In-process control of surface roughness due to tool wear using a new ultrasonic system

Int. J. Math. Tools Manufact. Vol. 36, No. 3, pp. 411-422, 1996 Copyright ~) 1995 Elsevier Science Lid Printed in Great Britain. All rights reserved 0890-6955196515.00 + .00

lPergamon

0890-6955(95)0005%7

IN-PROCESS CONTROL OF SURFACE ROUGHNESS DUE TOOL WEAR USING A NEW ULTRASONIC SYSTEM

TO

SCOTI" A. C O K E R t and YUNG C. SHINt (Received 1 December 1994)

Abstract--This paper presents in-process monitoring and control of surface roughness during machining processes via ultrasonic sensing. A newly developed system is utilized to measure the reflected intensity of ultrasonic beams from the surface and in-process measurement capability is evaluated under machine vibration and tool wear. The experimental results show that the system is able to detect the changes in surface roughness caused by tool wear and can possibly be used to monitor tool wear. Finally, in-process control of surface roughness using the ultrasonic system is demonstrated by adapting the system to a CNC machining center and performing geometric adaptive control. The results show it is possible to maintain surface roughness within 10% of the target value for tool wear up to 0.3 mm.

INTRODUCTION

With increasing demands for higher productivity and quality, there has been increased interest in monitoring all aspects of the machining process. Some of the sensors in use to this end include; bearing, cutting and spindle force sensors, dimensional and proximity sensors, acoustic emission sensors and surface roughness sensors [1]. The purpose of using these sensors in machining processes is to increase part quality while decreasing cost and time of manufacture. Among various process conditions, surface finish is a very important factor determining the quality of a piece. Many life attributes can be determined by how well the surface finish is maintained. To ensure that a part is machined with the desired surface, an in-process measuring system must be designed and implemented. This system would ideally be able to measure the surface in-process in real time. This should give immediate feed back on the surface and allow adaptive control to be used to maintain the desired results. An alternative to in-process measurement is an on-line system, that would measure the roughness after the part has been machined and before it goes on to the next production step. This type of system would allow geometric control that would change the process parameters for the next part based on measurements from the existing part. Both of these systems would be much more desirable than the procedure used in the majority of the machining operations today. The present technique is to measure one out of maybe fifty to one hundred parts. The part is removed from the manufacturing line and measured with a stylus based profilometer in a "clean" environment. This procedure is time consuming and can often result in damage to the part. Statistical process control is another method that is used to "predict" that the desired results are obtained based on previous performance by the machines. An in-process or on-line system would allow 100% inspection of all machined surfaces while ensuring that unnecessary machining is not performed and the part not made too smooth. This paper is concerned with on-line surface roughness measurement and control of a machined part by means of an ultrasonic system. To this end tool wear effects on surface roughness are examined.

Surface roughness Direct surface roughness measurement is needed for machining control as there is no absolute, robust way to predict the roughness analytically or experimentally. There tSchool of Mechanical Engineering, Purdue University, West Lafayette, IN, U.S.A. Hm ~-3-1

411

412

s.A. Coker and Yung C. Shin

seem to be numerous factors that can affect the surface roughness. Controllable process parameters include; feed, cutting speed, tool geometry and setup. Other factors which are harder to control include tool, workpiece and machine vibrations, tool wear and degradation, and workpiece and tool material variability. There has been some work done to attempt to predict the roughness, but each approach seems to have its limitations. The analytical approach depends on geometrical work to derive an ideal profile from which the roughness can be predicted. A turning example of such an approach was shown by Vajpayee [2]. This can be limited by any number of unpredictable events and can become quite complex if all contributing factors are attempted to be modeled. A more complex scheme for peripheral milling that includes tool wear, cutter vibrations and run out is presented by Ismail et al. [3]. The approach starts with analytical formulas that are supplemented by the machine tool characteristics and experimental data. Factorial regression is another way to predict the surface roughness [4, 5]. For this approach, experiments must be conducted with all the factors of significance considered. This can lead to a lot of experiments and tedious work that may not be transferable to other machine tools and processes. In addition, some of the controlling factors themselves may be hard to quantify and control. For both analytical and experimental prediction, most of the work has been done on turned surfaces as the surface is basically described by a two-dimensional (2D) model. With other 3D surfaces such as ground and milled pieces, the prediction can become much more complex. It is, therefore, advantageous to measure the roughness in-process or on-line.

