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Tool wear analysis in micromilling of titanium alloy
Precision Engineering 57 (2019) 83–94
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Tool wear analysis in micromilling of titanium alloy ∗
T
Alessandro Colpani , Antonio Fiorentino, Elisabetta Ceretti, Aldo Attanasio Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123, Brescia, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: Micromilling Tool wear Titanium alloy Cutting force Roughness
Micromachining is a key technology in contemporary society, due to the new requirements of the modern industry. The need of higher accuracy and precision, even for details on the finished product (which may also have very small dimensions), can be fulfilled only with micromechanical machining. Among this family of technologies, micromilling is one of the most important and widespread: the potential of this process is due to the great precision level that can be obtained, when machining high strength materials too. One of the main challenges launched by micromilling concerns the understanding of the processes that generate breakages and deterioration of the tool, and the study of their causes, that are usually different from those observed in conventional milling processes, for which the definition and the prediction of the tool-life are regulated by the ISO-8688 standard (part 1 and 2). This standard is not referred to micromilling and a dedicated regulation has not been compiled yet. In this article the results of an experimental campaign, made to investigate the tool wear in micromilling process, are presented. The aim of the work is to provide fundamental knowledge for the development of a future standard that can fill the normative gap. In particular, results describe that the flank wear evolution follows the typical trend characterized by the decreasing, constant and increasing tool wear slope regions. Cutting force, roughness and tool corner radius evolution are related with tool wear. In particular, a statistical analysis based on the Pearson correlation coefficient is presented in order to quantify the correlation between the flank wear and the considered parameters. Details about the influence of feed rate and mill type in flank wear evolution are also provided. Furthermore, results show that flank wear could actually be used for a tool-life criterion in micromilling processes.
1. Introduction In order to meet the demand for high accuracy reducing component size, the miniaturization of products has become increasingly important for several modern technologies. Micromechanical machining is getting greater employed, due to its ability of fabricating complex 3D components, its capability of machining a variety of engineering materials, and its high material removal rate [1]. Reducing size and weight can substantially increase the convenience and value of some components. Many products have been already miniaturized in order to increase their market share, and the future of many manufacturers will rely on how quickly and successfully they can implement micromachining in their operations [2]. Modern manufacturing advances allowed to reach a lot of goals such as better product quality, increased productivity and a high level of flexibility [3]. Such benefits are dependent on troublefree operations of the various machine elements [4]. In machining industry, 20% of downtime is attributed to tool failures [5]. Therefore, tool condition monitoring and life prediction play an important role in
improving machine productivity, maintaining the quality and integrity of the machined part, minimizing material waste, and reducing cost for sustainable manufacturing [6]. Wear and tool failure mechanisms are known to be complicated in micromachining. Tool life is acceptable at a very low feed-per-tooth. At higher feed-per-tooth values, tool life becomes unpredictable and short [7]. In the world of micromechanical machining, micromilling is among the principal manufacturing processes which allowed the development of components with micrometric dimensions, being used to manufacture dies and final products. Several works are present in literature analysing the tool behaviours [8], the material influence on the process [9,10], and the machine design [11]. In fact, the downsizing of the process up to the micro scale needs a full review of all the knowledge coming from the macro-scale. In conventional end milling studies have been conducted with different purposes: to model cutting mechanism [12], to study the characteristics of cutting forces [13], to detect tool failure [14], and so on. Cutting force characteristics of micro-end-milling operations are almost the same as those of conventional milling
∗
Corresponding author. E-mail addresses: [email protected] (A. Colpani), antonio.fi[email protected] (A. Fiorentino), [email protected] (E. Ceretti), [email protected] (A. Attanasio). https://doi.org/10.1016/j.precisioneng.2019.03.011 Received 19 March 2019; Received in revised form 25 March 2019; Accepted 28 March 2019 Available online 02 April 2019 0141-6359/ © 2019 Elsevier Inc. All rights reserved.
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[15]; however, the wear and breakage mechanisms are very different. So, the traditional approaches used to describe the phenomena involved in conventional (or macro) machining do not apply in this case [16]. Size effect is certainly among the principal issues, probably the most relevant aspect, to be addressed in microcutting. Furthermore, there are other important characteristics of micro technologies: micromachining forces are low because of the small shear area, however, in microcutting, specific cutting force greatly increases [17] as the uncut chip thickness is reduced [18]. Cutting temperature is considerably lower in comparison with conventional technologies, but its accurate determination presents difficulties related to the small shear area and the need of measuring equipment with high sampling rates [19]. Special attention must be paid to the surface finish [20], the burr formation [21], and the prediction of tool failure [22]. Unpredictable tool life and premature tool failure are major problems in micromachining [23]. This research wants to deepen the tool wear knowledge in the micromilling process of titanium alloy, describing the experimental campaign results and providing some data which can give the fundamentals for the development of a future standard for tool-life criterion in micromilling. Starting from the ISO standard currently available, experimental tests were planned, in order to focus on the tool wear in micromilling. The experimental tests performed in the present research, the monitored quantities, and the systems used for the data acquisition are presented. Then, the data analysis procedures are provided and the results are shown, with particular attention on the relations between the variables and the measured quantities.
Fig. 1. Definition of various wear parameters [24].
extended over those portions of the tool flanks adjoining the entire length of the active cutting edge; non-uniform flank wear (Fig. 2b), wear land which has an irregular width and for which the profile generated by the intersection of the wear land and the original flank varies at each position of measurement; localized flank wear (Fig. 2c), that develops in a limited part of the flank. According to the standard, a tool-life criterion is a predetermined numerical value of any type of tool deterioration parameter which can be measured. A certain width of the flank wear land (VB) is the most commonly used criterion, and the recommended values are 0,3 mm for uniform flank wear and 0,5 mm for non-uniform flank wear; the use of VB3, with a limit value of 0,5 mm, is recommended only when chipping occurs. ISO-8688-2, even if not specifically for micromilling, contains some recommendations for cutters with small diameters (a case similar to the micromilling condition): the standard advises against the use of deterioration phenomena that produce abrupt breakages (for example clogging) as tool-life criteria. Some indications about how to measure the different quantities are also provided. In particular, flank wear measurement must be carried out in parallel to the surface of the wear land and in a direction perpendicular to the original cutting edge. Although the flank wear land on a significant portion of the flank may be of uniform size, there will be variations in its value at other portions of the flanks depending on the tool profile and edge chipping. Values of flank wear measurements should therefore be related to the area or position along the cutting edges at which the measurement is made. Before providing details about the experimental campaign performed in this work, it is important to underline that, while in conventional milling tool wear is a commonly used criterion to determine the tool life duration, in micromilling it is not always effective. In fact, microtools frequently run into premature failures, due to various mechanisms, not always related to wear. Excessive stress-related breakage occurs very quickly when the cutting force exceeds the strength of the tool, which is usually quite low because of the small tool diameter. The reasons which can cause this force increase are many: the cutting edge has lost its sharpness or it has been partially damaged (studying the wear process of the tool this phenomenon is predictable); deposition of chips fill the tool grooves stopping the chip flow, so the workpiece starts to push the tool increasing the static component of the force in the feed direction (this phenomenon is called chip clogging and it is almost unpredictable) [25]; during cutting the presence of oxides or carbides in the material matrix can cause a sudden increase of cutting forces that
2. Tool life in micromilling In the field of tool wear, ISO-8688-2 is the standard for tool life testing in end milling [24]. It contains the recommendations to unify procedures in order to increase the reliability and comparability of milling test results. Tests conducted in our research are not included in the field of application of ISO-8688-2, but this standard was chosen as a benchmark because no regulation about micromilling has been published yet. In particular, this work will refer to chapter 7 of the standard (Tool deterioration and tool-life criteria). ISO-8688-2 standard specifies recommended procedures for tool-life testing in milling processes. It can be applied to tests conducted with HSS tools and steel or cast iron workpieces. The type of tests named in the standard are slot milling, end milling in which tool periphery is predominantly used, and end milling in which the end teeth of a tool are predominantly used. The ISO standard is also clear about the definition of some important terms, starting from the tool deterioration, that consists in all the changes in a cutting part of a tool caused by a cutting process. Two major classes of tool deterioration are distinguished: tool wear, that is the change in tool cutting edge shape, resulting from the progressive tool material loss during cutting operation, and the brittle fracture (chipping), occurrence of cracks in the cutting part of a tool followed by the loss of small fragments of tool material, resulting from crack that takes origin during cutting operation. About the tool deterioration measure (quantity used to express the magnitude of a certain aspect of tool deterioration by a numerical value), it is important to define two other terms: tool life criterion (predetermined value of a specific tool deterioration measure or the occurrence of a specified phenomenon), and tool life (total cutting time of the cutting part required to reach a specified tool-life criterion). Wear on end milling cutters and slot drills, and many deterioration phenomena are described in the ISO standard, and each of them can be quantified with some parameters (Fig. 1). In this work the phenomenon that is analysed is the flank wear (VB), that is defined as the loss of tool material from the tool flanks during the cutting operation: it results in progressive development of flank wear land. There are three different typologies of flank wear: uniform flank wear (Fig. 2a), wear land that usually has a constant width and is 84
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Fig. 2. Different typologies of flank wear: (a) uniform flank wear, (b) non-uniform flank wear, (c) localized flank wear [24].
