eScholarship provides open access, scholarly publishingservices to the University of California and delivers a dynamicresearch platform to scholars worldwide.

Laboratory for Manufacturing andSustainabilityUC Berkeley

Title:A Study of Surface Roughness in the Micro-End-Milling Process

Author:Lee, Kiha, University of California, BerkeleyDornfeld, David A, UC Berkeley

Publication Date:05-31-2004

Series:Consortium on Deburring and Edge Finishing

Publication Info:Laboratory for Manufacturing and Sustainability

Permalink:http://escholarship.org/uc/item/51r6b592

Keywords:burr, roughness, micro end milling

Abstract:Micro-end-milling is emerging as an important fabrication process. Its benefits include the abilityto fabricate micro and meso-scale parts out of a greater range of materials and with more variedgeometry than is possible with lithography and etching. It also enables the creation of microand meso-scale molds for injection molding. Factors affecting surface roughness have not beenstudied in depth for this process. A series of experiments has been conducted in order to beginto characterize the factors affecting surface roughness and determine the range of attainablesurface roughness values for the micro-end-milling process. A 229 ?m diameter end mill was usedto cut slots into aluminum (6061) samples. The machining factors studied were chip load (feedper tooth), cutting speed, and depth of cut. A two level factorial experiment was run, and it wasdetermined that while chip load was the dominating factor, the interaction between chip load andcutting speed was also significant. Further experiments allowed the generation of a second orderrelationship between chip load and surface roughness. The model, which includes the effect ofchip load, cutting speed, and the interaction between the two, predicted the surface roughnessvalues with an accuracy of about +/- 10%. The surface roughness values ranged from 600 Å allthe way to 3800 Å over the span of the studied parameters.

It has previously been shown that run-out creates a greater problem for the dimensional accuracyof parts created by a micro-end-milling process as compared to parts created by a traditional end-milling process (Lee, et al 2001). It appears that run-out also has a more significant effect on thesurface quality of micro-end milled parts. The surface roughness traces reveal large peak to valleyvariations with a period of twice the chip load. This means that one of the two cutting edges on

eScholarship provides open access, scholarly publishingservices to the University of California and delivers a dynamicresearch platform to scholars worldwide.

the tool creates a deeper cut than the other. Cutting marks from the non-dominant cutting edgeare also visible on the surface roughness traces as small steps between the much larger marksfrom the dominant cutting edge. It is postulated that the effect is due to run-out, and that improvingmachine tool run-out will have a very significant effect on the surface quality of micro-end-milledfeatures.

Copyright Information:All rights reserved unless otherwise indicated. Contact the author or original publisher for anynecessary permissions. eScholarship is not the copyright owner for deposited works. Learn moreat http://www.escholarship.org/help_copyright.html#reuse

A STUDY OF SURFACE ROUGHNESS IN THE MICRO-END-MILLINGPROCESS

Kiha Lee and David A. Dornfeld

Laboratory for Manufacturing AutomationDepartment of Mechanical Engineering

University of California at BerkeleyBerkeley, CA 94720, USA

dornfeld@me.berkeley.edu

ABSTRACT

Micro-end-milling is emerging as an importantfabrication process. Its benefits include theability to fabricate micro and meso-scale partsout of a greater range of materials and withmore varied geometry than is possible withlithography and etching. It also enables thecreation of micro and meso-scale molds forinjection molding.Factors affecting surface roughness have notbeen studied in depth for this process. Aseries of experiments has been conducted inorder to begin to characterize the factorsaffecting surface roughness and determine therange of attainable surface roughness valuesfor the micro-end-milling process. A 229 mmdiameter end mill was used to cut slots intoaluminum (6061) samples. The machiningfactors studied were chip load (feed per tooth),cutting speed, and depth of cut. A two levelfactorial experiment was run, and it wasdetermined that while chip load was thedominating factor, the interaction between chipload and cutting speed was also significant.Further experiments allowed the generation ofa second order relationship between chip loadand surface roughness. The model, whichincludes the effect of chip load, cutting speed,and the interaction between the two, predictedthe surface roughness values with an accuracy

of about +/- 10%. The surface roughnessvalues ranged from 600 Å all the way to 3800Å over the span of the studied parameters.

