EXPERIMENTAL AND MODELING ANALYSIS OF MICRO-MILLING OF HARDENED H13 TOOL STEEL

Transcription

1 Proceedings of the ASME 2011 International Manufacturing Science and Engineering Conference MSEC2011 June 13-17, 2011, Corvallis, Oregon, USA MSEC EXPERIMENTAL AND MODELING ANALYSIS OF MICRO-MILLING OF HARDENED H13 TOOL STEEL Hongtao Ding, Ninggang Shen and Yung C. Shin Center for Laser-Based Manufacturing Purdue University West Lafayette, Indiana, 47907, USA Abstract This study is focused on experimental evaluation and numerical modeling of micro-milling of hardened H13 tool steels. Multiple tool wear tests are performed in a micro side cutting condition with 100 µm diameter endmills. The machined surface integrity, part dimension control, size effect and tool wear progression in micromachining of hardened tool steels are experimentally investigated. A strain gradient plasticity model is developed for micromachining of hardened H13 tool steel. Novel 2D FE models are developed in software ABAQUS to simulate the continuous chip formation with varying chip thickness in complete micro-milling cycles under two configurations: micro slotting and micro side cutting. The steady-state cutting temperature is investigated by a heat transfer analysis of multi micro-milling cycles. The FE model with the material strain gradient plasticity is validated by comparing the model predictions of the specific cutting forces with 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 micro-milling cycle, including the size effect. It is also shown that built-up edge in micromachining can be predicted with the FE model. 1. Introduction As miniaturization of products grows in complexity and shrinks in micro-size in many applications, the need to manufacture parts with complex features as small as a few microns to a high precision has expanded from conventional soft materials like aluminum and copper to much stronger engineering materials such as high-temperature superalloys [1], hardened tool steels [2-4], stainless steels [1, 5], titanium alloys [1] and ceramics [6]. Micromachining, micro-milling in particular, due to its great process flexibility, is a promising technology for the manufacture of durable, high temperature and wear resistant micro-dies and micro-molds made of hardened tool steels with relative high accuracy. However, micro-milling of hardened tool steels still remains a great technological challenge in industry due to unpredictable tool life of micro endmills, machined surface integrity and part dimension accuracy. The size effect contributes to the fundamental difference in the process mechanism between micromachining and conventional macro-machining, and the analytical and numerical solutions available for macro-machining cannot be assumed to be valid for micromachining operations particularly for the small undeformed chip thickness. In micromachining, the cutting edge radius (r e ) of the micro tools is comparable to the undeformed chip thickness (h) and in some occasions less than the size of the workpiece material grain size. The substantial reduction in the ratio (λ) of undeformed chip thickness to cutting edge radius has a profound influence on the specific cutting force, chip formation and surface integrity in micromachining. Fig. 1 illustrates the change of material removal mechanism in micromachining for a constant chip load but with varying tool cutting edge radii. Cutting is the dominant mechanism for a fresh tool with h greater than r e, but ploughing with workpiece material elastic recovery plays a more important role as r e increases to a size similar to h. Ploughing eventually becomes dominant as r e increases to be much greater than h and no chip forms beyond this condition. More specific cutting energy will be spent in the material plastic deformation due to ploughing than shearing in cutting. The size effect in micromachining has been extensively studied theoretically and experimentally, but the focus has been mainly on soft materials like aluminum alloys [7], copper [8] and mild carbon steels [9]. Only a handful of studies have investigated the size 1 Copyright 2011 by ASME

