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Hongtao DingNinggang Shen

Yung C. Shin1

Professor ASME Fellow

e-mail: [email protected]

Center for Laser-based Manufacturing,

School of Mechanical Engineering,

Purdue University,

West Lafayette, IN 47907

Experimental Evaluationand Modeling Analysis ofMicromilling of Hardened

H13 Tool SteelsThis study is focused on experimental evaluation and numerical modeling of micromillingof hardened H13 tool steels. Multiple tool wear tests are performed in a microside cuttingcondition with 100 lm diameter endmills. The machined surface integrity, part dimensioncontrol, size effect, and tool wear progression in micromachining of hardened tool steelsare experimentally investigated. A strain gradient plasticity model is developed formicromachining of hardened H13 tool steel. Novel 2D finite element (FE) models aredeveloped in software ABAQUS to simulate the continuous chip formation with varying chipthickness in complete micromilling cycles under two configurations: microslotting andmicroside cutting. The steady-state cutting temperature is investigated by a heat transferanalysis of multi micromilling cycles. The FE model with the material strain gradient plasticity is validated by comparing the model predictions of the specific cutting forceswith the measured data. The FE model results are discussed in chip formation, stress,temperature, and velocity fields to great details. It is shown that the developed FE model

is capable of modeling a continuous chip formation in a complete micromilling cycle,including the size effect. It is also shown that the built-up edge in micromachining can bepredicted with the FE model. [DOI: 10.1115/1.4004499]

Keywords: size effect, micromilling, finite element model, thermal analysis, straingradient

1 Introduction

As miniaturization of products grows in complexity and shrinksin microsize in many applications, the need to manufacture partswith complex features as small as a few microns to a high preci-sion has expanded from conventional soft materials like aluminumand copper to much stronger engineering materials such as high-temperature superalloys [1], hardened tool steels [24], stainlesssteels [1,5], titanium alloys [1], and ceramics [6]. Micromachin-ing, micromilling in particular, due to its great process flexibility,is a promising technology for the manufacture of durable, high-temperature, and wear resistant microdies and micromolds madeof hardened tool steels with relative high accuracy. However,micromilling of hardened tool steels still remains a great techno-logical challenge in industry due to the unpredictable tool life ofmicroendmills, machined surface integrity and part dimensionaccuracy.

The size effect contributes to the fundamental difference in theprocess mechanism between micromachining and conventionalmacromachining, and the analytical and numerical solutions avail-able for macromachining cannot be assumed to be valid for micro-

machining operations particularly for the small undeformed chipthickness. In micromachining, the cutting edge radius (re) of themicrotools is comparable to the undeformed chip thickness (h) andin some occasions less than the size of the workpiece material grainsize. The substantial reduction in the ratio (k) of undeformed chipthickness to cutting edge radius has a profound influence on thespecific cutting force, chip formation, and surface integrity inmicromachining. Figure 1 illustrates the change of material

removal mechanism in micromachining for a constant chip loadbut with varying tool cutting edge radii. Cutting is the dominantmechanism for a fresh tool with h greater than re, but ploughingwith workpiece material elastic recovery plays a more importantrole as re increases to a size similar to h. Ploughing eventually

becomes dominant as re increases to be much greater than h and nochip forms beyond this condition. More specific cutting energy willbe spent in the material plastic deformation due to ploughing thanshearing in cutting. The size effect in micromachining has beenextensively studied theoretically and experimentally, but the focushas been mainly on soft materials like aluminum alloys [7], copper[8], and mild carbon steels [9]. Only a handful of studies haveinvestigated the size effect in micromachining of difficult-to-machine materials. Aramcharoen and Mativenga [2] experimen-tally explored the size effect on the specific cutting force, surfacefinish and burr formation in microslotting of hardened H13 toolsteel with a hardness of 45 Rockwell Hardness C Scale (HRC)using a 900 lm diameter tungsten carbide endmill. Their study hasshown that the specific cutting force of hardened H13 steelincreased drastically to around 100 GPa as the ratio k decreases to

0.2. Shelton and Shin [5] conducted laser-assisted microslottingexperiments of difficult-to-machine materials such as titaniumalloy Ti6Al4V, AISI 316, and 422 stainless steels with 100 lm di-ameter tungsten carbide endmills and numerically modeled the sizeeffect on specific cutting force under orthogonal cutting conditions.

Many theoretical and experimental attempts have been made toanalyze surface integrity in micromachining. Liu et al. [911]studied the surface roughness achieved in micromachining of alu-minum alloy 6082-T6 and carbon steel 1041 and their studyshowed that the resultant surface roughness was a product of thetradeoff between the effect of minimum chip thickness and thetraditional effect of feed rate. For cutting ratio k greater than 1,the surface roughness increased with increasing feed per tooth,while for cutting ratio k less than 1, roughness increased again

1Corresponding author.Contributed by the Manufacturing Engineering Division of ASME for publication

in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript receivedMarch 7, 2011; final manuscript received June 24, 2011; published online July 27,2011. Assoc. Editor: Burak Ozdoganlar.