It has previously been shown that run-outcreates a greater problem for the dimensionalaccuracy of parts created by a micro-end-millingprocess as compared to parts created by atraditional end-milling process (Lee, et al 2001).It appears that run-out also has a moresignificant effect on the surface quality of micro-end milled parts. The surface roughness tracesreveal large peak to valley variations with aperiod of twice the chip load. This means thatone of the two cutting edges on the tool createsa deeper cut than the other. Cutting marks fromthe non-dominant cutting edge are also visibleon the surface roughness traces as small stepsbetween the much larger marks from thedominant cutting edge. It is postulated that theeffect is due to run-out, and that improvingmachine tool run-out will have a very significanteffect on the surface quality of micro-end-milledfeatures.

INTRODUCTION

The end-milling process is one of the mostwidely used material removal processes inindustry. Recently the micro-end-milling processhas received increased attention (Damazo, et al

1999; Friedrich and Vasile 1996; Schaller, et al1999). Micro-end-milling refers to a basic endmilling process using tools down to 10 mm indiameter. Because the geometries that can beproduced by micro-end-milling are more flexiblethan those produced by lithography, this processis potentially useful as a companion tol i thography based MEMS processingtechniques. Furthermore, a larger range ofmaterials can be processed using this process.This process is also important for the productionof meso-scale parts (parts on the order of 1mmto 1cm) which are too large for lithographytechniques, but too small for many othertraditional processing techniques.

Micro-end-milling is essentially the sameprocess as end-milling on the macro scale.However, there are a few important differences.As the tool diameter becomes smaller, therotational speed theoretically required to achievethe recommended cutting speed is far above thetechnical limit of the available spindles. Forinstance, vc = 6m/min calculated fromn=40,000rpm for a 50 mm diameter tool, ascompared to the recommended vc = 100 m/minfor cutting aluminium in a conventionalmachining process. Another concern with micro-milling is that run-out can become comparable tothe diameter of tools. The run-out to tooldiameter ratio becomes much larger for micro-end-milling than for traditional milling.

Many applications that could benefit from themicro-end-milling process (optical systems forexample) demand extremely good surfacequality. Macro scale parts that require a verygood surface finish often undergo processingafter milling to improve the surface roughness.However, it is more difficult to apply such postprocessing techniques to features on the microscale. Therefore, the surface roughnessgenerated by the milling tool is perhaps evenmore vital at very small scales. With this in mind,the following study to characterize the surfacequality produced by the micro-end-millingprocess has been undertaken.

SURFACE ROUGHNESS IN END MILLING

The typical slot-end-milling process is shown inFigure 1. The surface along the bottom of theslot will be scalloped. As the tool passesthrough the workpiece, each tooth creates a

semi-circular scratch along the bottom of theslot. Thus, the bottom surface will be scalloped.

FIGURE 1. SLOT-END-MILLING PROCESS.

The theoretical surface roughness, Ra, can beestimated using the following equation(Montgomery and Altintas 1991):

( )p/32

2

tt

ta nfR

fR±

= Eq. (1)

where Ra is the surface roughness, nt is thenumber of teeth on the cutter, R is the radius ofthe cutter, ft is the feed per revolution, and the +sign refers to up-milling and the – sign refers todown milling. The above equation does notconsider many factors that in reality can affectthe surface roughness. For this reason thesurface roughness will generally be higher thanthat predicted by Eq. 1. Statistical models thatinclude such factors as depth of cut and cuttingspeed in addition to feed per revolution (chipload) have been developed (Alauddin et al1995). Although empirical models do notprovide as much insight into the physics of aparticular phenomenon, they are useful todetermine the effect of factors that are difficult tophysically model. Therefore, experiments toprovide a statistical model, similar to thatdeveloped by Alauddin, et al, and to providequalitative insight have been performed.

EXPERIMENTAL SETUP

The Mori Seiki CNC drilling center shown inFigure 2 was used for the experiments. Thedrilling center has a maximum spindle speed of8000 RPM; however an attachment (shownbelow in Figure 3) allows operation at 40000RPM.

A 229 mm diameter tool from RobbjackCorporation was used for the experiments. Theend-mills are made of 92% WC and 8% Co.Cobalt increases the toughness of tool. The toolused has a very strong inner adhesion and highedge stability. The tool is shown in Figure 4. Ascan be seen in the figure, the tool is a two flutedend-mill with more or less standard geometry.Slots in aluminum (6061) samples were cut withthis tool to determine the affect of machiningparameters on surface roughness.

FIGURE 2. MORI SEIKI CNC DRILLING CENTERTV-30, MAXIMUM 8,000RPM.

FIGURE 3. AIR TURBINE TOOL, MODEL 250,(SPEED = 40,000RPM).

FIGURE 4. ROBBJACK MICRO-END-MILL, 92%WCAND 8% CO.