2 (a) h > r e (b) h r e (c) h < r e Fig. 1 Chip formation relative to chip load and cutting edge radius effect in micromachining of difficult-to-machine materials. Aramcharoen and Mativenga [2] experimentally explored the size effect on the specific cutting force, surface finish and burr formation in micro slotting of hardened H13 tool steel with a hardness of 45 HRC using a 900 µm diameter tungsten carbide endmill. Their study has shown that the specific cutting force of hardened H13 steel increased drastically to around 100 GPa as the ratio λ decreases to 0.2. Shelton and Shin [5] conducted laser-assisted micro slotting experiments of difficult-to-machine materials such as titanium alloy Ti6Al4V, AISI 316 and 422 stainless steels with 100 µm diameter tungsten carbide endmills and numerically modeled the size effect on specific cutting force under orthogonal cutting conditions. Many theoretical and experimental attempts have been made to analyze surface integrity in micromachining. Liu et al. [9-11] studied the surface roughness achieved in micromachining of aluminum alloy 6082-T6 and carbon steel 1041 and their study showed that the resultant surface roughness was a product of the tradeoff between the effect of minimum chip thickness and the traditional effect of feed rate. For cutting ratio λ greater than 1, the surface roughness increased with increasing feed per tooth, while for cutting ratio λ less than 1, roughness increased again with decreasing feed due to the material elastic recovery. A similar phenomenon was observed for micro slotting of harden H13 steel [2]. The most frequently observed surface defects on the machined surface by micromachining were dimples, prows, microvoids, and microcracks [12]. For carbon steel with a dual phase structure of pearlite and ferrite, dimples were found on the machined surface at the pearlite-ferrite grain boundary, which indicated a great effect of the inhomogeneous microstructure on machined surface integrity undergoing severe plastic deformation. Their study showed that prows resulted from the broken-down built-up edge (BUE) from the tool tip. Prows were hardened workpiece materials that had undergone severe plastic deformation under the tool nose with a hardness value 2 to 3 times greater than that of the original workpiece [12]. Burr formation is another critical issue in micromachining processes since it affects the functionality of the microcomponent and damages the part dimension and geometric tolerance. The mechanism of burr formation in micromachining has been reported to be dominated by the interaction between cutting edge radius and feed per tooth, while cutting speed, undeformed chip thickness, tool edge radius, feed rate, and workpiece materials all contributed to burr formation in micromachining [13]. Micro-tools such as micro endmills and drills are generally made from tungsten carbide (WC) with cobalt as the binder. Progression of tool wear in micromachining is dominated by the friction between the tool and the workpiece. For a small depth of cut in micromachining, a tool with a greater edge radius with respect to undeformed chip thickness increases the tool-work friction and wears at a faster rate [14]. Filiz et al. [15] investigated the wear progression of 254 µm diameter WC endmills in cutting of copper at cutting speeds ranging from 40 to 120 m/min and feed ranging from 0.75 to 6 µm per tooth. Their study showed that WC tools wore at a 5- time faster rate when the ratio λ reduced from about 3 to 0.4 for all the cutting speeds investigated. A number of finite element (FE) models have been proposed to simulate the chip formation in micromachining by simplifying the 3D milling processes to 2D orthogonal cutting processes, but few of them modeled the actual chip formation with varying undeformed chip thickness in the milling cycle. Özel et al. developed a 2D FE model for micro slotting of aluminum alloy 2024-T6 and AISI 4340 steel to simulate the chip formation and cutting force within a complete slot cutting cycle of one flute using commercial software DEFORM-2D [16]. Although a complete chip formation was simulated with the DEFORM platform, the predicted cutting force was not validated with the cutting force data measured from their micro slotting tests. To model the size effect in micromachining at a micro length scale, Liu and Melkote [7, 17] and Lai et al. [8] applied material strain gradient plasticity models in 2D FE models to simulate orthogonal cutting of aluminum alloy 5083-H116 and copper, respectively. Liu and Melkote showed that the strain gradient plasticity model was able to simulate the drastic increase of the specific cutting as λ decreased from 4 to 0.6 in micromachining and their simulated specific cutting force matched well with the experimental data [17]. With the strain gradient plasticity 2 Copyright 2011 by ASME

3 model developed for copper at the micro level, Lai et al. predicted a great increase of specific cutting force to around 45 GPa as ratio λ decreased to about 0.2 in micro-milling by using an analytical slip line model. As discussed on the above 2D FE modeling work, the state of art FE modeling techniques still face a difficulty to correctly and efficiently model the chip formation with varying chip thickness and the significant size effect in micro-milling process. In this research, machined surface integrity, part dimension control, size effect and tool wear progression in micromachining of hardened tool steels are experimentally investigated by conducting micro side cutting tests of hardened H13 tool steel with a hardness of 42 HRC using 100 µm diameter endmills. A strain gradient plasticity model is developed for micromachining of hardened H13 tool steel. Novel 2D FE models are developed in software ABAQUS to simulate the continuous chip formation with varying chip thickness in a complete cutting cycle of one flute under two configurations: micro slotting and micro side cutting. The steady-state cutting temperature is investigated by a heat transfer analysis of multi micro-milling cycles. The FE model with the strain gradient plasticity model is validated by comparing the predicted specific cutting forces with the measured ones under various ratios of λ. The FE model results are discussed in chip formation, stress, temperature, and velocity fields in great details. 2. Micro-milling experiments Micro-milling experiments were carried out on a threeaxis CNC controlled micro-milling system that includes a Precise SC-40 spindle with a maximum rotation speed of 90k RPM and provides movement of the workpiece relative to the tool with a 1 µm resolution. A flexible nozzle was attached to the spindle mounting fixture allowing for an adjustable flow of assist gas during machining. A differential acoustic emission (AE) sensor with an operating frequency range of khz was securely mounted to the workpiece vise, and was connected to a matching preamplifier and data acquisition card with Physical Acoustics software being used for all signal processing. The AE sensor was used as an indicator of tool contact and to collect qualitative data during the cutting process. Post-inspections after micro-milling experiments were carried out on surface integrity, machined part size and tool wear. A JEOL JSM-T330 scanning electron microscope (SEM) and a Zeiss optical microscope were used to examine machined workpieces and tools in this study. Prior to imaging in the SEM, all samples were cleaned in an ultrasonic cleaner with Acetone and/or methyl alcohol. 3D surface maps and surface roughness measurements were obtained using a non-contact interferometric surface profiler (ADE Phase Shift Micro-XAM). Micro-milling of hardened H13 steel was studied for two test configurations as shown in Fig. 2: A, slotting and B, side cutting. The slotting experiments were conducted for hardened H13 steel with an average hardness of 45 HRC using 900 µm diameter micro endmills by Aramcharoen and Mativenga [2], while the side cutting tests were conducted for hardened H13 steel with an average hardness of 42 HRC using 100 µm diameter micro endmills by the authors. Twoflute endmills of ultra-fine tungsten carbide in a cobalt matrix were used. The composition of the tool was 92% tungsten carbide with an average grain size of 0.4 microns and 8% cobalt as a binder to hold carbide together. Fig. 3 shows typical tool geometry and dimensions for the 100 µm diameter endmills. Note the tool radial rake angle changes as the tool wears. For example, the radial rake angle is about 12 for a fresh tool with a cutting edge radius of 0.5 µm, while it decreases to about 5 as the tool wears to a cutting edge radius of 2 µm. (a) Slotting configuration (b) Side cutting configuration Fig. 2 Micro-milling experimental configurations 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 λ of maximum undeformed chip thickness (h max ) to cutting edge radius from 0.14 to Cutting forces were measured in these tests and the size effect on specific cutting 3 Copyright 2011 by ASME

