A multi-sensor strategy for tool failure detection in milling

Int. J. Mach.ToolsManufact, Vol. 35. No. 3, pp. 383-398. 1995 Copyright~) 1994ElsevierScienceLtd Printed in Great Britain. All rightsreserved 0890-6955/9559.50 + .00

0ag0-6955(~)F,0021.A A MULTI-SENSOR STRATEGY FOR TOOL DETECTION IN MILLING

FAILURE

DI YAN,t T. I. EL-WARDANYt and M. A. ELBESTAWlt (Received 20 July 1993; in final form 11 February 1994) Abstraet--A multi-sensor monitoring strategy for detecting tool failure during the milling process is presented. In this strategy, both cutting forces and acoustic emission signals are used to monitor the tool condition. A feature extracting algorithm is developed based on a first order auto-regressive (AR) model for the cutting force signals. This AR(1) model is obtained by using average tooth period and revolution difference methods. Acoustic emission (AE) monitoring indices are developed and used in determining the setting threshold level on-line. This approach was beneficial in minimizing false alarms due to tool runout, cutting transients and variations of cutting conditions. The proposed monitoring system has been verified experimentally by end milling Inconel 718 with whisker reinforced ceramic tools at spindle speeds up to 3000 rpm.

I. INTRODUCTION

TH~ recent introduction of new and difficult-to-machine materials (e.g. aerospace alloys and reinforced plastics), as well as new tool materials (e.g. ceramic and CBN inserts), has reduced considerably the predictability of cutting processes. Accordingly, on-line tool failure detection is becoming a critical requirement for improving the utilization and flexibility of present-day CNC machine tools. It is estimated that fracture will be the dominant failure mode for more than 25% of all advanced tooling materials by the year 1995 [1]. Therefore, tool breakage detection will be essential to the realization of untended machining. In-process detection of a milling cutter tooth breakage has been the subject of several research efforts. The majority of these efforts concentrated on cutting force and acoustic emission sensing. The research issue is to develop and select signature features which are sensitive to tool breakage. For force-based detection algorithms, two approaches were reported in the literature for processing the force signal. The first approach is deterministic and is based on the mechanics of the milling process, while the second is purely stochastic and considers the cutting force as a time series. Examples of the first approach are found in Refs [2-5]. Typically, processing of the signal is considerably simplified by synchronizing sampling of the force signature with spindle rotation. Various signal processing techniques are then used to separate cutting transients and tool run-outs from the breakage events, such as averaging the force per tooth period, normalized first order difference of the averaged cutting forces, and second order difference of the averaged forces. When the normalized first differential force exceeds a threshold level, the tool is judged to be damaged. This threshold level is typically a function of the depth of cut. Recently, Vierck and Tlusty [6] developed an improved thresholding scheme that corrects for axial and radial depth of cut. They acknowledge, however, the problems associated with exit transients at higher radial immersions. Clearly, developing an adaptive thresholding scheme is a key issue for the successful realization of tool breakage detection in milling. References [7, 8] are examples where the force signal is processed as a time series by using an auto-regressive (AR) model. In this case, the parameters of the AR model are updated continuously using a recursive least square procedure. The prediction error of the model is then used to indicate tool

tMechanicai Engineering Department, McMaster University, Hamilton, Ontario, Canada L8S 4L7. 383