Journal of Manufacturing Science and Engineering AUGUST 2011, Vol. 133 / 041007-1CopyrightVC 2011 by ASME

Downloaded 23 Oct 2011 to 117.211.123.42. Redistribution subject to ASME license or copyright; see http://www.asme.org/terms/Terms_Use.cfm

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with decreasing feed due to the material elastic recovery. A simi-lar phenomenon was observed for microslotting of harden H13steel [2]. The most frequently observed surface defects on themachined surface by micromachining were dimples, prows,microvoids, and microcracks [12]. For carbon steel with a dualphase structure of pearlite and ferrite, dimples were found on themachined surface at the pearlite-ferrite grain boundary, whichindicated a great effect of the inhomogeneous microstructure onmachined surface integrity undergoing severe plastic deformation.Their study showed that prows resulted from the broken-downbuilt-up edge (BUE) from the tool tip. Prows were hardenedworkpiece materials that had undergone severe plastic deforma-

tion under the tool nose with a hardness value 23 times greaterthan that of the original workpiece [12]. Burr formation is anothercritical issue in micromachining processes since it affects thefunctionality of the microcomponent and damages the part dimen-sion and geometric tolerance. The mechanism of burr formationin micromachining has been reported to be dominated by theinteraction between cutting edge radius and feed per tooth, whilecutting speed, undeformed chip thickness, tool edge radius, feedrate, and workpiece materials all contributed to burr formation inmicromachining [13].

Microtools such as microendmills and drills are generally madefrom tungsten carbide (WC) with cobalt as the binder. Progressionof tool wear in micromachining is dominated by the frictionbetween the tool and the workpiece. For a small depth of cut inmicromachining, a tool with a greater edge radius with respect to

undeformed chip thickness increases the tool-work friction andwears at a faster rate [14]. Filiz et al. [15] investigated the wearprogression of 254 lm diameter WC endmills in cutting of copperat cutting speeds ranging from 40 to 120 m=min and feed rangingfrom 0.75 to 6 lm per tooth. Their study showed that WC toolswore at a 5-time faster rate when the ratio k reduced from about 3to 0.4 for all the cutting speeds investigated.

A number of finite element (FE) models have been proposed tosimulate the chip formation in micromachining by simplifying the3D milling processes to 2D orthogonal cutting processes, but fewof them modeled the actual chip formation with varying unde-formed chip thickness in the milling cycle. Ozel et al. developed a2D FE model for microslotting of aluminum alloy 2024-T6 andAISI 4340 steel to simulate the chip formation and cutting forcewithin a complete slot cutting cycle of one flute using commercial

software DEFORM-2D [16]. Although a complete chip formationwas simulated with the DEFORM platform, the predicted cuttingforce was not validated with the cutting force data measured fromtheir microslotting tests. To model the size effect in micromachin-ing at a microlength scale, Liu and Melkote [7,17] and Lai et al.[8] applied material strain gradient plasticity models in 2D FEmodels to simulate orthogonal cutting of aluminum alloy 5083-H116 and copper, respectively. Liu and Melkote showed that thestrain gradient plasticity model was able to simulate the drasticincrease of the specific cutting as k decreased from 4 to 0.6 inmicromachining and their simulated specific cutting forcematched well with the experimental data [17]. With the strain gra-dient plasticity model developed for copper at the microlevel, Laiet al. [8] predicted a great increase of specific cutting force to

around 45 GPa as the ratio k decreased to about 0.2 in micromil-ling by using an analytical slip line model. As discussed on theabove 2D FE modeling work, the state-of-the-art FE modelingtechniques still face a difficulty to correctly and efficiently modelthe chip formation with varying chip thickness and the significantsize effect in micromilling process.

In this research, machined surface integrity, part dimensioncontrol, size effect, and tool wear progression in micromachiningof hardened tool steels are experimentally investigated by con-ducting microside cutting tests of hardened H13 tool steel with ahardness of 42 HRC using 100 lm diameter endmills. A straingradient plasticity model is developed for micromachining of

hardened H13 tool steel. Novel 2D FE models are developed insoftware ABAQUS to simulate the continuous chip formation withvarying chip thickness in a complete cutting cycle of one fluteunder two configurations: microslotting and microside cutting.The steady-state cutting temperature is investigated by a heattransfer analysis of multi micromilling cycles. The FE model withthe strain gradient plasticity model is validated by comparing thepredicted specific cutting forces with the measured ones undervarious ratios ofk. The FE model results are discussed in chip for-mation, stress, temperature, and velocity fields in great details.

2 Micromilling Experiments

Micromilling experiments were carried out on a three-axis com-puter numerical controlled (CNC) micromilling system that

includes a Precise SC-40 spindle with a maximum rotation speed of90k revolutions per minute (RPM) and provides movement of theworkpiece relative to the tool with a 1 lm resolution. A flexiblenozzle was attached to the spindle mounting fixture allowing for anadjustable flow of assist gas during machining. A differential acous-tic emission (AE) sensor with an operating frequency range of1001000 kHz was securely mounted to the workpiece vise and wasconnected to a matching preamplifier and data acquisition card withthe physical acoustics software being used for all signal processing.The AE sensor was used as an indicator of tool contact and to col-lect qualitative data during the cutting process. Post inspections aftermicromilling experiments were carried out on surface integrity,machined part size, and tool wear. A JEOL JSM-T330 scanningelectron microscope (SEM) and a Zeiss optical microscope wereused to examine machined workpieces and tools in this study. Prior

to imaging in the SEM, all samples were cleaned in an ultrasoniccleaner with acetone and=or methyl alcohol. 3D surface maps andsurface roughness measurements were obtained using a noncontactinterferometric surface profiler (ADE Phase Shift Micro-XAM).

