د عامر


Al-Khwarizmi Engineering Journal, Vol. 10, No. 2, P.P. 

 

Utilizing a Magnetic Abrasive Finishing Technique (M
Adaptive 

Department of Automated Manufacturing Engineering / Alkhwarzmi College of Engineering/ University of Baghdad
E-mail: amermoosa@kecbu.uobaghdad.edu.iq

(Received  3 October 

 
Abstract 
 

An experimental study was conducted for 
abrasive finishing technique (MAF) on brass 
process where the cost is high and much more susceptible to surface damage as compared to other materials. Four 
operation parameters were studied, the gap between the work
generate the flux, the rotational Spindale speed and amount of abrasive powder size considering constant linear feed 
movement between machine head and workpiece. Adaptive 
for evaluation of a series of experiments an
optimized and usefully achieved by obtained results 
predicted by model simulation and that of direct measure is 2.0222 %

 
Keywords:Magnetic abrasive finishing (MAF), adaptive 
roughness, (RMSE) root mean square error, membership
 
 
1. Introduction 

 
Magnetic abrasive finishing (MAF) is a super 

finishing method comparing with the other 
traditional operation of surface finishing
grading, lapping etc., due to the attitude
magnetic flux controlling to its cutting force 
combining with flexibility of magnetic 
and brush shape which minimize the possibility 
micro cracks on the surface of the workpiec
(MAF) the workpiece is held between the 
machine table that imposed to directional feed rate 
and the magnetic poles of the head nose (inductor) 
where the gap between the workpiece 
nose is filled with abrasive particle powder that 
shaped by the flux. This configuration act as 
smooth grinding brush and behaves
multipoint cutting tool operation [1][2].Wang 
Hu [3] described the principle of the process 

 

Khwarizmi Engineering Journal, Vol. 10, No. 2, P.P. 49- 56 (2014) 
 

Utilizing a Magnetic Abrasive Finishing Technique (MAF
Adaptive Nero Fuzzy(ANFIS) 

 
Amer A. Moosa 

Department of Automated Manufacturing Engineering / Alkhwarzmi College of Engineering/ University of Baghdad
amermoosa@kecbu.uobaghdad.edu.iq 

 
3 October 2013; accepted  15 April 2014) 

 

for measuring the quality of surface finishing roughness 
 plate which is very difficult to be polish by a conventional machining 

is high and much more susceptible to surface damage as compared to other materials. Four 
gap between the work piece and the electromagnetic inductor

speed and amount of abrasive powder size considering constant linear feed 
head and workpiece. Adaptive Neuro fuzzy inference system  (ANFIS) 

nd a verification with respect to specimen roughness 
obtained results were an average of the error between the surface roughne
t of direct measure is 2.0222 %. 

finishing (MAF), adaptive Neuro fuzzy inference system (ANFIS), (AR) average 
membership function (MF).  

(MAF) is a super 
the other 

finishing like 
attitude of the 

magnetic flux controlling to its cutting force 
 powder 

e possibility of 
the surface of the workpiece. At 

between the 
t imposed to directional feed rate 

(inductor) 
 and the 

powder that 
This configuration act as 

smooth grinding brush and behaves like 
Wang and 

of the process and 

finishing characteristics of unbounded magnetic 
abrasive within internal tubing finishing. They 
also deal with the design and fabrication of MAF 
setup for finishing three kinds of materials tubing, 
such as (Ly12) aluminum alloy, (
steel and (H62) brass. Geeng et a
described the process principles and its 
characteristics of unbounded magnetic abrasive 
within cylindrical magnetic abrasive finishing. 
They investigated the finishing characteristics on 
surface roughness and material removal as well as 
their mechanisms.Mori et al. [5] examined the 
magnetic field, acting forces and provides a 
fundamental understanding of the process 
mechanism. A planar type process for a non
magnetic material, stainless steel, was 
introduced.Maiboroda and Khomenko [6] 
investigated how the frictional force between
magnetic-abrasive powders and(Ti