3. Experimental procedure In order to reach the main objective of this work, that is the understanding of wear phenomenon in micromilling, an experimental campaign was planned with the purpose of investigating the relation between the tool wear and some relevant parameters and quantities measured during the process. The tests consisted in shoulder micromilling with 90° entering angle. During the experimental campaign some process parameters (depths of cut, material and geometry of the samples, tool diameter, cutting speed) were fixed, while others (tool geometry and feed rate) were varied. Quantities related to tool life (flank wear, cutting force, roughness and tool corner radius) were monitored during the process (on line monitoring), or at the end of each test (off line monitoring). Each testing condition was repeated three times. The milling machine where the tests were made was a KERN Pyramid Nano (Fig. 3), managed by an iTNC control. It can move on 5 axes (using hydrostatic guides) and it can automatically measure the tool parameters through a tool presetting laser system (BLUM LASER CONTROL NANO NT). The machine is located in a room with constant temperature (20 ± 0,5 °C) and humidity (50%), in order to prevent thermal expansions. Table 1 summarizes the main features of this milling machine. The samples were cubes (with a side of 14 mm) made of Ti-6Al-4V, which is a titanium α-β type alloy (Fig. 4a). The microstructure is formed by equi-axial grains characterized by a minimum diameter of 2 μm (Fig. 4b). Ti-6Al-4V is one of the most utilized titanium alloy, and it is also a common material for tests about micromilling: this makes the results of this work easily comparable with other studies conducted in the same field. Table 2 summarizes Ti-6Al-4V main properties. Some cutting parameters (Table 3) are fixed in all the tests, so their influence cannot be deduced from the results of this study. The values of these parameters were chosen according to the toolmaker guidelines. In particular, according to the ISO standard, the axial depth of cut (ap) was chosen equal to three times the corner radius of the mills (rε in Table 4).
Fig. 3. KERN pyramid nano. Table 1 KERN Pyramid Nano features. Position accuracy
± 0.3 μm
Maximum mandrel speed Maximum axis acceleration Maximum axis speed Work space
5000 rpm 10 m/s2 30 m/min 500 × 500 × 500 mm
could bring to a tool breakage [26]. Fatigue related breakage happens when cutting force increases and remains at high level for a long time. This increase is often due to tool wear. Since the tool is rotating, the stress will change cyclically and generate a fatigue fracture after a certain number of revolutions. The increase in the specific energy is also important in the tool failure: as the chip gets thinner the microtool will meet a greater resistance compared to conventional milling operations. This increase, especially if the depth of cut decreases under the minimum achievable chip thickness, can be responsible for the tool breakage [27].
Fig. 4. (a) One of the samples after the experiment, (b) micrograph of the sample. 85
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Table 2 Ti-6Al-4V properties. Chemical composition O (%max)
N (%max)
H (%max)
C (%max)
Fe (%max)
Al (%max)
V (%max)
Residues (%tot)
0.2
0.05
0.015
0.08
0.4
5.5–6.75
3.5–4.5
0.4
Mechanical properties Breaking load: 895 MPa Yield load (0.2%): 895 MPa Tensile modulus: 106000 MPa
Hardness: 300–400 HV Elongation: 10% Poisson coefficient: 0.34
Physical properties Density: 4.4 g/cm3 Melting point: 1650 °C Coefficient of thermal expansion: 9 μm/°C Specific heat: 586 J/Kg°C
Thermal conductivity: 6.6 W/m°C Electrical resistance: 1.71 μΩm Electrical conductivity: 0.98 (%IACS) Magnetic permeability (at 1.6 Kam): 1.00005
Table 3 Fixed cutting parameters. Cutting speed Rotation speed Tool diameter Nr. of cutting edges Axial depth of cut (ap) Radial depth of cut (ae)
100 m/min 39789 rpm 0.8 mm 2 150 μm 30 μm
Two different types of tool were used, namely SECO 103L008R005MEGA-64-T (hereinafter SECO 103) and SECO 905L008-MEGA-T (hereinafter SECO 905). Both these tools are characterized by two cutting edges and made by coated tungsten carbide. (Ti, Al)N coating was used because it is suitable for cutting hard materials. The tools differ in helix and rake angles, as shown in Fig. 5 and Table 4. Two different values of feed-per-tooth (and feed rate) were used in the tests: 10 μm/tooth/rev (LOW), and 20 μm/tooth/rev (HIGH). Being the measured cutting edge radius lower than 5 μm, these feed per tooth values guarantee shearing cutting mechanism. Being the depths of cut and the geometry of the sample equal in all the experiments, a variation in the feed rate value directly affects material removal rate (Table 5). The experimental plan consists in four different test configurations (Table 6). Each configuration was tested 3 times, so the total number of runs was equal to 12. Each test was carried out using a new tool and new samples. In order to have a planar surface, a preliminary facemilling operation (step 0 in Fig. 6a) was performed on each sample using a tool different from the tested one. After this first operation the testing tool was mounted on the machine for the experiment. The process was divided in steps (Fig. 6a), characterized by the same volume of removed material, and each step is composed by 57 passes (Fig. 6b). The volume of material that is removed in each step is a parallelepiped with a length of 14 mm (i.e. the total length of the sample), a height of 0,15 mm (i.e. the axial depth of cut), and a width of 1,71 mm (i.e. the radial depth of cut multiplied for the number of passes). Therefore, the amount of material removed during one step is equal to 3591 mm3. The time needed to complete one step depends on the feed rate: 60 s for the low feed rate, and 30 s for the high feed rate. Splitting the process in steps gives the possibility to remove the tool from the
Fig. 5. Tools geometry. Table 5 Differences between HIGH and LOW configurations of feed rate. Configuration
Feed rate [μm/tooth/rev]
MMR [mm3/min]
LOW HIGH
10 20
3.591 7.182
Table 6 Experimental plan. Mill type/Feed Rate
10 μm/tooth/rev
20 μm/tooth/rev
SECO 103 SECO 905
103 LOW 905 LOW
103 HIGH 905 HIGH
machine every 60 or 30 s (depending on the test feed rate), to measure the tool wear, and to verify the tool integrity. Four different quantities, which could be related to tool life and can help to understand the mechanisms that lead to breakage, were monitored during the tests. ⁃ Flank wear. According to ISO-8868-2 tool-life criteria based on flank wear are strongly recommended for conventional milling.
Table 4 SECO 103 and SECO 905 quotes. Mill
Dimensions [mm]
SECO 103 SECO 905
Dc = 0.8 Dc = 0.8
Angles [°] l3 = 4 l3 = 4
rε = 0.05 ± 0.01 rε = 0.05 ± 0.02
ap (max) = 1.2 ap (max) = 0.4
86
Helix angle = 0 Helix angle = 20
Rake angle = 0 Rake angle = 4
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Fig. 6. Experimental test: (a) process steps, (b) passes on each step. Table 7 Load cell main features. Range Fx, Fy
−50 ÷ 50 N
Range Fz Sensitivity Fx Sensitivity Fy Sensitivity Fz Linearity Natural frequency Fx, Fy Natural frequency Fz
−300 ÷ 300 N −24.54 pC/N −24.34 pC/N −11.05 pC/N ≤ ± 0.07% FSO 5 kHz 21 kHz
Table 8 KISTLER Charge Meter Type 5015 features. Output range
± 10 V
Max Voltage Output Error Bandwidth
0.1% 100 ÷ 3000 Hz
Fig. 7. Image from the optical microscope.
