Washing machines are a common feature today in our household. The most important utility a customer can derive from a washing machine is that he saves the effort he/she had to put in brushing, agitating and washing the cloth. Most of the people wouldn’t have noticed (but can reason out very well (that different type of cloth need different amount of washing time which depends directly on the type of dirt, amount of dirt, cloth quality etc. The washing machines that are used today (the one not using fuzzy logic control) serves all the purpose of washing, but which cloth needs what amount of agitation time is a business which has not been dealt with properly. In most of the cases either the user is compelled to give all the cloth same agitation or is provided with a restricted amount of control. The thing is that the washing machines used are not as automatic as they should be and can be

Alhaddad, Ahmed

Alhanjouri, Mohammed A.

English

Institutional Repository of the Islamic University of Gaza

   OPTIMIZE WASH TIME OF WASHING MACHINE  
USING FUZZY LOGIC 
Mohammed Alhanjouri*, and Ahmed A. Alhaddad** 
*Associate Professor, Computer Engineering Department, Islamic University of Gaza 
E-mail: mhanjouri@iugaza.edu.ps 
**Postgraduate Student, Electrical Engineering Department, Islamic University of Gaza 
E-mail: aah4190@hotmail.com 
 
 
ABSTRACT 
Washing machines are a common feature today 
in our household. The most important utility a 
customer can derive from a washing machine is that 
he saves the effort he/she had to put in brushing ,
agitating and washing the cloth. Most of the people 
wouldn’t have noticed (but can reason out very well( 
that different type of cloth need different amount of 
washing time which depends directly on the type of 
dirt, amount of dirt, cloth quality etc. The washing 
machines that are used today (the one not using 
fuzzy logic control) serves all the purpose of 
washing, but which cloth needs what amount of 
agitation time is a business which has not been dealt 
with properly. In most of the cases either the user is 
compelled to give all the cloth same agitation or is 
provided with a restricted amount of control.  The 
thing is that the washing machines used are not as 
automatic as they should be and can be. 
  
Keywords: Fuzzy Logic, Washing Machine, Optimization 
 
1. INTRODUCTION 
This paper aims at presenting the idea of 
controlling the washing time using fuzzy logic 
control. The paper describes the procedure that can 
be used to get a suitable washing time for different 
cloths. The process is based entirely on the principle 
of taking non-precise inputs from the sensors, 
subjecting them to fuzzy arithmetic and obtaining a 
crisp value of the washing time. It is quite clear from 
the paper itself that this method can be used in 
practice to further automate the washing machines. 
 
2. PROBLEM DEFINITION 
When one uses a washing machine, the person 
generally select the length of wash time based on the 
amount of clothes he/she wish to wash and the type 
and degree of dirt cloths have. To automate this 
process, we use sensors to detect these parameters 
(i.e. volume of clothes, degree and type of dirt). The 
wash time is then determined from this data. 
Unfortunately, there is no easy way to formulate a 
precise mathematical relationship between volume 
of clothes and dirt and the length of wash time 
required. Consequently, this problem has remained 
unsolved until very recently. Conventionally, people 
simply set wash times by hand and from personal 
trial and error experience. Washing machines were 
not as automatic as they could be. The sensor system 
provides external input signals into the machine 
from which decisions can be made. It is the 
controller's responsibility to make the decisions and 
to signal the outside world by some form of output. 
Because the input/output relationship is not clear, 
the design of a washing machine controller has not 
in the past lent itself to traditional methods of 
control design. We address this design problem 
using fuzzy logic. Fuzzy logic has been used 
because a fuzzy logic controlled washing machine 
controller gives the correct wash time even though a 
precise model of the input/output relationship is not 
available. 
 
 
3. DETAILS ABOUT THE PROBLEM 
The problem in this paper has been simplified by 
using only two variables. The two inputs are: 
 
1. Degree of dirt 
2. Type of dirt 
 
Figure (1) shows the basic approach to the 
problem. The fuzzy controller takes two inputs (as 
stated for simplification), processes the information 
and outputs a wash time. How to get these two 
inputs can be left to the sensors (optical, electrical or 
any type). The working of the sensors is not a matter 
of concern in this paper. We assume that we have 
these inputs at our hand. Anyway the two stated 
points need a bit of introduction which follows. The 
degree of dirt is determined by the transparency of 
the wash water. The dirtier the clothes, less 
transparent the water being analysed by the sensors 
is. On the other hand, type of dirt is determined by 
the time of saturation, the time it takes to reach 
saturation. Saturation is a point, at which there is no 
more appreciable change in the color of the water. 
3-02  Optimize Wash Time Of Washing Machine Using Fuzzy Logic
77
Degree of dirt determines how much dirty a cloth is. 
Where as 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. Thus a 
fairly straight forward sensor system can provide us 
the necessary input for our fuzzy controller. 
 