Previous work to monitor surface roughness in-process Optical techniques to measure roughness in-process will be reviewed first. With the range for common finish machining being 0.5 I~m to 5 ~tm in Ra, the present optical systems are mostly area-averaging in nature. The first of these methods uses a fibreoptic bundle to measure the intensity of a beam of light reflected from the machined surface in the specular direction [6-9]. The distance from the surface to the sensor affects the intensity and therefore must be kept constant. Another technique to use fibre-optics measures the diffuseness of the reflected light from the surface [10]. The reflectivity of the surface must also be taken into consideration with a correlation chart being generated for each material. Another system that measures the specular reflectance and the total reflected intensity from the surface uses a helium laser to generate the light beam and a lead selenide detector to receive the reflected signal [11]. A commercially available optical comparator has been used to characterize surface roughness [12]. This system also measures the specular reflectance from the surface and compares the data with those from standard surfaces. Two optical methods being developed have the possibility of profiling the surface. The first of these uses a system of photo diodes to measure the displacement of laser beams on the surface [13, 14]. Another set of optical methods use focus error detection to determine the profile of the surface, but seem to be more suited for measurements of smooth surfaces of less than 1 v,m Ra [15]. Another approach to roughness measurement with an optical technique utilizes machine vision systems to view the surface. A light source is used to illuminate the surface with a digital system viewing the surface and the data being sent to a computer to be analyzed [16-18]. The digitized data is then used with a correlation chart to get actual roughness values. In all of the optical techniques, there seem to be some limitations to their in-process use. In the harsh machining environment, cutting fluid or any extraneous materials seem to affect the measurements. Also, any of the parametric methods must be carefully correlated for each material with differing reflectances. Non-optical methods that are being researched include; an inductance pickup and a capacitance probe [19, 20]. The first of these, the inductance pickup, depends on the placement of a sensor in close proximity of the surface to measure the inductance. This measurement gives a parametric value that may be used to give a comparative

In-process Control of Surface Roughness

413

roughness. The inductive system is limited to measuring magnetic materials and would also be adversely affected by cutting fluid and chips. The capacitive systems also depend on the placement of a sensor in close proximity of the surface. The sensor measures the capacitance between the sensor and the surface and gives a comparative roughness parameter. Work has also been done by Garbini et al. [20] with a small sized capacitive sensor that would give profile information. This sensor is limited to conductive metals and would be adversely affected by fluids and chips. The last roughness measurement technique to be reviewed and used in the present work uses an ultrasonic sensor to provide a profile measurement [21-23]. A spherically focused ultrasonic sensor is positioned with a non-normal incidence angle above the surface. The sensor sends out an ultrasonic pulse to the surface and measures the amplitude of the returned signal. This data is sent to a personal computer for analysis and calculation of roughness parameters. The system, once calibrated with data from a stylus profilometer, produces the actual roughness values. IN-PROCESS MEASUREMENT BY ULTRASOUND