Table 9 NI9205 features. Measurements
Voltage
Isolating measures Resolution Single-ended channels Differential channels Maximum sampling frequency Maximum voltage in input Accuracy (interval ± 10 V)
Earthing 16 bit 32 16 250 kS/s 10 V 6220 μV
tool life, in particular in the micro scale, because a rapid increase of the cutting force is one of the most common causes of breakage. ⁃ Roughness of the horizontal surfaces. Surface roughness is one of the properties that can describe the quality of a process, so it is important to know how it evolves during the tests. Furthermore, the obtainable roughness in a process depends on the cutting parameters but it is also strongly connected with tool wear. The profilometer used for this study (Mitaka PF-60) permitted to measure the roughness on the horizontal surfaces of the samples. ⁃ Tool corner radius. Tool corner radius determines the shape of the
Fig. 8. Load cell KISTLER 9317C.
Even if, as seen, tool rupture in micromilling is not always depending on wear, it could be interesting to search a relation between VB and tool life. ⁃ Cutting force. Cutting force is a fundamental factor to determine 87
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Fig. 9. PF-60 output graph: roughness measurements (103 HIGH configuration, step 27).
Fig. 10. Tool corner radius measurements. Fig. 12. Flank wear vs. time, comparison between the trend lines (with the relative R2 value).
end mill, and it increases as the tool loses sharpness, so it can be seen as an alternative measure of wear.
Each test ended when one of the cutting edges reached the maximum VB value, or when the tool incurred in an abrupt breakage, or when high chipping was observed. For each test, the final output of this acquisition chain consisted in two series of VB values that represent the evolution of flank wear during the process of each tool tooth.
4. Measurement methods 4.1. Flank wear The wear data acquisition was carried out using the optical microscope of a nanoindentation tester (Anton Paar Tabletop) which guarantees a measuring accuracy of 0,8 μm. At the end of each step the tool was removed from the machine and cleaned with a polishing paste. Then the images of the flanks of the two cutting edges were acquired by the optical microscope and the corresponding wear measured (Fig. 7).
4.2. Cutting force The acquisition chain for the cutting force measurement consists in a series of devices that are able to measure and record force components. The chain is formed by a load cell (KISTLER 9317C), three
Fig. 11. Flank wear vs. time trend line obtained from a third order polynomial interpolation (103 HIGH configuration). 88
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Fig. 13. Flank wear vs. removed material, comparison between the trend lines (with the relative R2 value).
Fig. 15. Experimental and mean values of cutting force vs. time for test 103 HIGH, first repetition.
amplifiers (KISTLER Charge Meter Type 5015), an acquisition module (NI 9205) and a software for the data elaboration (LabVIEW). The load cell is a piezoelectric cell (Fig. 8), able to convert the three components of the force generated by the interaction between the tool and the sample in charge signals. Piezoelectric sensors are suitable for high frequency sampling. Table 7 summarizes the main features of the device. Hammer impact test was done to correctly define the bandwidth of the load cell-clamping system, obtaining a bandwidth range from 100 Hz to 3000 Hz. Being the cutting force signal frequency equal to 1326 Hz, the utilized system is adequate. The three charge signals coming from the load cell are sent to three laboratory amplifiers, that enhance the signals coming from the load cell in order to make their voltage suitable for the acquisition module. Table 8 summarizes the main features of the amplifiers. The acquisition module processes the signals received from the amplifiers to extract the force values and send them to the computer (Table 9). With LabVIEW the data from the acquisition module were acquired and showed to the user in three different graphs, which are the final output of this system and describe the three force components evolution in time.
interruption of the process but only at the end of the test, because the sample needs to be properly cleaned before the measuring. So roughness is detected only at the end of the process. These surfaces are those corresponding to the steps 7, 13, 18, 22, 25, 27 and 28 as shown in Fig. 6a. The instrument used in this study is a Mitaka PF-60, a non-contact profilometer that consists of an autofocus laser beam microscope (AF microscope) and a high precision XY scanning stage. The AF microscope measures height in the Z axis and the XY stage moves the sample in order to obtain XYZ coordinate values for 2D and 3D measurements. The laser beam incorporated in the AF microscope passes through the objective and forms a laser spot on the surface of the sample. The laser beam reflected from the sample surface passes through the objective again and forms an image on the autofocus sensor (AF sensor). The AF sensor detects the laser spot displacement in real time and adjusts the AF microscope back to the in-focus position (the laser spot forms its image at the centre of the AF sensor). The reflectivity of the sample does not affect the measurement, so it is possible to carry out roughness not in contact measurements on almost every kind of material, without any risk of possible damage to the surface (this can happen with touch probes). The output of the roughness measurement performed with PF60 is shown in Fig. 9 (that refers on 103 HIGH configuration, step 27).
4.3. Surface roughness
4.4. Tool corner radius
The roughness of a surface is quantified by the deviations in the direction of the normal vector of a real surface from its ideal form. The most common roughness parameter is Ra, which is defined as the arithmetical average of the absolute values of the vertical deviations of the profile from its mean line. Roughness cannot be measured at every
The tool corner radius was measured from the tool signs left on the intersections between horizontal and vertical worked surfaces. The acquisition of tool corner radius data was obtained by using a metallographic optical microscope (Leica DMI5000 M). The samples, properly cleaned in order to remove burrs and residual material, were
Fig. 14. (a) The last pass of the tool. (b) signal of the last pass (103 HIGH configuration). 89
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Fig. 16. Cutting force: comparison between the four configurations. (a) Cutting force vs. time. (b) Cutting force vs. removed material. (c) Cutting force vs. flank wear.
(Fig. 11). The trend line has the typical behaviour of a graph which describes wear evolution on a tool: three regions can be distinguished, the first region (primary on initial wear) is characterized by a relatively high wear rate, the second region (steady-state wear) is characterized by a constant wear rate, and the third region (tertiary or accelerated wear) is characterized by high cutting force, high temperature and severe tool wear. Figs. 12 and 13 show the evolution of the tool wear respectively in relation with cutting time and removed material. As expected, in configurations with higher feed rate tool wear grows more quickly than in those with lower feed rate, while 905 mills wear down more quickly than 103 mills. Relating tool wear with time the most influencing variable is the feed rate, while relating tool wear with removed material the most influencing variable is the mill geometry. Also tool angles determinates this behaviour: SECO 103 wears less rapidly due to the rake angle equal to 0° while SECO 905 wears more rapidly because of the rake angle equal to 4°.
analysed at the microscope: the images were taken using a Leica DFC 295, a digital camera that can be connected to all Leica microscopes. The Leica software allows to measure the tool corner radius from the picture identifying at least 3 points on the worked surface. In this research 5 points were acquired as shown in Fig. 10. 5. Data analysis, results and discussion All the data collected during and after the experiments are wear, forces, roughness and tool corner radius. The aim of this analysis was to discover how the monitored quantities evolve as the processing time increases, to find out if there is a correlation between the four monitored variables, to examine the influence of the tool type and the feed rate on the evolution of the process, and to try to use these features for the definition of a possible tool-life criteria suitable for micromilling. 5.1. Flank wear For each test condition two data series are present, one for the first cutting edge and one for the second cutting edge. Comparing the wear curves of the two cutting edges of the same mill differences can occur due to the presence of tool run out [8,15] and/or to the tool deflection [28]. In facts, if the tool bends, the two cutting edges are differently stressed, so they wear in a different way. As a consequence, for each test six data series (two for each repetition) were obtained (Fig. 11). For each test configuration, the six data series (that differ for the cutting edge and the repetition number) were interpolated by means of a polynomial fitting curve of the third order, passing through the origin
5.2. Cutting force Fig. 14 reports the typical output of the force measuring system. The graphs show the evolution in time of the three cutting force components. Fig. 14a reports the behaviour of the cutting force components for the first step of 103 HIGH configuration test. Fig. 14b shows the force signals at pass 57th (the final) of the same test. The obtained force signals were filtered from vibrations (band pass filter with a bandwidth from 100 Hz to 2000 Hz) and, applying equation (1), the total cutting force (Fc) was obtained. 90
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Fig. 17. Roughness: comparison between the four configurations. (a) Roughness vs. time. (b) Roughness vs. removed material. (c) Roughness vs. flank wear. (d) Roughness vs. cutting force.