Micromilling of hardened H13 steel was studied for two testconfigurations as shown in Fig. 2: A, slotting and B, side cutting.The slotting experiments were conducted for hardened H13 steelwith an average hardness of 45 HRC using 900 lm diametermicroendmills by Aramcharoen and Mativenga [2], while the sidecutting tests were conducted for hardened H13 steel with an aver-age hardness of 42 HRC using 100 lm diameter microendmills bythe authors. Two-flute endmills of ultrafine tungsten carbide in acobalt matrix were used. The composition of the tool was 92%tungsten carbide with an average grain size of 0.4 lm and 8%

Fig. 1 Chip formation relative to chip load and cutting edge radius

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cobalt as a binder to hold carbide together. Figure 3 shows typicaltool geometry and dimensions for the 100 lm diameter endmills.Note that the tool radial rake angle changes as the tool wears. Forexample, the radial rake angle is about 12 deg for a fresh tool witha cutting edge radius of 0.5 lm, while it decreases to about 5 degas the tool wears to a cutting edge radius of 2 lm.

Table 1 summarizes the cutting conditions used in these tests.Various feed rates were used in the slotting tests by Aramcharoen

and Mativenga, resulting in a broad range of ratio k of maximumundeformed chip thickness (hmax) to cutting edge radius from 0.14to 2.57. Cutting forces were measured in these tests and the sizeeffect on specific cutting force was examined. Tool wear testswere conducted for the side cutting configuration with multipletools. In these tests, the microendmills entered the workpiece atan angle of 26 deg and exited the workpiece at 0 deg for the radialdepth of cut of 5 lm. Figure 4 shows a schematic of tool path in

Fig. 2 Micromilling experimental configurations

Fig. 3 Geometry and dimensions of 100 lm diameter endmill. (a) SEM micrograph (b) Tooldimension in lm: radial rake angle, a512 deg; radial relief angle, b530 deg; helix angle, c535deg; Width of land, wol540 lm (c) 3D overview

Table 1 Test conditions of micromilling of hardened H13 steels

ConditionHardness

(HRC)Dtool(lm)

re(lm)

doc(lm)

V(m=min)

hmax(lm)

k(hmax=re)

A. Slotting [2] 45 900 %1.4 50 (Axial) 84.82 (30,000 rpm) 0.23.6 0.142.57B. Side cutting 42 100 %0.5 5 (Radical)

100 (Axial)18.85 (60,000 rpm) 0.83 1.67

Fig. 4 Schematic of tool path in side cutting

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the side cutting configuration. A wavy surface profile could begenerated at the entrance and exit of the machined slot due to toolchattering if a fast feed rate was applied. To avoid tool chatteringand to get a clean straight cut with the 100 lm diameter endmill, aslow feed rate of 35 mm=min was used when entering and exitingthe workpiece for a short distance of 0.5 mm, respectively. A fastfeed rate was applied in steady-state cutting of a distance of 24mm per pass, which resulted in a maximum undeformed chip loadof 0.83 lm.

3 Experimental Results

Experimental results of microside cutting tests are presented inprocess monitoring with an AE sensor, machined surface integ-rity, dimension control, and tool wear.

3.1 Real-Time Monitoring of Cutting Process With an AESensor. Acoustic emissions were recorded and analyzed for sidecutting experiments. Acoustic emissions are stress waves pro-duced by crack propagation in stressed materials. The most com-mon source of acoustic emissions during cutting are plastic

deformation in the workpiece or chip, frictional contact at the toolsurface, collisions between the chip and tool, chip breakage, andcrack growth in the tool [18]. The recorded AE root mean square(RMS) voltage indicates the acoustic emission signal strength.The AE RMS signals remained about constant within one cuttingpass before the tool was severely worn. For instance, the AE RMSremained constant at about 0.015 V during the 20th pass of sidecutting as can be seen in Fig. 5, which indicated a steady-state cut-ting process.

The relationship between AE RMS and machining time wasstudied over the total cutting time for multiple tool wear experi-ments. The AE RMS signal increased at a steady rate as the cut-ting time increased and the tool wore, as shown in Fig. 6. Afterthe tool was severely worn, the AE RMS signal increased drasti-cally. Good repeatability can be observed from the AE signals

recorded for the two tools. For tool 1, after 11 min cutting time,the RMS voltage was as high as 0.05 V and the tool fracturedwithin 1 min. For tool 2, the tool fractured immediately as soon asthe RMS signal increased to above 0.03 V. Figure 6 indicates thatthe AE signal can be used to monitor tool wear. When the RMSsignal increases by 200%, the tool has been worn out. Althoughcutting force was not measured for side cutting experiments, theinarguable relation between continued tool wear and increasingAE RMS signal allows Fig. 6 to be interpreted as the positive cor-relation between AE RMS and cutting force during micromilling.

Although acoustic emission is a useful tool to assess the toolwear and cutting force, several considerations must be taken intoaccount when using AE RMS as an analytical tool for micromil-ling. Workpiece size, position, and clamping load in the vise can

all have an effect on the absolute value of AE RMS. Also, varia-tions in tool geometry (particularly the cutting edge radius), wear,and runout can have significant impacts on AE RMS values.Therefore, the AE sensor was primarily used as a monitoring toolfor the micromilling experiments.

3.2 Dimension Control in Side Cutting. The AE sensor wasalso used as an indicator of tool-work contact to precisely set thedepth of cut and workpiece surface zeros prior to the micromillingtests. The machining cycle was controlled by a CNC program andthe origin of the workpiece in the CNC program was determinedby multiple contacts between the tool and workpiece. The posi-tions of contacts were precisely set by monitoring the AE signaland the error was generally less than 1 lm. The slopes of theworkpiece along tool axial and feed directions were calibratedwith the AE sensor as well. These slopes came from the imperfectflatness of the workpiece and the geometric error of the vice andCNC stages. These defects and errors were compensated by thesloped tool path, which was precisely controlled by the CNCprogram.