The original system as described by Shin and Oh [21] was an off-line system which utilized an immersion tank on a x - y - z movement table. The immersion tank provided the necessary fluid couplant for the ultrasonic sensor while the x - y - z table provided the movement of the surface for the measurements. Water was used as the original coupling medium as it provided better sound transmission than air. This system was updated as described in Coker et al. [24] and shown in Fig. 1 to be used on-line and possibly in-process. The immersion tank was replaced by a nozzle that squirted the necessary fluid onto the surface in a steady stream. Cutting fluid provided by the machine was used as the couplant, as it was readily available and the circulating system of the machine could be utilized. This stream of cutting fluid provided more than just the medium for the ultrasonic beam. The fluid also cleared any cutting chips from the surface and provided necessary flooding to the cutting tool. The movement provided by the x - y - z table was replaced by the movement of the spindle housing of a CNC machining center. The ultrasonic sensor was mounted on the spindle housing close to the point of actual machining to provide measurement immediately after cutting. Initial tests were completed to ensure that the ultrasonic sensor would measure the roughnesses on-line on milled aluminum surfaces. Results to these experiments are shown in Fig. 2. The same correlation curve is present with the spindle running at 2500 rpm as with the spindle stopped. This trend is the same that is obtained off-line on the x-y-z table. These results confirm that the system has the capability to measure surfaces on-line and has some tolerance to vibrations of the running machine. To correlate the roughness values given by the ultrasonic software, a Rank Taylor Hobson

Machine

1Coolant

Holding

Pulser Receiver

l/ Fig. 1. In-process setup.

414

S . A . Coker and Yung C. Shin

20

•

s=0 RPM

•

s=2500 RPM

~15 e-.

10 ,~"~ 5 pj,ql -

0

....

I ....

0.5

I

I~Aj,I

1.5

....

2

I ....

2.5

3

R a (I.tm) Fig. 2. Ra correlation curve of on-line measurement with the ultrasonic system.

Surtronic 3 plus contact stylus profilometer was used to measure the roughness of specimens. For the measurements with both the profilometer and the ultrasonic sensor, the milled surfaces were scanned along the centerline of the cut with three measurements averaged to reduce errors from local surface variations. The profilometer scanned 8 mm (0.32 in) in length while the ultrasonic system scanned 6.35 mm (0.25 in) with an ISO-2CR filter being used for both systems. In-process experiments have been presented in Coker et al. [24], but will be briefly reviewed here to show the capabilities of the ultrasonic system. The feed was varied from 0.0762 mmpr (0.003 ipr) to 0.2794 mmpr (0.011 ipr) while maintaining the feedrate of 10.82 cm/min (4.26 in/min). The procedure for these tests was to measure the roughness with the ultrasonic system while the surface was being machined with the measurements repeated on-line with the spindle turned off. The surface was then cleaned, dried and measured with the contact profilometer to obtain the corresponding roughness. For each of the measuring techniques, three spots were measured and the data were averaged with the results presented in Fig. 3. This plot shows the same trend exists for the on-line and in-process data. It can be observed that for some of the rougher surfaces, the in-process data have slightly higher values, which can be attributed to the additional noise present during machining. While machining it was evident that the stream of coolant from the nozzle was sufficient to wash away any extraneous chips and to provide the necessary coolant to the cutting tool. TOOL WEAR EXPERIMENTS

A beneficial aspect of surface measurement is the possibility of checking tool quality and wear. While many methods have been proposed and studied to monitor tool wear on-line and in-process including monitoring acoustic emission [25, 26] and monitoring the vibration of the tool holder [27], these methods require a good correlation derived through complete experiments with all extraneous sounds and vibrations reduced. It is also likely that any correlation data obtained will be very machine specific and results 25

In-Process On-Line

•

20

•

5 10 a~" 5 •

0

Ill

II

0.5

l

I

1.5

I,,,,I

2

L

2.5

3

R (l.tm) Fig. 3. R. correlation curve for ultrasonic system, in-process test.