384

D. YAN et al.

breakage. A higher order model (at least 15) was used in these studies, which results in significant difficulties during real time implementation. As mentioned earlier, acoustic emission (AE) sensing was also used to detect tool breakage in milling [9-12]. Statistical and spectral characteristics of AE signals generated during cutting processes were analyzed and related to tool wear and failures. It has been found that variations in the cutting tool condition result in changes in the pattern of the AE signal spectrum in the range of 100-300 kHz [ 10]. The intensity of AE root-mean-square (RMS) values was also utilized to indicate tool failures during cutting processes. To monitor tool breakage, a relationship between the fractured area AA and the amplitude of the emitted stress wave was developed in [l 1]. Another statistical model employing true-mean-square (TMS) values of AE signals was proposed in Ref. [12]. Different relationships between the TMS values, the cutting condition, and the chip formation process were established. This research work provided a better understanding of the physical relationship between the generated AE signal and the cutting processes. However, the main difficulty in using AE signals for tool condition monitoring has been identified as the sensitivity of the signal to cutting conditions as well as sensor location (transmission path). Recently, research efforts have been directed towards the development of multisensor monitoring strategies. In this case, an important research issue is integrating the information from various sensors (sensor fusion). Various approaches towards sensor fusion can be found in Refs [13-17]. These approaches include rule-based expert system, fuzzy decision trees, and neural network techniques. In general the performance of sensor fusion schemes depends on sensor reliability and model adequacy [5]. This paper proposes a multi-sensor strategy for tool failure monitoring in milling. Cutting force and acoustic emission sensors are used to develop the monitoring system. A stochastic model for the cutting force has been developed and applied to detection of tool breakage and chipping. AE monitoring indices are developed and used to assist in setting the alarm triggering threshold level and to improve the reliability of tool failure detection. The threshold level is automatically adjusted during the cutting process for variable cutting conditions, transients, and cutter run out. The results of experimental verification of the proposed scheme are presented in this paper. 2. STRATEGY FOR DETECTION OF TOOL BREAKAGE

2.1. Signal processing of cutting force The machining process is described by combining two components: deterministic process dynamics, represented by a transfer function; and process disturbance, represented by a stochastic model. Accordingly, the cutting force of such a process is expressed as: l'~s(B) . .

F(t) - A ~

Fq(B)

u(t) + ~ ( ~ v ( t ) ,

(1)

where B is a backward operator, and Os, A, Fq and *p are polynomials of orders s, r, q and p respectively. In the milling process, F(t) corresponds to the instantaneous resultant cutting force at time instant t, u(t) is a function which represents the effects of cutting conditions, such as feed, depth of cut, cutting speed, and material properties, and v(t) represents the process disturbance. On the right side of equation (1), the first term is the system dynamic response to steady state cutting u(t), and the second term is the response due to disturbances produced by cutting transients and unexpected events such as tool breakage and chipping, in addition to sensor measurement errors. The disturbance input v(t) is assumed to be Gaussian white noise with a zero mean and a constant variance. In stable milling (i.e. no chatter is present), equation (1) reduces to the form Fq(B)

F(t) = u(t) + ~

v(t),

(2)

Tool Failure Detection in Milling

385

where the deterministic component u(t) can be expressed by the well known equation

[4] u(t)

=

C

. a

• b

•

(3)

c(t),

where C is a material constant (N/ram3), a is the radial depth of cut (mm), b is the axial depth of cut (mm), and c(t) is the variable chip load (mm). When runout exists, the chip load c(t) is a periodic variable which includes spindle revolution and toothpassing frequencies and their harmonics. It has been suggested that the optimum order for a time series model describing the milling process should be high enough to cover the amount of sample points in 2.5 spindle revolutions [7]. For example, in the case of using a cutter with two teeth and a sampling frequency of eight points per tooth period, the optimum order will be 40. Clearly, this order is quite high for on-line parameter estimation. In order to remove the effect of chip load variation, the average force F, within a single tooth period is considered.

F.(j) = ~

F(j - 1) • (T + "r)d'r,

(4)

where T is the tooth period, and j is the sequential number of the tooth period. Thus, equation (2) reduces to the form below. If M is the number of sample points per tooth period, then the tooth averaging technique reduces the model order by a factor M.

~q(a)

F,(j) = u.(j) + ~

..,

vu) .

