and the tangential force (F t) were measured using grinding dynamometer