NEURO-FUZZY STUDIES OF THE ROLE OF FLEXIBILITY ON

PERFORMANCE OF FMS

Submitted By :-VIKAS- 0814340053AJAY YADAV- 0814340005SHIVANI YADAV- 0814340044JAGDEEP SINGH- 0814340018

CONTENTS

• OBJECTIVE

• MOTIVATION

• LITRATURE SURVEY

• METHODOLOGY

• IMPLEMENTATION PLAN

• EXPECTED OUTCOME

• 1965 - Fuzzy Sets ( Lofti Zadeh, seminar)

• 1966 - Fuzzy Logic ( P. Marinos, Bell Labs)

• 1972 - Fuzzy Measure ( M. Sugeno, TIT)

• 1974 - Fuzzy Logic Control (E.H. Mamdani)

• 1980 - Control of Cement Kiln (F.L. Smidt, Denmatk)

• 1987 - Sendai Subway Train Experiment ( Hitachi)

• 1988 - Stock Trading Expert System (Yamaichi)

• 1989 - LIFE ( Lab for International Fuzzy Eng)

HISTORY OF FUZZY LOGIC

OBJECTIVE

• TO MAKE INDIAN INDUSTRIES CAST EFFECTIVE

• FMS is considered to be highly flexible and highly integrated

system , but they cost heavy and most of the Indian industries

can not afford this. So it is relevant to find a solution for Indian

industries which could offer cost efficient ways to achieve this.

MOTIVATION• The machine learning technique in the field of artificial

intelligence

• Approaches used include fuzzy logic approaches, artificial

neural networks, and the application of adaptive-network-

based fuzzy inference systems (ANFIS)

• Fuzzy logic approaches easily deal with uncertain and

incomplete information

• Approaches in scheduling of flexible manufacturing

systems increased

LITRATURE SURVEY

• FMS(FLEXIBLE MANUFACTURING SYSTEM)

• ARTIFICIAL INTELLIGENCE

• NEURO-FUZZY

FMS(FLEXIBLE MANUFACTURING SYSTEM)

• A manufacturing system in which there is some amount of flexibility

that allows the system to react in the case of changes, whether

predicted or unpredicted

• Comes in the middle of the 1960s

• Philosophically, FMS incorporates a system view of manufacturing

• We must become managers of technology not merely users of

technology by Peter Drucker

• Today flexibility means to produce reasonably priced customized

products of high quality that can be quickly delivered to customers

• Workstations

• Material handling and storage system

• Computer control system

• People are required to manage and operate the system.

BASIC COMPONENTS OF FMS

Machine Work table

Machine Tool

Parts CarouselRobot

AUTOMATED MANUFACTURING CELL

• Load/Unload Stations - Physical interface: FMS and factory

• Machining Stations - Most common is the CNC machining centre

• Other Processing Stations – sheet-metal fabrication, forging

• Assembly - Industrial robots, component placement machines

• Other Stations and Equipment -inspection stations, cleaning

stations, central coolant delivery and chip removal systems

WORKSTATIONS

ADVANTAGES OF FMS

• Increased machine utilization

• Fewer machines required

• Reduction in factory floor space required

• Greater responsiveness to change

• Reduced inventory requirements

• Lower manufacturing lead times

• Reduced direct labor requirements and higher labor

productivity

• Opportunity for unattended production

DISADVANTAGES OF FMS

• Substantial pre-planning activity

• Expensive, costing millions of dollars

• Sophisticated manufacturing systems

• Limited ability to adapt to changes in product or product mix

• Technological problems of exact component positioning and

precise timing necessary to process a component

ARTIFICIAL INTELLIGENCE

• “AI is the activity of providing such machines as computers

with the ability to display behaviours that would be regarded

as intelligent if it were observed in humans” (R. McLeod)

• “AI is the study of agents that exist in an environment,

perceive and act.” (S. Russel and P. Norvig)

ARTIFICIAL NEURAL NETWORK• Computational models that try to emulate the structure of the

human brain wishing to reproduce at least some of its flexibility

and power.

• ANN consist of many simple computing elements – usually

simple nonlinear summing operations – highly connected by

links of varying strength

• ANNs are able to learn from examples

• Function approximations

• Solutions not always correct

• ANNs are able to generalize the acquired knowledge

TRAINING

• Weight values change during the training process

• Values are presented at the inputs and outputs are compared to the

desired values.

• Wrong outputs cause weights to change in order to reduce the

error

• Process is repeated with different inputs till the ANN is able to

give the correct answers

• Hopefully the ANN will be able to give the correct answer even to

inputs that were not trained.

FUZZY LOGIC : AN IDEA

1.0

FUZZY LOGIC

• Introduced by Lofti Zadeh (1965)

• It is a powerful problem-solving methodology• Builds on a set of user-supplied human language rules

• It deals with uncertainty and ambiguous criteria or values• Example: “the weather outside is cold”

• but, how cold is actually the coldness you described?• What do you mean by ‘cold’ here?

• As you can see a particular temperature is cold to one person but it is not to another

• It depends on one’s relative definition of the said term

• Formal definition:• A fuzzy set A in X is expressed as a set of ordered pairs:

A x x x XA {( , ( ))| }

Universe oruniverse of discourse

Fuzzy setMembership

function(MF)

A fuzzy set is totally characterized by amembership function (MF).