Fc =
Fx2 + Fy2 + Fz2
material (Fig. 17b), wear (Fig. 17c) and forces (Fig. 17d). These graphs show that roughness has a slightly decreasing trend during the micromilling operation, in particular when time and VB increase. This trend is linked to the tool corner radius evolution. In fact, tool corner radius value affects the roughness as shown in Eq. (2) [29]. As described in the next paragraph, in this work a tool corner radius increasing was observed. Therefore, accordingly to tool corner radius increasing, roughness decreases.
(1)
Where Fx, Fy, and Fz are the cutting force components. Also in this case tool run out determines different values of cutting force for the cutting edges due to unbalanced chip thickness values. Fig. 15 reports the different peak values of each cutting edge as a function of cutting time (the cross marks in the figure are the average values). Fig. 16 reports for all the cutting conditions the average values obtained considering the three test repetitions. Fig. 16a shows the relation between forces and time, Fig. 16b the relation between forces and removed material, while Fig. 16c is about the correlation between forces and wear. As expected, forces slightly increase while the tools wear. The differences between the configurations are quite clear (Fig. 17): the 103 HIGH configuration has the highest force, the 905 LOW configuration has the lowest force, and 103 LOW and 905 HIGH configurations are in the middle. This is because forces increase with the feed rate, while at constant feed rate 103 mills generate higher forces than 905 mills. Also tool angles influence this behaviour: SECO 103 generates higher forces due to the rake angle equal to 0° and helix angle equal to 0°, while SECO 905 generates lower cutting forces because of the rake angle equal to 4° and helix angle equal to 20°.
Rmax ≈
f z2 8·TR
(2)
Where: ⁃ Rmax is the maximum roughness; ⁃ fz is the feed per tooth; ⁃ TR is the tool corner radius.
5.4. Tool corner radius Tool corner radius was investigated in relation to time (Fig. 18a), removed material (Fig. 18b), wear (Fig. 18c) and forces (Fig. 18d). Tool corner radius increases during the process. All the experimental configurations show this trend, and this fact confirms that the tool is uniformly wearing since no chipping phenomena occurs affecting tool corner radius value. Tool corner radius evolution is linked to the roughness decreasing above described.
5.3. Surface roughness Roughness was investigated in relation to time (Fig. 17a), removed 91
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Fig. 18. Tool corner radius: comparison between the four configurations. (a) Tool corner radius vs. time. (b) Tool corner radius vs. removed material. (c) Tool corner radius vs. flank wear. (d) Tool corner radius vs. cutting force.
standardization of the covariance obtained using the standard deviations of the variables. Correlation coefficient definition is shown in Eq. (3) where x and y are the two examined parameters, ??x and ??y are their standard deviations, S(xx) and S(yy) respectively are x and y variances (Eqs. (4) and (5)), while S(xy) is their covariance (Eq. (6)). In those equations n is the sample size (i.e. the number of observations for each examined parameter). Pearson coefficient so defined allows to quantify the correlation between x and y variables with no dependence on the measurement scales. The r value is usually calculated by applying Eq. (7), obtained from an elaboration of the previous equations that allows to find out an operative formula for correlation coefficient evaluation.
Table 10 Critical values of correlation coefficient [30]. Sample size (n)
Critical value (r)
5 10 15 20 25 30 50 100
0.88 0.63 0.51 0.44 0.39 0.36 0.28 0.20
5.5. Results discussion
r= A statistical analysis of the above presented data was conducted. In order to evaluate the correlation between tool wear and force, roughness, and tool corner radius, the Pearson correlation coefficient r [30] was calculated. In particular, r coefficient represents the
S (xy ) = σx σy
S (xx ) =
1 n−1
S (xy ) S (xx ) S (yy ) n
∑ (xi − x¯)2 = i=1
− 1 ≤ r ≤ +1 (3) n
n (∑i = 1 x i )2 1 (∑ x i2 − ) n n − 1 i=1
(4)
Table 11 Correlation coefficient of force, roughness and tool corner radius in relation with tool wear. Parameters
Sample size (n)
Critical value (interpolated data)
Correlation coefficient (r)
Force vs. Wear Roughness vs. Wear Tool Radius vs. Wear
129 62 21
0.15 0.26 0.43
0.45 −0.29 0.68
92
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S (yy ) =
S (xy ) =
n
n
n (∑i = 1 yi )2 1 (∑ yi2 − ) n n − 1 i=1
1 n−1
∑ (yi − y¯)2 =
1 n−1
∑ (xi − x¯)(yi − y¯)
i=1 n i=1 n
n
n
(∑i = 1 x i )(∑i = 1 yi ) 1 (∑ x i y − ) = n n − 1 i=1 i
r=
1 n−1
n
∑( i=1
because the tool is more smoothed, and cutting force increases, because the ratio of edge radius to chip thickness becomes higher. These results mean that tool deterioration (and tool life), could be described using flank wear, as ISO 8688-2 recommends for standard milling. In fact, this quantity seems to have the features to be used in a tool-life criterion, also in micromilling. The results give also some clear information about the influence of the helix and rake angles. In fact, increasing the helix angle allows to reduce the cutting force improving the chip evacuation. The same influence on cutting force was observed for the rake angle, while increasing this angle the tool wear increases. Further tests and studies should concentrate on some specific aspects. With regard surface quality, it will be important to design tests that consider the need to measure roughness on the vertical surface, in order to put it in relation with flank wear. Other important aspects are related to abrupt breakages and run out. It will be interesting to determine the odds of the cases in which the tool incurs in an abrupt breakage or becomes completely worn in a very short time, and to determine if these events are completely accidental or if they are related to specific causes. Moreover, it will be important to consider the need to analyse the influence of the variables on the odds to incur in a consistent run out.
(5)
x i − x¯ yi − y¯ )( ) σx σy
(6)
(7)
In particular, Pearson coefficient allows to quantify the correlation between the considered parameters by a comparison between the absolute value of r (calculated with Eq. (7)) and an r critical value [30]. Correlation is present when the r absolute value is between 1 and the r critical value, while there is no correlation between the examined parameters if the r absolute value is minor than the critical value. Furthermore, when correlation is observed, it is positive or negative respectively when the calculated r (Eq. (7)) is positive or negative. The r critical value depends on the desired confidence level. The confidence level value usually adopted is the 95%. Once fixed the desired confidence level, tables reporting the r critical value depending on the considered sample size are available. Table 10 gives the 95% critical points for the absolute value of the correlation coefficient for different sample size [30]. Applying the above described procedure, experimental results were analysed and the correlation between the examined parameters was evaluated. Table 11 shows the results about correlation between tool wear and force, roughness, and tool corner radius. Regarding the correlation between force and wear, the value of r is higher than the critical value, and this means that the two parameters are actually related. The positive correlation between force and wear is explained because the tool surfaces are no longer optimal when tool wear occurs, and this fact leads to an increase in friction between tool and workpiece, and therefore an increase in force. The value of the correlation coefficient for roughness in relation to wear is higher than the critical value, so the correlation between these two parameters, that was not exactly clear from the graphs shown in Fig. 18, is confirmed from this analysis. An aspect that can be underlined is the negative value of the correlation coefficient. The negative sign denotes that there is a negative correlation between roughness and tool wear. In fact, as can be seen from Fig. 18c, roughness decreases as the tool wear increases. Finally, the r value shows the correlation between tool corner radius and tool wear. This phenomenon is explained because all the cutting edge is engaged in this cutting operation, so the decrease in volume that occurs during the process (as consequence of tool wear) results in an increase in the radius of the tool itself.