(5)

In order to remove the periodicity and the DC component in the cutting force, a seasonal time series is defined as: f(j) = Vz(B)F.(j)

= F.(j) - F . ( j -

Z) = ,l,,,( "yq(B) a ) v(j),

(6)

where Z is the number of teeth, and Vz(B) = 1 - B z is the difference operator. The time series given by equation (6) decreases the model order by a factor Z. The time series f ( j ) depends only on the random characteristics of the cutting process which include tool breakage. A first order auto-regressive time series model is used to define f(j). The model parameter ~b is estimated on-line using the recursive least square method [8]. C ( j - 1) • f ( j - 1) 4~(j) = 'b(J - 1) + X + C ( j - 1) • j ~ ( j - 1)

(7)

c~(j- 1). ~ ( y - ~) ],. c(j) -- c ( / - 1) - ~X c(7:1~ : ~ - ( ~ - 1 ) ] "

(8)

where C(j) is the covariance of f ( j ) , and h is a forgetting factor. The initial value of C(j) is C(0) = 100. k is chosen as 0.95. Accordingly, a seasonal time series model is obtained as follows. An optimal predictor for f ( j ) at sample j can be obtained as: 1

Fa(j) = Vz(B) • 6 1 ( B ) v ( j )

(9)

D. YANel al.

386

(10)

f(j) = $ ( j - 1) • f ( j - 1 ) , where d ~ ( j - 1) is the model parameter estimated at sample ( j prediction error e(j) at the ]th sample is defined as: e(j) = f(j)

- f(j)

1). The model

.

(I I)

Since the disturbance signal v(j) is assumed to be white noise, the prediction error will follow a normal distribution. A threshold level L(j) for the jth prediction error e(j) is given by:

L(j) = +- 3s(j - 1),

(12)

where s is the estimated variance of the prediction error e. s is calculated as follows: 1

s2(j) = ~

w~

'~ o~2(j - k), k=O

(13)

where Ws is a moving window and a ( j ) is defined by:

~e(j) or(j) = [ L ( j )

if le(J)l < IL(J)[~ ifle(j)l >-IL(j)iJ"

(14)

Equations (12) and (13) insure that the threshold level is calculated only under stable cutting and "healthy" tool conditions. Large prediction errors of the AR(1) model will occur during cutting transients, i.e. at entry and exit conditions, where changes in the nature of force patterns occur. In order to avoid false alarms during this transient period, the adaptive threshold needs to be relaxed. On-line identification of the transient periods can be accomplished by means of monitoring the time-variant model parameter qfft). Typically, when the cutter enters or exits the workpiece, the parameter 9(t) will remain positive because the amplitude of cutting force changes only in one direction. On the other hand, the revolution difference signal f(j) of the cutting forces is positive as the cutter enters the workpiece, but becomes negative during cutter exit (see Fig. 1). Therefore, tracking the revolution difference signal of cutting forces f(j) and the parameter qfft) of the AR(1) model

Average Force Fa(t)

. . . . . . .

I

........tt

I .trill .......

FIG. 1. Transient detection using AR(1) model parameterd~(t).

Tool Failure Detection in Milling

387

will assist in identifying the transient periods. It is also possible for the prediction error to exceed the set threshold level when there is a sudden change in cutting conditions such as the depth of cut and feed. The distinction between the cases of tool failure and those of sudden changes in cutting conditions can be accomplished by examining the pattern of the prediction error. The prediction error will exceed both upper and lower threshold levels at tool failure, while it will exceed only one level due to a change in cutting conditions. Accordingly, checking two successive tooth periods will separate tool failure from sudden changes of cutting conditions.

2.2. Signal processing of acoustic emission Previous studies have shown that the envelope of the RMS signature of the acoustic emission signal typically contains information related to tool failure in milling [10]. This low frequency signal (termed AE in this paper) is suitable for on-line monitoring schemes. The AE signal generated during the milling process is considered as a periodic random signal, and can be expressed as: AE(t)

=

AEp(t) + AEr(t),

(15)

where AEp(t) is a periodic component and AE,(t) is a random one. The periodic component possesses a predominant frequency which corresponds to the engagement, the cutting and the disengagement of the cutting edges. Sources of the random component include tool breakage, chipping, sensor noise, etc. A time domain averaging (TDA) technique can be used to extract the periodic component from a stationary, noisy periodic signal. Its function is similar to that of comb filtering around the fundamental frequency and its higher harmonics. The use of the sampfing synchronization method is a prerequisite for the T D A technique. Given a tooth passing period T, the periodic and random portions of AE signals that cover N tooth passing periods can be expressed by: 1N--1

AEp(x) = ~ .i~o A E ( j . T + -c) 0 < ' r < AE,(t) = AE(t) - AEp(T)

T

(16)

0 < "r < T .