4 (a) SEM micrograph of a new tool (b) Tool dimensions in µm (c) 3D overview Fig. 3 Geometry and dimensions of 100 µm diameter endmill. (a) SEM micrograph (b) Tool dimension in µm: radial rake angle, a =12 ; radial relief angle, b = 30 ; helix angle, c = 35 ; Width of land, wol = 40 µm (c) 3D overview Fig. 4 Schematic of tool path in side cutting Table 1. Test conditions of micro-milling of hardened H13 steel Condition Hardness (HRC) D tool (µm) r e (µm) doc (µm) A. Slotting [2] (Axial) B. Side cutting (Radical) X 100 (Axial) V (m/min) (30,000 rpm) (60,000 rpm) h max (µm) λ (h max / r e ) force was examined. Tool wear tests were conducted for the side cutting configuration with multiple tools. In these tests, the micro endmills entered the workpiece at an angle of 26 from the workpiece surface normal direction for the radial depth of cut of 5 µm. Fig. 4 shows a schematic of tool path in the side cutting configuration. A wavy surface profile could be generated at the entrance and exit of the machined slot due to tool chattering if a fast feed rate was applied. To avoid tool chattering and to get a clean straight cut with the 100 µm diameter endmill, a slow feed rate of 35 mm/min was used when entering and exiting the workpiece for a short distance of 0.5 mm, respectively. A fast feed rate was applied in steady-state cutting of a distance of 24 mm per pass, which resulted in a maximum undeformed chip load of 0.83 µm. 4 Copyright 2011 by ASME

5 3. Experimental results Experimental results of micro side cutting tests are presented in process monitoring with an AE sensor, machined surface integrity, dimension control and tool wear. 3.1 Real-time monitoring of cutting process with an AE sensor Acoustic emissions were recorded and analyzed for side cutting experiments. Acoustic emissions are stress waves produced by crack propagation in stressed materials. The most common source of acoustic emissions during cutting are plastic deformation in the workpiece or chip, frictional contact at the tool surface, collisions between the chip and tool, chip breakage, and crack 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 cutting pass before the tool was severely worn. For instance, the AE RMS remained constant of about V during the 20th pass of side cutting as can be seen in Fig. 5, which indicated a steady-state cutting process. interpreted as the positive correlation between AE RMS and cutting force during micro-milling. Although acoustic emission is a useful tool to assess the tool wear and cutting force, several considerations must be taken into account when using AE RMS as an analytical tool for micro-milling. Workpiece size, position, and clamping load in the vise can all have an effect on the absolute value of AE RMS. Also, variations 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 tool for the micro-milling experiments. Fig. 6 Average AE RMS signals recorded over multiple-pass in side cutting Fig. 5 AE RMS signals recorded during the 20th pass side cutting The relationship between AE RMS and machining time was studied over the total cutting time for multiple tool wear experiments. The AE RMS signal increased at a steady rate as the cutting time increased and the tool wore, as shown in Fig. 6. After the tool was severely worn, the AE RMS signal increased drastically. Good repeatability can be observed from the AE signals recorded for the two tools. For tool 1, after 11 minutes cutting time, the RMS voltage was as high as 0.05 volts and the tool fractured within one minute. For tool 2, the tool fractured immediately as soon as the RMS signal increased to above 0.03 volts. Fig. 6 indicates that the AE signal can be used to monitor tool wear. When the RMS signal increases by 200%, the tool has been worn out. Although cutting force was not measured for side cutting experiments, the inarguable relation between continued tool wear and increasing AE RMS signal allows Fig. 6 to be 3.2 Dimension control in side cutting The AE sensor was also used as an indicator of tool-work contact to precisely set the depth of cut and workpiece surface zeros prior to the micro-milling tests. The machining cycle was controlled by a CNC program and the origin of the workpiece in the CNC program was determined by multiple contacts between tool and workpiece. The positions of the contacts were precisely set by monitoring the AE signal and the error was generally less than 1 µm. The slopes of the workpiece along tool axial and feed directions were calibrated with the AE sensor as well. These slopes came from the imperfect flatness of workpiece and the geometric error of the vice and CNC stages. These defects and errors were compensated by the sloped tool path, which was precisely controlled by the CNC program. Fig. 7 shows the workpiece geometry machined after 15 side cutting passes or 3 minutes of cutting time. The picture also shows the coordinates and the definitions used in the side cutting tests. Clean step geometry with burrs largely remaining on the top surface was observed along the whole machined section. Machining marks can be observed on the end surface and the small steps on the machined end surface were caused by the change of surface contacts after loading and unloading the workpiece. The machined section geometry 5 Copyright 2011 by ASME