In-process Control of Surface Roughness

415

will not transfer well to other machines. The ideal situation would be to measure the surface roughness directly and correct for the effects of tool wear or other disruptions. If all of the process parameters are kept constant, a change in the surface would indicate tool wear. If this relation is examined thoroughly, then the amount of tool wear could be predicted from the roughness. On the other hand, if it is desirable to maintain roughness at a specific level as the tool wears, the process parameters may be changed with a simple geometric control scheme. This procedure would measure the roughness on-line and compensate for tool wear by changing the process parameters for the next part. As the first step towards using the ultrasonic sensor for this purpose, an initial evaluation test was conducted to see if the system would provide measurements sensitive to the changes in the surface caused by the changing tool properties. Aluminum was used as the measuring surface and machined on a Mazak CNC machining center with a single K68 carbide insert in a 7.62 cm (3 in) face mill. The surfaces were measured on-line after machining was completed. With free machining 6061 aluminum as the test material, a different material had to be used to accelerate the wear of the carbide tool between cuts. Nodular gray cast iron was chosen, as it provided high machining forces and caused little built up edge. To isolate the effects of tool wear, a consistent set of process parameters were chosen. For the initial experiment, the feed was set to 0.018 cmpr (0.007 ipr) with 1300 rpm used for the rotational speed of the cutter. A specific procedure was followed for the experiments to ensure repeatability. The aluminum block was machined first with the face mill using the specified process parameters and then measured with the ultrasonic and profilometer systems. Once the surface was measured, the cutting insert was artificially worn by milling the cast iron "wear" block to induce wear on the insert and then the flank wear was measured with a toolmakers microscope. The procedure was repeated until the tool wear reached a preset value. With both measuring systems, the data was collected along the centerline of the cut in three spots with the results averaged to remove effects of local surface irregularities. When cutting the aluminum, a depth of cut of 0.076 cm (0.030 in) was used with a taper of 0.102 mm/30.4 cm (0.004 in/ft) programmed to remove the effects of backstreaking from the face mill. The purpose of the slight taper was to remove backstreaking that would randomly occur and greatly change the surface periodicity. The results from this experiment are shown in Figs 4 and 5. Figure 4 shows the roughness values plotted against the tool wear. From this graph it can be observed that the ultrasonic values followed the profilometer values very well. The need for direct roughness measurement and control can also be observed since there is no linear straightforward correlation between roughness and tool wear. The curve is very irregular and shows no general trend for roughness as the cutting tool wears. The initial improvement in surface roughness is due to the increased corner radius with tool wear. As the flank wear develops further and the cutting edge becomes dull, cutting by tear dominates, thereby

4.5 43.5 3 i2.5 2

•

Ultra.

-----

Prof.

1.2 0.8 ~a

0.6~

o.45

~" 1.5

0.2 1 0.5 ~'''~ .... i .... ,... , I . . . . l l t l , I . . . . I , , 0 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Wear (rnm) Fig. 4. Roughnessvs tool wear.

416

S.A. Coker and Yung C. Shin

3.5 3 "E 2.5 O I

~1.5 e~ 1

0.5 0.2

0.4 0.6 0.8 Ra (lira)

1

1.2

Fig. 5. R. correlation curve for ultrasonic system for initial wear test.

deteriorating the surface roughness. Figure 5 shows the correlation between the ultrasonic (URa) values and the profilometer (Ra) values. This initial experiment showed that the ultrasonic system would be able to detect the changes in the surface caused by tool wear. SURFACE ROUGHNESS CONTROL WITH TOOL WEAR

The next step in the research was to use the roughness information to control the finish as the tool wears. To maintain the roughness, it was decided to change the feed to either increase or decrease the roughness as needed. A simple geometric control scheme was implemented (Fig. 6). For these experiments, this control scheme was implemented manually by adjusting the feed after machining each block, but could easily be automated by using a personal computer that would give the necessary feeds. The roughness was evaluated by the ultrasonic system and the error between this roughness and the desired roughness was calculated. A new feed was then decided on and the piece was remachined. To automate this procedure, a simple proportional control could be implemented to take the error value and output the necessary feed. In order to control the surface roughness, the correlation among actual roughness, ultrasonic roughness and feed had to be established first. For this purpose three experiments were conducted in the same manner as the wear test mentioned before with each experiment having a different feed. The feeds chosen were 0.0178 cmpr (0.007 ipr), 0.0203 cmpr (0.008 ipr) and 0.0229 cmpr (0.009 ipr) with the data from each of these experiments shown in Figs 7-12. The same format for the plots is used as the previous test with the first plots showing roughness values vs wear data. These plots show how well the ultrasonic measurements followed the profilometer values for each of the feeds used. The second set of plots show the correlation curve for each test with the ultrasonic (URa) data plotted vs the profilometer (R~) data. Figure 13 shows all of the correlation curves plotted on one graph with straight lines and their individual fits added also. With this single plot (Fig. 13) a factorial regression was performed to obtain an equation relating the feeds, the ultrasonic values and the profilometer values. For the fitted equation, the feeds ( f ) and the ultrasonic values (URn) are the independent