(17)

The time domain averaging technique is effective for tracking the local deviations during metal cutting processes caused by noisy periodic AE signals [11]. A typical AE signal obtained during a milling process is shown in Fig. 2. The maximum value of AE signal within a tooth period 6.0

5.0 i

4.o

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-1.11 1

2

~me (sec)

FIG.2. AE signalduring milling.

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=

max

{AE((j-I).

T+,r)}

(18)

O<~r
AEm~.(j) = ~

AE(j-

1) • ( T + 'r)d,

(19)

reflects the intensity of a burst type signal during this period. Strong signal intensity will indicate abnormal conditions such as tool failure. The mean value of the AE signal within a tooth period shows the energy level of the acoustic emission signal within that period. The AE peak and mean values within the jth tooth period are given by the following equation. A ratio between the AE mean value and the AE peak value can be expressed by equation (19). AEmea,(j) AEratio(j) -- A E ~ k ( j ) .

(20)

During cutting transients, the cutting interval is shorter than during steady state cutting. As a result, the mean value of an acoustic emission signal during the transients is expected to be lower than that for the case of steady state cutting. On the other hand, the AE signal reaches its peak value when the cutting edge engages into, or disengages from, the workpiece for one tooth period, regardless of the cutting interval length. Thus, the ratio given by equation (19) can be used to identify cutting transients. Compared with cutting forces, AE signals are more affected by ambient noise. A moving average technique is utilized to minimize random noise caused by material inhomogeneity, built-up-edge and chip congestions, as well as other mechanical and electrical noise. Taking the average of a feature such as AE ratio will increase the signal to noise ratio. Given a window width Ww, moving averages of the AE peak values and the AE ratios can be obtained by: Ww-I

AEp~ak(/)- Ww k=o AEpeak(J- k)

j = W w , Ww+ 1 ..... N

(21)

j = W w , W~ + 1 , . . . , N .

(22)

Ww--1

AEratio(j) -

1 2 Ww i,=o

AEr,tio(j- k)

The AE sensor can also be used as a touch sensor. Accordingly, the AE peak value for a tooth period can be utilized as an index CUtAE to identify the cutting duration. CUtAE(j) = AEpeak(J)

j = 1, 2 ..... N .

(23)

Obviously, tool failure alarms detected outside this cutting duration are false alarms, and therefore should be ignored. During a transient period such as cutter entry or exit, the AE ratios of the mean value over the maximum value will be much lower than those obtained during steady state cutting. Thus, transients can be identified when the index TransAE given below exceeds a preset threshold level.

ZransAE(J) =AEratio(j)

j = Ww, Ww + 1 . . . . . N .

(24)

Since information about cracking, chipping and breakage is contained in the burst type AE signal, the AE peak value is an indicator of tool failure. This can be developed to obtain a tool failure index as follows, where w is a weighting factor that takes the effect of the cutting transients into account. The weighting factor can be taken as ZransAE. The index FailAE

Tool Failure Detection in Milling

FailAE(J) = W" AEpc.k(j) - AEpc.k(j) AEp=.k(j)

389

j = I4I,, Ww + 1 ..... N

(25)

will extract the sudden change in the AE peak value caused by tool failure. 2.3. Fusion of force and AE signals In this section, both force and AE signals are integrated to develop a tool condition monitoring strategy. The advantages of the proposed sensor fusion scheme include: (i) improved identification of the transient and cutting periods; (ii) increased sensitivity of the proposed strategy in detecting tool failure. Referring to Fig. 3, Itl and I~2 represent the force and acoustic emission sensor inputs respectively. In the second level of the proposed strategy, indices are first calculated, and then compared with threshold levels as follows. (i) Calculate prediction error e(j) [equation (10)] IF e(j) > ThresholdERROR,

THEN E21 = 1

ELSE EEl = 0 ThresholdEggOR is estimated on-line as given by equation (11). (ii) Calculate parameter tb(j) [equation (7)] IF tb (j) > ThresholdpARAM,

