Paper Daaam
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The 19th INTERNATIONAL DAAAM SYMPOSIUM"Intelligent Manufacturing & Automation: Focus on Next Generation of Intelligent Systems and Solutions"
22-25th October 2008
APPLYING SIGNALS OF CONTROL SYSTEM FOR TOOL WEAR MONITORING
Udiljak, T[oma]; Mulc, T[ihomir]
Abstract: Open structure of modern digital controls open up
new possibilities and prospects in monitoring of machining
systems and processes. In many cases, the combination of
digital plant signals and internal data of the machine control
system, along with advanced methods of signal analysis can
replace the external control systems. The integration of process
control software module into the machine control system allows
fast reactions should there be any process disturbances,
without any additional hardware expansion. This paper studies
the sensitivity of signals contained in the control system to the
cutting tools wear processes in face turning.
Key words: tool condition monitoring, open control, cutting
forces, turning
1. INTRODUCTION
Over the recent years, machine tools and production systems
have gone through dramatic changes caused to the greatest
extent by the development of information technology and
flexible automation. Control of high-speed machines is a very
demanding task which requires powerful and efficient systems
of process monitoring and diagnostics. Basic conditions for
good management of machining monitoring include knowledge
about the process state and undertaking of adequate actions.
The diversity of input parameters, constant development of new
materials, geometry and new tool materials, as well as highermachining speeds, with simultaneous setting of increasingly
strict standards regarding safety, complicate the control process
monitoring, so that process monitoring remains one of the most
demanding tasks in further development of machining devices.
Controller significantly affects the capabilities of machining
systems. It offers some possibilities for establishing simple,
inexpensive and easy-to-manage monitoring systems. Thus,
standard functions library can be supplemented by specific
modules for tool monitoring in order to provide the users with
new possibilities in the field of “on-line” process monitoring
with regard to avoiding collision, breakdown, overload and
monitoring of tool wear. However, the sensitivity and
applicability of such systems in various processing conditions
need to be checked for every individual case.
2. ESTIMATION OF THE FEED CUTTING FORCE
2.1 Modeling of the Feed and Main Drive SystemReliability of monitoring process is strongly dependent on
quality of information extracted from the measuring signals.
With adequate procedure it is possible to extract the influence
of inertial forces, influence of friction of moving components
(eq. guideways, bearings, spindles), and influence of static
coefficient of friction. Mechanical chain of servo axis consists
of slider, transmission and electro motor, Fig. 1.
Taking in consideration the momentum of inertia, momentum
of friction on the motor side, and momentum of load, the
mechanical equation for the i-th axis could be as follows:
pmiTmimimimi T T q J T ++= &&
(1)
Fig.1 Mechanical chain of the servo axis
The momentum of load, reduced to motor axis, is expressed as:
1 pmi poi
i
T T N
= , (2)
TmiT represents momentum of friction on the motor side,
poiT momentum of load, andi N transmission ratio.
Momentum of load consists of inertial part, friction resistance
TiT , gravitational influence G , and cutting resistance force
riT :
GT T q J T riTioioi poi+++= && (3)
For horizontally arranged feed drives, the influence of
gravitation could be neglected, G=0. The same could be done
for vertically arranged feed drives with compensation (electricalor mechanical) of slider weight.
The friction is very complex phenomena and it is difficult to
express it mathematically. According to [ ], the losses caused by
friction could be presented as follows:
3
03010 )( iiT iiT iToiTi qT qT qsignT T &&& ++= (4)
)( 00 iiT qsignT & - dry friction, (Coulomb’s friction)
iiT qT 01& - viscous friction depending on velocity
and temperature3
03 iiT qT & - friction in guideways
Motor torque must overcome the resistance cutting forces,inertial forces and friction forces. The resistance cutting forces
are:
ri firi F K T = (5)
where coefficient fiK depends on transmission. Having in
mind the transmission ratio it could be written:
oiimi q N q = ,oiimi q N q && = ,
oiimi q N q &&&& = . (6)
By including the equations (2), (3), (4), (5) and (6) in equation
(1) we obtain:
ri
i
iT
i
miiTemieimieimiT
N qT
N qsignT q Dq J T
11)(
3
30++++= &&&&&
(7)
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Motor torque is proportional to current:
aimimi I K T = (8)
Including equation (8) in equation (7) results with mathematical
model of servo axis, (9):
rii
fi
iT i
miTeoimieimieiaimi F N
K
qT N qsignT q Dq J I K ++++=
3
3
1
)(&&&&&
(9)
Each particular realization of servo axis needs estimation of
influence of individual load component.