~

Feed

I Pr Ch ic

J Correlation L . Ultrasonic ]Ultrasonic

Roughness J

J-goughnes, I

System

Fig. 6. Control scheme implemented to maintain roughness.

I ~

In-process Control of Surface Roughness

•

Ultrasonic

-

417

Profilometer 1.8 1.6 1.4 1.2

-

2.5

1

- 0.8 - 0.6 0.4 1 , , , i .... i ,,,1 . . . . i .... i,, 0.2 0 0.1 0.2 0.3 0.4 0.5 0.6 Wear (mm) Fig. 7. Roughness vs tool wear, 0.0178 cmpr (0.(107 ipr). ~ 1.5

- -

y --0.40216 + 1.6211x R =

3

2.5 0 2

5 ¢~ 1.5 1

'

0

'=if'

0.4

'

'

I

'

'

'

i

0.8 1.2 Ra (llm)

J

J

i

I

i

~

A

1.6

I

2

Fig. 8. Ra correlation curve for ultrasonic system, 0.0178 cmpr (0.(107 ipr) wear test. Ultrasonic 9

--~

Profilometer - 1.6 1.4 1.2 1

~

~"

4

0.8 ""

3 2

0.6 0.4

0

,,, I , , , ,I , ,~/, I . . . . ~;2,, 0.1 0.2 0.3 0.4 0.5 Wear 0.008ipr # 14

Fig. 9. Roughness vs tool wear, 0.0203 cmpr (0.008 ipr). variables with the dependent variable being the profilometer values (Ra). The statistical package Minitab was used to run the factorial regression and find the "best" fit for the data. The data was entered into Minitab with various combinations of the independent variables created including terms up to third order such as UR 3, ]3, UR2.f and URa*f2. The highly correlated terms were removed from the fit including UR 2, f3 and UR*ff"z as they interfered with the calculation of the fitted equation. Minitab was then used to find the best combination of the remaining independent variable by running various subsets. Based solely on the coefficient of multiple determination (R2), two best fits were chosen, both of which had R 2 of 91.4% with the first including f, URa, .f2, UR$ and UR] with the second adding UR*~f. The first set was decided upon and a full A N O V A table was calculated with the fitted equation given by

418

S.A. Coker and Yung C. Shin - -

y = -0.47507 + 5.4383x

98 I

R= 0.944 RL-0.892

°

5

"~4 e~ 3 2 0.4

0.6

0.8

1

1.2

,i 1.6

1.4

R a (gm)

Fig. 10. R, correlation curve for ultrasonic system, 0.0203 cmpr (0.008 ipr) wear test. Ultrasonic

--~

15

Profilometer 2 1.8

"~ 12.5

x~

1.6 ~ 1.4 ~"

lO

1.2 1

"'* 7.5

1 0.8 0.1 0.2 0.3 0.4 0.5 Wear (mm) Fig. 11. Roughness vs tool wear, 0.0229 cmpr (0.009 ipr).

5 "' -0.1 0

y= -2.0038 + 8.7493x 15

~

12.5 1o

~

7.5 iol I i i i I L L i I , , , I , , , I , ~ , I

0.8

1

1.2

1.4

1.6

1.8

2

R a (lain)

Fig. 12. R. correlation curve for ultrasonic system, 0.0229 cmpr (0.009 ipr) wear test. PRa = 19.0 - 4 4 0 8 " f + 0.599* URa + 242561" f ~

(1)

- 0.0542* UR 2 + 0.00187* UR 3 w h e r e PRa = predicted roughness f = feed URa = ultrasonic roughness. T h e results of the analysis f r o m Minitab are p r e s e n t e d in T a b l e s 1-3. T a b l e 1 shows the various predictor coefficients and the standard deviation for each. Also included in the table is the t-ratio which is the coefficient divided by the standard deviation. This t-ratio and the n u m b e r of samples are used to d e t e r m i n e the p - v a l u e which in turn gives the significance of each predictor. E a c h of the listed predictors is d e t e r m i n e d

In-process Control of Surface Roughness •

15 "6" "r- 10

0.007ipr

--~

0.008ipr

....