THEN E22 = 1

ELSE E22 = 0 ThresholdpARAM depends on the feed rate and is defined by the user from cutting tests. Referring to Fig. 8(b), ThresholdvARAM was set at 0.6. (iii) Calculate CUTAE [equation (22)] IF C U T A E ~> ThresholdcuT,

THEN E23 = 1

ELSE E23 = 0 (iv) Calculate TRANSAE [equation (23)] IF TRANSAE < ThresholdTRANS,

THEN E24 = 1

ELSE E24 = 0 (v) Calculate FAILAE [equation (24)]

Detection Results

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T H E N E25 = 1

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The last three indices are all user defined, based on a set of calibration cutting tests. The monitoring strategy starts by determining if the tool is in cut. This is identified if E23 = 1. If the tool is not in cut (E23 = 0), then both ThresholdERRoa and ThresholdrAIL are set to infinity. On the other hand, if the tool is in cut, then monitoring strategy identifies whether this is a transient situation (i.e. entry or exit condition) or steady state cutting. Transient cutting is identified if E31 = 1, and it is evaluated from:

(26)

E31 = (E22 k/E24) A E23,

where V indicates an OR gate, and A is an A N D gate. During transient cutting the threshold levels, ThresholdERl~Oa and ThresholdvAm are both relaxed to minimize false alarms, Tool breakage is then identified from:

E32

=

(E21 V E2s) A E23

(27)

•

A flow chart of the proposed monitoring scheme is shown in Fig. 4. 3. REAL-TIME IMPLEMENTATION

3.1. Experimental set-up and test conditions A schematic representation of the monitoring system is shown in Fig. 5. Cutting tests were performed using a YAM CNC-5-A vertical knee type machining center. An optical shaft encoder mounted on the machine spindle supplies 1024 pulses per revo-

Input parameters Z,M

[

- [ datm lUXlulSltlon

force feature ex~actlon

Fa

!

AE feature extraction

~-

F AR(1) model

=

.

Cut Trans. Fall

Routine of logic inference Set threshold

No

_1

-I

FIG. 4. Flow chart of the on-line monitoring procedure.

Alarm

Tool Failure Detection in Milling

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lution. The encoder was used as an external clock to synchronize sampling with spindle rotation. Eight force samples were taken every tooth period. A table dynamometer was used to measure two orthogonal cutting force components Fx and Fy. An acoustic emission sensor was mounted on the table dynamometer to measure high frequency elastic waves generated during machining. An AE analyzer was utilized for the preproeessing of acoustic emission signals. The workpiece material was high nickel-based super-alloy (Inconel 718). The tool material was WG-300 whisker-reinforced ceramic composite. Round inserts were used with two end mills, 38.1 and 76.2 mm diameter respectively. Although the first end mill had two teeth, and the second one had four teeth, not all available insert stations were used during actual cutting. Square inserts of the same material were also used to compare the performance of inserts with different geometries. Several cutting tests were performed to investigate the influence of process parameters, such as cutter geometry, cutting speeds, feeds, axial and radial depths of cut, on the proposed tool failure detection algorithm. These cutting conditions are listed in Table 1. 3.2. Software structure A modular design is used to develop the monitoring system as shown in Fig. 6. Knowledge organized in the structured modular form is more easily managed and maintained. The system contains four modules to execute functions of data acquisition, feature extraction, decision making and man-machine interface respectively. In the Data Acquisition Module, a continuous DMA (direct memory access) routine, executes A/D conversion with a double buffering technique. When the sampled data are being filled in one buffer, the data stored in the other buffer are used as inputs for signal processing. Real-time signal processing is performed in the Feature Extraction Module. CalcuTABLE1. ~ Parameters Cutter diameter, D (mm) Type of inserts No. of inserts in cutter, Z Spindle speed, n (rpm) Feed, St (mm/tooth) Axial depth of cut, DOC (mm) Immersion ratio, (a/D)

CONDITIONS Levels 38, 76 round, square 1,2 500, 1656, 2000, 2930 0.075, 0.150, 0.200, 0.250 0.25, 0.75, 1.25, 2.00, 2.50 0.25, 0.50, 1.0

