Sensing Cutter Breakage in Milling

Sensing Cutter Breakage in Milling Jiri Tlusty (I), Y. S. Tarng; University of Florida Received on January 19,1988

Abstract The paper describes a system for sensing milling cutter breakage. It is based on recognizing changes in the pattern of the cutting force, The regular periodic variation per tooth is filtered out by synchronizedsampling, averaging per tooth and first differencing and the change due to the broken tooth stands out. The threshold for alarm is set with respect to the mean force per revolution (moving average) to make it independent of changes in radial and axial deDths of cut and in chip load, Due to practical difficulties with force measurements the use of vibrational signal is explored and found feasible. Keywords : M i l l i n g , C u t t e r Breakaxe Sensnr, C u ~ s l n y .Force Kecognitioil, VLbrationai s i g n a l .

1. Task Specificatlon

The problem of sensing milling cutter breakage and deciding on how to react to such an event has been the subject of quite a number of research projects in recent years. We will briefly review the various efforts and describe our own approach. First, however, let us state the goal and the desired characteristics of the sensing and control system. On one hand, there are those who do not consider the development of such a system an important task and point out that there are in existence a large number of machining centers or even a number of Flexible Machining Systems which work to some extent unattended and still there do not seem to be complaints about this problem. Here we respond by pointing out that in almost all these cases the machined materials are aluminum and cast iron which are rather non aggressive on cutting edge breakage and mainly, they are run under very moderate conditions, at very small chip loads. The practitioners who program these machines know well the effect of the chip load on breakage and the necessity to try and avoid edge breakage. For a more explicit discussion of this effect see, e.g., Ref (11, For more efficient production heavier chip loads should be used under which, however, the probability of chipping increases. In machining steel these occurrences are unavoidable. One cannot then exclude the possibility of the damage accumulating and of massive damage which may have catastrophic consequences for the tool, the workpiece and the machine. For unattended machining under these conditions a system for sensing cutter damage and for control of protective actions is necessary. The significance of the system Increases with the amount of milling operations on the given machine and it is highest for such operations in large scale productions like those on transfer lines. The fundamental requirement for such a system is that it be very highly reliable, on the safe side. It should signal alarm for every damage beyond a certain level and may do it even if some noise and false signals occur. However, it should not produce false alarms to often otherwise the operator would switch it off. In this respect one of the difficult research tasks beyond the recognitionof an event is how to formulate and set the alarm threshold under varying operating conditions. The system must be robust in that sense that it is sensitive to the controlled event, the tool breakage, while being lnsensltive to noise and to signals produced by the transients inherent in a milllng operation llke entering the cut, exiting from it, milling over a slot or over a hole. Also it must not be sensitive to variations in workpiece material, in cutting conditions. in cut geometry. Furthermore, the system should be based on a practical form of sensor employable over a wide range of

Annals of the ClRP Vol. 37/1/1988

conditions. Here we mention the trends to high and very high spindle speeds. Finally. the system must react fast, within about one cutter revolution to prevent a local breakage to spread on the following teeth. It is, of course, feasible to have one fast algorithm, on the safe side, triggering a first kind of reaction like slowing down the feed rate and protecting in this way the other teeth. followed by another slower algorithm which can decide whether the alarm was justified and it is necessary to stop or whether it was false and it is possible to continue. The requirements on the system are summarized in Table 1.

Table 1 Requirementson the System 1. Reliability,on the safe side. 100% alarm on damage beyond a set level. Minimum of false alarms

2. Highly automated threshold setting Independent of changes in radial and axial depths of cut

3. Robustness High sensitivity to breakage. Very low sensitivity to: transients (entry, exit, interruptedcuttings) and to variations in cutting parameters: workpiece mat., feed, speed, up -, down-milling, radial immersion 4. Practicality of sensor Easy to locate in the machine. Applicability to high speed milling

