Introduction - Semantic Scholar · Faran Baig 1, Muhammad Waseem Ashraf 1, 2, *, Zahoor Ahmed , Muhammad Imran , Shahzadi Tayyaba2, Muhammad Saleem Khan1, Shan-ur-Rahman1, Yasir Noor1,

International Journal of Advanced Technology & Engineering Research (IJATER)

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ISSN No: 2250-3536 Volume 3, Issue 1, Jan. 2013 79

DESIGN AND SIMULATION OF FUZZY LOGIC BASED

ELID GRINDING CONTROL SYSTEM

Faran Baig1, Muhammad Waseem Ashraf1, 2, *, Zahoor Ahmed1, Muhammad Imran1, Shahzadi Tayyaba2, Muhammad Saleem Khan1, Shan-ur-Rahman1, Yasir Noor1, Assad Ullah Masood1, Ammar Haider1 and Nitin Afzulpurkar2

1Department of Physics (Electronics), GC University Lahore, Pakistan 2School of Engineering and Technology, Asian Institute of Technology, Bangkok, Thailand

*Email: [email protected]

Abstract

This research work deals with the design and simulation of

fuzzy logic based elid grinding control system. Elid tech-

nique is used for betterment of surface quality and metal removal rate in brittle materials. The presented control sys-

tem uses fuzzy logic design: fuzzifier, inference engine, rule

base and defuzzification. The defuzzification is capable to be

used in grinding purpose by taking four inputs roughness,

hardness, material removal rate (MMR) and tangential force.

Fuzzy rules are formulated and applied by using MATLAB

simulation for this industrial control system. The presented

work provide useful information and predicted data to de-

velop fuzzy logic based control system for enhancement of

the surface quality and MMR in real time application

Introduction

Fuzzy logic control was originally introduced and devel-

oped as a model free control design approach. It has been

used with great success in industry applications. In the past

ten years, prevailing research efforts on fuzzy logic control

have been devoted to model-based fuzzy control systems

that guarantee not only stability but also performance of

closed-loop fuzzy control systems. Fuzzy logic starts with and build on user supplied human language and convert

these rules into mathematical equivalent. Fuzzy logic has a

unique feature of simplicity and its flexibility to handle

problems with precision and accuracy with its simulation

results. It can be performed in hardware or software or by

combination of both of them. The development of fuzzy

logic has been enthusiastic and dramatic with its applications

on various aspects of life like control, automobiles, decision

making systems and medical field. Dressing (ELID) grind-

ing can be used in machine to make hard and brittle mate-

rials to achieve high surface quality and high MMR. ELID grinding is efficient method that uses a metal bonded di-

amond grinding wheel in order to achieve a mirror surface

finish especially on hard and brittle materials. Feng et al. [1]

reported a survey on analysis and design of model-based

fuzzy control systems. The study was performed for the sta-

bility analysis and controller design. That was based on the

fuzzy dynamic models. Simoes and Spiegel presented a sur-

vey on fuzzy logic based intelligent control of a variable

speed cage machine wind generation system. The work de-

scribed a variable speed wind generation system where

fuzzy logic principles are used for efficiency optimization

and performance enhancement control [2]. Kim et al [3] re-

ported a study on the estimation of wheel state in electrolytic

in-process dressing (elid) grinding. Lim et al. [4] described a survey on fundamental study on the mechanism of electro-

lytic in-process dressing (ELID) grinding. Lee et al. [5] re-

ported a survey on a design by applying fuzzy control tech-

nology to achieve biped robots with fast and stable footstep.

This study used fuzzy logic control and linear quadratic reg-

ulator (LQR) control theory on biped robot system to

achieve the development of balanced and fast footsteps.

Chen et al. [6] presented a survey on fuzzy logic based On-

line efficiency optimization control of a ball mill grinding

circuit. It was reported that the fuzzy logic based on line

optimization control integrated in an expert system was de-veloped to control product particle size while enhancing mill

efficiency in a ball mill grinding circuit. Saleh et al. [7]

present a study on in-process truing for elid grinding by

pulse width control. Hseng et al. [8] described a report on

combination of fuzzy logic control and back propagation

neural networks for the autonomous driving control of car

like mobile robot systems. The designs of sensor based be-

havior fusion mechanism for car like mobile robot was pre-

sented to implement the autonomous driving mission. Khan

et al. [9] proposed grinding and mixing system using fuzzy

time control discrete event model for industrial applications.

Aurtherson et al. [10] presented a survey on optimization of elid grinding process of al/sic composite through neuro-

fuzzy network. Abbas et al. [11] reported a survey on auto-

nomous room air cooler using fuzzy logic control system.