6 and errors are shown in Table 2. A precise dimension control with an accumulated error of about 1% was achieved in the micro side cutting tests. the machined surface, which was caused by the change of material removal mechanism. As the tool wore and the tool edge radius increased, the ploughing became dominant than cutting, which produced lots of prows as can be seen in Fig. 9 (b). Fig. 7 Machined slot geometry produced after about 3 minutes side cutting (15 passes) Fig. 8 Typical 3D surface profile produced by side cutting Table 2. Measurement and error of machined workpiece geometry Accumulated radial doc (µm) Axial doc (µm) Expected Actual Error (%) 1.3% 1.0% 3.3 Surface roughness and surface defects The surface roughness of the machined side surface was measured with a non-contact interferometric surface profiler. The surface roughness on the machined side surface was found to be constantly around 0.5 µm in multiple-pass side cutting. The 3D surface profile of the machined side surface is shown in Fig. 8. Fig. 9 (a) and (b) compare the SEM micrographs of the machined surface generated after 3 and 12 minutes side cutting with the same micro endmill. A cleaner cut surface was generated after 3-minute side cutting, while as the tool wore more severely after 12-minute cutting, more material tearing can be observed on the machined surface. Surface defects on the machined side surface are defined in Fig. 10. Prow was caused by workpiece material plastic deformation, while voids and dimples were caused by fracture. Burrs and material side flow can be observed on the boundary of the side cutting as can be seen in Fig. 10. The length of burrs was measured with SEM and the length ranged from 10 to 90 µm. The length of burr increased as the tool wore because a sharper tool generally created a cleaner surface. However, the burr could be depressed and torn in the following cuts and hence its size did not always increase as the tool wore. As the tool had severely worn, lots of prows could be observed on (a) after 3 min (b) after 12 min Fig. 9 Micrographs of machined surface generated by side cutting (a) after 3-minute (b) after 12-minute 6 Copyright 2011 by ASME

7 Fig. 10 Close look of SEM image of surface defects on the machined surface after 6-minute side cutting 3.4 Tool Wear The tool flank wear and tool nose wear in edge radius during side cutting were investigated in the side cutting experiments. Tool wear patterns are defined in Fig. 11. The flank wear was measured with the microscope or SEM by calculating the decrease from the width of land of the tool flank surface before cutting to the one after cutting. The tool cutting edge radius was measured with SEM. Fig. 12 shows the tool wear progression in side cutting. A gradual tool wear progression in the tool nose and flank surface can be observed after 3, 6 and 9 minutes of cutting time. Note that after 3 minutes of cutting time, the two flutes of tool 1 developed wear at different rates with one flute more severely worn than the other, which was mainly caused by the large runout of the new tool and also the shallow radial depth of cut of 5 µm. However, as cutting time increased to about 6 minutes, both flutes developed similar wear. Some built-up edge was seen remaining left on the tool nose after 3 and 9 minutes cutting, while no BUE can be seen after 6 minutes cutting, which indicated a frequent welded-on and break-off of the BUE during cutting. The tool cutting edge radius, r e, and maximum flank wear, VB max, vs. cutting time are shown in Fig. 13 (a) and (b), respectively. More data was measured for flank wear with the optical microscope, while less data was collected with SEM for tool nose radius. Good repeatability can be observed in tool wear for the two tools investigated. Maximum tool cutting edge radius was measured at the tool end, while the average edge radius was assessed about µm above the tool end. It can be seen that the tool nose wear and flank wear developed at a steady rate prior to tool catastrophic failure. The maximum tool flank wear, maximum tool edge radius and average tool edge radius gradually reached to about 25 µm, 10 µm and 4 µm for their last measurement before tool fracture, respectively. The ratio of maximum undeformed chip thickness to average cutting edge radius, λ, decreased from about 2 to 0.2 as the tool cutting edge radius increased from 0.4 to 4 µm. As the tool wore and λ decreased to 0.2, ploughing and rubbing played the dominant role, no chip would form, and eventually the tool fractured due to the dramatic increase of the specific cutting force. Fig. 11 Tool wear measurements for a tool after 6-minute side cutting Fig. 12 Tool wear progress in side cutting for tool 1. 7 Copyright 2011 by ASME