   
Figure 1 The shape of the membership function. 
 
4. DETAILS ABOUT THE SET APPLIED 
Before the details of the fuzzy controller are 
dealt with, the range of possible values for the input 
and output variables are determined. These (in 
language of Fuzzy Set theory) are the membership 
functions used to map the real world measurement 
values to the fuzzy values, so that the operations can 
be applied on them. Values of the input variables 
degree_of_dirt and type_of_dirt are normalized 
range -1 to 100) over the domain of optical sensor 
(see figure 2). 
The decision which the fuzzy controller makes is 
derived from the rules which are stored in the 
database. These are stored in a set of rules. Basically 
the rules are if-then statements that are intuitive and 
easy to understand, since they are nothing but 
common English statements. Rules used in this 
paper are derived from common sense, data taken 
from typical home use, and experimentation in a 
controlled environment.  
 
The sets of rules used here to derive the output are: 
1. If dirtness_of_clothes is Large and type_of_dirt 
is Greasy then wash_time is VeryLong; 
2. If dirtness_of_clothes is Medium and 
type_of_dirt is Greasy then wash_time is Long; 
3. If dirtness_of_clothes is Small and type_of_dirt 
is Greasy then wash_time is Long; 
4. If dirtness_of_clothes is Large and type_of_dirt 
is Medium then wash_time is Long; 
5. If dirtness_of_clothes is Medium and 
type_of_dirt is Medium then wash_time is Medium;  
6. If dirtness_of_clothes is Small and type_of_dirt 
is Medium then wash_time is Medium; 
7. If dirtness_of_clothes is Large and type_of_dirt 
is NotGreasy then wash_time is Medium; 
8. If dirtness_of_clothes is Medium and 
type_of_dirt is NotGreasy then wash_time is Short; 
9. If dirtness_of_clothes is Small and type_of_dirt 
is NotGreasy then wash_time is VeryShort 
 
The rules too have been defined in imprecise 
sense and hence they too are not crisp but fuzzy 
values. (See figure 3) The two input parameters after 
being read from the sensors are fuzzified as per the 
membership function of the respective variables. 
These in additions with the membership function 
curve are utilized to come to a solution (using some 
criteria). At last the crisp value of the wash_time is 
obtained as an answer. 
 
 
 
  
 
(a) 
 
 
(b) 
 
 
(c)  
Figure 2 (a) A membership for input variable Type_of_dirt. 
(b) A membership for input variable Dirtness_of_colthes. 
(c) A memberships for output Wash_ Time. 
 
The Proceedings of The 7th ICTS, Bali, May 15th-16th, 2013 (ISSN: 9772338185001)
78
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
In this paper we use a FUZZY TOOLBOX in 
MATLAB software program to present our model 
system. This tool allowed us to justify inputs and 
outputs depend on our own design. So in (figure 2,a 
and b) you can see that we apply triangular 
membership for input permeates and we get the 
resulted output as seen in (figure 2, c). 
 
5. RESULTS AND DISCUSSION 
The sensors sense the input values and using the 
above model the inputs are fuzzyfied and then by 
using simple if-else rules and other simple fuzzy set 
operations the output fuzzy function is obtained and 
using the criteria the output value for wash time is 
obtained. Figure 4 shows the response surface of the 
input output relations as determined by Fuzzy 
Interface Unit. This is the fundamental unit in which 
the application interface encodes controller 
information. 
 
 
   
Figure 4 Response surface of the input output relations. 
 
The results (the above plot) shows the way the 
machine will response in different conditions. For 
example, if we take type_of_dirt and dirtness value 
both to be 100, the wash_time which the model 
output is equivalent to 60 mins. This is quite 
convincing and appropriate 
 
6. MODEL AND CALCULATION 
 
Like a real fuzzy washing machine would, our 
model first tests how dirty the laundry is. Once it 
knows how dirty the laundry is, it can easily 
calculate how long it should wash it. To calculate 
this it uses the graph below:  
 
First it always takes a base of 10 minutes. It does 
this so that people are happy with its work even if 
they put completely clean laundry in to wash. It then 
calculates to what degree it is dirty. If it is 100% 
dirty it adds two minutes per piece of laundry. Of 
course a real washing machine would just do these 
calculations in the end, but our model does it for 
each individual piece so you can keep track of what 
is going on easier.  
So if you now add a piece which is only 50% 
dirty, it will add 50% of 2 minutes; it adds 1 minute 
instead of 2 minutes to the base of 10 minutes.  
Our washer, however, doesn't only check for dirt 
but also for grease. Laundry which is greasy has to 
be washed longer too. Since the laundry can be 
greasy and dirty at the same time, we have to put 
them on the same graph. When we do this, we get a 
graph as shown in figure 5. 
 