FUZZY SETS

• Most natural language is bounded with vague and imprecise

concepts

• Example:

• “He is quite tall”

• “The student is intelligent”

• “Today is a very hot day”

• These statements are difficult to translate into more precise

language

• Fuzzy logic was introduced to design systems that can demonstrate

human-like reasoning capability to understand such vague terms

• CRISP LOGIC• precise properties

• Full membership• YES or NO• TRUE or FALSE• 1 or 0

• Crisp Sets• she is 18 years old• man 1.6m tall

• FUZZY LOGIC• Imprecise properties

• Partial membership• YES ---> NO• TRUE ---> FALSE• 1 ---> 0

• Fuzzy Sets• she is about 18 years old• man about 1.6m tall

DIFFERENCES BETWEEN FUZZY LOGIC AND CRISP LOGIC

HOW DOES FUZZY LOGIC RESEMBLES HUMAN INTELLIGENCE?

• It can handle at certain level of imprecision and uncertainty

• By clustering & classification

• dividing the scenario/problems into parts

• focusing on each part with rank of importance and alternatives to solve

• combining the parts to as an integrated whole

• It reflects some forms of the human reasoning process by

• Setting hypothetical rules

• Performing inferencing

• Performing logic reasoning on the rules

• Inputs to a fuzzy system can be:

– fuzzy, e.g. (Score = Moderate), defined by membership

functions;

– exact, e.g.: (Score = 190); defined by crisp values

• Outputs from a fuzzy system can be:

– fuzzy, i.e. a whole membership function.

– exact, i.e. a single value is produced

EXAMPLE: FUZZY INFERENCE

METHODOLOGY

• Inputs to a fuzzy system can be:

– fuzzy, e.g. (Score = Moderate), defined by membership

functions;

– exact, e.g.: (Score = 190); defined by crisp values

• Outputs from a fuzzy system can be:

– fuzzy, i.e. a whole membership function.

– exact, i.e. a single value is produced

EXAMPLE: FUZZY INFERENCE

Consider the rules in fuzzy form, as follows:

Rule 1 Rule 2

IF driving_speed is fast IF driving_speed is slow

THEN stop_distance is long THEN stop_distance is short

In fuzzy rules, the linguistic variable speed can have the range between 0 and 220 km/h, but the range includes fuzzy sets,

such as slow, medium, fast. Linguistic variable stop_distance can take either value: long or short. The universe of discourse of the linguistic variable stop_distance can

be between 0 and 300m and may include such fuzzy sets as short, medium, and long.

WHAT IS THE DIFFERENCE BETWEEN CLASSICAL AND FUZZY RULES?

• Set the boundaries between two values(cold and hot) which

will show the degrees of temperature

• A sample set of rules

• IF temperature is cold THEN set fan speed to zero

• IF temperature is cool THEN set fan speed to low

• IF temperature is warm THEN set fan speed to medium

• IF temperature is hot THEN set fan speed to high

FUZZY LOGIC METHODOLOGY

IF Load Weight is heavy THEN set Water Amount to maximum

IF Load Weight is medium THEN set Water Amount to regular

IF Load Weight is light THEN set Water Amount to minimum

IF Load_Weight is heavy THEN set Water_Amount to full IF Load_Weight is not_so_heavy THEN set Water_Amount to three_quarter IF Load_Weight is not_so_light THEN set Water_Amount to half IF Load_Weight is light THEN set Water_Amount to quarter

DESIGN A SET OF FUZZY RULES FOR AN ELECTRICAL WASHING MACHINE

Or

• A fuzzy set A can be alternatively denoted as follows:

A x xAx X

i i

i

( ) /

A x xA

X

( ) /

X is discrete

X is continuous

Note that S and integral signs stand for the union of membership grades; “/” stands for a marker and does not imply division.

ALTERNATIVE NOTATION

• Fuzzy Logic Operators are used to write logic combinations between

fuzzy notions (i.e. to perform computations on degree of membership)

• Zadeh operators

1. Intersection: The logic operator corresponding to the intersection

of sets is AND

µ(A AND B) = MIN (µA,µB)

2. Union: The logic operator corresponding to the union of sets is OR

µ(A OR B) = MAX (µA,µB)

3. Negation: The logic operator corresponding to the complement of

a set is the negation

FUZZY LOGIC OPERATIONS

µ(NOTA) = 1-µA

FUZZY LOGIC OPERATIONS

task aug sept oct nov dec jan feb mar april may june

problem search      

problem identification    

litreture survey            

learning of anfis      

data collection                

experimentation                

analysis                

result of inference        

report writing        

final submission                      

IMPLEMENTATION PLAN

EXPECTED OUTCOME

• Fuzzy Logic Decision Making is used in many applications

• Implemented using fuzzy sets operation(if , then , else

statements & logical operators)

• Resembles human decision making with its ability to work

from approximate data and find a precise solutions

• Cost effective FMS(Flexible Manufacturing System) system may

be dsign

SOME SNAP SHOTS training the data in anfis editor

SOME SNAP SHOTS structure of the trained data

RULES of the fuzzy-logic

SURFACE of the fuzzy-logic