8 (a) Tool edge radius (b) Maximum flank wear Fig. 13 Tool edge radius and maximum flank wear progress in side cutting 4 Finite element model analysis of micro-milling Chip formation during micro-milling with varying uncut chip thickness was simulated with a strain gradient based finite element model for both micro side cutting and micro slotting configurations. Two cases were studied: 1), the FE model with the strain gradient plasticity model was validated by comparing the model predictions of the specific cutting force with the measured data in the micro slotting tests by Aramcharoen and Mativenga [2]; 2), the FE simulations of micro side cutting were carried out to study chip formation, stress, temperature and BUE formation. The size effect was modeled through the strain gradient plasticity model for the maximum chip load as small as 0.83 µm in micro cutting of hardened H13 tool steel. An arbitrary-lagrangian Eulerian (ALE) based finite element explicit scheme was developed with the commercial software ABAQUS to model the chip formation in micro-milling. The technique of remesh/solution mapping was developed to remesh the workpiece domain to enable a continuous simulation of chip formation and transfer the simulation results between ABAQUS explicit and implicit analyses. The steady-state cutting temperature was investigated by a heat transfer analysis of multi micro-milling cycles. 4.1 Material constitutive modeling Table 3 shows the Johnson-cook type material constitutive plasticity models for hardened H13 steel with a hardness of 45 HRC [19]. For hardened H13 steel with a hardness of 42 HRC, the material constants A and B were estimated by scaling down the values of those of 45 HRC to compensate for the difference in hardness. These constitutive models describe the material flow stress at various strains, strain rates and temperatures occurring in cutting. However, the modeled flow stress is non-dimensional and independent of the length scale in the FE simulation and hence the models are not suitable for describing the significant size effect in micro cutting. The strain gradient plasticity model is briefly presented in this section and more detailed descriptions can be found from the work of Liu and Melkote [7] and Lai et al. [8]. In strain gradient plasticity, a length scale is introduced through the coefficients of spatial gradients of strain components and can be used to model the size effect in micro-milling. The strain gradient constitutive model can be expressed explicitly as m T T n ref σ = ( A+ Bε )( 1+ clog ε) 1 i Tm Tref m 2 2 μ T T μ 1+ ( 18 ) / n ref ( + ε )( 1+ log ε) 1 abg L A B c Tm Tre f where L is the length parameter. The strain gradient plasticity was programmed as a material subroutine in ABAQUS. Tables 4 gives the model parameters for the hardened H13 steel used in the simulations. The length L used in the simulation was chosen to be the maximum uncut chip thickness. For the maximum uncut chip thickness of 0.83 µm, with the strain gradient plasticity model, the average von Mises stress was simulated to be about 2,500 MPa in the primary shear zone, while with the conventional Johnson- Cook model, the average von Mises stress was simulated to be about 1,400 MPa in the primary shear zone. The Johnson- Cook shear failure model parameters for hardened H13 steel [21] are given in Table 5. The thermo-mechanical properties of the tool material (WC-Co) [22] used in the simulation are shown in Table 6. 2μ 1/ 2 (1) 8 Copyright 2011 by ASME

9 Table 3. JC constitutive plasticity model parameters for H13 steel Material Model Hardness (HRC) A (MPa) B (MPa) n C m H H13 [19] Material Table 4. Strain gradient parameters for H13 steel [8, 20] Material G (GPa) b (nm) α µ H Table 5. JC shear failure model parameters for H13 steel [21] Material d 1 d 2 d 3 d 4 d 5 H Elastic modulus (GPa) Table 6. Thermo-mechanical properties of tungsten carbide (WC-Co) tool [22] Poisson s ratio Micro hardness HV30 Specific heat (J/kg C) Thermal conductivity (W/m K) Thermal expansion (µm/m C) Density (g/cm 3 ) WC-Co FE models of chip formation in micro-milling The 3D micro-milling process as illustrated in Fig. 2 can be approximated as the sum of a deck of 2D deformationprocess sections with finite sectional heights twisted at the helix angle of the endmill in an orderly fashion. Because the sectional height is very small, the tool helix angle has little effect and the section can be treated as straight one in the tool axial direction. Fig. 14 shows the 2D FE model setups for the two micro-milling configurations. Simplified geometry of one cutting flute was modeled in the analysis, while the actual tool cutting edge radius, tool radial rake angle and relief angle were used in the simulation. For slotting, only half of the workpiece geometry was modeled due to the symmetry in slotting and the simulation started from 92 to 80 to ensure that cutting at 90 would be properly simulated at the maximum uncut chip thickness. For side cutting, the simulation started from 26 to 0 to simulate a complete cutting cycle of one flute with the radial depth of cut of 5 µm. Fully coupled thermo mechanical Abaqus/Explicit analysis was carried out for micro-milling simulations. Quadrilateral, four-node, bilinear displacement and temperature elements with automatic hourglass control and reduced integration were used. The ALE technique was used in the Abaqus/Explicit analysis step to simulate chip formation. During cutting simulation, the number of elements in the workpiece domain remained the same, but some workpiece elements behind the tool flank surface would flow around the tool nose to the front of the tool, and become chip. In another word, the ALE algorithm in Abaqus/Explicit smoothes the mesh distortions due to deformation by re-adjusting the element positions. Initial mesh has to be fine enough in the workpiece; otherwise the chip formation will not be simulated well with ALE. Hence, the workpiece domain was divided into two sections, with ALE applied on the top section A with a fine mesh and the bottom section B with a coarse mesh fixed in space to work as a heat sink. A high thermal conductance of 1E5 W/K mm 2 was used to define the interface between sections A and B to ensure the continuity of temperature. As a result, no stress would be simulated in section B, while temperature was simulated properly due to heat thermal conduction. For the slotting model, the top section A ranged from 92 to 75 with a sectional thickness of 10 µm, while for the side cutting model, the top section A ranged from 26 to -5 with a sectional thickness of 6 µm. Room temperature was used as the initial temperature condition in the simulation of the first milling cycle, while the steady-state temperature after many milling cycles was analyzed by a thermal model as will be discussed in Section 4.3. A rotational speed was applied to the tool center and workpiece elements in section A deformed into the chip with the smoothing techniques of ALE. A constant frictional coefficient of 0.65 was adopted for the tool-work interface [16]. No chip separation criterion was required by the FE model. An ABAQUS/Explicit simulation step of 20 µs or 7 tool rotation of side cutting at a speed of m/min can be completed with the ALE technique in a reasonable computation time. However, a longer step cannot be 9 Copyright 2011 by ASME