2.2 Estimation of the Cutting ForceFor proper estimation of parameters by using control system
signals, equation (9) should be modified in matrix form:
i
T
ii t t y θ )()( Ψ= (10)
)()( t I t y aii = - output vector (11)
1,),(,,)( 3mimimimi
T i signt ω ω ω ω &=Ψ - measuring vector (12)
=
fi
imi
ri
mii
iT
mi
Teo
mi
ei
mi
eiT
i
K N K
F
K N
T
K
T
K
D
K
J ,,,, 31
θ (13)
-vector of parameters for i-th joint
Estimation of n unknown in parameters vector for i-th joint
demands acquisition of at least n measuring values in various
measuring points: t=k T 0 , k=1, 2,.....4,. By applying least square
method, the equation (10) gives following solution:
[ ]$∆ Φ Φ Φ=−
T T Y 1(14)
Equation (14) is suitable for on-line estimation of the dynamic
parameters. The estimated parameters are consecutively
compared with previous values by applying equation 15:
d n n
n
$( $ $ )
$∆
∆ ∆
∆=
− 0
0
(15)
The process could be monitored by analyzing the magnitude
and direction, sign d n( $ )∆ , of deviation of the estimated
parameter. The change of parameter is a change generated in
the observed system which does not couse imidiate systemfailure, but has negative impact on system behaviour.
3. EXPERIMENT PLANNING
The aim of the experiment is to determine the sensitivity of
drive system parameters to tool edge wear in process of fine
turning. The turning unit was fitted within the unit of special
Fig. 2. Unit for fine turning (SAS-Zadar)
machine tool controlled by Siemens digital control system ,
Sinumerik 840D, Fig. 2.
4. RESULT ANALYSIS
During the period of automatic working of the system (till the
tool wear out) the system stores the correction values, i.e. tool
wear values suitable for wear curve, Fig. 3.
Fig.3 Dependence of relative power consumption on tool wear
It has been shown that tool wear mostly influence main spindle,
i.e. main drive. Current signal of the main drive shows increase
of approx. 30% during increase of tool wear. It is a significant
increase and could be used for judging on tool condition. Theexperimental results confirm that feed drive signal is not
suitable for the judging on tool condition in fine turning.
Because the share of power necessary to prevail friction and
mechanical loses in feed drive is very high, it is not possible to
isolate the power changes in feed drive that are consequence of
increase in tool wear.
5. CONCLUSION
Open control with digital drive system open up new
possibilities and prospects in “on-line” monitoring of the
machining systems. By combination of digital drive systems
with additional information from the control system, methods
of isolating characteristic features from the signal andsophisticated data processing technologies, high reliability and
safety of signal analysis is achieved. Further development of
such systems, and the method of isolating characteristic
features, at the same time applying the technologies of artificial
intelligence, present a significant step towards realizing a
simple, reliable, user friendly way of monitoring of cutting
tools and machining processes.
6. REFERENCES
1. Isermann R., Uberwachtung und Fehlerdiagnose, VDI-
Verlag, Dusseldorf 1994.
2. Stute G., Regelung an Werkzeugmaschinen, Carl Hanser
Verlag Munchen Wien 1981.3. Cuppini D., D'Errico G., Rutelli G., Tool wear monitoring
based on cutting power measurement, Wear, 139(1990)
303-311.
4. Damodarasamy S., Raman S., An inexpensive system for
classifying tool wear states using pattern recognition, Wear,
170(1993) pp.149-160
5. Mulc, T., Udiljak, T., Čuš, F., Milfelner, M.. Monitoring
Cutting Tool Wear Using Signals from the Control System,
Strojniški vestnik, 50(2004)12, ISSN 0039-2480, p. 568-579
6. Brezak, D., Udiljak, T., Mihoci, K., Majetic, T., Novakovic,
B., Kasac, J.(2004). Tool Wear Monitoring Using Radial
Basis Function Neural Network, International Joint
Conference on Neural Networks & IEEE International
Conference on Fuzzy Systems, Budapest 2004,
Author: prof.dr Toma UDILJAK, FSB-Zagreb, Tihomir
MULC, chief of research department, SAS-Zadar dd. Marka
Oreskovica 1, 2300 Zadar, Croatia, Tel+385 23 200 128
0,14
0,12
0,1
0,08
0,06
0,04
0,02
00 0,1 0,2 0,3
Tool flank wear VB, mm
R e l a t i v e p o w e r c o n s u m o t i o n
Feed drive
Main drive