Baig et al. [12] presented a design model of fuzzy logic

medical diagnosis control system. The work was proposed to

develop a control system to enhance the efficiency to diag-

nose a disease related to human brain. Batayneh1 et al. [13]

exhibited a survey on fuzzy logic approach to provide safe

and comfortable indoor environment. The study was per-

formed on the bases of a fuzzy logic approach that aimed to

control the indoor air quality to provide a safe and comforta-ble environment. Abbas et al. [14] described a survey on

fuzzy logic based hydroelectric power dam control system.


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Nabi et al. [15] presented a survey on development of a

brake control system for a series hybrid electric city bus us-

ing fuzzy logic. Here, the design of fuzzy logic based elid

grinding control system has been presented for high surface

quality and high MMR for hard and brittle materials.

Design Model and Methodology

A. Design Model

The design model of elid grinding fuzzy logic control sys-

tem is established. This is used to measure the roughness,

hardness, MMR and tangential force of the material. It also

gives us the probability material smoothness. The member-

ship function of input variables such as roughness, hardness,

MMR and tangential force of the material is given in the

Table 1.

Table 1. Membership function of input variables

The output range of the membership function of fuzzy log based elid grinding control system is given in Table 2.

Table 2. Output of the membership function

Three memberships function low, medium, high has been

used to demonstrate the different ranges of input fuzzy vari-

able. The plot of input membership function for fuzzy varia-

ble like roughness, hardness, MMR and tangential force are

shown in Fig. 1.

Figure 1. Plot of input membership function (a) roughness, (b)

hardness (c) MMR (d) tangential force

The output variable varies very low, Low, Medium, high and very high. The output shows the quality of the surface

that is shown in Fig. 2.

Figure 2. Output membership function plot

B. Fuzzification


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The apparent elid grinding fuzzy logic control system lies

among four input variables. Both the region has values for

variables. Linguistic variable f1 and f2 are for the input vari-

able like roughness. Linguistic variable f3 and f4 are for the

input variable like hardness. Linguistic variable f5 and f6 are

for the input variable like MMR. Linguistic variable f7 and f8 are for the input variable like tangential force. The map-

ping values through membership function were named as

linguistic values. Here, four input variables was used which

represent eight linguistic values as shown in Fig. 3.

Figure 3. Four- input crisp values correspond to eight output

linguistic variables of fuzzifier

In two regions, the relationship of input variables with

membership function is shown in Table 3.

Table 3. Linguistic values of fuzzifier outputs

Table 3 shows the relationship between eight linguistic va-

riables in accordance of four input crisp values. Every input

variable made its own effect on the output. Every variable is

free and not depend on each other. That’s why every varia-

ble may be lies in any of the region. So it has its own effect

at the output. Hence, 16 rules had to establish because none

of our input depends upon each other.

C. Inference Engine (IE)

Sixteen AND operators of inference engine did not fallow

the AND logic, but they select minimum input from the giv-

en input for output. Fuzzifier gives eight inputs to inference

engine and maximum-minimum method used to get the out-

put values of R. Minimum-maximum process between the

four inputs has used by the method mentioned in Fig. 4.

Figure 4. Minimum- maximum process method

Here we use a process minimum AND which is denoted

by the sign ^ between membership function values. Mamda-

ni-min process is used in which the minimum of function

values has been obtained by AND process end. The diagram

of interference process is shown in Fig. 5.


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Fig.ure 5. Inference procedure diagram

D. Rule Selector

For elid grinding control system selector of rules gets four

crisp values of roughness, hardness, MMR and tangential

force. The singleton values of the output function from the

rule selector on the bases of rules has been obtained. For

four input variables 16 rules had to design to get the values of S1, S2, S3, S4, S5, S6, S7, S8, S9, S9, S10, S11, S12,

S13, S14, S15 and S16. The block diagram is singleton is

shown in Fig 6.

Figure 6. Diagram of singleton.

These 16 rules for region 1 and region 2 are shown in the Table 4 and Table 5.

Table 4. Complete rules of region 1

Table 5. Complete rules of region 2

The rule rule base of fuzzy logic based elid grinding sys-

tem is shown in Fig. 7.

Figure 7. Diagram of rule base Fuzzifier consists of a multiplier, comparator, subtrac-

tors, divider and fuzzy set selector. The fuzzifier design of

fuzzy logic based elid grinding control system is for rough-ness is shown in Fig. 8.


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Figure 8. Diagram of fuzzifier design for roughness

Multiplier converts 0-5 input voltage into the crisp values

0-1.7 for roughness by multiplying it with the 0.85. Subtrac-

tor subtracts the crisp values from the end point values of

each region selection and input from two subtractors. The

region selection will give the information of address to the

multiplexer and multiplexer also have input from two sub-tractors. Now it will multiplex two values because it is de-

signed for two regions. Divider will divide each input by

0.85, to find out the mapping values of membership function

and input variable values of roughness in that particular re-

gion. To find the second active fuzzy set values, subtract the

first active set values from 1 this is done in second fuzzy set

subtractor. The internal hardware structure scheme for hard-

ness of fuzzifier for two regions is shown in the above Fig 9.