10 simulated due to excessive distortion of the workpiece mesh around the tool nose even with ALE. To simulate the chip formation continuously for a longer period of time, for instance, 26 tool rotation for a complete cutting cycle of one flute in micro side cutting, remeshing the deformed workpiece is required; however the mesh-to-mesh solution mapping technique is only available in ABAQUS/Implicit. An ABAQUS/Implicit step was developed between two continuous explicit steps for remeshing the distorted workpiece mesh and mapping the simulation results from the previous explicit step to the following one. A very short period of time, say µs, was simulated for the implicit step and remesh was optimized in the deformed workpiece domain using ABAQUS/CAE. The Johnson-Cook shear failure model for hardened H13 steel was applied in the last explicit step with a step time of 2 µs or a tool rotation of less than 1 to simulate the separation of the chip from the bulk material. (a) Slotting FE setup (b) Side cutting FE setup Fig. 14 FE model setups 4.3 Thermal analysis of micro-milling The FE chip formation simulation was limited to one micro-milling cycle of both micro slotting and side cutting configurations, because coupled thermo-mechanical analysis is too expensive in computation using any commercial finite element software. To correctly model the steady-state cutting temperature only achieved after many milling cycles, a heat transfer analysis was performed on the bulk workpiece after the chip formation analysis for further milling cycles at a low computation cost. In the chip formation analysis, the modeled workpiece was smaller than the actual one to save computation cost, but it was extended to close to the actual size in the heat transfer analysis to properly set the thermal boundary conditions. The ABAQUS/Explicit solver was used in the heat transfer analysis of the bulk workpiece for a long period of time. In every milling cycle of micro slotting and side cutting configurations, the workpiece material is heated locally by heat generation due to plastic deformation and friction at the tool-chip and tool-workpiece interfaces as the tool flute approaches, while it cools down due to heat conduction to the bulk material and heat convection to the air as the flute leaves. If the local heat generation is not dissipated completely to the bulk material by heat conduction and to the environment by heat convection, the temperature of the workpiece will get an increase in the following milling cycle due to the remaining heat. As the cutting flute approaches, the total heat flux to the local material is composed of heat generation term q pl converted from plastic deformation and frictional heat term q. Deformation heat flux is given by Eqn. (2) f pl q = η σε (2) pl where η pl specifies the fraction of deformation energy converted into thermal energy (0.9 was used), σ is the material flow stress, and ε is the material strain rate. Frictional heat flux is created due to the sliding friction between the workpiece material and the tool face. The amount of frictional heat flux into the workpiece is given by Eqn. (3) q f = ξη f τγ s (3) where η f specifies the fraction of mechanical energy converted into thermal energy (0.9 was used), τ s is the frictional stress, γ is the slip rate, and ξ gives the fraction of the generated heat flowing into the workpiece (0.5 was used). Both heat flux components are varying from node to node and the nodal heat flux data were obtained from the FE chip formation analysis of one milling cycle. A time-dependent nodal heat flux subroutine was created for the heat transfer analysis of multi cycles, in which the heat flux was used as periodic heat input along the milling paths. Fig. 15 shows the simulated workpiece temperature fields at different tool rotation angles of the 16th micro slotting cycle, in which counter-clockwise tool rotation was specified for a cutting speed of m/min and a feed of 2.8 µm/tooth. It can be seen in Fig. 15 (a) that as tool flute-1 rotates 90 and cuts the material in the center of the slot, the material peak temperature near flute-1 is increased to about 300 C. As flute-1 rotates 180 and leaves the slot before flute-2 enters, the heat is dissipated to the bulk material but the average temperature of the material around the slot remains as high as about C. To determine if the workpiece temperature field reaches its quasi steady state, the temperature histories of several nodes near the slot were tracked with the heat transfer analysis. Fig. 16 (a) shows the nodal temperature histories of 16 micro slotting cycles, with N1 designated as the node at the slot wall at θ of 90 and N2 as a node of 10 µm away from N1. It can be seen that after 12 micro slotting cycles, quasi steady state periodic temperature patterns are maintained in the further milling cycles and the temperatures at both nodes 10 Copyright 2011 by ASME

11 drop to the same level of 90 C as the flute leaves. In another word, the temperature field after 12 micro slotting cycles can be used for setting the temperature conditions in the chip formation analysis of micro slotting. Fig. 16 (b) shows the nodal temperature histories of three micro side cutting cycles, with N1 designated as the node on the machined chamfer at θ of 20 and N2 as a node of 8 µm away from N1. It can be seen that the material temperature increases to about 75 C as the flute approaches but drops to the ambient temperature as the flute leaves within the same milling cycle. This finding tells that, differently than slotting, the ambient temperature can be used as the workpiece temperature conditions in the chip formation analysis of micro side cutting. A flute cuts 180 and idles for 180 in slotting, while it cuts about 26 and idles for 334 in side cutting. As a result, the workpiece material is heated for a much shorter period of time in micro side cutting compared with micro slotting, but cools long enough to dissipate the heat completely. The cutting temperature of 75 C in side cutting is significantly lower than that of 300 C in slotting, mainly because a low cutting speed of m/min is used in micro side cutting. The simulated steady-state temperature fields and ambient temperature were then used for setting the temperature conditions in the chip formation analysis of micro slotting and side cutting configurations, respectively. (a) Slotting of 16 milling cycles (b) Side cutting of 3 milling cycles Fig. 16 Workpiece nodal temperature histories of multiple milling cycles Fig. 15 Workpiece temperature field during the 16th micro slotting cycle of condition A 4.4 Specific cutting force in micro slotting To assess the validity of the developed FE model with the strain gradient plasticity, ten slotting tests were simulated for hardened steel of a hardness of 45 HRC with various feed rates for tool rotation from 92 to 80. The simulated ratio of maximum undeformed chip thickness to cutting edge radius, λ, varied from 0.2 to 2. The simulated specific cutting forces were calculated by dividing the simulated cutting force at 90 by the product of the maximum uncut chip thickness at 90 and the axial depth of cut. Fig. 17 shows the comparison of the specific cutting force predictions with the experimental data of Aramcharoen and Mativenga [2]. Micro-milling simulation with the strain gradient plasticity model shows a significant size effect in the specific cutting force in micromilling and matches well with the experimental data for various λ ratios. Simulations with the Johnson-Cook model predict the size effect to some extent due to the increase of tool cutting edge radius in micro slotting; however, it was not 11 Copyright 2011 by ASME