5. Fast Response Within one revolutionfor the first alarm

2 Review of the Various Approaches The approaches may be divided in two groups, one based on Acoustic Emlsslon (AE) as a one time sudden sharp signal coinciding with the breakage and the other based on the analysis of changes in the Periodlclty of the Milllng Force as detected either directly in the force or torque signal or indirectly from a vibration or sound signal. There are then further distinctions in the processing algorithm employing either fundamentally a stochastic (black box type) method or a method based on the analytical understanding of the signal changes. The use of the AE signal was discussed typically in papers (21, (31,(41. (51, (6). (71. The AE is obtained from a very high

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frequency pieuoelectric transducer, in the 0.1 to 1 MHz range. Lower frequencies are filtered out. The transducer is thus sensitive to very sudden events like micro-and macra fractures due to chip formation, chip breaking, tool wear and tool breakage. Various processing techniques both timedomain and spectral type have been applied with multifeature extractions and correlations. It seems that the problems of robustness are still not satisfactorily solved and the signals are sensitive to variations of cutting data. In (5) the authors have simultaneously measured cutting forces in order to approach the problem of threshold setting. Sensing itself is comparatively easy in turning, less so In milling where the signal has to pass through spindle bearings to the housing. The use of the changes in the milling force periodic variations is much more straightforward. This will be amply illustrated in this paper. Stochastic processing leads to very high order time series modelling , see Ref (81, (91. and the robustness of the algorithm is not very high. Once the basic features of the milling force variations are recognized from the analysis of the milling process much simpler and more robust processing is possible, see (10). (1 1). The algorithms used are based on synchronous sampling and they are further described in the following text, The question of the threshold setting is fully recognized along with all the requirements as presented in Table 1. The main problem remaining is the impracticality of the measurement of the milling force. Therefore this present paper concentrates on discussing the feasibility of using vibration signals instead, The system si developed well into satisfying most of the criteria of Table 1. At the end of the paper further potential research and development work is discussed.

3. Solution based on sensing the milling force and its limitations. The milling force offers extremely good evidence of milling cutter damage. This will be illustrated using an upmilling operation with 1/2 immersion (radial depth of cut = 1/2 cutter diameter) and with 8 teeth on the cutter. Milling of cast iron to an axial depth of cut 2.5 mm with a chip load c = 0.25 mm is considered. Spindle speed n = 2250 rpm is used which gives a tooth frequency f = 300 Hz. Analogous illustrations not included here for lack of space were derived and also obtained experimentally for 1/8. 1/4 and 1/1 immersions both up- and down-milling and the conclusions presented here for the chosen illustrative case apply very well to all those other cases. Except, in the 1/1 immersion case the variable component of the force is theoretically zero (for a cutter with no radial run-outs). Even there, however the effect of breakage on the signal is the same. The effects to be discussed apply even more strongly for cutters with lesser number of teeth. Except for the above mentioned 1/1 (slotting) case the milling force signal contains a strong periodically variable component once per tooth period. The 'tooth frequency' is ft = nm/60 (Hz)

(2)

AFav = Fav.i - Fav,i-1

At the same time, the DC component is also removed and what remains is the signal for the broken tooth clearly standing out: see diagrams c) and h). For comparison, for an undamaged cutter the AFav signal is a steady zero (not considering noise nor run-out ) see diagram d). This is a very efficient way of extracting the signal for the damaged tooth. The same kind of graphs as in Fig. 1 are presented in Fig. 2 except they have been obtained experimentally, from a cutting test. The left hand column contains records for an undamaged cutter and the right hand column those for a cutter with a rather badly damaged tooth as shown in the

1

F

e,

2.0

0.0

1 0.0

Fa V

')

I ,

1

FaV

g,

(1)

where n (rpm) is the spindle speed and m is the number of teeth on the cutter. This uniform periodicity is distorted if a part of one of the cutting edges is 'chipped' or broken away. This is illustrated in Fig. 1. It contains, in the first row, the force signal. In the left hand column the case a) of a fully developed milling operation is shown. In the right hand column the case e) of the transient of the cutter entering the cut is shown. During the entry a breakage occurred. The signals in Fig. 1 were obtained by simulation. The technlques of simulating forces in milling were well developed in the past