  
Al-Khwarizmi 
Engineering   

Journal 

AF) Via 

Department of Automated Manufacturing Engineering / Alkhwarzmi College of Engineering/ University of Baghdad 

roughness using magnetic 
conventional machining 

is high and much more susceptible to surface damage as compared to other materials. Four 
piece and the electromagnetic inductor, the current that 

speed and amount of abrasive powder size considering constant linear feed 
(ANFIS) was implemented 

 change has been 
between the surface roughness 

(ANFIS), (AR) average 

finishing characteristics of unbounded magnetic 
rasive within internal tubing finishing. They 

also deal with the design and fabrication of MAF 
setup for finishing three kinds of materials tubing, 

(316L) stainless 
Geeng et al. [4] Also 

and its finishing 
characteristics of unbounded magnetic abrasive 
within cylindrical magnetic abrasive finishing. 
They investigated the finishing characteristics on 
surface roughness and material removal as well as 

al. [5] examined the 
magnetic field, acting forces and provides a 
fundamental understanding of the process 
mechanism. A planar type process for a non-
magnetic material, stainless steel, was 
introduced.Maiboroda and Khomenko [6] 

onal force between 
Ti) alloy surface 

mailto:amermoosa@kecbu.uobaghdad.edu.iq
mailto:amermoosa@kecbu.uobaghdad.edu.iq


 Amer A. Moosa                                Al-Khwarizmi Engineeri
 
varies during magnetic-abrasive machining in 
relation to the technological parameters. They 
studied experimentally the effects of five types of 
powders with different grain sizes.  

Dhirendra et al. [7] Reported the experimental 
findings about the forces acting during MAF and 
provides correlation between the surface finish 
and the forces. 
Nazar [8]Reported the experimental findings 
the forces acting during MAF and provides 
correlation between the surface finish and this
force. It is concluded that forces and the change in 
surface roughness (ΔRa) increase with 
increasing of the current value impose 
electromagnet (or magnetic flux density) and 
decreasing in the working gap. The researchers 
filled the working gap with a homogeneous 
mixture of silicon carbide abrasives and 
ferromagnetic iron particles at a ratio of 25:75 
weight, respectively. 

In this paper a model based on (ANFIS) for 
(AMF) process was performed using brass metal 
workpieces to estimate its surface roughness
an adoption of objective simulation is carried
to optimize the solution obtained by the model.

 
 

2. Experiments 
 
An electromagnetic inductor was designed and 

manufactured to implement (MAF) on workpiece 
by milling machine as shown in Fig. 1. Consist of 
(1) inductor of steel road wrapped with a coil of 
wire (2) work piece (brass) (3) D.C power supply 
(4) machine spindle (5) inductor body (6) shank 
(7) milling table. While Fig. 2. Shows (1) 
magnetic powder particle(2) abrasive brush (3) 
gap between head nose and work piece.  

The inductor material is low carbon steel (C15) 
with a cross section of (A) =14cm2 and long of 
(L)is (75mm) and copper wire diameter of 
(ø)=1mm and number of turns is (N 2400) while 
the powder is (40%) iron and (60%) quartz 
centered in (1200 c°) and then were crushed to 
(150μm) of approximated diameter shows under 
SEM, JSM-6360 LV scanning microscope
in Fig. 3. 

The process parameters has been changed 
during the operation as follows: the working gap 
from (10 to 20)mm,current responsible to change 
the flux from (1.5 to 3.5) Amp, volume of the 
powder from (2 to 4) cm3 and the rotation speed 
from (175 to 5250) RPM with a feed rate of (30) 
mm/min. 

 

Khwarizmi Engineering Journal, Vol. 10, No. 2, P.P. 

50 
 

abrasive machining in 
relation to the technological parameters. They 
studied experimentally the effects of five types of 

the experimental 
findings about the forces acting during MAF and 
provides correlation between the surface finish 

the experimental findings of 
the forces acting during MAF and provides 

tween the surface finish and this 
change in 

surface roughness (ΔRa) increase with the 
value impose to the 

electromagnet (or magnetic flux density) and the 
The researchers 

with a homogeneous 
mixture of silicon carbide abrasives and 

of 25:75 in 

In this paper a model based on (ANFIS) for 
(AMF) process was performed using brass metal 

ghness and 
carried out 

solution obtained by the model. 