O.O09ipr

O

5

d.

,

,~

0

11

~

0.4

~

I

",s'o

~

,

.

.I,

o 1[ ~

.

.,/"

0

> ~.

•t , j

./

~

419

,

,

,

I

0.8 1.2 R a (t.tm)

,

,

,

I

1.6

,

1

2

Fig. 13. R, correlation curve of ultrasonic system, three experiments combined. Table 1. Results from statistical package Minitab Predictor

Coefficient

Constant f

18.970 -4408.1 0.59876 242561 -0.05423 0.0018697

UR,, f'2

UR*.2 UR*,3

Standarddeviation

t-ratio

3.445 884.0 0.07779 55162 0.01062 0.0004409

5.51 -4.99 7.7 4.40 -5.11 4.24

0.000 0.000 0.000 0.000 0.000 0.000

Table 2. Analysis of variance for factorial regression Source

DF

Regression Error Total

5 35 40

SS 6.0160 (SSR) 0.5686 (SSE) 6.5846 (SSTO)

MS

F

p

1.2032 0.0162

74.06

0.000

Table 3. Continued analysis of variance table Source f

UR,

I'2

UR*~2 UR*,3

DF

Seq SS

1 1 1 1 1

1.4309 3.8682 0.0167 0.4079 0.2922

to be significant for p-values less than 5% and is included in the final equation. The p-value had shown that the term UR*~f was insignificant in the prediction and was therefore removed. Tables 2 and 3 present the analysis of variance table generated by Minitab. Table 2 examines the complete regression and the total error and shows that with a p-value less than 5% the overall fit is significant. The sum of squares for the regression (SSR) and the sum of squares total (SSTO) are used to determine the coefficient of multiple determination (R 2) as mentioned before. The high F value (74.06) also shows that the fit is significant for the data. Table 3 shows the sequential sum of squares for each of the predictors which gives an idea of how much each predictor is contributing to the regression fit. With the necessary relationship available, the next step was to conduct an experiment to control the surface roughness with increasing tool wear. An Ra value of 1 . 4 g m was

420

S.A. Coker and Yung C. Shin PredictedR,

•

MeasuredR, .7

1.7 1.5

i

1.5

1.3 E

1.1

1.1

0.9

o.9

~.

0.7

0.7 ":

0.5 0 5 10 15 20 25 30 35 Run Number Fig. 14. Predicted roughness and measured roughness against run number. 0.5

,,,I

....

J ....

I ....

J ....

,

.... ,,,,

chosen to be the desired roughness to maintain as it allowed the greatest flexibility within the available data. This would allow the changing of the feed in either direction without having to extrapolate the regression fit. The procedure for this experiment was similar to that of the previous ones. To start the test, a feed of 0.0178 cmpr (0.007 ipr) was chosen to cut the aluminum measuring block. Three measurements w e r e taken along the centerline of the cut with the ultrasonic system. The average of the ultrasonic data was used in equation (1) along with the feed used to obtain the predicted surface roughness (PR,). The surface was cleaned, dried and measured with the contact profilometer to check the predicted value. For all the experiments the regression formula was able to predict the surface to within 20% of the actual value. Once the measurements were complete, the difference between the predicted roughness and the desired roughness was determined and this value was used to decide the next part's process parameters. For these tests the cutting tools were artificially worn by machining a cast iron block after the surface roughness was controlled within an acceptable level, since the wear rate with the aluminum blocks was slow and would not allow the completion of a test in a reasonable amount of time. The difference between the desired and predicted roughness values was controlled to within plus or minus 6% before the experiment was continued. Figure 14 shows the actual profilometer roughness (MR,) and the ultrasonic predicted roughness (PR,) plotted vs the run number. This plot shows that the predicted roughness values follow the actual values very well and leads to Fig. 15 which shows the percentage difference between the actual and predicted roughness values. Figure 16 shows the percentage difference b e t w e e n the actual measured roughness and the desired roughness values on the left axis with the wear amounts on the right axis. The wear points in this plot indicate a test run when the cutting tool has been worn with the corresponding measuring surface generated with the previous feed. This plot demonstrates that each time incremental tool wear is introduced the actual measured roughness may deviate from the desired