Figure 7 shows the workpiece geometry machined after 15 side

cutting passes or 3 min of cutting time. The picture also shows thecoordinates and the definitions used in the side cutting tests. Cleanstep geometry with burrs largely remaining on the top surface wasobserved along the whole machined section. Machining markscan be observed on the end surface and the small steps on themachined end surface were caused by the change of surface con-tacts after loading and unloading the workpiece. The machinedsection geometry and errors are shown in Table 2. A precise

Fig. 5 AE RMS signals recorded during the 20th pass sidecutting

Fig. 6 Average AE RMS signals recorded over multiple-pass inside cutting

Fig. 7 Machined slot geometry produced after about 3 minside cutting (15 passes)



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dimension control with an accumulated error of about 1% wasachieved in the microside cutting tests.

3.3 Surface Roughness and Surface Defects. The surfaceroughness of the machined side surface was measured with a non-contact interferometric surface profiler. The surface roughness onthe machined side surface was found to be constantly around 0.5lm in multiple-pass side cutting. The 3D surface profile of themachined side surface is shown in Fig. 8.

Figures 9(a) and 9(b) compare the SEM micrographs of the

machined surface generated after 3 and 12 min side cutting withthe same microendmill. A cleaner cut surface was generated after3-min side cutting, while as the tool wore more severely after 12-min cutting, more material tearing can be observed on themachined surface. Surface defects on the machined side surfaceare defined in Fig. 10. Prow was caused by workpiece materialplastic deformation, while voids and dimples were caused by frac-ture. Burrs and material side flow can be observed on the bound-ary of the side cutting as can be seen in Fig. 10. The length ofburrs was measured with SEM and the length ranged from 10 to90 lm, which increased as the tool wore. However, the burr couldbe depressed and torn in the following cuts and hence its size didnot always increase as the tool wore. As the tool had severely

worn, lots of prows could be observed on the machined surface,which was caused by the change of material removal mechanism.As the tool wore and the tool edge radius increased, the ploughingbecame dominant than cutting, which produced lots of prows ascan be seen in Fig. 9(b).

3.4 Tool Wear. The tool flank wear and tool nose wear inedge radius during side cutting were investigated in the side cut-ting experiments. Tool wear patterns are defined in Fig. 11. The

Table 2 Measurement and error of machined workpiecegeometry

Accumulated radial doc (lm) Axial doc (lm)

Expected 75 100Actual 76 99Error (%) %1.3% %1.0%

Fig. 8 Typical 3D surface profile produced by side cutting

Fig. 9 Micrographs of machined surface generated by side cutting

Fig. 10 Close look of SEM image of surface defects on themachined surface after 6-min side cutting

Fig. 11 Tool wear measurements for a tool after 6-min side

cutting



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flank wear was measured with the microscope or SEM by calcu-lating the decrease from the width of land of the tool flank surfacebefore cutting to the one after cutting. The tool cutting edge radiuswas measured with SEM. Figure 12 shows the tool wear progres-sion in side cutting. A gradual tool wear progression in the toolnose and flank surface can be observed after 3, 6, and 9 min ofcutting time. Note that after 3 min of cutting time, the two flutesof tool 1 developed wear at different rates with one flute moreseverely worn than the other, which was mainly caused by thelarge runout of the new tool [13, 5] and also the shallow radialdepth of cut of 5 lm. However, as cutting time increased to about6 min, both flutes developed similar wear. Some built-up edgewas seen remaining left on the tool nose after 3 and 9 min cutting,while no BUE can be seen after 6 min cutting, which indicated afrequent welded-on and break-off of the BUE during cutting.

The tool cutting edge radius, re, and maximum flank wear,VBmax, vs. cutting time are shown in Figs. 13(a) and 13(b), respec-tively. More data was measured for flank wear with the opticalmicroscope, while less data was collected with SEM for tool noseradius. Good repeatability can be observed in tool wear for thetwo tools investigated. Premature tool breakage was successfullyavoided in these side cutting experiments by carefully determiningthe surface contacts and controlling the depth of cut using the AEsensor and by employing slower feed rate when entering and leav-ing the workpiece. The maximum tool cutting edge radius wasmeasured at the tool end, while the average edge radius wasassessed about 3050 lm above the tool end. It can be seen thatthe tool nose wear and flank wear developed at a steady rate priorto tool catastrophic failure. The maximum tool flank wear, maxi-

mum tool edge radius, and average tool edge radius graduallyreached about 25 lm, 10 lm, and 4 lm in their last measurementbefore tool fracture, respectively. The ratio of maximum unde-formed chip thickness to average cutting edge radius, k, decreasedfrom about 2 to 0.2 as the tool cutting edge radius increased from0.4 to 4 lm. As the tool wore and k decreased to 0.2, ploughingand rubbing played the dominant role, no chip would form, andeventually the tool fractured due to the dramatic increase of thespecific cutting force.

4 Finite Element Modeling Analysis of Micromilling

Chip formation during micromilling with varying uncut chipthickness was simulated with a strain gradient based finite elementmodel for both microside cutting and microslotting configurations.