An electromagnetic inductor was designed and 
manufactured to implement (MAF) on workpiece 
by milling machine as shown in Fig. 1. Consist of 
(1) inductor of steel road wrapped with a coil of 
wire (2) work piece (brass) (3) D.C power supply 

le (5) inductor body (6) shank 
(7) milling table. While Fig. 2. Shows (1) 
magnetic powder particle(2) abrasive brush (3) 

The inductor material is low carbon steel (C15) 
with a cross section of (A) =14cm2 and long of 
L)is (75mm) and copper wire diameter of 

1mm and number of turns is (N 2400) while 
the powder is (40%) iron and (60%) quartz 

) and then were crushed to 
m) of approximated diameter shows under 

6360 LV scanning microscope shown 

The process parameters has been changed 
during the operation as follows: the working gap 
from (10 to 20)mm,current responsible to change 
the flux from (1.5 to 3.5) Amp, volume of the 
powder from (2 to 4) cm3 and the rotation speed 

75 to 5250) RPM with a feed rate of (30) 

 
Fig. 1. Magnetic abrasive Devices.

 
 

 
Fig. 2. Magnetic brush of electromagnet poles.

 
 

                                       
 Fig. 3. SEM (X 100) of magnetic abrasive particles.
 
 
 
 
 

3 

2 

ng Journal, Vol. 10, No. 2, P.P. 49- 56 (2014) 

 

Fig. 1. Magnetic abrasive Devices. 

 

Fig. 2. Magnetic brush of electromagnet poles. 

 

Fig. 3. SEM (X 100) of magnetic abrasive particles. 

1 



 Amer A. Moosa                                Al-Khwarizmi Engineering Journal, Vol. 10, No. 2, P.P. 49- 56 (2014) 
 

51 
 

The work piece is divided into nine parts represent 
the three level configuration as shown in Tables (1and 
2) respectively. Some of them were operated 
traditionally and the other has been simulated as an 
artificial intelligent base of (ANFIS) (Fig. 4.) Each 
piece is fixed in such a way that the center of the work 
piece  coincides with the center of the head nose. The 
required gap between them is filled with powder 
abrasive particles. After each experiment, the change in 
surface roughness value (ΔRA) is determined by 
measuring (RA) via tester TR220 (Fig. 5). 
 
Table 1, 
Three Level parameters. 

 
 
Table 2, 
 Parameters configuration. 

 
 

 

 

 

 

 
 

Fig. 4. Photo of some of the work pieces. 
 
 

 
 

Fig. 5. Surface roughness tester, (TR-220). 
 
 

3. ANFIS Optimization Technique  
 
ANFIS is a hybrid predictive method that 

combines the neural network tool to the fuzzy 
approaches to generate mapping scheme between 
input parameters and output results. The structure 
of this model consist of five layers, each layer is 
constructed by several nods. ANFIS behave just 
like the neural network where the structure of 
each layer is obtained by the node of the previous 
layers as shown in Fig. 6.A Numbers of initiating 
data among all data set have been selected as 
training data, and then the trained network was 
validated by other data set. The root mean square 
error (RMSE) is applied to this work for 
inspection purposes of the trained model as 
follows : 

RMSE = √ [1/Tr∑ TRi = 1(ti − Yi)^2]               …(1) 
Where (Tr) are the total number of training 
samples, (ti)is the real output value, and (Yi) is 
the ANFIS output value in training from matlab 

Parameters Units Levels 

Rotational speed (P1) Rpm 175 - 350 - 525 

Coil current (P2) Amp 1.5 - 2.5 - 3.5 

Volume of powder (P3) cm
3 2.0 – 3.0 – 4.0 

Working gap (P4) Mm 1.0 - 1.5 - 2.0 

Exp. Factors 

Rotati-
onal 
speed 
(P1) 
(rpm) 