12 able to simulate the extreme high specific cutting force occurring in micro cutting for ratio λ less than 0.5. The simulation results thus validated the efficacy of the FE model with the strain gradient plasticity model for simulating micromilling. Fig. 17 Comparison between measured and predicted (with and without strain gradient plasticity) specific cutting forces in micro-milling of hardened H13 steel of 45 HRC 4.5 Chip formation, stress, velocity and temperature fields in micro side cutting Continuous chip formation within a complete side cutting cycle for one flute rotation from 26 to 0 with a 0.5 µm edge radius micro tool is shown in Fig. 18. Eight ABAQUS/Explicit steps were simulated for the 72 µs side cutting cycle with first seven 10-µs steps and one last 2-µs step. Seven ABAQUS/Implicit intermittent steps were used to remesh the distorted workpiece mesh from the previous explicit step and map the simulation results from the earlier step to the following one. Strain gradient plasticity was used in all the time steps with the varying uncut chip thickness as the material length scale L. The material shear failure criterion was used in the last explicit step of 2 µs to simulate the separation of the chip from the bulk material. As can be seen in Fig. 18, a seamless chip formation was simulated with a smooth transition between two neighboring explicit steps. Deformed chip grew thicker in the beginning 30 µs cutting time even with a decreasing uncut chip thickness. The model simulated necking of chip formation after 50 µs. The chip separation in the last 2 µs was simulated with a breakage of the neck at the primary shear zone. The simulated von Mises stress in the primary shear zone was constantly about 2,500 MPa for this condition. No stress was simulated in the bottom section B of the workpiece because of the displacement constraints applied there. Fig. 18 Chip formation and von Mises stress distributions during one side cutting cycle with a 0.5 µm edge radius endmill 12 Copyright 2011 by ASME

13 Material removal mechanism transition from cutting to ploughing was investigated by the FE model. Fig. 19 shows the chip formation within the first 10 µs side cutting with different tool edge radii from 0.5 to 4.2 µm. Cutting was the main material removal mechanism when the ratio λ was greater than 1. Ploughing played a more important role as λ decreased to below 1. No chip would form as λ decreased to below 0.2. The simulated maximum von Mises stresses in the primary shear zone were about the same at 2,500 MPa for different tool edge radii because the same uncut chip thickness of 0.83 µm was used as the length scale parameter in the strain gradient models in these first explicit steps. However, as shown in Fig. 19 (a-c), a larger highly stressed deformation zone was predicted for side cutting with a r e of 4.2 µm than that of 0.5 µm, which lead to a greater reaction force. The maximum temperatures in the chip and tool were predicted to be around 75 C and 50 C, respectively, for various cutting ratios. Velocity fields simulated with various cutting ratios as can be seen in Fig. 19 (g-i) show a large triangle zone of stagnant workpiece material for side cutting with a small λ less than 1, which indicated a built-up edge would form more often as tool wore to have a large r e. The experimental observations of BUE formed on the worn tool discussed earlier confirmed the model predictions of BUE. The surface defect of prows remaining on the machined surface was the result of BUE s that have broken off from the tool nose. The model prediction that a large BUE could form for a large tool edge radius corroborates the experimental observation of more and larger prows remaining on the machined surface produced by a worn tool than a fresh one. 5 Conclusions In this research, multiple micro-milling tests under progression of tool wear were performed with 100 µm diameter tungsten carbide endmills under a micro side cutting condition. A precise dimension control of the machined part was achieved with an accumulated error of about 1%. The surface roughness on the machined side surface was found to be constantly around 0.5 µm. As the tool wore and ploughing became dominant, the length of burr increased with the longest one of 90 µm remaining on the top surface, larger and more prows were observed on the machined surface and large BUE was observed on the tool nose. A gradual tool wear progression in both the tool nose and flank face was observed in the wear tests for multiple tools. The maximum tool flank wear, maximum tool edge radius and average tool edge radius gradually reached about 25 µm, 10 µm and 4 µm, respectively, and the ratio λ decreased from about 2 to 0.2 before the tool catastrophic failure. Novel 2D FE models with a strain gradient plasticity model were developed to simulate the continuous chip formation using ALE technique for a complete micro-milling cycle of hardened H13 tool steel with varying chip thickness under two configurations: micro slotting and micro side cutting. The heat transfer analysis of multi milling cycles showed that the steady-state workpiece temperature reaches about 300 C as the flute approaches and drops to about 90 C as the flute leaves in micro slotting at a cutting speed of 85 m/min, while the workpiece temperature increases to 75 C in the cutting phase but drops to the ambient temperature in the cooling phase in micro side cutting at a cutting speed of 19 Fig. 19 Deformation fields during side cutting with different tool edge radii 13 Copyright 2011 by ASME