Two cases were studied: (1) the FE model with the strain gradientplasticity model was validated by comparing the model predic-tions of the specific cutting force with the measured data in themicroslotting tests by Aramcharoen and Mativenga [2]; (2) the FEsimulations of microside cutting were carried out to study chipformation, stress, temperature, and BUE formation. The sizeeffect was modeled through the strain gradient plasticity modelfor the maximum chip load as small as 0.83 lm in microcutting ofhardened H13 tool steel. An arbitrary-LagrangianEulerian (ALE)based finite element explicit scheme was developed with the com-mercial software ABAQUS to model the chip formation in micromil-ling. The technique of remesh=solution mapping was developedto remesh the workpiece domain to enable a continuous simula-tion of chip formation and transfer the simulation results between

Fig. 12 Tool wear progress in side cutting for tool 1

Fig. 13 Tool edge radius and maximum flank wear progress in side cutting



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ABAQUS explicit and implicit analyses. The steady-state cuttingtemperature was investigated by a heat transfer analysis of multimicromilling cycles.

4.1 Material Constitutive Modeling. Table 3 shows theJohnsonCook type material constitutive plasticity models forhardened H13 steel with a hardness of 45 HRC. For hardened H13steel with a hardness of 42 HRC, the material constants A and Bwere estimated by scaling down the values of those of 45 HRC tocompensate for the difference in hardness. These constitutivemodels describe the material flow stress at various strains, strainrates and temperatures occurring in cutting. However, the mod-eled flow stress is nondimensional and independent of the lengthscale in the FE simulation and hence the models are not suitablefor describing the significant size effect in microcutting.

The strain gradient plasticity model is briefly presented in thissection and more detailed descriptions can be found from thework of Liu and Melkote [7] and Lai et al. [8]. In strain gradientplasticity, a length scale is introduced through the coefficients ofspatial gradients of strain components and can be used to modelthe size effect in micromilling. The strain gradient constitutivemodel can be expressed explicitly as

r A Ben 1 c log _e 1 T Tref

Tm Tref

m

1 18a2bG2

L A Ben 1 c log _e 1 TTrefTmTref

m 20B@

1CAl0

B@1CA

1=2

(1)

where L is the length parameter. The strain gradient plasticity wasprogrammed as a material subroutine in ABAQUS. Table 4 gives themodel parameters for the hardened H13 steel used in the simula-tions. The length L used in the simulation was chosen to be the

maximum uncut chip thickness. For the maximum uncut chipthickness of 0.83 lm, with the strain gradient plasticity model, theaverage von Mises stress was simulated to be about 2500 MPa inthe primary shear zone, while with the conventional JohnsonCook model, the average von Mises stress was simulated to beabout 1400 MPa in the primary shear zone. The JohnsonCookshear failure model parameters for hardened H13 steel are givenin Table 5. The thermo-mechanical properties of the tool material(WC-Co) used in the simulation are shown in Table 6.

4.2 FE Models of Chip Formation in Micromilling. The3D micromilling process as illustrated in Fig. 2 can be approxi-mated as the sum of a deck of 2D deformation-process sectionswith finite sectional heights twisted at the helix angle of the end-mill in an orderly fashion. Because the sectional height is verysmall, the tool helix angle has little effect and the section can betreated as straight one in the tool axial direction. Figure 14 showsthe 2D FE model setups for the two micromilling configurations.Simplified geometry of one cutting flute was modeled in the anal-ysis, while the actual tool cutting edge radius, tool radial rakeangle, and relief angle were used in the simulation. For slotting,only the half of the workpiece geometry was modeled due to thesymmetry in slotting and the simulation started from 92 deg to 80

deg to ensure that cutting at 90 deg would be properly simulatedat the maximum uncut chip thickness. For side cutting, the simula-tion started from 26 deg to 0 deg to simulate a complete cuttingcycle of one flute with the radial depth of cut of 5 lm.

Fully coupled thermo-mechanical ABAQUS=explicit analysis wascarried out for micromilling simulations. Quadrilateral, four-node,bilinear displacement, and temperature elements with automatichourglass control and reduced integration were used. The ALEtechnique was used in the ABAQUS=explicit analysis step to simu-late chip formation. During cutting simulation, the number of ele-ments in the workpiece domain remained the same, but someworkpiece elements behind the tool flank surface would flowaround the tool nose to the front of the tool and become chip. Inanother word, the ALE algorithm in ABAQUS=explicit smoothes themesh distortions due to deformation by re-adjusting the element

positions. Initial mesh has to be fine enough in the workpiece; oth-erwise, the chip formation will not be simulated well with ALE.Hence, the workpiece domain was divided into two sections, withALE applied on the top section A with a fine mesh and the bottomsection B with a coarse mesh fixed in space to work as a heat sink.A high thermal conductance of 105 W=K mm2 was used to define

Table 3 JC constitutive plasticity model parameters for H13steel

Materialmodel

Hardness(HRC) A (MPa) B (MPa) n C m

H13 %42 810.6 286.8 0.278 0.028 1.18H13 [19] %45 908.54 321.39 0.278 0.028 1.18

Table 4 Strain gradient parameters for H13 steel [8,20]

Material G (GPa) b (nm) a l

H13 81 0.304 0.5 0.38

Table 5 JC shear failure model parameters for H13 steel [21]

Material d1 d2 d3 d4 d5

H13 0.8 2.1 0.5 0.0002 0.61

Table 6 Thermo-mechanical properties of tungsten carbide (WC-Co) tool [22]

MaterialElastic

modulus (GPa)Poissons

ratioMicrohardness

HV30Specific

heat (J=kg C)Thermal

conductivity (W=m K)Thermal

expansion (lm=m C)Density(g=cm3)