Coil 
current 
(P2)               
(Amp) 

Volume 
of 
powder 
(P3) 
(Cm3) 

Worki
ng gap 
(P4) 
(mm) 

1 175 1.5 2 2 

2 350 2.5 3 3 

3 525 3.5 4 4 

4 350 1.5 4 4 

5 525 2.5 2 2 

6 175 3.5 3 3 

7 525 1.5 3 3 

8 175 2.5 4 4 

9 350 3.5 2 2 



 Amer A. Moosa                                Al-Khwarizmi Engineeri
 
platform using fuzzy tool representing by ANFIS 
guide user interface with the adoption the attitude 
of E AND C. A fuzzy inference system of sugeno 
model is conducted as follows: 

A two rule sugeno ANFIS has rules of the 
form  
If x is A1 and y1 is B1 THEN  
f = p1 x + q1 y +r1                                         

If x is A2 and y2 is B2 THEN  
f = p2 x + q2 y + r2                                        

For the training network fig 3E the RMSE was set 
to (0.02) and the iteration number was (30)epochs 
where the layers act as follow: 
Layer 1 (Fuzzification layer): It transforms the 
crisp inputs 
(Xi) to linguistic labels (   , like small, medium, 
large etc.) with a degree of membership. The 
output of node (Oij) or could represent by (ki) is 
expressed as follows: 
Oij1=Ok1= μij(Xi) , i= 1...m , j = 1...n-         

Where (μij)is the (jth) membership function 
the input (Xi). 
Layer 2 (Product layer): For each node (k) in this 
layer, the 
The output represents a weighting factor (e) 
(firing strength) of the rule 
(k). The output (Wk) is the product of all its 
inputs as follows: 

 

                                                    
Fig. 6.

4. Results and Discussion  
 
After using several types of member

function, the Gaussian function Fig. 7. W
selected to be more accumulate with modeling 
behavior as follows: 
 

Khwarizmi Engineering Journal, Vol. 10, No. 2, P.P. 

52 
 

platform using fuzzy tool representing by ANFIS 
guide user interface with the adoption the attitude 

f sugeno 

A two rule sugeno ANFIS has rules of the 
  

                                        …(2) 

                                        …(3)  

For the training network fig 3E the RMSE was set 
to (0.02) and the iteration number was (30)epochs 

Layer 1 (Fuzzification layer): It transforms the 

, like small, medium, 
large etc.) with a degree of membership. The 
output of node (Oij) or could represent by (ki) is 

         …(4) 

th) membership function for 

Layer 2 (Product layer): For each node (k) in this 

The output represents a weighting factor (e) 

(k). The output (Wk) is the product of all its 

Ok2 = Wk = u1e1(X1)u2e2(X2)…um em
                                                                        
K=1…n ,e1,e2…em , =1…n 
 
Layer 3 (Normalized layer): The output of each 
node (k) in 
This layer represents the normalized weighting 
factor (Wk|) of the 
(kth) rule as follows: 
Ok3 = Wk| = (Wk) / (W1+W2+…Wn)

Layer 4 (De-fuzzification layer): Each node of 
this layer gives a weighted output of the first order 
TSK-type fuzzy if then rule as follows:
Ok4 = Wk| fk                                         

Where fk represents the output of (kth) TSK 
(Takagi-sugeno-Kang)-type fuzzy rules.
Layer 5 (Output layer): This single
represents the overall output (Y) of the network as 
the sum of all weighted outputs of the rules:
O5 = Y = ∑ 

nk = 1 (Wk| fk)                            
It is inevitable to consider that the fuzzy set is a 
decision-making process comparison with ANFIS, 
which raise the ability of the knowledge base 
decision-making system with its capability to 
produce the rules for simulation process.
 

 

Fig. 6. ANFIS network structure. 
 
