8/3/2019 Paper Daaam

http://slidepdf.com/reader/full/paper-daaam 1/2

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)

8/3/2019 Paper Daaam

http://slidepdf.com/reader/full/paper-daaam 2/2

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