~

40

-- ~ •

20 30

~

%cliffMeas. &Pred. R WearAmounts 0.25 a

i li

o

O.2

o.1 ~

~-,o

o.O5~o

.

,,, l . . .". .. . . .i.. . . .'. . . . ' , , ,

-40 0

5

10 15 20 25 Run Number

~

-0.05

30 35

Fig. 15. Percentage difference between measured and predicted roughness and tool wear.

In-process Control of Surface Roughness --o--

.J

50 40 30 20 10 0 -10 -20 -30

421

%diff Given&Measured 0.25 0.2 0.15 0.1

~g

0.05

I,,,,I .... Ittjil .... I,tt -0.05 10 15 20 25 30 35 Run Fig. 16. Percentage difference between measured and desired roughness and tool wear vs run number.

,,I

0

....

5

roughness, but it is quickly brought back by changing the feed values and remachining the surface. To assure that the roughness had been sufficiently controlled and to aid in the presentation of the data, once the desired roughness was obtained, the aluminum block was remachined at least three times with the same process parameters before further wearing the tool. During actual production it is not likely that such large tool wear would occur between roughness measurements and therefore the roughness would not deviate as much from the desired value. With smaller changes in the tool condition between measurements, the actual roughness would be much more controlled and closer to the desired value. At a wear amount of approximatley 0.3 mm (0.0118 in) the experiment was halted as the roughness values were getting smoother than the fitted data obtained earlier and the prediction was not accurate because the fit was extrapolating too far. However, this problem can be alleviated by adding more experimental data to the correlation chart. CONCLUSION The ultrasonic system has been shown to be able to measure surface roughness online and in-process. In comparison to other in-process methods, it does not suffer from common set-backs such as interference by cutting fluids and chips. The system is easy to set-up and does not significantly alter the machine tool it is mounted on. A standard, inexpensive personal computer is used for data collection and analysis with an off-theshelf spherically focused ultrasonic sensor being used to generate the signals. An added benefit of the technique is indirect measurement of tool wear. A geometric control scheme has been demonstrated to maintain a desired surface finish throughout tool degradation. This work has the possibility of being extended to a full in-process monitoring system that would counteract any troublesome external factors. Process parameters could be varied in-process with an adaptive control scheme or a simpler geometric control scheme. Both of these methods could be used to ensure consistent part quality. REFERENCES [1] J. Tlusty and G. C. Andrews, A critical review of sensors for unmanned machining, Ann. CIRP 32(2), 563-572 (1983). [2] S. Vajpayee, Analytical study of surface roughness in turning, Wear70, 165-175(1981). F. Ismail, M. A. Elbestawi, R. Du and K. Urbasik, Generation of milled surfaces including tool dynamics and wear, .I. Engng Ind. Trans. A S M E 115, 245-252 (1993). [4] R. M. Sundaram and B. K. Lambert, Surface roughness variability of AISI 4140 steel in fine turning using carbide tools, Int..I. Prod. Res. 17(3), 249-258 (1979). [5] S. K. Dontamsetti and G. W. Fischer, Factors affecting surface roughness in finish turning of gray iron, Adv. Mater. Mfg Processes 3(4), 689-725 (1988). [6] D. Spurgeon and R. A. C. Slater, In-process indication of surface roughness using a fibre-optics transducer, Proc. 15th Int. MTDR Conf., pp. 339-347 (1974). [7] I. Ins~ki, Development of in-proce~ sensor for surface roughness measurement, Proc. 23rd Int. MTDR Conf., pp. 109-113 (1982).