WCCo 696 0.25 1700 260 28.4 5.2 14.5

Fig. 14 FE model setups



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the interface between sections A and B to ensure the continuity oftemperature. As a result, no stress would be simulated in sectionB, while temperature was simulated properly due to heat thermalconduction. For the slotting model, the top section A ranged from92 deg to 75 deg with a sectional thickness of 10 lm, while forthe side cutting model, the top section A ranged from 26 deg to5 deg with a sectional thickness of 6 lm. Room temperaturewas used as the initial temperature condition in the simulation ofthe first milling cycle, while steady-state temperature after many

milling cycles was analyzed by a thermal model as will be dis-cussed in Sec. 4.3. A rotational speed was applied to the tool cen-ter and workpiece elements in section A deformed into the chipwith the smoothing techniques of ALE. A constant frictional coef-ficient of 0.65 was adopted for the tool-work interface [16]. Nochip separation criterion was required by the FE model.

An ABAQUS=explicit simulation step of 20 ls or 7 deg tool rota-tion of side cutting at a speed of 18.85 m=min can be completedwith the ALE technique in a reasonable computation time. How-ever, a longer step cannot be simulated due to excessive distortionof the workpiece mesh around the tool nose even with ALE. Tosimulate the chip formation continuously for a longer period oftime, for instance, 26 deg tool rotation for a complete cuttingcycle of one flute in microside cutting, remeshing the deformedworkpiece is required; however, the mesh-to-mesh solution map-

ping technique is only available in ABAQUS=implicit. An ABA-QUS=implicit step was developed between two continuous explicitsteps for remeshing the distorted workpiece mesh and mappingthe simulation results from the previous explicit step to the fol-lowing one. A very short period of time, say 0.001 ls, was simu-lated for the implicit step and remesh was optimized in thedeformed workpiece domain using ABAQUS=CAE. The JohnsonCook shear failure model for hardened H13 steel was applied inthe last explicit step with a step time of 2 ls or a tool rotation ofless than 1 deg to simulate the separation of the chip from thebulk material.

4.3 Thermal Analysis of Micromilling. The FE chip forma-tion simulation was limited to one micromilling cycle of bothmicroslotting and side cutting configurations, because coupled

thermo-mechanical analysis is too expensive in computation usingany commercial finite element software. To correctly model thesteady-state cutting temperature only achieved after many millingcycles, a heat transfer analysis was performed on the bulk work-piece after the chip formation analysis for further milling cycles ata low computation cost. In the chip formation analysis, the mod-eled workpiece was smaller than the actual one to save computa-tion cost, but it was extended to close to the actual size in the heattransfer analysis to properly set the thermal boundary conditions.The ABAQUS=explicit solver was used in the heat transfer analysisof the bulk workpiece for a long period of time. In every millingcycle of microslotting and side cutting configurations, the work-piece material is heated locally by heat generation due to plasticdeformation and friction at the tool-chip and tool-workpiece inter-

faces as the tool flute gets engaged into the workpiece, while itcools down due to heat conduction to the bulk material and heatconvection to the air as the flute leaves. If the local heat genera-tion is not dissipated completely to the bulk material by heat con-duction and to the environment by heat convection, thetemperature of the workpiece will get an increase in the followingmilling cycle due to the remaining heat.

As the cutting flute approaches, the total heat flux to the localmaterial is composed of heat generation term _qpl converted from

plastic deformation and frictional heat term _qf. Deformation heat

flux is given by Eq. (2)

_qpl gplr _e (2)

where gpl specifies the fraction of deformation energy convertedinto thermal energy (0.9 was used), r is the material flow stress,and _e is the material strain rate. Frictional heat flux is created dueto the sliding friction between the workpiece material and the toolface. The amount of frictional heat flux into the workpiece isgiven by Eq. (3)

_qf ngfss _c (3)

where gf specifies the fraction of mechanical energy converted

into thermal energy (0.9 was used), ss is the frictional stress, _c isthe slip rate, and n gives the fraction of the generated heat flowinginto the workpiece (0.5 was used). Both the heat flux componentsare varying from node to node and the nodal heat flux data wereobtained from the FE chip formation analysis of one milling cycle.A time-dependent nodal heat flux subroutine was created for theheat transfer analysis of multiple cycles, in which the heat fluxwas used as periodic heat input along the milling paths.

Figure 15 shows the simulated workpiece temperature fields atdifferent tool rotation angles of the 16th microslotting cycle, inwhich counter-clockwise tool rotation was specified for a cuttingspeed of 84.82 m=min and a feed of 2.8 lm=tooth. It can be seenin Fig. 15(a) that as tool flute-1 rotates by 90 deg and cuts the ma-terial in the center of the slot, the material peak temperature nearflute-1 is increased to about 300 C. As flute-1 rotates by 180 deg

and leaves the slot before flute-2 enters, the heat is dissipated tothe bulk material but the average temperature of the materialaround the slot remains as high as about 8090 C.