Surface roughness measurements were takenwith a diamond stylus (Tencor P-10), travelingalong a straight line over the surface. It featuresthe ability to measure micro-roughness with upto 0.5 Å (0.002 µin.) resolution over shortdistances as well as waviness over a full, 60 mm(2 inch) maximum scan length.

A three factor full factorial experiment wasperformed. The factors included in theexperiment were chip load (or feed per tooth) ft,cutting speed vt, and depth of cut ac. Themachine tool used can only operate at twospindle speeds. Since it was desired to keepthe tool radius constant, only two levels for thecutting speed parameter were used. Since itwas considered that the chip load wouldprobably be the most dominant factor, theexperiments covered four different chip loads.Furthermore, it has been shown that for macroscale end-milling the surface roughnessresponse to chip load is nonlinear (Alauddin et al1995). Two levels and a center point were usedfor depth of cut. Taking a subset of the datacollected, a two level factorial analysis wasperformed, and a linear model was developed.Then, a more detailed analysis was performedon the chip load factor using all four levels. Afinal model was developed with a quadraticrelationship for chip load, and linear relationshipfor the other two factors.

RESULTS AND ANALYSIS

Results of 2 Level Factorial Analysis

A two level factorial analysis was performed ona subset of the data. The high and low values

for depth of cut correspond to 1/2 and 1/4 thetool diameter respectively. The cutting speedvalues correspond to 7500 RPM and 40000RPM with a 229 mm diameter tool.

The chip load is by far the most dominant factoraffecting the surface roughness. However, bothcutting speed and the cutting speed X chip loadinteraction appear to be significant as shown onthe normal probability plot in Figure 5. Anypoints that lie off a straight line in this plot can beconsidered a real effect and not due to randomvariation in the process. The effects of the otherfactors (those points not labeled in Figure 5) arewithin the noise of the process and so havebeen left out of the models developed.

FIGURE 5. NORMAL PROBABILITY PLOT OF MAINEFFECTS AND INTERACTIONS. POINTS THATLIE OFF A STRAIGHT LINE ARE THESIGNIFICANT EFFECTS AND ARE LABELED.

Although cutting speed and the cutting speed Xchip load interaction are significant, their affectis far smaller than the effect of chip load. Alinear model, shown in Eq. 2, was developedusing the data from the 2 level factorial analysis.

ctcta vfvfR 8185896975.96 -++= Eq. (2)

The model predicts the surface roughness withinabout plus or minus 10% of the measured value.

Tipnis, et al. (1976) developed a multiplicativemodel, given by Eq. 3, to model surfaceroughness.

ma

lt

kca afcvR = Eq. (3)

c, k, l, and m are experimentally determinedconstants. Alauddin, et al (1995) used alogarithmic transformation to convert the datafrom a factorial experiment into this form. Theyfound good correlation between this model andtheir experimental data. A similar logarithmictransformation was used in this analysis todetermine the constants for Eq. 3 based onmeasured data. However, it was found that thedeviations between the predicted values and themeasured values were about twice as high usingthe multiplicative model compared to the linearpolynomial model. Therefore, it was thoughtmore appropriate to use the simple linearpolynomial model.

Higher Order Factorial Analysis

Four different chip load levels were tested inorder to generate a quadratic model for thisfactor. The extra two levels were added inbetween the high and low level used for the twolevel factorial. Surface roughness results for twodifferent depths of cut are shown in Figure 6.

d=229um, DOC = D/2

0500

10001500200025003000350040004500

0 2 4 6Chip Load (um)

Ra

(Ang

stro

ms)

Vc = 0.09 m/sVc = 0.48 m/s

d=229 um, DOC = D/4

0500

10001500200025003000350040004500

0 2 4 6Chip Load (um)

Ra

(Ang

stro

ms)

Vc = 0.09 m/sVc = 0.48 m/s

FIGURE 6. SURFACE ROUGHNESS AS AFUNCTION OF CHIP LOAD FOR 2 DIFFERENTDEPTHS OF CUT AND CUTTING SPEEDS. THETREND LINE IS THE VALUE PREDICTED BY AQUADRATIC POLYNOMIAL MODEL.

The relationship between chip load and surfaceroughness appears slightly non-linear,particularly at high cutting speed. A polynomialmodel was developed incorporating a secondorder term for chip load. The model is given byEq. 4.

ctctta vfvffR 99012563.464396.43 2 -+++=Eq. (4)

As shown in Figure 6, the model fits themeasured data very well at low cutting speeds.The model does not fit the data quite as well athigh cutting speeds probably due to theincreased variation in the measured values athigher cutting speeds. The figure alsodemonstrates the chip load X cutting speedinteraction effect. At high chip loads, the effectof cutting speed is much more pronounced.