14 m/min. The FE model with the strain gradient plasticity model was validated by comparing the model predictions of the specific cutting forces with the measured data in micro slotting. The specific cutting force was predicted to increase from about 15 to about 100 GPa as λ decreased from 2 to 0.2. Ploughing and no chip formation were simulated with the FE model as ratio λ decreased to about 0.2. The maximum temperatures in the chip and tool were predicted to be around 75 C and 50 C, respectively, for various cutting ratios in micro side cutting. The model simulations showed that a large triangle zone of workpiece material was stagnant in front of the tool nose for side cutting with a large tool edge radius, which indicated a built-up edge would form more often as the tool wore. References: [1] Shelton, J. A., and Shin, Y. C., 2010, "Comparative evaluation of laser-assisted micro-milling for AISI 316, AISI 422, Ti-6Al-4V and inconel 718 in a side-cutting configuration," Journal of Micromechanics and Microengineering, 20, pp [2] Aramcharoen, A., and Mativenga, P. T., 2009, "Size effect and tool geometry in micromilling of tool steel," Precision Engineering, 33(4), pp [3] Aramcharoen, A., Mativenga, P. T., Yang, S., Cooke, K. E., and Teer, D. G., 2008, "Evaluation and selection of hard coatings for micro milling of hardened tool steel," International Journal of Machine Tools and Manufacture, 48(14), pp [4] Melkote, S., Kumar, M., Hashimoto, F., and Lahoti, G., 2009, "Laser assisted micro-milling of hard-to-machine materials," CIRP Annals - Manufacturing Technology, 58, pp [5] Shelton, J. A., and Shin, Y. C., 2010, "Experimental evaluation of laser-assisted micromilling in a slotting configuration," Journal of Manufacturing Science and Engineering, Transactions of the ASME, 132, [6] Jeon, Y., 2008, Laser-assisted micro end milling, Ph.D. thesis, The University of Wisconsin Madison, Madison, WI. [7] Liu, K., and Melkote, S. N., 2007, "Finite element analysis of the influence of tool edge radius on size effect in orthogonal micro-cutting process," International Journal of Mechanical Sciences, 49(5), pp [8] Lai, X., Li, H., Li, C., Lin, Z., and Ni, J., 2008, "Modelling and analysis of micro scale milling considering size effect, micro cutter edge radius and minimum chip thickness," International Journal of Machine Tools and Manufacture, 48(1), pp [9] Liu, X., DeVor, R. E., and Kapoor, S. G., 2006, "An analytical model for the prediction of minimum chip thickness in micromachining," Journal of Manufacturing Science and Engineering, Transactions of the ASME, 128, pp [10] Liu, X., DeVor, R. E., Kapoor, S. G., and Ehmann, K. F., 2004, "The mechanics of machining at the microscale: assessment of the current state of the science," Transactions of the ASME. Journal of Manufacturing Science and Engineering, 126, pp [11] Liu, X., Devor, R. E., and Kapoor, S. G., 2007, "Modelbased analysis of the surface generation in microendmilling - Part II: Experimental validation and analysis," Journal of Manufacturing Science and Engineering, Transactions of the ASME, 129, pp [12] Simoneau, A., Ng, E., and Elbestawi, M. A., 2006, "Surface defects during microcutting," International Journal of Machine Tools and Manufacture, 46(12-13), pp [13] Lee, K., and Dornfeld, D. A., 2005, "Micro-burr formation and minimization through process control," Precision Engineering, 29(2), pp [14] Chae, J., Park, S. S., and Freiheit, T., 2006, "Investigation of micro-cutting operations," International Journal of Machine Tools and Manufacture, 46(3-4), pp [15] Filiz, S., Conley, C. M., Wasserman, M. B., and Ozdoganlar, O. B., 2007, "An experimental investigation of micro-machinability of copper 101 using tungsten carbide micro-endmills," International Journal of Machine Tools and Manufacture, 47(7-8), pp [16] Özel, T., 2007, "Modelling and Simulation of Micro- Milling Process," 4th International Conference and Exhibition on Design and Production of Machines and Dies/Molds, 21-23, Cesme, TURKEY. [17] Liu, K., and Melkote, S. N., 2006, "Material strengthening mechanisms and their contribution to size effect in micro-cutting," Transactions of the ASME. Journal of Manufacturing Science and Engineering, 128, pp [18] Liang, S. and Dornfeld, D., 1981, Tool wear detection using time series analysis of acoustic emission, Journal of Engineering for Industry, Transactions of the ASME, 111, pp [19] Yan, H., Hua, J., and Shivpuri, R., 2007, "Flow stress of AISI H13 die steel in hard machining," Materials & Design, 28(1), pp [20] T.B. Zorc et al. (Eds), Properties and Selection: Irons, Steels, and High-performance Alloys, Metals Handbook, v1, 10th ed., ASM International, [21] Ng, E.-G., and Aspinwall, D. K., 2002, "Modelling of hard part machining," Journal of Materials Processing Technology, 127(2), pp [22] Park, S., 2007, Development of a microstructure-level finite element model for the prediction of tool failure by chipping in tungsten carbide-cobalt systems, Ph.D. thesis, University of Illinois at Urbana-Champaign, Urbana, IL. 14 Copyright 2011 by ASME