Acknowledgments The Authors wish to thank Simitecno and Seco Tools which provided respectively the PF-60 non-contact profilometer and the tools used in these tests. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References [1] Malekian M, Park SS, Jun MBG. Tool wear monitoring of micro-milling operations. J Mater Process Technol 2009;209:4903–14. [2] Tansel I, Rodriguez O, Trujillo M, Paz E, Li W. Micro-end-milling–I. Wear and breakage. Int J Mach Tool Manuf 1998;38:1419–36. [3] Wiendahl HP, Elmaraghy HA, Nyhuis P, Zäh MF, Wiendahl HH, Duffie N, Brieke M. Changeable manufacturing – classification, design, and operation. CIRP Ann Manuf Technol 2007;56(2):783–839. [4] Zhou ZD, Chen YP, Fuh JYH, Nee AYC. Integrated condition monitoring and fault diagnosis for modern manufacturing systems. CIRP Ann - Manuf Technol 2000;49(1):387–90. [5] Kurada S, Bradley C. A review of machine vision sensors for tool condition monitoring. Comput Ind 1997;34:55–72. [6] Haapala KR, Zhao F, Camelio J, Sutherland JW, Skerlos SJ, Dornfeld DA, Jawahir IS, Clarens AF, Rickli JL. A review of engineering research in sustainable manufacturing. J Man Sci Eng 2013;135(4). [7] Altintas Y, Yellowley I, Tlusty J. The detection of tool breakage in milling operations. J Eng Ind Trans ASME 1988;110:271–7. [8] Attanasio A. Tool run-out measurement in micro milling. Micromachines 2017;8(7). [9] Attanasio A, Gelfi M, Pola A, Ceretti E, Giardini C. Influence of material microstructures in micromilling of Ti6Al4V alloy. Materials 2013;6(9):4268–83. [10] Gelfi M, Attanasio A, Ceretti E, Garbellini A, Pola A. Micromilling of lamellar Ti6Al4V: cutting force analysis. Mater Manuf Process 2016;31(7):919–25. [11] Venkatesh V, Swain N, Srinivas G, Kumar P, Barshilia HC. Review on the machining characteristics and research prospects of conventional microscale machining operations. Mater Manuf Process 2017;32(3):235–62. [12] Sutherland JW, DeVor RE. An improved method for cutting force and surface error prediction in flexible end milling systems. J Eng Ind Trans ASME 1986;108: [269]–79]. [13] Gygax PE. Cutting dynamics and process–structure interactions applied to milling. Wear 1980;64:161–84. [14] Takata S, Ogawa M, Bertok P, Ootsuka J, Matushima K, Sata T. Real-time monitoring system of tool breakage using kalman filtering. Rob Comp-Integr Manuf 1985;2(1):[33]–40]. [15] Attanasio A, Garbellini A, Ceretti E, Giardini C. Force modelling in micromilling of channels. Int J Nanomanuf 2015;11:275–96. [16] Oliaei S, Karpat Y. Influence of tool wear on machining forces and tool deflections during micro milling. Int J Adv Manuf Technol 2016;84. [17] Lucca DA, Seo YW. Effect of tool edge geometry in energy dissipation in ultraprecision machining. CIRP Ann 1993;42:83–6. [18] Kim CJ, Bono M, Ni J. Experimental analysis of chip formation in micro-milling. Trans NAMRI/SME 2002;30:1–8. [19] Gandarias E. Micromilling technology: global review. ed. Semantic scholar online 2009. [20] Vogler MP, Devor RE, Kapoor SG. On the modeling and analysis of machining performance in micro end milling, Part I: surface generation. J Manuf Sci Eng
6. Conclusions The aim of this work was to provide results that could be a basis for the development of a standard in the field of tool wear in micromilling. The research presented makes some aspects of the problem more evident. The experimental results give many interesting information about tool deterioration mechanism in micromilling. Firstly, the graphs that describe the evolution of flank wear in the micromilling process used in these experiments are very similar to the graphs that describe the evolution of flank wear in conventional milling processes. In particular, the three characteristic sections with decreasing, constant and increasing slope of tool wear curve are clearly recognizable. Moreover, flank wear is conditioned by the feed rate and the mill type. Cutting force, roughness and tool corner radius evolution are coherent with flank wear evolution. In fact, while the tool wears, tool corner radius increases, because the tool loses sharpness, roughness decreases, 93
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micromanufacturing. 2006. [26] Chae J, Park SS, Freiheit T. Investigation of micro-cutting operations. Int J Mach Tool Manuf 2006;46:313–32. [27] Hesselbach J, Raatz A, Herrmann H, Weule H, Weck M. Investigation of the international state of the art of micro production technology. In: Weck M, Kunzmann H, editors. Proceedings of the Euspen international topical conference. 2003. [Aachen, Germany]. [28] Mamedov A, Ehsan Layegh KS, Lazoglu I. Machining forces and tool deflections in micro milling. Proc CIRP 2013;8:147–51. [29] Cheng K. Machining dynamics: fundamentals, applications and practices. Springer Series in Advanced Manufacturing; 2009. DOI 10.1007/978-1-84628-368-0. [30] Chatfield C. Statistics for technology. third ed. London: Chapman & Hall; 1983.
2004;126(4):684–93. [21] Lee K, Dornfeld D. An experimental study on burr formation in micromilling aluminium and copper. Trans North Am Manuf Res Inst 2002;30:255–62. [22] Tansel IN, Arkan TT, Bao WY, Mahendrakar N, Shisler B, Smith D, McCool M. Tool wear estimation in micro-machining, Part I: tool usage-cutting force relationship. Int J Mach Tool Manuf 2000;40(4):599–608. [23] Wu X, Li L, He N, Zhao G, Jiang F, Shen J. Study on the tool wear and its effect of PCD tool in micro milling of tungsten carbide. Int J Refract Metals Hard Mater 2018;77:61–7. [24] ISO 8688 : Tool life testing in milling. [25] Rusnaldy, Ko T, Kim HS. Cutting tool wear and vibration in micro end milling of silicon wafer. In: Proceedings of the 1st international conference on
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Tool wear analysis in micromilling of titanium alloy ∗
T
Alessandro Colpani , Antonio Fiorentino, Elisabetta Ceretti, Aldo Attanasio Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123, Brescia, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: Micromilling Tool wear Titanium alloy Cutting force Roughness
Micromachining is a key technology in contemporary society, due to the new requirements of the modern industry. The need of higher accuracy and precision, even for details on the finished product (which may also have very small dimensions), can be fulfilled only with micromechanical machining. Among this family of technologies, micromilling is one of the most important and widespread: the potential of this process is due to the great precision level that can be obtained, when machining high strength materials too. One of the main challenges launched by micromilling concerns the understanding of the processes that generate breakages and deterioration of the tool, and the study of their causes, that are usually different from those observed in conventional milling processes, for which the definition and the prediction of the tool-life are regulated by the ISO-8688 standard (part 1 and 2). This standard is not referred to micromilling and a dedicated regulation has not been compiled yet. In this article the results of an experimental campaign, made to investigate the tool wear in micromilling process, are presented. The aim of the work is to provide fundamental knowledge for the development of a future standard that can fill the normative gap. In particular, results describe that the flank wear evolution follows the typical trend characterized by the decreasing, constant and increasing tool wear slope regions. Cutting force, roughness and tool corner radius evolution are related with tool wear. In particular, a statistical analysis based on the Pearson correlation coefficient is presented in order to quantify the correlation between the flank wear and the considered parameters. Details about the influence of feed rate and mill type in flank wear evolution are also provided. Furthermore, results show that flank wear could actually be used for a tool-life criterion in micromilling processes.