Figure 9. Fuzzifier design diagram for hardness

Here, multiplier converts 0-5 input voltage into the crisp

values 0-130 for hardness by multiplying it with the 70. Sub-tractor subtracts the crisp values from the end point values of

each region selection and input from two subtractors. The

region selection will give the information of address to the

multiplexer and multiplexer also have input from two sub-

tractors. Now it will multiplex two values because it is de-

signed for two regions. Divider will divide each input by 70,

to find out the mapping values of membership function and

input variable values of hardness in that particular region. To

find the second active fuzzy set values, subtract the first ac-

tive set values from 1 this is done in second fuzzy set sub-

tractor. The internal hardware structure scheme for MMR of fuzzifier for two regions is shown in the above Fig. 10. For

material removal ratio, multiplier converts 0-5 input voltage

into the crisp values 0-4 for material removal ratio by mul-

tiplying it with the 2. Subtractor subtracts the crisp values

from the end point values of each region selection and input

from two subtractors. The region selection will give the

information of address to the multiplexer and multiplexer

also have input from two subtractors. Now it will multiplex

two values because it is designed for two regions. Divider

will divide each input by 2, to find out the mapping values of

membership function and input variable values of MRR in

that particular region. To find the second active fuzzy set values, subtract the first active set values from 1 this is done

in second fuzzy set subtractor.

Figure 10. Fuzzifier design diagram for MMR

The internal hardware structure scheme for tangential

force of fuzzifier for two regions is shown in the above Fig.

11. Here, multiplier converts 0-5 input voltage into the crisp values 0-1.5 for Tangential Force by multiplying it with the

0.75. Subtractor subtracts the crisp values from the end point

values of each region selection and input from two subtrac-

tors. The region selection will give the information of ad-

dress to the multiplexer and multiplexer also have input from

two subtractors. Now it will multiplex two values because it

is designed for two regions. The divider will divide each

input by 0.75, to find out the mapping values of membership

function and input variable values of tangential force in that

particular region. To find the second active fuzzy set values,


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subtract the first active set values from 1 this is done in

second fuzzy set subtractor as shown in table.

Figure 11. Fuzzifier design diagram for tangential force

The results of fuzzification are shown in Table 6 for ma-

thematical analysis.

Table 6. Results for fuzzification

E. Defuzzifier

The presented system has one output that describes the function of elid grinding control. It explains roughness,

hardness, MMR and tangential force on the material. By the

defuzzification process input values are estimated to the out-

put crisp values. Inference engine gives 32 inputs to defuz-

zifier in which sixteen values of R1, R2, R3, R4, R5, R6, R7,

R8, R9, R10, R11, R12, R13, R14, R15 and R16 and from

selector of rules sixteen values for S1, S2, S3, S4, S5, S6,

S7, S8, S9, S10, S11, S12, S13, S14, S15 and S16. The cen-

ter of average method has been used for presented system. It

is mathematically shown as

∑ Si × Ri / ∑ Ri (1) (1)

Here, i= 1 to 16

By use of center of average method defuzzifier calculate

the crisp value of output. The block diagram of defuzzifier

using center of average method is shown in the Fig. 11.

Figure 12 Defuzzifier diagram for center of average method

Simulation and Results

Presented system based on fuzzy logic model. It is used to

measure the roughness, and hardness of the material. The

system consists of four inputs like roughness, hardness,

MRR and tangential force. The rules are formulated in such

a way to get the output. In this system we have four fuzzifier

and one defuzzifier calculation. For the input values we con-

sidered roughness = 0.296, hardness = 31, MRR = 0.697 and

tangential force =. 0358. To apply sixteen inference rules four fuzzifier inference engines were used in sixteen linguis-

tic variable values as shown in Fig. 13.

Figure 13. Calculation by using sixteen rules

For four variables the singleton values S1, S2, S3, S4, S5,

S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, S16 were

found by the sixteen rules using rules base. Crisp vales are

found when defuzzifier accept the values of R1, R2, R3, 4,

R5, R6, R7, R8, R9, R10, R11, R12, R13, R14, R15, R16


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and S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13,

S14, S15, S16. Crisp values output are estimated by the de-

fuzzifier using the center of average method (C.O.A). The

simulated results that have been achieved with the help of

sixteen rules using MATLAB is given here. Fig. 14a and Fig

14b show all the dependencies of the output variable on the input variables for region 1 and region 2 respectively.

