NEURO FUZZY STUDIES OF FMS SYSTEM

1. NEURO-FUZZY STUDIES OF THE
ROLE OF FLEXIBILITY ON
PERFORMANCE OF FMS
Submitted By :-
VIKAS- 0814340053
AJAY YADAV- 0814340005
SHIVANI YADAV- 0814340044
JAGDEEP SINGH- 0814340018

2. CONTENTS
• OBJECTIVE
• MOTIVATION
• LITRATURE SURVEY
• METHODOLOGY
• IMPLEMENTATION PLAN
• EXPECTED OUTCOME

3. HISTORY OF FUZZY LOGIC
• 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)

4. 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.

5. 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

6. LITRATURE SURVEY
• FMS(FLEXIBLE MANUFACTURING SYSTEM)
• ARTIFICIAL INTELLIGENCE
• NEURO-FUZZY

7. 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

8. BASIC COMPONENTS OF FMS
• Workstations
• Material handling and storage system
• Computer control system
• People are required to manage and operate the system.

9. AUTOMATED
MANUFACTURING CELL
Machine Tool
Parts Carousel
Robot
Machine Work
table

10. WORKSTATIONS
• 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

11. 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

12. 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

13. 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)

14. 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

15. 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.

16. FUZZY LOGIC : AN IDEA
1.0

17. 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

18. FUZZY SETS
• Formal definition:
• A fuzzy set A in X is expressed as a set of ordered
pairs:
A {( x, A ( x ))| x X}
Membership Universe or
Fuzzy set
function universe of discourse
(MF)
A fuzzy set is totally characterized by a
membership function (MF).

19. • 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

20. DIFFERENCES BETWEEN FUZZY
LOGIC AND CRISP LOGIC
• CRISP LOGIC • FUZZY LOGIC
• precise properties • Imprecise properties
• Full membership • Partial membership
• YES or NO • YES ---> NO
• TRUE or FALSE • TRUE ---> FALSE
• 1 or 0 • 1 ---> 0
• Crisp Sets • Fuzzy Sets
• she is 18 years old • she is about 18 years old
• man 1.6m tall • man about 1.6m tall

21. 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

22. METHODOLOGY
EXAMPLE: FUZZY INFERENCE
• 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

23. EXAMPLE: FUZZY INFERENCE
• 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

24. WHAT IS THE DIFFERENCE BETWEEN
CLASSICAL AND FUZZY RULES?
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.

25. FUZZY LOGIC METHODOLOGY
• 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

26. DESIGN A SET OF FUZZY RULES FOR
AN ELECTRICAL WASHING MACHINE
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
Or
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

27. ALTERNATIVE NOTATION
• A fuzzy set A can be alternatively denoted
as follows:
A A ( xi ) / xi
X is discrete xi X
X is continuous A A (x) / x
X
Note that S and integral signs stand for the union of
membership grades; “/” stands for a marker and does
not imply division.

28. FUZZY LOGIC OPERATIONS
• 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
µ(NOTA) = 1-µA

29. FUZZY LOGIC OPERATIONS

30. IMPLEMENTATION PLAN
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

31. 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

32. SOME SNAP SHOTS training the data in anfis editor

33. SOME SNAP SHOTS structure of the trained data

34. RULES of the fuzzy-logic

35. SURFACE of the fuzzy-logic