D. YAN et al.

392

Decision Making

Man-Machine Interface Data

F:oatu~

Acquisition

Extm~ion

FIG. 6. Modular structure of software.

lation of all feature indices of cutting force and AE signals is completed within one tooth passing period. This extracted dynamic information will pass on directly to the Decision Making Module. In the Decision Making Module, logic reasoning in the form of Boolean functions is executed to guarantee the real-time implementation of onestep-decision-making within a limited interval of one tooth passing period in milling operations. The Man-Machine Interface Module assists in the selection of the types and values of process variables, such as the external clock divider, the range of band pass filters, and system gains. 3.3. On-line monitoring scheme Two orthogonal components of the cutting force are obtained using the table dynamometer, namely, Fx and Fy. Averaging the force components within a tooth period before calculating the average resultant force reduces the errors due to phase lags (between the two force components), and also increases on-line computation efficiency. 1

M

Fx,a(j) = ~ ~ F~[(j - 1) • M + k] k=l

j = 1,2 ..... N . I

M

Fy,.(j) = ~ ~ Fy[(j - 1 ) . M + k].

(28)

k=l

The average resultant forces can be acquired as follows:

Fa(j) = ~(F~,a(j) +F~,.(j))

j = 1,2 .... ,N.

(29)

Figure 7(a) shows an example of the average cutting forces in milling. Figure 7(b) shows the revolution difference of the average cutting force. The value of f(t) is affected by cutting conditions. The relative change in cutting conditions can be obtained through signal normalization defined by:

Vz(B)F.(j) x(j)

-

Fz(j)

'

(30)

where Fz(j) is the mean value of F,(j) for one revolution, which is defined by equation

(30): Z

1 k~,=F.(j _ k) Fz(j) = -Z

(31)

Tool Failure Detection in Milling

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Runout developing due to chipping [

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30O 2OO 100 0

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20

40

60

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0.6 chipping

0.4 0.2

g

o 41.2 -0.4 breakages -11.6

20

40

60

Time (see) (b) Revolution difference of the average force

FIG. 7. In-process tool breakage detection ( D = 38 mm, Z -- 2, round insert, n = 500 rpm, D O C = 2.5 ram, St = 0.175 mm t -1, a/D = 0.5).

Figure 8(a) shows the AR(1) prediction errors and thresholds. As shown in Fig. 8(b), during a steady state cutting period, the model parameters are changing in the range of -0.5-0.5, and the monitored signal x ( j ) is stochastic. Figure 9(a,b,c) show the indices of CutAr~, TransAE and FailAE during the milling process respectively. Threshold levels of feature indices are determined according to the sensitivity of the acoustic sensor and the gain of the amplifier. With a system gain of 20 dB, the threshold levels of indices CUtAE, TransAE and FailAE were set as 0.2, 0.3 and 1.0 respectively. The threshold for tool failure detection is given by: L(j)

= - 3.0. g ( j - 1)

L(j)

= +- r .

L(j)

= +-

3.0- d ( j - 1)

for steady state cutting for transient for non-cutting period,

(32)

where the variable r determines the relaxed threshold level. Since, in most cases, the chip load variation during transient cutting will not exceed twice the nominal chip load, it was found from calibration tests that setting r = 2 gave consistently satisfactory detection results. 4. E X P E R I M E N T A L V E R I F I C A T I O N O F T H E SYNTHESIS S T R A T E G Y

To verify the proposed monitoring scheme, cutting tests were performed under the various cutting conditions listed in Table 1. Results from typical tests are presented in Figs 10-15. Each figure shows the average cutting force, and the tool failure detection index. Figures 10 and 11 show two cases of normal cutting processes during which no tool breakage or severe chipping was detected. Figure 10 illustrates a fly-cutting process, HTB 35:3-C

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Tool Failure Detection in Milling