46

and described, e.g.. in (13) and (141. Looking at the case a) we see a DC level and a periodic component Identical for six subsequent teeth. On the seventh tooth (the one with the broken edge) the force drops to a low value and the next tooth takes a double load. On a cutter with undamaged teeth there would b e a constant periodicity per tooth. This constant periodic component of the force c a n b e very effectively filtered out. We sample the force in synchronization with the cutter rotation, from an encoder on the spindle, taking 9 samples per tooth period (72 samples per revolution). By adding all 9 samples per each tooth the 'average force per tooth' Fav.1 is obtained. The subscript i is incremented once per tooth period. These average forces are shown in diagram b) for the steady state cut and in 0 for the entry transient. In the former case the Fav,/ values are equal for six teeth in a row. The next Fav,i ( for the damaged insert) drops and the next one shoots up. This pattern Is also seen in the transient where the signal is further improved by low pass filtering, diagram g). The regular periodic signal is effectively filtered out by producing the first difference AFav of the average (per tooth period) forces:

,--

t-

-2.0

0.80

0.85

(SW

0.90 0.00

I

1

0.05

0.10

(SW

1. The force signals. Left, full cut. Right, entry transient. All signals for damaged cutter except d). Simulation.

The same cases are shown once again on a compressed time scale in Fig. 4 which contains the whole cycle of entry. constant cut and exit. The left column is for the good cutter. right column for the damaged one. First row Is the force signal as sampled, second row is the Favj signal and third row the AFav signal. Again, the difference between the AFav signals is very clear. Still one more illustration of the effect of tooth breakage on the force signal is presented in Fig. 5. It shows the spectra of the force signals as they were given in Figs. 4 c) and 9). With the unbroken cutter, graph 50) the most prominent component is the tooth frequency ft = 300 Hz and its second harmonic 600 Hz. With one tooth broken we have an event passing once per revolution with a frequency 2250/60 = 37.5 Hz. In Fig. 5b), apart from the ft = 300 Hz component the 37.5 Hz component stands out as well as 2nd and 3rd harmonics, all of them pretty strong. In our processing method we filter out fully the ft component and all its harmonics by producing the Favj and AFav which leaves graph 50) practically blank. In the graph 5b) the (l/n) = 37.5 Hz and its harmonics dominate. This further explains how powerful our method is in distinguishingthe case with the broken tooth.

2. Force signal and its processing for an undamaged and a damaged cutter. Test data.

3. Determining the Threshold It is obvious that it is possible to establish a threshold level, like the one indicated by the lines L (for limit) in the bottom row of Fig. 4. Once the AFav values exceed these limits alarm is indicated. The important question arises as how to determine the proper threshold level. It must be set so as to safely determine chipping or breakage exceeding a certain desired minimum but it also has to be comfortably above the level of the signal for the undamaged cutter so as not to cause too many false alarms. First is then the question of the minimum admissible

photograph Fig. 3. The first row shows the force during the entry transient and the second row the same signal processed into the AFav. The clear distinction of the breakage is observed. In the third row is the force during the fully developed cut and in the bottom row the AFav signal for the same case. Again the evidence of the damage is very clear.

4. Signals for the good and the damaged cutter through the entry-fullcut-exit cycle. Test data.

3. The damaged insert used in the experiment.

chipping or breakage. It might be expressed by the length Id of the damaged edge. This value will be different for different tool and workpiece materials and the criterion may be how fast would the damage spread on the same tooth and, due to the consequent overload of the following tooth, how fast it would extend to other teeth of the cutter, eventually spreading in a catastrophic chain type fashion. This can only be established by tests but, usually a small chip

47

up to about 1 mm length is tolerable. Having decided on Id we may derive the corresponding value of AFav and of L for any particular cutting conditions. The scale of diagrams like those in Fig. 4 is proportional to the axial depth of cut b and to the chip load c because, actually the cutting force in any instant is proportionalto (bc). It is also approximately proportional to the radial depth of cut

