The document discusses the implementation of fuzzy logic in washing machines, highlighting its advantages such as improved performance and automation through sensors that monitor dirt levels and adjust operations accordingly. It explains the processes of fuzzification, defuzzification, and the use of fuzzy inference systems to enhance washing efficiency without requiring human intervention. Additionally, it mentions the use of tools like MATLAB and Scikit-fuzzy for developing and testing fuzzy logic applications in washing machines.

1. FUZZY LOGIC
IN
WASHING MACHINE
PRESENTED BY: M.S.C. I.T
SEM 2
02 KHYAT ANJARIA
09 DEEP CHOTHANI
11 HARSH GOR

2. INTRODUCTION TO FUZZY LOGIC IN
WASHING MACHINE
• Fuzzy logic washing machines are gaining popularity. These
machines offer the advantages
of performance, productivity, simplicity and less cost. Sensors
continually monitor varying conditions inside the machine and
accordingly adjust operations for the best wash results.
• The fuzzy logic checks for the extent of dirt and grease, the amount
of soap and water to add, direction of spin, and so on. The machine
rebalances washing load to ensure correct spinning. Neuro fuzzy
logic incorporates optical sensors to sense the dirt in water and
a fabric sensor to detect the type of fabric and accordingly adjust
wash cycle.

3. WASHING MACHINE MODEL REFERENCE

4. BLOCK DIAGRAM OF WASHING MACHINE

5. WASH SENSOR
• Assume that we have these inputs at our hand,
• The degree of dirt is determined by the transparency of the wash
water. The dirtier the clothes, less transparent the water being
analyzed by the sensors is.
• Type of dirt determines the quality of dirt. Greasy cloths, for
example, take longer for water transparency to reach transparency
because grease is less soluble in water than other forms of dirt. type
of dirt is determined by the time of saturation.
• Because the input/output relationship is not clear, the design of a
washing machine controller has not in the past lent itself to

6. FUZZY LOGIC APPROACH
• Convert crisp values in Fuzzy values
• Rule Evaluation (Rule Application)
• Use membership functions
• Fuzzification
• Defuzzification (Obtaining crisp or actual results)

7. FUZZY INFERENCE SYSTEM
• Fuzzy Inference System (FIS) is a way of mapping and input space to
an output space using fuzzy logic
• The rules in FIS are fuzzy production rules like
• if (antedecent) then (consequence)
• If x is low and y is high then z is medium
• Set of rules in a FIS is known as knowledge base

8. FUZZY LOGIC TOOLBOX IN MATLAB

9. • Using MATLAB’s Fuzzy Logic Toolbox we can create and edit
fuzzy inference systems with Fuzzy Logic Toolbox Software.
• Simulink Software helps us to test our fuzzy system in a block
diagram simulation environment.
• It can work as a stand-alone fuzzy inference engine.

10. FUZZY LOGIC DESIGNER

11. Fuzzy Membership Function Editor

12. Fuzzy Membership Function Editor

13. Fuzzy Rule Editor

14. Fuzzy Rule Viewer

15. Fuzzy Surface viewer

16. SCIKIT-FUZZY [THE PROGRAMMING PART]
• A fuzzy logic toolbox for SciPy.
• Collection of fuzzy logic algorithms written In
Python.
• Despite some of the lengthy rule sets, Scikit-
Fuzzy’s control system can execute and finish
calculations in miliseconds.

17. HARDWARE USED
Arduino 16x2 LCD

18. Ultrasonic Sensor DC Motor

19. L298N Motor Controller

20. LCD Circuit Diagram

21. Ultrasonic Sensor Circuit Diagram

22. CONCLUSION
• By the use of fuzzy logic control we have been able to obtain a wash
time for different type of dirt and different degree of dirt.
• The conventional method required the human interruption to decide
upon what should be the wash time for different cloths.
• The situation analysis ability has been incorporated in the machine
which makes the machine much more automatic and represents the
decision taking power of the new arrangement.
• The strength of fuzzy logic is that we are able to model words by the
use of fuzzy sets.