Applying signals of control system in tool wear monitoring.

Udiljak, Toma ; Mulc, Tihomir ; Ciglar, Damir 等

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 (Isermann, 1994, Stute, 1981, Mulc et al. 2004). The diversity of input parameters, constant development of new materials, geometry and new tool materials, as well as higher machining 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 System

Reliability 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

[FIGURE 1 OMITTED]

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:

[T.sub.mi] = [J.sub.mi][[??].sub.mi] + [T.sub.Tmi] + [T.sub.pmi] (1)

The momentum of load, reduced to motor axis, is expressed as:

[T.sub.pmi] = 1/[N.sub.1] [T.sub.poi] (2)

[T.sub.Tmi] represents momentum of friction on the motor side, T momentum of load, and N transmission ratio.

[T.sub.poi] Momentum of load consists of inertial part, friction resistance [T.sub.Ti], gravitational influence G, and cutting resistance force [T.sub.ri]:

[T.sub.poi] = [J.sub.oi][[??].sub.oi] + [T.sub.Ti] + [T.sub.ri] + G (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 (electrical or mechanical) of slider weight.

The friction is very complex phenomena and it is difficult to express it mathematically. According to [Isermann, 1994], the losses caused by friction could be presented as follows:

[T.sub.Ti] = [[T].sub.Toi]sign([[??].sub.0i]) + [[T.sub.T1i][[??].sub.0i] + [[T.sub.T3i][[??].sup.3.sub.0i] (4)

[[T.sub.T0i]sign[[??].sub.0i]--dry friction, (Coulomb's friction)

[T.sub.T1i][[??].sub.0i]--viscous friction depending on velocity and temperature

[T.sub.T3i][[??].sup.3.sub.0i]--friction in guideways

Motor torque must overcome the resistance cutting forces, inertial forces and friction forces. The resistance cutting forces are:

[T.sub.ri] = [K.sub.fi][F.sub.ri] (5)

where coefficient [K.sub.fi] depends on transmission. Having in mind the transmission ratio it could be written:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)

By including the equations (2), (3), (4), (5) and (6) in equation (1) we obtain:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)

Motor torque is proportional to current:

[T.sub.mi] = [K.sub.mi] [I.sub.ai] (8)

Including equation (8) in equation (7) results in mathematical model of servo axis, (9):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9)

Each particular realization of servo axis needs estimation of influence of individual load component.

2.2 Estimation of the Cutting Force

For proper estimation of parameters by using control system signals, equation (9) should be modified in matrix form:

[y.sub.i](t) = [[PSI].sup.T.sub.i](t)[[theta].sub.i] (10)

[y.sub.i](t) = [I.sub.a](t) - output vector (11)

[[PSI].sup.T.sub.i](t) = [[[??].sub.mi], [[omega].sub.mi], sign([[omega].sub.mi]), [[omega].sup.3.sub.mi],1] - measuring vector (12)

[[theta].sup.t.sub.i] = [[J.sub.ei]/[K.sub.mi], [D.sub.ei]/[K.sub.mi], [T.sub.Teo1]/[K.sub.mi], [T.sub.T3i/[N.sub.i][K.sub.mi], [F.sub.ri]/[K.sub.mi[N.sub.i/[K.sub.fi] (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.sub.0], k=1, 2,.... 4,. By applying least square

method, the equation (10) gives following solution:

[DELTA] = [[[PHI].sup.T][PHI]].SUP.-1] [[PHI].sup.T] Y (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[DELTA] = ([absolute value of [[DELTA].sub.n] - [absolute value of [[DELTA].sub.n0])/[[DELTA].sub.n0] (15)

The process could be monitored by analyzing the magnitude and direction, sign(d[[DELTA].sub.n]), of deviation of the estimated parameter. The change of parameter is a change generated in the observed system which does not cause immediate system failure, but has negative impact on system behavior.

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 machine tool controlled by Siemens digital control system (Mulc et al., 2004), Sinumerik 840D, Fig. 2.

[FIGURE 2 OMITTED]

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.

[FIGURE 3 OMITTED]

It has been shown that tool wear mostly influence main spindle, i.e. main drive (Cuppini et al., 1990). 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. The experimental 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 (Shi & Gindy, 2007). By combination of digital drive systems with additional information from the control system, methods of isolating characteristic features from the signal and sophisticated data processing technologies, high reliability and safety of signal analysis is achieved. Further development of such systems, and the methods of isolating characteristic features, together with technologies of artificial intelligence (Brezak et al., 2004), presents a significant step towards realizing a simple, reliable, and user friendly way of monitoring of cutting tools and machining processes.

6. REFERENCES

Isermann R.(1994). Monitoring and Diagnostics, VDI-Verlag, Dusseldorf

Stute G.(1981). Control of Machine Tools, Carl Hanser Verlag Munchen Wien

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

Cuppini D., D'Errico G., Rutelli G.(1990). Tool wear monitoring based on cutting power measurement, Wear, 139(1990), 303-311.

Damodarasamy S., Raman S.(1993). An inexpensive system for classifying tool wear states using pattern recognition, Wear, 170(1993), 149-160

Mulc, T., Udiljak, T., Cus, F.(2004). Milfelner, M. Monitoring Cutting Tool Wear Using Signals from the Control System, Strojniski vestnik, 50(2004)12, ISSN 0039-2480, p. 568-579

Shi, D., N.N. Gindy. (2007). Industrial Applications of Online Machining Process Monitoring System, IEEE/ASME Transactions on Mechatronics, Vol. 12, No. 5, October 2007, 561-564