To determine if the workpiece temperature field reaches itsquasi steady-state, the temperature histories of several nodes nearthe slot were tracked with the heat transfer analysis. Figure 16(a)shows the nodal temperature histories of 16 microslotting cycles,with N1 designated as the node on the slot wall at h of 90 deg andN2 as a node of 10 lm away from N1. It can be seen that after 12microslotting cycles, quasi steady-state periodic temperature pat-terns are maintained in the further milling cycles and the tempera-tures at both nodes drop to the same level of 90 C as the fluteleaves. In another word, the temperature field after 12 microslot-ting cycles can be used for setting the temperature conditions in

Fig. 15 Workpiece temperature field during the 16th microslotting cycle of condition A



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the chip formation analysis of microslotting. Figure 16(b) showsthe nodal temperature histories of three microside cutting cycles,with N1 designated as the node on the machined chamfer at h of20 deg and N2 as a node of 8 lm away from N1. It can be seenthat the material temperature increases to about 75 C as the fluteapproaches but drops to the ambient temperature as the flute

leaves within the same milling cycle. This finding tells that, differ-ently than slotting, the ambient temperature can be used as theworkpiece temperature conditions in the chip formation analysisof microside cutting. A flute cuts 180 deg and idles for 180 deg inslotting, while it cuts about 26 deg and idles for 334 deg in sidecutting. As a result, the workpiece material is heated for a muchshorter period of time in microside cutting compared with micro-slotting, but cools long enough to dissipate the heat completely.The cutting temperature of 75 C in side cutting is significantlylower than that of 300 C in slotting, mainly because a low cuttingspeed of 18.85 m=min is used in microside cutting. The simulatedsteady-state temperature fields and ambient temperature were thenused for setting the temperature conditions in the chip formationanalysis of microslotting and side cutting configurations,respectively.

4.4 Specific Cutting Force in Micro Slotting. To assess thevalidity of the developed FE model with the strain gradient plas-ticity, ten slotting tests were simulated for hardened steel of ahardness of 45 HRC with various feed rates for tool rotation from92 deg to 80 deg. The simulated ratio of maximum undeformed

chip thickness to cutting edge radius, k, varied from 0.2 to 2. Thesimulated specific cutting forces were calculated by dividing thesimulated cutting force at 90 deg by the product of the maximumuncut chip thickness at 90 deg and the axial depth of cut. Figure17 shows the comparison of the specific cutting force predictionswith the experimental data of Aramcharoen and Mativenga [2].

Micromilling simulation with the strain gradient plasticity modelshows a significant size effect in the specific cutting force inmicromilling and matches well with the experimental data for var-ious k ratios. Simulations with the JohnsonCook model predictthe size effect to some extent due to the increase of tool cuttingedge radius in microslotting; however, it was not able to simulatethe extreme high specific cutting force occurring in microcuttingfor ratio k less than 0.5. The simulation results thus validated theefficacy of the FE model with the strain gradient plasticity modelfor simulating micromilling.

4.5 Chip Formation, Stress, Velocity, and TemperatureFields in Micro Side Cutting. Continuous chip formation withina complete side cutting cycle for one flute rotation from 26 deg to0 deg with a 0.5 lm edge radius microtool is shown in Fig. 18.

Eight ABAQUS=explicit steps were simulated for the 72 ls side cut-ting cycle with first seven 10-ls steps and one last 2-ls step.Seven ABAQUS=implicit intermittent steps were used to remesh thedistorted workpiece mesh from the previous explicit step and mapthe simulation results from the earlier step to the following one.Strain gradient plasticity was used in all the time steps with thevarying uncut chip thickness as the material length scale L. Thematerial shear failure criterion was used in the last explicit step of2 ls to simulate the separation of the chip from the bulk material.As can be seen in Fig. 18, a seamless chip formation was simu-lated with a smooth transition between two neighboring explicitsteps. The deformed chip grew thicker in the beginning 30 ls cut-ting time even with a decreasing uncut chip thickness. The modelsimulated necking of chip formation after 50 ls. The chip separa-tion in the last 2 ls was simulated with a breakage of the neck at

the primary shear zone. The simulated von Mises stress in the pri-mary shear zone was constantly about 2500 MPa for this condi-tion. No stress was simulated in the bottom section B of theworkpiece because of the displacement constraints applied there.

Material removal mechanism transition from cutting to plough-ing was investigated by the FE model. Figure 19 shows the chipformation within the first 10 ls side cutting with different tooledge radii from 0.5 to 4.2 lm. Cutting was the main material re-moval mechanism when the ratio k was greater than 1. Ploughingplayed a more important role as k decreased to below 1. No chipwould form as k decreased to below 0.2. The simulated maximumvon Mises stresses in the primary shear zone were about the sameat 2500 MPa for different tool edge radii because the same uncutchip thickness of 0.83 lm was used as the length scale parameter

Fig. 16 Workpiece nodal temperature histories of multiple milling cycles

Fig. 17 Comparison between measured and predicted (withand without strain gradient plasticity) specific cutting forces inmicromilling of hardened H13 steel of 45 HRC



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Fig. 18 Chip formation and von Mises stress distributions during one side cutting cycle with a 0.5 lm edge radius endmill

Fig. 19 Deformation fields during side cutting with different tool edge radii



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in the strain gradient models in these first explicit steps. However,as shown in Figs. 19(a)19(c), a larger highly stressed deforma-tion zone was predicted for side cutting with a re of 4.2 lm thanthat of 0.5 lm, which lead to a greater reaction force. The maxi-mum temperatures in the chip and tool were predicted to bearound 75 C and 50 C, respectively, for various cutting ratios.Velocity fields simulated with various cutting ratios as can beseen in Figs. 19(g)19(i) show a large triangle zone of stagnantworkpiece material for side cutting with a small k less than 1,which indicated a built-up edge would form more often as tool

wore to have a large re. The experimental observations of BUEformed on the worn tool discussed earlier confirmed the modelpredictions of BUE. The surface defect of prows remaining on themachined surface was the result of BUEs that have broken offfrom the tool nose. The model prediction that a large BUE couldform for a large tool edge radius corroborates the experimentalobservation of more and larger prows remaining on the machinedsurface produced by a worn tool than a fresh one.