1.5

1

3.0 b,

HZ 1

0

200

400

600

800

1

1000

5. The force spectra for the good and the damaged cutter. Test data. a. This has been established through simulations for various values of radial immersions a/d. We should then keep changing the levels L with changes in a and b. However, mostly we do not know those latter values and their variation through the cut. It is therefore better to express the limit in a relative way US a ratio over the force averaged per the whole revolution of the cutter (the mean force);

4 Using Vibration Signal Instead of Force Obtaining the force signal is not easy especially if higher

spindle speeds are used. In the paper (8)the torque signal as obtained from the current of the spindle motor was used. However, the motor with its inertia acts as a low-passfilter with a corner frequency of only about 5 Hz, see, e.g. Ref (15). Even the best dynamometers work well only in the lower frequency range. They are difficult to build into the structure of the machine and they may be limiting the workspace and they are expensive and difficult to maintain. Therefore, we have explored the use of a vibration signal instead and considered the use of an accelerometer attached to the headstock as the practically simplest solution, or of a relative displacement pick-up between the housing and the spindle close to its flange. First, let us look at some theoretical results. We are assuming a Transfer Function between the force on the cutter and the transducer (either acceleration or displacement) of a single-degree-of-freedomtype with a natural frequency fn = 200Hz and a damping ratio E, = 0.04. This may correspond to the basic spindle mode. The results are shown in Fig. 60) for spindle speed n = 75 rpm which gives tooth frequency ft = 10 Hz = fn/20. We are running very much below resonance. Graph 01) is the milling force F. Graph 02) is acceleration a and 03) is displacement, both filtered through the spindle system response. It is seen that the force signal is rather well reproduced by the displacement. In acceleration, not only is the DC component lost (depriving us of the reference for the threshold) but the whole character of the signal is changed so that it contains bursts of the natural frequency 200 Hz excited by the individual teeth. It still shows clearly the fact that one tooth is missing and the next one gets double load. Then we increase the spindle speed to n = 300 rpm, ft = 40 Hz = fn/5 and obtain signals as in Fig 6b). The time scale is 4 times signifying that the Condition for AFav is not applied if Fm < Fm where Fm has to be chosen according to the nature of the operation.

1 m-1 Fm = Fav,(i-k) (3) m k=o where m is the number of the teeth on the cutter and as mentioned above Fm z Cabc (4) where C is essentially a moterlal constant. It is then L=fAFm (5) where the value of the coefficient A has to be chosen mainly with respect to the noise affecting the signal obtained from the undamaged cutter. Also, it is obvious that this criterion cannot be used outside of a cut where Fm = 0 and any amount of noise would trigger an alarm. It is necessary to set a minimum: if L c Lmin then L = h i n (6) We are now faced with the problem of choosing Lmin which, we believe, can only be resolved by letting this value to be set individually for each operation considering the potential noise a the potential danger caused by small damages which could pass through the tolerance 6) In very shallow Cvts.

The formulas (5) and (6) can also be written as a condition for the magnitude of the difference average force AFav as related to the 'mean' force I AFav I < I L I = AFm, I AFav I < A, for Fm>Fm,min (7) Fm

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6. Force, accelerationand vibration signals for a) ft = fn/20, b) ft = fn/5. Simulation.

7. Force, acceleration and vibration signals for a) ft = fn/2, b) ft = fn. Simulation.

faster, the force signal is the same as before, both acceleration and displacement increase and they show the same characteristics as in the preceding case. Now we increase the spindle speed to n = 750 rpm, ft = 100 Hz = fn/2, see Fig 7a). The character of the signals is still essentially the same except that there is a higher content of the fn = 200 Hz vibration in the displacement signal. Finally, we reach the resonant case with n = 1500 rpm, ft = 200 Hz = fn, see Fig 7b). Now, the acceleration signal loses almost completely the ability to show the fact of the broken tooth while the displacement signal still retains it. These examples should be interpreted in a relative wuy so that if, e,g,,the spindle natural frequency were 600 Hz the Individual cases would correspond to three times higher spindle speeds. The conclusion to make is that the displacement signal is much more useful for our purpose than the acceleration signal because it reproduces the DC component and because it still distinguishes the event of broken tooth quite well even at ft = fn. Therefore, in the following we will discuss