1. Introduction In order to meet the demand for high accuracy reducing component size, the miniaturization of products has become increasingly important for several modern technologies. Micromechanical machining is getting greater employed, due to its ability of fabricating complex 3D components, its capability of machining a variety of engineering materials, and its high material removal rate [1]. Reducing size and weight can substantially increase the convenience and value of some components. Many products have been already miniaturized in order to increase their market share, and the future of many manufacturers will rely on how quickly and successfully they can implement micromachining in their operations [2]. Modern manufacturing advances allowed to reach a lot of goals such as better product quality, increased productivity and a high level of flexibility [3]. Such benefits are dependent on troublefree operations of the various machine elements [4]. In machining industry, 20% of downtime is attributed to tool failures [5]. Therefore, tool condition monitoring and life prediction play an important role in
improving machine productivity, maintaining the quality and integrity of the machined part, minimizing material waste, and reducing cost for sustainable manufacturing [6]. Wear and tool failure mechanisms are known to be complicated in micromachining. Tool life is acceptable at a very low feed-per-tooth. At higher feed-per-tooth values, tool life becomes unpredictable and short [7]. In the world of micromechanical machining, micromilling is among the principal manufacturing processes which allowed the development of components with micrometric dimensions, being used to manufacture dies and final products. Several works are present in literature analysing the tool behaviours [8], the material influence on the process [9,10], and the machine design [11]. In fact, the downsizing of the process up to the micro scale needs a full review of all the knowledge coming from the macro-scale. In conventional end milling studies have been conducted with different purposes: to model cutting mechanism [12], to study the characteristics of cutting forces [13], to detect tool failure [14], and so on. Cutting force characteristics of micro-end-milling operations are almost the same as those of conventional milling
∗
Corresponding author. E-mail addresses: [email protected] (A. Colpani), antonio.fi[email protected] (A. Fiorentino), [email protected] (E. Ceretti), [email protected] (A. Attanasio). https://doi.org/10.1016/j.precisioneng.2019.03.011 Received 19 March 2019; Received in revised form 25 March 2019; Accepted 28 March 2019 Available online 02 April 2019 0141-6359/ © 2019 Elsevier Inc. All rights reserved.
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[15]; however, the wear and breakage mechanisms are very different. So, the traditional approaches used to describe the phenomena involved in conventional (or macro) machining do not apply in this case [16]. Size effect is certainly among the principal issues, probably the most relevant aspect, to be addressed in microcutting. Furthermore, there are other important characteristics of micro technologies: micromachining forces are low because of the small shear area, however, in microcutting, specific cutting force greatly increases [17] as the uncut chip thickness is reduced [18]. Cutting temperature is considerably lower in comparison with conventional technologies, but its accurate determination presents difficulties related to the small shear area and the need of measuring equipment with high sampling rates [19]. Special attention must be paid to the surface finish [20], the burr formation [21], and the prediction of tool failure [22]. Unpredictable tool life and premature tool failure are major problems in micromachining [23]. This research wants to deepen the tool wear knowledge in the micromilling process of titanium alloy, describing the experimental campaign results and providing some data which can give the fundamentals for the development of a future standard for tool-life criterion in micromilling. Starting from the ISO standard currently available, experimental tests were planned, in order to focus on the tool wear in micromilling. The experimental tests performed in the present research, the monitored quantities, and the systems used for the data acquisition are presented. Then, the data analysis procedures are provided and the results are shown, with particular attention on the relations between the variables and the measured quantities.
Fig. 1. Definition of various wear parameters [24].
extended over those portions of the tool flanks adjoining the entire length of the active cutting edge; non-uniform flank wear (Fig. 2b), wear land which has an irregular width and for which the profile generated by the intersection of the wear land and the original flank varies at each position of measurement; localized flank wear (Fig. 2c), that develops in a limited part of the flank. According to the standard, a tool-life criterion is a predetermined numerical value of any type of tool deterioration parameter which can be measured. A certain width of the flank wear land (VB) is the most commonly used criterion, and the recommended values are 0,3 mm for uniform flank wear and 0,5 mm for non-uniform flank wear; the use of VB3, with a limit value of 0,5 mm, is recommended only when chipping occurs. ISO-8688-2, even if not specifically for micromilling, contains some recommendations for cutters with small diameters (a case similar to the micromilling condition): the standard advises against the use of deterioration phenomena that produce abrupt breakages (for example clogging) as tool-life criteria. Some indications about how to measure the different quantities are also provided. In particular, flank wear measurement must be carried out in parallel to the surface of the wear land and in a direction perpendicular to the original cutting edge. Although the flank wear land on a significant portion of the flank may be of uniform size, there will be variations in its value at other portions of the flanks depending on the tool profile and edge chipping. Values of flank wear measurements should therefore be related to the area or position along the cutting edges at which the measurement is made. Before providing details about the experimental campaign performed in this work, it is important to underline that, while in conventional milling tool wear is a commonly used criterion to determine the tool life duration, in micromilling it is not always effective. In fact, microtools frequently run into premature failures, due to various mechanisms, not always related to wear. Excessive stress-related breakage occurs very quickly when the cutting force exceeds the strength of the tool, which is usually quite low because of the small tool diameter. The reasons which can cause this force increase are many: the cutting edge has lost its sharpness or it has been partially damaged (studying the wear process of the tool this phenomenon is predictable); deposition of chips fill the tool grooves stopping the chip flow, so the workpiece starts to push the tool increasing the static component of the force in the feed direction (this phenomenon is called chip clogging and it is almost unpredictable) [25]; during cutting the presence of oxides or carbides in the material matrix can cause a sudden increase of cutting forces that
2. Tool life in micromilling In the field of tool wear, ISO-8688-2 is the standard for tool life testing in end milling [24]. It contains the recommendations to unify procedures in order to increase the reliability and comparability of milling test results. Tests conducted in our research are not included in the field of application of ISO-8688-2, but this standard was chosen as a benchmark because no regulation about micromilling has been published yet. In particular, this work will refer to chapter 7 of the standard (Tool deterioration and tool-life criteria). ISO-8688-2 standard specifies recommended procedures for tool-life testing in milling processes. It can be applied to tests conducted with HSS tools and steel or cast iron workpieces. The type of tests named in the standard are slot milling, end milling in which tool periphery is predominantly used, and end milling in which the end teeth of a tool are predominantly used. The ISO standard is also clear about the definition of some important terms, starting from the tool deterioration, that consists in all the changes in a cutting part of a tool caused by a cutting process. Two major classes of tool deterioration are distinguished: tool wear, that is the change in tool cutting edge shape, resulting from the progressive tool material loss during cutting operation, and the brittle fracture (chipping), occurrence of cracks in the cutting part of a tool followed by the loss of small fragments of tool material, resulting from crack that takes origin during cutting operation. About the tool deterioration measure (quantity used to express the magnitude of a certain aspect of tool deterioration by a numerical value), it is important to define two other terms: tool life criterion (predetermined value of a specific tool deterioration measure or the occurrence of a specified phenomenon), and tool life (total cutting time of the cutting part required to reach a specified tool-life criterion). Wear on end milling cutters and slot drills, and many deterioration phenomena are described in the ISO standard, and each of them can be quantified with some parameters (Fig. 1). In this work the phenomenon that is analysed is the flank wear (VB), that is defined as the loss of tool material from the tool flanks during the cutting operation: it results in progressive development of flank wear land. There are three different typologies of flank wear: uniform flank wear (Fig. 2a), wear land that usually has a constant width and is 84
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Fig. 2. Different typologies of flank wear: (a) uniform flank wear, (b) non-uniform flank wear, (c) localized flank wear [24].
3. Experimental procedure In order to reach the main objective of this work, that is the understanding of wear phenomenon in micromilling, an experimental campaign was planned with the purpose of investigating the relation between the tool wear and some relevant parameters and quantities measured during the process. The tests consisted in shoulder micromilling with 90° entering angle. During the experimental campaign some process parameters (depths of cut, material and geometry of the samples, tool diameter, cutting speed) were fixed, while others (tool geometry and feed rate) were varied. Quantities related to tool life (flank wear, cutting force, roughness and tool corner radius) were monitored during the process (on line monitoring), or at the end of each test (off line monitoring). Each testing condition was repeated three times. The milling machine where the tests were made was a KERN Pyramid Nano (Fig. 3), managed by an iTNC control. It can move on 5 axes (using hydrostatic guides) and it can automatically measure the tool parameters through a tool presetting laser system (BLUM LASER CONTROL NANO NT). The machine is located in a room with constant temperature (20 ± 0,5 °C) and humidity (50%), in order to prevent thermal expansions. Table 1 summarizes the main features of this milling machine. The samples were cubes (with a side of 14 mm) made of Ti-6Al-4V, which is a titanium α-β type alloy (Fig. 4a). The microstructure is formed by equi-axial grains characterized by a minimum diameter of 2 μm (Fig. 4b). Ti-6Al-4V is one of the most utilized titanium alloy, and it is also a common material for tests about micromilling: this makes the results of this work easily comparable with other studies conducted in the same field. Table 2 summarizes Ti-6Al-4V main properties. Some cutting parameters (Table 3) are fixed in all the tests, so their influence cannot be deduced from the results of this study. The values of these parameters were chosen according to the toolmaker guidelines. In particular, according to the ISO standard, the axial depth of cut (ap) was chosen equal to three times the corner radius of the mills (rε in Table 4).