These results are similar to those of Alauddin, etal. (1995). They found that chip load is the mostimportant factor. They also found a nonlinearrelationship between chip load and surfaceroughness, and a negative relationship betweencutting speed and chip load (higher cuttingspeed results in lower roughness). However,the results of these experiments differ in that theinteraction between chip load and cuttingvelocity is important. Alauddin, et al found noneof the interactions to be significant. While theyfound that cutting speed had the same effect onsurface roughness regardless of chip load, theexperiments show that for the micro-end millingprocess, the effect of cutting speed on surfaceroughness is more pronounced at higher chiploads.

Additional runs were done with a tool of smallerdiameter for comparison. The results of thosetests are shown in Figure 7. Note that becausethe tool diameter is smaller, the cutting speedsare lower. The surface roughness valuescompare fairly well with those using a larger toolfor comparable chip loads. An interestingfeature of these extra runs is that surfaceroughness appears to be linear with chip load.Notice also that the model does not predict thesurface roughness well for a cutting velocity of0.05 m/s. This is expected since the model wasgenerated using data only in the range of about0.1 m/s to 0.5 m/s. It is possible that one of thereasons that the surface roughness seems to bemore linear at lower cutting velocities is thattemperature effects are less noticeable.

Whatever the reason, the relationship betweensurface roughness and chip load seems to belinear at lower cutting speeds and lower chiploads.

Effect of Runout on Surface Roughness

Figures 8 and 9 show surface roughness tracesfor several slots machined with differentcombinations of chip load. Large marks fromthe cutting tooth (a deep valley followed by ahigh peak) are easily visible on the surfaceroughness traces. Interestingly, the period fromlarge peak to large peak is twice the chip load,

d=127 um, DOC = D/2

0500

10001500200025003000350040004500

0 2 4 6Chip Load (um)

Ra

(Ang

stro

ms)

Vc = 0.05m/sVc = 0.27m/s

FIGURE 7. SURFACE ROUGHNESS AS AFUNCTION OF CHIP LOAD & CUTTINGVELOCITY FOR A SMALLER TOOL DIAMETER.

which means that the large marks are createdonce per revolution rather than once for eachtooth. In many of the surface roughness traces,a step is clearly visible midway between largerpeaks. This affect is most likely the result ofrun-out.

-500-400-300-200-1000

100200300400500

0 10 20 30 40

Feed distance [um]

Surf

ace

roug

hnes

s [n

m]

1/2d DOC; 7500RPM; 0.847 mm/tooth

-500-400-300-200-1000

100200300400500

140 150 160 170 180

Feed distance [um]

Surf

ace

roug

hnes

s [n

m]

1/2d DOC, 7500RPM; 4.233 mm/tooth

FIGURE 8. SURFACE ROUGHNESS TRACES ANDSEM MICROGRAPHS AT 7500 RPM FOR LOWAND HIGH CHIP LOADS.

-500-400-300-200-1000

100200300400500

0 10 20 30 40

Feed distance [um]

Surf

ace

roug

hnes

s [n

m]

40,000RPM; 0.826 mm/tooth

-500-400-300-200-1000

100200300400500

0 10 20 30 40

Feed distance [um]

Surf

ace

roug

hnes

s [n

m]

1/2d DOC; 40,000RPM; 4.128 mm/tooth

FIGURE 9. SURFACE ROUGHNESS TRACES ANDSEM MICROGRAPHS AT 40000 RPM FOR LOWAND HIGH CHIP LOADS.

w w+runout

FIGURE 10. COMPARISON OF THE IDEALCHANNEL AND THE CHANNEL MACHINED WITHRUN-OUT.

Run-out of a machine tool results in amachined feature that is larger than the diameterof the tool. This is caused by imperfect toolalignment, asymmetric tool geometry, mismatchbetween tool and machine tool, and vibration oftools during machining (Stephenson, andAgapiou 1997). Figure 10 shows how run-outaffects the size a machined slot. In addition,radial cutting forces deflect cutting tools like acantilever beam. The deflection can be reducedthrough minimizing the length of the tool andtoolholder, the use of a stiffer toolholder andclamping unit for the cutter, and the use of atoolholder and tool materials with higher moduliof elasticity. For a conventional macro-scalemachining process, the run-out, typically on theorder of micrometers, has a small (oftennegligible) effect on the dimensional accuracy ofthe machined feature. For micro machining,however, the tool run-out to tool diameter ratiobecomes much larger. According to the testresults by Bao and Tansel (2000), run-outs ofthe holder with a collet were 0% to 65% and run-outs of the conventional holder were between40% and 87%.