 

embership 
Fig. 7. Was 

selected to be more accumulate with modeling 

U(x)=exp[-(x-c)^2/2σ^2]                                

Where: 
U(X) represents the MF input and x,
parameters for Gaussian function shape
selected from the platform. 

ng Journal, Vol. 10, No. 2, P.P. 49- 56 (2014) 
Ok2 = Wk = u1e1(X1)u2e2(X2)…um em(Xm)                  
                                                                        …(5) 

Layer 3 (Normalized layer): The output of each 

This layer represents the normalized weighting 

Ok3 = Wk| = (Wk) / (W1+W2+…Wn)          …(6) 

fuzzification layer): Each node of 
this layer gives a weighted output of the first order 

type fuzzy if then rule as follows: 
                                                  …(7) 

Where fk represents the output of (kth) TSK 
type fuzzy rules. 

Layer 5 (Output layer): This single-node layer 
represents the overall output (Y) of the network as 
the sum of all weighted outputs of the rules: 

                            …(8) 

It is inevitable to consider that the fuzzy set is a 
making process comparison with ANFIS, 

which raise the ability of the knowledge base 
making system with its capability to 

simulation process. 

 

                                …(9) 

the MF input and x,σ,c are 
function shape that 



 Amer A. Moosa                                Al-Khwarizmi Engineeri
 

Fig. 7. 

The experimental data are mapped to ANFIS 
and evaluates as patterns tanning/testing 
vectors where the training and testi
performance for ANFIS was checked by 
equation number (1). 

The topology of the number of sets and epoch
dedicates the number of rules used by 
ANFIS that reach 81 rules and its relate to the 
number of data and a comparison of experimental
training /testing measured with those estimated by 
           
 

                  

Khwarizmi Engineering Journal, Vol. 10, No. 2, P.P. 

53 
 

 
 

 Gaussian function selection box. 
 

to ANFIS 
/testing formed 

and testing 
ked by the 

The topology of the number of sets and epochs 
used by sugeno 

and its relate to the 
experimental 

training /testing measured with those estimated by 

ANFIS network as shown in F
respectively. 

It is obvious to recognize the simulation fitting 
that shows good agreement for a wide range of 
acceptability and reliability.  

Table 3. Show the RMSE process result of 
ANFIS model for a different type
Fig. 10. Show the training error curve.
After carrying out the process some of work 
pieces are shown in Fig. 11. 

 
 

Fig. 8. Training session. 

ng Journal, Vol. 10, No. 2, P.P. 49- 56 (2014) 

Fig. (8 and 9) 

simulation fitting 
a wide range of 

the RMSE process result of 
a different type of MF. Where 

the training error curve. 
carrying out the process some of work 



 Amer A. Moosa                                Al-Khwarizmi Engineeri
 

                                                             
Fig. 

 
  

Fig. 10. The error curve during the ANFIS process
 
 
Table 3, 
Parameters results of ANFIS model . 

Current mAmp Gap mmm Powder cc

1.5 1 2 
1.75 1.12 2.25 
2 1.24 2.5 
2.25 1.36 2.75 
2.5 1.48 3 
2.75 1.6 3.25 
3 1.72 3.5 
3.25 1.84 3.75 
3.5 2 4 
   
   
 

Khwarizmi Engineering Journal, Vol. 10, No. 2, P.P. 

54 
 

 

Fig. 9. Verification session. 

 
 

Fig. 10. The error curve during the ANFIS process. 

Powder cc Speed r/rev Rf u error 

175 0.13 2.1 
218.75 0.21 2.4 
262.5 0.16 2.2 
306.25 0.111 1.7 
350 0.136 1.2 
393.75 0.123 2.6 
437.4 0.113 2.1 
481.25 0.209 1.4 
525 0.117 2.5 
  Sum.18.2
  18.2/9=2.02

ng Journal, Vol. 10, No. 2, P.P. 49- 56 (2014) 

Sum.18.2 
18.2/9=2.02 



 Amer A. Moosa                                Al-Khwarizmi Engineeri
 

                 
Fig. 11. Pieces show the location of smooth mirror

 
 