Fig. 3. KERN pyramid nano. Table 1 KERN Pyramid Nano features. Position accuracy
± 0.3 μm
Maximum mandrel speed Maximum axis acceleration Maximum axis speed Work space
5000 rpm 10 m/s2 30 m/min 500 × 500 × 500 mm
could bring to a tool breakage [26]. Fatigue related breakage happens when cutting force increases and remains at high level for a long time. This increase is often due to tool wear. Since the tool is rotating, the stress will change cyclically and generate a fatigue fracture after a certain number of revolutions. The increase in the specific energy is also important in the tool failure: as the chip gets thinner the microtool will meet a greater resistance compared to conventional milling operations. This increase, especially if the depth of cut decreases under the minimum achievable chip thickness, can be responsible for the tool breakage [27].
Fig. 4. (a) One of the samples after the experiment, (b) micrograph of the sample. 85
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Table 2 Ti-6Al-4V properties. Chemical composition O (%max)
N (%max)
H (%max)
C (%max)
Fe (%max)
Al (%max)
V (%max)
Residues (%tot)
0.2
0.05
0.015
0.08
0.4
5.5–6.75
3.5–4.5
0.4
Mechanical properties Breaking load: 895 MPa Yield load (0.2%): 895 MPa Tensile modulus: 106000 MPa
Hardness: 300–400 HV Elongation: 10% Poisson coefficient: 0.34
Physical properties Density: 4.4 g/cm3 Melting point: 1650 °C Coefficient of thermal expansion: 9 μm/°C Specific heat: 586 J/Kg°C
Thermal conductivity: 6.6 W/m°C Electrical resistance: 1.71 μΩm Electrical conductivity: 0.98 (%IACS) Magnetic permeability (at 1.6 Kam): 1.00005
Table 3 Fixed cutting parameters. Cutting speed Rotation speed Tool diameter Nr. of cutting edges Axial depth of cut (ap) Radial depth of cut (ae)
100 m/min 39789 rpm 0.8 mm 2 150 μm 30 μm
Two different types of tool were used, namely SECO 103L008R005MEGA-64-T (hereinafter SECO 103) and SECO 905L008-MEGA-T (hereinafter SECO 905). Both these tools are characterized by two cutting edges and made by coated tungsten carbide. (Ti, Al)N coating was used because it is suitable for cutting hard materials. The tools differ in helix and rake angles, as shown in Fig. 5 and Table 4. Two different values of feed-per-tooth (and feed rate) were used in the tests: 10 μm/tooth/rev (LOW), and 20 μm/tooth/rev (HIGH). Being the measured cutting edge radius lower than 5 μm, these feed per tooth values guarantee shearing cutting mechanism. Being the depths of cut and the geometry of the sample equal in all the experiments, a variation in the feed rate value directly affects material removal rate (Table 5). The experimental plan consists in four different test configurations (Table 6). Each configuration was tested 3 times, so the total number of runs was equal to 12. Each test was carried out using a new tool and new samples. In order to have a planar surface, a preliminary facemilling operation (step 0 in Fig. 6a) was performed on each sample using a tool different from the tested one. After this first operation the testing tool was mounted on the machine for the experiment. The process was divided in steps (Fig. 6a), characterized by the same volume of removed material, and each step is composed by 57 passes (Fig. 6b). The volume of material that is removed in each step is a parallelepiped with a length of 14 mm (i.e. the total length of the sample), a height of 0,15 mm (i.e. the axial depth of cut), and a width of 1,71 mm (i.e. the radial depth of cut multiplied for the number of passes). Therefore, the amount of material removed during one step is equal to 3591 mm3. The time needed to complete one step depends on the feed rate: 60 s for the low feed rate, and 30 s for the high feed rate. Splitting the process in steps gives the possibility to remove the tool from the
Fig. 5. Tools geometry. Table 5 Differences between HIGH and LOW configurations of feed rate. Configuration
Feed rate [μm/tooth/rev]
MMR [mm3/min]
LOW HIGH
10 20
3.591 7.182
Table 6 Experimental plan. Mill type/Feed Rate
10 μm/tooth/rev
20 μm/tooth/rev
SECO 103 SECO 905
103 LOW 905 LOW
103 HIGH 905 HIGH
machine every 60 or 30 s (depending on the test feed rate), to measure the tool wear, and to verify the tool integrity. Four different quantities, which could be related to tool life and can help to understand the mechanisms that lead to breakage, were monitored during the tests. ⁃ Flank wear. According to ISO-8868-2 tool-life criteria based on flank wear are strongly recommended for conventional milling.
Table 4 SECO 103 and SECO 905 quotes. Mill
Dimensions [mm]
SECO 103 SECO 905
Dc = 0.8 Dc = 0.8
Angles [°] l3 = 4 l3 = 4
rε = 0.05 ± 0.01 rε = 0.05 ± 0.02
ap (max) = 1.2 ap (max) = 0.4
86
Helix angle = 0 Helix angle = 20
Rake angle = 0 Rake angle = 4
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Fig. 6. Experimental test: (a) process steps, (b) passes on each step. Table 7 Load cell main features. Range Fx, Fy
−50 ÷ 50 N
Range Fz Sensitivity Fx Sensitivity Fy Sensitivity Fz Linearity Natural frequency Fx, Fy Natural frequency Fz
−300 ÷ 300 N −24.54 pC/N −24.34 pC/N −11.05 pC/N ≤ ± 0.07% FSO 5 kHz 21 kHz
Table 8 KISTLER Charge Meter Type 5015 features. Output range
± 10 V
Max Voltage Output Error Bandwidth
0.1% 100 ÷ 3000 Hz
Fig. 7. Image from the optical microscope.
Table 9 NI9205 features. Measurements
Voltage
Isolating measures Resolution Single-ended channels Differential channels Maximum sampling frequency Maximum voltage in input Accuracy (interval ± 10 V)
Earthing 16 bit 32 16 250 kS/s 10 V 6220 μV
tool life, in particular in the micro scale, because a rapid increase of the cutting force is one of the most common causes of breakage. ⁃ Roughness of the horizontal surfaces. Surface roughness is one of the properties that can describe the quality of a process, so it is important to know how it evolves during the tests. Furthermore, the obtainable roughness in a process depends on the cutting parameters but it is also strongly connected with tool wear. The profilometer used for this study (Mitaka PF-60) permitted to measure the roughness on the horizontal surfaces of the samples. ⁃ Tool corner radius. Tool corner radius determines the shape of the
Fig. 8. Load cell KISTLER 9317C.
Even if, as seen, tool rupture in micromilling is not always depending on wear, it could be interesting to search a relation between VB and tool life. ⁃ Cutting force. Cutting force is a fundamental factor to determine 87
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Fig. 9. PF-60 output graph: roughness measurements (103 HIGH configuration, step 27).
Fig. 10. Tool corner radius measurements. Fig. 12. Flank wear vs. time, comparison between the trend lines (with the relative R2 value).
end mill, and it increases as the tool loses sharpness, so it can be seen as an alternative measure of wear.
Each test ended when one of the cutting edges reached the maximum VB value, or when the tool incurred in an abrupt breakage, or when high chipping was observed. For each test, the final output of this acquisition chain consisted in two series of VB values that represent the evolution of flank wear during the process of each tool tooth.
4. Measurement methods 4.1. Flank wear The wear data acquisition was carried out using the optical microscope of a nanoindentation tester (Anton Paar Tabletop) which guarantees a measuring accuracy of 0,8 μm. At the end of each step the tool was removed from the machine and cleaned with a polishing paste. Then the images of the flanks of the two cutting edges were acquired by the optical microscope and the corresponding wear measured (Fig. 7).
4.2. Cutting force The acquisition chain for the cutting force measurement consists in a series of devices that are able to measure and record force components. The chain is formed by a load cell (KISTLER 9317C), three
Fig. 11. Flank wear vs. time trend line obtained from a third order polynomial interpolation (103 HIGH configuration). 88
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Fig. 13. Flank wear vs. removed material, comparison between the trend lines (with the relative R2 value).