422

S. A. Coker and Yung C. Shin

[8] I. Inasaki, In-process measurement of surface roughness during cylindrical grinding process, Precision Engng 7(2), 73-76 (1985). [9] D. A. Dornfield and R. Y. Fei, In-process surface finish characterization, Mfg Simulation Processes, ASME, PED 20, 191-204 (1986). [10] H. Takeyama, H. Sekiguchi, R.Murata and H. Matsuzaki, In-process detection of surface roughness in machining, Ann. CIRP 25(1), 467-471 (1976). [11] D. G. Jansson, J. M. Rourke and A. C. Bell, High-speed surface roughness measurement, J. Engng Ind. Trans. ASME 106, 34-39 (1984). [12] K. J. Stout, Optical assessment of surface roughness: the effectiveness of a low-cost, commerciallyavailable instrument, Precision Engng 6(1), 35-39 (1984). [13] K. Mitsui and H. Sato, Development of an in-process sensor for surface roughness by laser beam, Proc. 16th Int. MTDR Conf., pp. 171-177 (1976). [14] M. Shiraishi, In-process measurement of surface roughness in turning by laser beams, J. Engng Ind. Trans. ASME 103, 203-209 (1981). [15] K. Mitsui, In-process sensors for surface roughness and their applications, Precision Engng 8, 212-220 (1986). [16] F. Luk, V. Huynh and W. North, Measurement of surface roughness by a vision system, Computers in Engineering, Proc. Int. Comp. Engng Conf., ASME vol. 2, pp. 61-65 (1987). [17] S. M. Sundar and S. Raman, Analysis for vision assisted optical characterization of machined surfaces, Mfg Sci. Engng., ASME PED, 64, 43-58, New Orleans (1993). [18] D. L. Devoe and G. Zhang, Optical area-based surface quality assessment for in-process measurement, Mfg Sci. Engng, ASME, PED 64, 413-420 (1993). [19] G. Sathyanarayanan and V. Radhakrishnan, Roughness measurement with non-contacting inductive pickup, Mfg Metrology, ASME PED 29, 75-100 (1988). [20] J. L. Garbini, S. Koh, J. E. Jorgensen and M. Ramulu, Surface profile measurement during turning using fringe-field capacitive profilometry, Trans. ASME 114, 234-243 (1992). [21 ] Y. C. Shin and S. J. Oh, Surface roughness measurement by ultrasonic sensing for in-process monitoring, Mfg Sci. Engng, ASME PED, 64, 3-12 (1993). [22] Y. C. Shin, S. J. Oh and S. C. Coker, Surface roughness measurement by ultrasonic sensing for inprocess monitoring, J. Engng Ind. Trans. ASME 117, 439-447 (1995). [23] S. J. Oh, Y. C. Shin and E. S. Furgason, Surface roughness evaluation via ultrasonic scanning, IEEE Trans. Ultrasonics, Ferroelectrics and Frequency Control 41, 863-871 (1994). [24] S. A. Coker, S. J. Oh and Y. C. Shin, In-process monitoring of surface roughness utilizing ultrasound, Mfg Sci. Engng, ASME PED, 68-1, 145-154, Chicago IL (1994). [25] K. lwata and T. Moriwaki, An application of acoustic emission measurement to in-process sensing of tool wear, Ann. CIRP 25(1), 21-26 (1977). [26] A. E. Diniz, J. J. Liu and D. A. Dornfeld, Correlating tool life, tool wear and surface roughness by monitoring acoustic emission in finish turning, Wear 152, 395-407 (1992). [27] S. M. Pandit and S. Kashou, A data dependent systems strategy of on-line tool wear sensing, J. Engng Ind. Trans. ASME 104, 217-223 (1982).