only the results obtained experimentally of a system based on a capacitance type displacement transducer located between the housing and the spindle In front of the front bearing. The Transfer Function measured between the force F on the cutter and the displacement X sensed on the spindle, (X/F), is presented in Fig. 8. Within our range of interest it contains two resonant peaks: at 258 Hz, denoted c),d) and at 492 Hz denoted b). Measurements were done with milling where the tooth frequency ft was well below these resonances. at ft = 80 Hz. denoted b y a) on the Transfer Function as well with ft = 258 Hz and ft = 492 Hz. The latter two instances obviously are the most difficult ones as was illustrated in Fig 7b). In Fig 9 results of tests done at spindle speed n = 600 rpm, ft = 80 Hz. point a) of Fig 8, are shown. The left hand column corresponds to the cutter with one broken tooth and the right hand column to an undamaged cutter. Records are presented from the steady state part of the cut, in rows from top to bottom : the displacement signal X(t). the XaV.1, and AXav and, finally the spectrum of the top row. The distinction in the AXav signal between the damaged and undamaged cutter is clearly seen and it is well explained in the spectral graph by the presence of the l/n, l/n, 3/n. 4/n components for the damaged tool. For the case at n = 1935 rpm, ft = 258 Hz, point c,d, similar and analogous graphs are shown both for the force and the displacement signals in Fig 10. It is seen that the Xav and AXav are naisier than the Fav and AFav. This is caused by the larger vibrational noise at the resonant vibration. However, there is still enough distinction available for the broken tooth. Finally, in Fig 11 the case of the most dominant resonance, at n = 3690 rpm. ft =492, point b) of the Transfer Function. is shown. This one shows the least margin between the

25

u x lo3

20

9 15 rn

7 7.5 0.0 .5

10 X

4

5

0 200

400

600

800

1000

HZ

8. Transfer Function X/F of the relative vibration between spindle and housing and the force on the cutter. Measured.

HZ 9. Vibration signals for the good and the damaged cutter at ft = 80 Hz. point a) of Fig. 8. Test data.

49

~ 1 0 '( N ~)

damaged and undamaged cutter due to the large vibration a1 ft = fn It will be best if the setting of the threshold is

4.50 7

2.25r

arranged differently for the resonant cases than for those

away from resonance The system will include an A1 circuit which would provide the various settings of the values of L

0.00

and L min Finally. in Fig 12 graphs are presented from the same experiments as in Figs 9 and 1 1 , This time, however, thc ratio of AXav/Xm IS plotted which corresponds to the formulas and

-----1

conditions opplied to the cutting force, Egs (2) through (7). Ikxapituloting. it is X0v.i is the sum of all the somples of X (t) pcr onc tooth ~ is the first difference period, the i-th one, AXav = X ~ V-,Xav,l-l of the Xav signals,

1

I

1

Xav

m-l Z Xov,(i-k) is the mean m k=o (moving average) of all the Xav,l values per one revolution. Xm =

Xav

An alarm is released if the following condition is not satisfied. 1 Aov(X) 1 R= < B, for Xm>Xm,min (8) Xm where the values B and Xm,min have to be selected for a given type of operation. The condition of Xm>Xm,min is not satisfied outside of a cut where the alarm should be disabled. The graphs in Fig 12 encompass the whole cycle of entry, full cut, exit as it is obvious from the top row expressing Xav The left hand side corresponds to a cutter

t--I

L----

0 00

0 08

1

with one damaged tooth and the right hand side to an undamaged cutter The second row is the ratio R from Fig (7) for the test at ft = 80 Hr (point a) in Fig 8) and the third row for the test at the dominant resonance, ft = 492 Hz (point b) in Fig

0 16

(SEC)

10 Vibration and force signals for the good and the damaged cutter at f = 258 Hz point c d in Flg 8 Test data

20'o

1

8). These graphs are limited to the duration of the actual cut because R is not defined outside of the cut. Even so, it is seen that in the transients and mainly in the exit phase R is increasing due to the strongly diminishing value of the mean vibrulion Xm and the persisting noise in the AXav signal. Nevertheless, it is possible to chose the values B. In the

1

resonant case the margin for false alarm is strongly diminished Further effort will be devoted to finding a formulation which could increase the margin in the transients.