In addition to producing features with inaccuratedimensions, run-out also negatively impactssurface roughness. Because the tool is notperfectly orthogonal to the surface being cut, theone side of the tool will cut deeper than the otherside. This can be clearly seen in many of thesurface roughness traces of Figures 8 and 9.Reducing runout would most likely have abeneficial effect on surface roughness. Valuesfor surface roughness range from about 600 Å to3800 Å for this experiment. It is reasonable to

assume that with improved run-out, microfeatures with an optical quality surfaceroughness below 500 Å could be produced.Unfortunately, run-out is primarily a function ofmachine tool design, and cannot be improvedmuch by changing machining parameters.

CONCLUSION

A set of experiments designed to begin thecharacterization of surface quality for the micro-end-milling process have been performed. Theeffect of chip load, cutting speed, and depth ofcut on surface roughness of aluminum (6061)samples was studied. An initial 2 level factorialexperiment shows that chip load (or feed pertooth) is by far the most dominant factor of thosestudied. Cutting speed and the chip load Xcutting speed interaction were also significanteffects. Further experiments were performedallowing the generation of a second orderrelationship between surface roughness andchip load. The second order model generated,which includes the effect of chip load, cuttingspeed, and the interaction between the two,predicts surface roughness reasonably well.The deviation between predicted and measuredsurface roughness values was within an errorband of about plus or minus 10%. The surfaceroughness values varied from about 600 Å toabout 3800 Å over the experimental range.

Run-out appears to play a significant role in thesurface quality of micro-milled parts. Thedominant cutting marks (as seen on the surfaceroughness profiles) have a period of twice thechip load, meaning that one cutting edge ismaking a deeper cut than the other cutting edge.The cutting marks of the non-dominant edge arealso visible as small steps on the surfaceroughness profiles. This effect is most likely dueto run-out. Improving run-out could easily leadto optical quality surfaces having a roughness ofless than 500 Å. Unfortunately, run-out isprimarily a function of machine tool design, andnot cannot be improved much by changingmachining parameters. However, by using theopt imal optimal machining parametersdetermined by these experiments along withimproved run-out, features with very goodsurface quality can be produced with the micro-end-milling process.

ACKNOWLEDGMENTS

This work was supported by the Consortium onDeburring and Edge Finishing (CODEF). Theauthors also thank Air Turbine Technology andRobbjack Corp. for their support.

REFERENCES

Alauddin, M., Baradie, M., Hashmi, M.S.J.,(1995), “Computer-aided analysis of a surface-roughness model for end milling,” Journal ofMaterials Processing Technology, 55, 123-127.

Bao, W.Y., Tansel, I.N., (2000), “Modellingmicro-end-milling operations. Part II: Tool run-out,” International Journal of Machine Tools &Manufacture, Vol. 40, pp. 2175-2192.

Damazo, B. N., Davies, M.A., Dutterer, B.S.,Kennedy, M. D., (1999), “A summary of micro-milling studies,” 1st international conference andgeneral meeting of the European society forprecision engineering and nanotechnology,Bremen, Germany, 31 May – 4 June, 1999.p.322-324.

Friedrich, C. R., Vasile, M.J., (1996),“Development of the micromilling process forhigh –aspect-ratio microstructures,” Journal ofMicroelectromechanical Systems, Vol. 5, no. 1,March, 1996.

Lee, K., Ahn, S.-H., Dornfeld, D., Wright, P.K.,(2001), “The effect of run-out on design formanufacturing in micro-machining process,”Proc. ASME IMECE 2001, MED-Vol. 12, ASME,New York, pp.11-17.

Montgomery, D., Altintas, Y., (1991),“Mechanism of cutting forces and surfacegeneration in dynamic milling,” ASME J. Eng.Ind., 113, 160-168.

Schaller, Th., Bohn, L., Mayer, J., Schubert, K.,(1999), “Microstructure grooves with a width ofless than 50 mm cut with ground hard metalmicro end mills,” Precision Engineering, Vol. 23,pp. 229-235.

Stephenson, D. A., Agapiou, J.S., (1997), Metalcutting theory and practice, Marcel Dekker, Inc.

Tipnis, V.A., Buescher, S.C., Garrison, R.C.,(1976), “Mathematically modelled machiningdata for adaptive control of end millingoperations,” Proc. NAMRC-iv, pp. 279-286.