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n = 2930rpm,

DOC

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396

D. YAN et al.

in which only one cutting insert was used in the milling operation. As shown in Fig. ll(a), some runout is seen in the force signal. In the proposed strategy, and in contrast to previously published work, runout is not taken as an indication of tool failure but as process noise. Figure 12 shows a case of tool breakage during the steady state cutting period when using two round inserts. Figure 13 shows a case of tool breakage near the cutter exit when machining with a square insert. Once the tool breakage incident occurs, the monitored index exceeds the threshold as shown in Figs 12(b) and 13(b). A case of edge chipping during a cutting process using two inserts is shown in Fig. 14. In this figure, the instant of tool chipping is easily detected. Figure 15 shows a case of edge chipping at the transient of cutter entry. Although the threshold was relaxed during the transient period, the chipping incident was again detected by the proposed scheme. 5. C O N C L U S I O N S

A multi-sensor tool condition monitoring system has been presented in this paper. This system has been successively implemented for the milling process with spindle speeds up to 3000 rpm. In this monitoring scheme, a sampling technique of synchronization with spindle position is used to develop a seasonal time series model for the cutting force signal. A first order auto-regressive model is capable of detecting tool breakage and chipping. The low model order is possible by preprocessing the force signature using the average tooth period and the revolution difference methods. The AE signal is used for the identification of the cutter engagement. The AE signal also assists in identification of transient periods, and tool failure detection. The threshold levels of tool failure detection are adapted on-line automatically without operator interference. The proposed monitoring scheme has been successfully verified experimentally during ceramic milling of Inconel 718.

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Fit;. 15. Chipping at cutter entry ( D = 3 8 m m , Z = 2 , round insert, n = 1656rpm, D O C = 2 m m , St = 0.25 mm t -~, a/D = 0.5). Acknowledgement--This work was carried out with financial support from the Natural Science and Engineering Council of Canada through grant no. A6470. REFERENCES [I] J. COLGAN, H. CHIN, K. DANAI and S. HAVASlU,Sensors and Signal Processing for Manufacturing, PED Volume 55, pp. 33-48, ASME (1992). [2] Y. ALT]NTASand I. YELLOWLEV, Trans. ASME, J. Engng Ind. 110, 271-277 (1988). [3] Y. ALTINTASand I. YELLOWLEY,Trans. ASME, J. Engng lnd. 111, 149-157 (1989). [4] J. TLUSTVand Y. S. TA~NO, Ann. CIRP 37, 45-51 (1988). [5] G. CHRYSSOLOUVJS,M. DOMROESEand P. BEAULIEU,Trans. ASME, J. Engng Ind. 114, 158-174 (1992). [6] K. C. VIEnCK and J. TLUSTV, Sensors and Signal Processing for Manufacturing, PED Volume 55, pp. 17-32, ASME (1992). [7] M. S. LAN and Y. NAERHEIM, Trans. ASME, J. Engng Ind. 108, 191-197 (1986). [8] Y. ALTINTAS,lnt. J. Mach. Tools Manufact. 28, 157-172 (1988). [9] S. RAMALINGAM,T. Sin, D. A. FROHRmand T. MosE~, Proc. NAMRC XV, pp. 245-255 (1987). [10] T. BLUM and I. INASAK1,Proc. of the USA-Japan Symposium of Flexible Automation, Volume 2, pp. 1017-1024, Minneapolis (1988). [11] E. N. DxEz and D. A. DORNFELD, Trans. ASME, J. Engng Ind. 109, 227-233 (1987). [12] M. LIu and S. Y. LXANG,Int. J. Mach. Tools Manufact. 31,589-606 (1991). [13] A. M. AOOGINOand S. SVJNIVAS,Mech. Syst. Signal Processing 2, 165-185 (1988). [14] S. B. BILLATOSand PIA-CHUNGTSENO, J. Manufact. Syst. 10, 464-475 (1992). [15] S. RANGWALAand D. DORNn~LD, Trans. ASME, J. Engng Ind. 112, 219-228 (1990). [16] E. EMEL and E. KANNATEV-AsmuJR., Int. J. Mech. Sci. 31, 795-809 (1989). [17] R. X. Du, M. A. ELRESTAWland S. LI, Int. J. Mach. Tools Manufact. 32, 781-796 (1992).