Figure 14. Dependencies of the output variables on the inputs

(a) region 1 (b) region 2

Fig. 15 and Fig. 16 show the plots among different quanti-

ties. The discussion of these plots is given here. Fig. 15a

shows that the output changes with the slight change in

roughness and hardness and after some time it become con-

stant. Fig. 15b shows that the output changes with the slight change in roughness and MRR and after some time it be-

come constant. Fig. 16c shows that the output changes with

the slight change in roughness and tangential force and after

some time it become constant. Fig. 15d shows that the out-

put changes with the slight change in roughness and hard-

ness and after some time it become constant. Fig. 15e shows

that the output changes with the slight change in MRR and

hardness and after some time it become constant. Fig. 15f

shows that the output changes with the slight change in tan-

gential force and hardness and after some time it become constant. Fig. 15g shows that the output changes with the

slight change in roughness and MRR and after some time it

become constant. Fig. 15h shows that the output changes

with the slight change in hardness and MRR and after some

time it become constant. Fig. 15i shows that the output

changes with the slight change in hardness and MRR and

after some time it become constant. Fig. 15j shows that the

output changes with the slight change in roughness and tan-

gential force and after some time it become constant. Fig.

15k shows that the output changes with the slight change in

hardness and tangential force and after some time it become

constant. Fig. 15l shows that the output changes with the slight change in MRR and tangential force and after some

time it become constant.

Fig. 16a shows that the output changes with the slight

change in roughness and hardness and after some time it

become constant. Fig. 16b shows that the output changes

with the slight change in roughness and MRR and after some

time it become constant. Fig. 16c shows that the output

changes with the slight change in tangential force and

roughness and after some time it become constant. Fig. 16d

shows that the output changes with the slight change in

roughness and hardness and after some time it become con-stant. Fig. 16e shows that the output changes with the slight

change in MRR and hardness and after some time it become

constant. Fig. 16f shows that the output changes with the

slight change in tangential force and hardness and after some

time it become constant. Fig. 16g shows that the output

changes with the slight change in roughness and MRR and

after some time it become constant. Fig. 16h shows that the

output changes with the slight change in hardness and MRR

and after some time it become constant. Fig. 16i shows that

the output changes with the slight change in tangential force

and MRR and after some time it become constant. Fig. 16j shows that the output changes with the slight change in

Roughness and tangential force and after some time it be-

come constant. Fig. 16k shows that the output changes with

the slight change in hardness and tangential force and after

some time it become constant. Fig. 16l shows that the output

changes with the slight change in MRR and tangential force

and after some time it become constant.


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Figure 16. Plots for outputs for region 1 (a) Plot between hardness-roughness (b) Plot between roughness-MRR (c) Plot between

tangential force-roughness (d) Plot between roughness-hardness (e) Plot between MRR-hardness (f) Plot between tangential force-

hardness (g) Plot between MRR-roughness (h) Plot between hardness-MRR (i) Plot between tangential force-MRR (j) Plot between

roughness-tangential force (k) Plot between hardness-tangential force (l) Plot between MRR-tangential force


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Figure 17. Plots for outputs for region 2 (a) Plot between hardness-roughness (b) Plot between MRR-roughness (c) Plot between

tangential force-roughness (d) Plot between roughness-hardness (e) Plot between MRR-hardness (f) Plot between tangential force-

hardness (g) Plot between roughness-MRR (h) Plot between hardness-MRR (i) Plot between tangential force-MRR (j) Plot between

roughness-tangential force (k) Plot between hardness-tangential force (l) Plot between MRR-tangential force

The contrast between calculated and simulated values for

surface quality for region 1 and region 2 is given in Table 7.


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Table 7. Comparison of calculated and simulated results for

region 1 and region 2

The above table shows that the simulated and calculated results are close agreement.

Conclusion

This paper presents the design and simulation of fuzzy

logic based elid grinding control system. MATLAB has

been used for simulation. The designed system has an inter-

active relationship between its output values with its input values. All output values are dependent on input values. This

industrial controlled system model based on fuzzy logics for

any number of inputs can be designed. By the help of this

increment in input output relationship, the system can be

make more precise and accurate for industrial uses. For fu-

ture evaluation and up gradation of designed industrial con-

trol system, FPGA technology can be used. The presented

methods of fuzzy logic based control system for industrial

application is also suitable to design an algorithm that helps

to upgrade and maintain the nature of decision making.

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Documents

Introduction - Semantic Scholar · Faran Baig 1, Muhammad Waseem Ashraf 1, 2, *, Zahoor Ahmed , Muhammad Imran , Shahzadi Tayyaba2, Muhammad Saleem Khan1, Shan-ur-Rahman1, Yasir Noor1,