5. Conclusion and Future Work 

 
The most effective factor acting on ANFIS 

the accuracy of the simulation model which is 
depend on the type of MF of Gaussian shape and 
the value of RMSE which is presume to be low on 
this operation which is mean more reliability
acceptability.The value number of Gaussian MF is 
twice the number of inputs, which are 
current research and need to be examining
to find if this happened by chance or there is a 
case of correlation. To explain this phenomenon,
the researcher intends to approve in future 
work.The number of RMSE is dedicated to make 
the training epochs continue until it become 
the target and that corresponding to the amount of 
epochs been settled and this arise the question
the epochs cycle change with the respect to time 
series how could this situation effect the 
prediction confidence.This neuro fuzzy model 
enhance the result with respect to other model 
such as the traditional neural network because
its rabidadaptation too bserve the structure 
process, also it is adapted to the increasing or 
adding  of new inputs parameters regards to the 
expansion ability of fuzzy sets numbers and
learning rules.More expected future result 
evaluating a good (quality and quantity) data 
sampling to obtain smaller results error (less than 
the acquired value of 0.022) in addition to how 
the flux shape could effect the powder figuration.

 
 

 

 

 

Khwarizmi Engineering Journal, Vol. 10, No. 2, P.P. 

55 
 

show the location of smooth mirror. 

on ANFIS is 
the accuracy of the simulation model which is 

Gaussian shape and 
to be low on 

reliability and 
Gaussian MF is 

inputs, which are four for 
examining more 

by chance or there is a 
ain this phenomenon, 

in future 
s dedicated to make 

continue until it become below 
amount of 

arise the question if 
the respect to time 

fect the 
euro fuzzy model 

enhance the result with respect to other model 
because of 

structure of the 
also it is adapted to the increasing or 

regards to the 
s numbers and 

expected future result is to 
a good (quality and quantity) data 

results error (less than 
in addition to how 

ld effect the powder figuration. 

6. Reference  
 

[1] S.C. Jayswal, V.K. Jain, and P.M. Dixit, 
“Modeling and simulation of magnetic 
abrasive finishing process”, International 
Journal of Advanced Manufacturing 
Technology, Vol.26 (2005), pp. 477

[2] Ching-Tien Lin, Lieh-Dai Yang, and Han
Ming Chow, “Study of magnetic abrasive 
finishing in free-form surface operations 
using the Taguchi method”, International 
Journal of Advanced Manufacturing 
Technology, (2006). 

[3] Yan Wang, and Dejin Hu, ”Study on the 
inner surface finishing of tubing by magnetic 
abrasive finishing”, International Journal of 
Machine Tools & Manufacture, Vol.45 
(2005), pp. 43–49. 

[4] Geeng-Wei Chang, Biing-Hwa, and Yan, 
Rong-Tzong Hsu, “Study on cylindric
magnetic abrasive finishing using unbounded 
magnetic abrasives”, International Journal of 
Machine Tools & Manufacture, Vol.42 
(2002),  pp. 575–583. 

[5] T. Mori, K. Hirota, and Y. Kawashima, 
“Clarification of magnetic abrasive finishing 
mechanism”, Journal of Materials Processing 
Technology, Vols.143-144 (2003), pp. 682
686. 

[6] V. S. Maiboroda and E. A. Khomenko, 
“Tribotechnical characteristics of 
ferroabrasive powders in magnetic
machining”, Journal of Powder Metallurgy 
and Metal Ceramics, Vol.42 (200

[7] Dhirendra K. Singh, V.K. Jain, and V. 
Raghuram., “Parametric study of magnetic 
abrasive finishing process”, Journal of 
Materials Processing Technology, Vol.149 
(2004), pp. 22–29. 

[8] Nazar kais M.naif“Study on the Parameter 
Optimization in Magnetic Abrasive Polishing 
for Brass Plate Using Taguchi Method 
“journal of college of engineering, 
vol,3(2011). 

[9] Jae-SeobKwak and Tae-Kyung Kwak, 
“Parameter Optimization in Magnetic 
Abrasive Polishing for Magnesium Plate”, 
IEEE, Vol.5 (2010), pp.544–547.