On the graph in figure 5 (b), you can see once 
more the base of ten minutes. The point 0,0 is where 
the laundry is completely clean; non-dirty and non-
greasy. The point 0,1 is where the laundry is non-
greasy, but dirty. The point 1,0 is where it is greasy 
but not dirty and 1,1 is greasy and dirty. The 
washing machine adds 2 minutes per piece for 100% 
dirty or 100% greasy and 4 minutes for 100% dirty 
and greasy.  
 
 
 
 
 
 
 
 
 
 
 (a) 
 
 
 
 
 
 
 
  
 
 
(b) 
 
Figure 5 (a) Choosing base time as reference. 
(b) Relation between base time and the system inputs 
 
 
 
Figure 3 Rules of the system. 
3-02  Optimize Wash Time Of Washing Machine Using Fuzzy Logic
79
So now if you have a piece of laundry which is 
100% dirty and 50% greasy, you first go to the point 
0,1 (100% dirty) and then go halfway towards point 
1,1. If the cube would contain more information, you 
could now see that the washer has to wash it for 3 
minutes more than the base of 10 minutes. 
 
After all our calculations we built the interface 
model for make test for these data. This interface 
allows any designers to adjust the optimal time 
needed for a number of clothes that could be cleaned 
(figure 6). 
On the other hand this application can work with 
our internet access. You can enter only the piece of 
dirty clothes with different degree of dirtiness. When 
you put a new piece the result or output (wash time) 
updated. We must refer to important point that we 
do optimize to get more performance, productivity, 
simplicity, and less cost. 
 
 
Figure 6 Model interface for testing wash time 
 
7. SUMMARY 
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. In other words this 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. 
Though the analysis in this paper has been very 
crude, but this clearly depicts the advantage of 
adding the fuzzy logic controller in the conventional 
washing machine. Also we do interface to validate 
our model. This interface makes the system easy to 
deal with it for user and any interested in this sector 
of engineering.  
 
 
 
 
8. FUTURE DIRECTIONS 
A more fully automatic washing machine is 
straightforward to design using fuzzy logic 
technology. Moreover, the design process mimics 
human intuition, which adds to the ease of 
development and future maintenance. Although this 
particular example controls only the wash time of a 
washing machine, the design process can be 
extended without undue complications to other 
control variables such as water level and spin speed. 
The formulation and implementation of membership 
functions and rules is similar to that shown for wash 
time. 
REFERENCES 
 
[1] Bart Kosko, Fuzzy Thinking: The New Science of 
Fuzzy Logic, Hyperion Year of Publication, 1993. 
 
[2] Bart Kosko, in Heaven in a Chip: Fuzzy Visions 
of Science & Society in the Digital Age, New 
York: Three Rivers Press, 2000. 
 
[3] Masao Mukaidono, Fuzzy Logic for Beginners, 
Singapore: World Scientific, 2001.  
 
[4] Timothy J. Ross, Fuzzy Logic with Engineering 
Applications, 3rd edition, John Wiley & Sons, 
2010 
 
[5] Bart Kosko, Fuzzy Logic, Scientific American 
journal, July 1993. 
 
[6] Badami et al., Fuzzy Logic Control Method for 
Reducing Water Consumption in a Machine for 
Washing, General Electric Company, 
Schenectady, NY, 1992. 
 
[7] Fuzzynet technical case studies 
(http://www.aptronix.com), access date; May 24, 
2010. 
 
[8] Fuzzy logic, 
(http://mathematica.ludibunda.ch/fuzzy-
logic6.html), access date: April 6, 2013. 
 
[9] Technical manual of washing machines, 
Samsung electronics, at 
(http://www.samsungelectronics.com.my/ 
washing_machine/tech_info/index.html), access 
date: April 6, 2013. 
 
[10] Weijing Zhang, Applications Engineer, 
Aptronics Incorporated, Copyright © 1992 by 
Aptronix Inc. 
The Proceedings of The 7th ICTS, Bali, May 15th-16th, 2013 (ISSN: 9772338185001)
80


Optimize Wash Time of Washing Machine Using Fuzzy Logic

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Optimize Wash Time of Washing Machine Using Fuzzy Logic

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