5 Conclusions

In this research, multiple micromilling tests under progressionof tool wear were performed with 100 lm diameter tungsten car-bide endmills under a microside cutting condition. The precisedimension control of the machined part was achieved with an

accumulated error of about 1%. The surface roughness on themachined side surface was found to be constantly around 0.5 lm.As the tool wore and ploughing became dominant, the length ofburr increased with the longest one of 90 lm remaining on the topsurface, larger and more prows were observed on the machinedsurface, and large BUE was observed on the tool nose. A gradualtool wear progression in both the tool nose and flank face wasobserved in the wear tests with multiple tools. The maximum toolflank wear, maximum tool edge radius and average tool edge ra-dius gradually reached about 25 lm, 10 lm, and 4 lm, respec-tively, and the ratio k decreased from about 2 to 0.2 before thetool catastrophic failure.

Novel 2D FE models with a strain gradient plasticity modelwere developed to simulate the continuous chip formation usingthe ALE technique for a complete micromilling cycle of hardened

H13 tool steel with varying chip thickness under two configura-tions: microslotting and microside cutting. The heat transfer anal-ysis of multi milling cycles showed that the steady-stateworkpiece temperature reaches about 300 C as the fluteapproaches and drops to about 90 C as the flute leaves in micro-slotting at a cutting speed of 85 m=min, while the workpiece tem-perature increases to 75 C in the cutting phase but drops to theambient temperature in the cooling phase in microside cutting at acutting speed of 19 m=min. The FE model with the strain gradientplasticity model was validated by comparing the model predic-tions of the specific cutting forces with the measured data inmicroslotting. The specific cutting force was predicted to increasefrom about 15 to about 100 GPa as k decreased from 2 to 0.2.Ploughing and no chip formation were simulated with the FEmodel as the ratio k decreased to about 0.2. The maximum tem-peratures in the chip and tool were predicted to be around 75 Cand 50 C, respectively, for various cutting ratios in microside cut-ting. The model simulations showed that a large triangle zone ofworkpiece material was stagnant in front of the tool nose for side

cutting with a large tool edge radius, which indicated a built-upedge would form more often as the tool wore.

Acknowledgment

The authors wish to gratefully acknowledge the financial sup-port provided for this study by the National Science Foundation(Grant Nos: 0538786-IIP, 0917936-IIP), State of Indiana throughthe 21st Century R&T Fund, and Industrial Consortium membersof the Center for Laser-based Manufacturing.

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http://dx.doi.org/10.1088/0960-1317/20/7/075012http://dx.doi.org/10.1016/j.precisioneng.2008.11.002http://dx.doi.org/10.1016/j.ijmachtools.2008.05.011http://dx.doi.org/10.1016/j.cirp.2009.03.053http://dx.doi.org/10.1115/1.4001142http://dx.doi.org/10.1016/j.ijmecsci.2006.09.012http://dx.doi.org/10.1016/j.ijmecsci.2006.09.012http://dx.doi.org/10.1016/j.ijmachtools.2007.08.011http://dx.doi.org/10.1115/1.2162905http://dx.doi.org/10.1115/1.2162905http://dx.doi.org/10.1115/1.1813469http://dx.doi.org/10.1115/1.2716706http://dx.doi.org/10.1115/1.2716706http://dx.doi.org/10.1016/j.ijmachtools.2005.10.001http://dx.doi.org/10.1016/j.precisioneng.2004.09.002http://dx.doi.org/10.1016/j.ijmachtools.2005.05.015http://dx.doi.org/10.1016/j.ijmachtools.2006.09.024http://dx.doi.org/10.1115/1.2193548http://dx.doi.org/10.1115/1.2193548http://dx.doi.org/10.1115/1.3188750http://dx.doi.org/10.1016/j.matdes.2005.06.017http://dx.doi.org/10.1016/S0924-0136(02)00146-2http://dx.doi.org/10.1016/S0924-0136(02)00146-2http://dx.doi.org/10.1016/j.matdes.2005.06.017http://dx.doi.org/10.1115/1.3188750http://dx.doi.org/10.1115/1.2193548http://dx.doi.org/10.1115/1.2193548http://dx.doi.org/10.1016/j.ijmachtools.2006.09.024http://dx.doi.org/10.1016/j.ijmachtools.2005.05.015http://dx.doi.org/10.1016/j.precisioneng.2004.09.002http://dx.doi.org/10.1016/j.ijmachtools.2005.10.001http://dx.doi.org/10.1115/1.2716706http://dx.doi.org/10.1115/1.1813469http://dx.doi.org/10.1115/1.2162905http://dx.doi.org/10.1115/1.2162905http://dx.doi.org/10.1016/j.ijmachtools.2007.08.011http://dx.doi.org/10.1016/j.ijmecsci.2006.09.012http://dx.doi.org/10.1016/j.ijmecsci.2006.09.012http://dx.doi.org/10.1115/1.4001142http://dx.doi.org/10.1016/j.cirp.2009.03.053http://dx.doi.org/10.1016/j.ijmachtools.2008.05.011http://dx.doi.org/10.1016/j.precisioneng.2008.11.002http://dx.doi.org/10.1088/0960-1317/20/7/075012

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