-20.04

-

r--

It has been demonstrated that the system presented here should b e successful in practical use for most milling operations. We need to improve the margin bctween the

-1

good and damaged cutter in the resonant cases and, to some extent also in the tronsients. Further research will concentrate on the use of the noise signal and the spectrum of noise combined with the signals described in this paper. The resolution of the damaged tooth in the transients may improve in this way.

-1 2.5

0.00

IL----

I

0.00

0.04

0.08

0.00

0.08

(SW 11. Vibration signals at the resonant case ft = 482 Hz. point b)

fo Fig 8 Test data

50

10. Altintas. Y.. Yellowley. I., Tlusty, J., 'Detection of Tool Breakage in Milling,' ASME 1985 WAM, PED-Vol. 18, pp, 4148. 11. Altintas. Y . , Yellowley. I, 'In-Process Detection of Tool Failure in Milling Using Cutting Force Models,' ASME 1987 WAM, PED-Vol.26, pp. 1-16. 12. Tlusty, J., MacNeil. P., 'Dynamics of Cuttlng Forces in End Milling,' ClRP Annals, Vol. 24/1/1975. 13. Fu, H.J.. DeVor. R.E., Kapoor, S.Cr., 'Mechanistic Model for Prediction of the Force System in Face Milling.' ASME J. Eng. Ind. 106, 1984. 14. Stein. J.L., Shin, K.-C., 'Current Monitoring of Field Controlled DC Spindle Drives,' ASME 1984 WAM. PED-Vol. 18, pp. 57-66.

12. Vibrotion signals for the cases of Figs 9 and 1 1 , Ratios of AXavIXm.

References 1. Tlusty, J., Massood, Z., 'Chipping and Breakage of Carbide Tools,' ASME J. Eng f. Ind., Nov, 1980, Vol. 10, p p 403412.

2. Iwata, K, Moriwaki, T., 'Application of Acoustic Emission to In-Process Sensing of Tool Wear,' ClRP Annals, Vol. 2611/ 1977.

3. Moriwaki, T., "Detection of Tool Fracture by Acoustic Emission Measurement,' ClRP Annals, Vol. 2911/1980. 4. Kannatey - Assibu, Jr., Dornfeld. D.A., 'Quantitative Relationship for Acoustic Emission from Metal Cutting,' ASME J. Eng. Ind. 103, 1981, pp. 33&340. 5. Liang, S.Y.. Dornfeld. D.A., 'Detection of Cutting Tool Wear Using Adaptive Time Series Modeling of Acoustic Emission Signal,' ASME 1987, WAM, PED - Vol. 26. pp. 27-38.

6. Emel, E., Kannatey - Assibu, Jr., 'Characterization of Tool Wear and Breakage. , .', 14th NAMRC. 1986, pp. 266-272. 7. Emel, E., Kannatey, - Assibu, Jr., 'Tool Failure Monitoring in Turning by Pattern Recognition Analysis of AE Signols,' ASME 1987 WAM PED-Vol. 26, pp. 39-55.

8. Bertok. P., Takata. S. Matsushima, K, Ootsuka, J.. Sata. T.. 'System for Monitoring the Machining Operation b y Referring to a Predicted Cutting Torque Pattern,' ClRP Annals, Vol. 32/1/1983. 9. Lon, M.S.. Naerheim. Y., 'In-Process Detection of Tool Breakage in Milling,' ASME 1985 WAM, PED-Vol. 18. pp. 49-

56.

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