 

ng Journal, Vol. 10, No. 2, P.P. 49- 56 (2014) 

S.C. Jayswal, V.K. Jain, and P.M. Dixit, 
“Modeling and simulation of magnetic 
abrasive finishing process”, International 
Journal of Advanced Manufacturing 
Technology, Vol.26 (2005), pp. 477–490. 

Dai Yang, and Han-
Ming Chow, “Study of magnetic abrasive 

form surface operations 
using the Taguchi method”, International 
Journal of Advanced Manufacturing 

an Wang, and Dejin Hu, ”Study on the 
inner surface finishing of tubing by magnetic 
abrasive finishing”, International Journal of 
Machine Tools & Manufacture, Vol.45 

Hwa, and Yan, 
Tzong Hsu, “Study on cylindrical 

magnetic abrasive finishing using unbounded 
magnetic abrasives”, International Journal of 
Machine Tools & Manufacture, Vol.42 

T. Mori, K. Hirota, and Y. Kawashima, 
“Clarification of magnetic abrasive finishing 

of Materials Processing 
144 (2003), pp. 682–

V. S. Maiboroda and E. A. Khomenko, 
“Tribotechnical characteristics of 
ferroabrasive powders in magnetic-abrasion 
machining”, Journal of Powder Metallurgy 

2 (2003), pp.9-10.  
Dhirendra K. Singh, V.K. Jain, and V. 
Raghuram., “Parametric study of magnetic 
abrasive finishing process”, Journal of 
Materials Processing Technology, Vol.149 

Study on the Parameter 
ic Abrasive Polishing 

for Brass Plate Using Taguchi Method 
llege of engineering, 

Kyung Kwak, 
“Parameter Optimization in Magnetic 
Abrasive Polishing for Magnesium Plate”, 

547. 



 )2014( 49- 56، صفحة 2، العدد10دجلة الخوارزمي الھندسیة المجلمعامر عبد المنعم موسى                                                       

56 
 

 
 
 التنفیع باسلوب العصیبات المضببة لتطویر التشغیل بطریقة التنعیم باالحتكاك المغناطیسي

 
 عامر عبد المنعم موسى

جامعة بغداد/ كلیة الھندسة الخوارزمي / قسم ھندسة التصنیع المؤتمت   
  amermoosa@kecbu.uobaghdad.edu.iq :البرید االلكتروني

  
 
 

 الخالصة
 

الى تم اجراء دراسھ تطبیقیھ لقیاس نوعیة السطوح المنعمھ بطریقة االحتكاك المغناطیسي على عینات لوحیھ من البراص المعروفھ بكونھا تحتاج 
  .التنعیم لمثل ھذا النوع من المعادن من كلف تشغلیھ ومھاره عالیھ وضروف تشغیلیھ خاصھمتطلبات خاصھ في عملیات 

بین القطب تم اختیار مجموعھ من المعایر المؤثره على ضروف التشغیل لمثل ھذا النوع من التنعیم والذي یتم عن طریق وضع مادة ابریھ لتملى الفراغ 
اذ تعتبر ھذة الفجوة من المعایر الحساسھ في ھذا النوع من التشغیل فضال عن شدة التیار المستخدم ، مراد تشغیلھا المغناطیسي الدوار للماكنھ وبین العینھ ال

  .لتولید الحث المغناطیسي وكمیة المادة االبریھ المستخدمھ وسرعة دوران القطب المغناطیسي الحامل لمادة التنعیم
على تولیفة مثلى من المعایر ضمن تشكیلة المعایر المستخدمھ في الحالھ العملیھ ووصل االنتفاع الى تم االنتفاع من اسلوب العصیبات المضببھ للحصول 

  % .٢.٠٢٢٢تولیفة مقاربھ للتجارب العملیھ بنسبة خطا 
الكلفھ التي التحتمل اجراء  وان التولیفھ قابلة للتغیر بمعایر مختلفة دون الرجوع الى التطبیق العملي مما یوفر الوقت والمجھود خاصھ للمعادن العالیھ

  .التجارب العملیھ دون التاكد من صحة المعطیات

 

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