Fig. 15. Experimental and mean values of cutting force vs. time for test 103 HIGH, first repetition.
amplifiers (KISTLER Charge Meter Type 5015), an acquisition module (NI 9205) and a software for the data elaboration (LabVIEW). The load cell is a piezoelectric cell (Fig. 8), able to convert the three components of the force generated by the interaction between the tool and the sample in charge signals. Piezoelectric sensors are suitable for high frequency sampling. Table 7 summarizes the main features of the device. Hammer impact test was done to correctly define the bandwidth of the load cell-clamping system, obtaining a bandwidth range from 100 Hz to 3000 Hz. Being the cutting force signal frequency equal to 1326 Hz, the utilized system is adequate. The three charge signals coming from the load cell are sent to three laboratory amplifiers, that enhance the signals coming from the load cell in order to make their voltage suitable for the acquisition module. Table 8 summarizes the main features of the amplifiers. The acquisition module processes the signals received from the amplifiers to extract the force values and send them to the computer (Table 9). With LabVIEW the data from the acquisition module were acquired and showed to the user in three different graphs, which are the final output of this system and describe the three force components evolution in time.
interruption of the process but only at the end of the test, because the sample needs to be properly cleaned before the measuring. So roughness is detected only at the end of the process. These surfaces are those corresponding to the steps 7, 13, 18, 22, 25, 27 and 28 as shown in Fig. 6a. The instrument used in this study is a Mitaka PF-60, a non-contact profilometer that consists of an autofocus laser beam microscope (AF microscope) and a high precision XY scanning stage. The AF microscope measures height in the Z axis and the XY stage moves the sample in order to obtain XYZ coordinate values for 2D and 3D measurements. The laser beam incorporated in the AF microscope passes through the objective and forms a laser spot on the surface of the sample. The laser beam reflected from the sample surface passes through the objective again and forms an image on the autofocus sensor (AF sensor). The AF sensor detects the laser spot displacement in real time and adjusts the AF microscope back to the in-focus position (the laser spot forms its image at the centre of the AF sensor). The reflectivity of the sample does not affect the measurement, so it is possible to carry out roughness not in contact measurements on almost every kind of material, without any risk of possible damage to the surface (this can happen with touch probes). The output of the roughness measurement performed with PF60 is shown in Fig. 9 (that refers on 103 HIGH configuration, step 27).
4.3. Surface roughness
4.4. Tool corner radius
The roughness of a surface is quantified by the deviations in the direction of the normal vector of a real surface from its ideal form. The most common roughness parameter is Ra, which is defined as the arithmetical average of the absolute values of the vertical deviations of the profile from its mean line. Roughness cannot be measured at every
The tool corner radius was measured from the tool signs left on the intersections between horizontal and vertical worked surfaces. The acquisition of tool corner radius data was obtained by using a metallographic optical microscope (Leica DMI5000 M). The samples, properly cleaned in order to remove burrs and residual material, were
Fig. 14. (a) The last pass of the tool. (b) signal of the last pass (103 HIGH configuration). 89
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Fig. 16. Cutting force: comparison between the four configurations. (a) Cutting force vs. time. (b) Cutting force vs. removed material. (c) Cutting force vs. flank wear.
(Fig. 11). The trend line has the typical behaviour of a graph which describes wear evolution on a tool: three regions can be distinguished, the first region (primary on initial wear) is characterized by a relatively high wear rate, the second region (steady-state wear) is characterized by a constant wear rate, and the third region (tertiary or accelerated wear) is characterized by high cutting force, high temperature and severe tool wear. Figs. 12 and 13 show the evolution of the tool wear respectively in relation with cutting time and removed material. As expected, in configurations with higher feed rate tool wear grows more quickly than in those with lower feed rate, while 905 mills wear down more quickly than 103 mills. Relating tool wear with time the most influencing variable is the feed rate, while relating tool wear with removed material the most influencing variable is the mill geometry. Also tool angles determinates this behaviour: SECO 103 wears less rapidly due to the rake angle equal to 0° while SECO 905 wears more rapidly because of the rake angle equal to 4°.
analysed at the microscope: the images were taken using a Leica DFC 295, a digital camera that can be connected to all Leica microscopes. The Leica software allows to measure the tool corner radius from the picture identifying at least 3 points on the worked surface. In this research 5 points were acquired as shown in Fig. 10. 5. Data analysis, results and discussion All the data collected during and after the experiments are wear, forces, roughness and tool corner radius. The aim of this analysis was to discover how the monitored quantities evolve as the processing time increases, to find out if there is a correlation between the four monitored variables, to examine the influence of the tool type and the feed rate on the evolution of the process, and to try to use these features for the definition of a possible tool-life criteria suitable for micromilling. 5.1. Flank wear For each test condition two data series are present, one for the first cutting edge and one for the second cutting edge. Comparing the wear curves of the two cutting edges of the same mill differences can occur due to the presence of tool run out [8,15] and/or to the tool deflection [28]. In facts, if the tool bends, the two cutting edges are differently stressed, so they wear in a different way. As a consequence, for each test six data series (two for each repetition) were obtained (Fig. 11). For each test configuration, the six data series (that differ for the cutting edge and the repetition number) were interpolated by means of a polynomial fitting curve of the third order, passing through the origin
5.2. Cutting force Fig. 14 reports the typical output of the force measuring system. The graphs show the evolution in time of the three cutting force components. Fig. 14a reports the behaviour of the cutting force components for the first step of 103 HIGH configuration test. Fig. 14b shows the force signals at pass 57th (the final) of the same test. The obtained force signals were filtered from vibrations (band pass filter with a bandwidth from 100 Hz to 2000 Hz) and, applying equation (1), the total cutting force (Fc) was obtained. 90
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Fig. 17. Roughness: comparison between the four configurations. (a) Roughness vs. time. (b) Roughness vs. removed material. (c) Roughness vs. flank wear. (d) Roughness vs. cutting force.
Fc =
Fx2 + Fy2 + Fz2
material (Fig. 17b), wear (Fig. 17c) and forces (Fig. 17d). These graphs show that roughness has a slightly decreasing trend during the micromilling operation, in particular when time and VB increase. This trend is linked to the tool corner radius evolution. In fact, tool corner radius value affects the roughness as shown in Eq. (2) [29]. As described in the next paragraph, in this work a tool corner radius increasing was observed. Therefore, accordingly to tool corner radius increasing, roughness decreases.
(1)
Where Fx, Fy, and Fz are the cutting force components. Also in this case tool run out determines different values of cutting force for the cutting edges due to unbalanced chip thickness values. Fig. 15 reports the different peak values of each cutting edge as a function of cutting time (the cross marks in the figure are the average values). Fig. 16 reports for all the cutting conditions the average values obtained considering the three test repetitions. Fig. 16a shows the relation between forces and time, Fig. 16b the relation between forces and removed material, while Fig. 16c is about the correlation between forces and wear. As expected, forces slightly increase while the tools wear. The differences between the configurations are quite clear (Fig. 17): the 103 HIGH configuration has the highest force, the 905 LOW configuration has the lowest force, and 103 LOW and 905 HIGH configurations are in the middle. This is because forces increase with the feed rate, while at constant feed rate 103 mills generate higher forces than 905 mills. Also tool angles influence this behaviour: SECO 103 generates higher forces due to the rake angle equal to 0° and helix angle equal to 0°, while SECO 905 generates lower cutting forces because of the rake angle equal to 4° and helix angle equal to 20°.
Rmax ≈
f z2 8·TR
(2)
Where: ⁃ Rmax is the maximum roughness; ⁃ fz is the feed per tooth; ⁃ TR is the tool corner radius.
5.4. Tool corner radius Tool corner radius was investigated in relation to time (Fig. 18a), removed material (Fig. 18b), wear (Fig. 18c) and forces (Fig. 18d). Tool corner radius increases during the process. All the experimental configurations show this trend, and this fact confirms that the tool is uniformly wearing since no chipping phenomena occurs affecting tool corner radius value. Tool corner radius evolution is linked to the roughness decreasing above described.
5.3. Surface roughness Roughness was investigated in relation to time (Fig. 17a), removed 91
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Fig. 18. Tool corner radius: comparison between the four configurations. (a) Tool corner radius vs. time. (b) Tool corner radius vs. removed material. (c) Tool corner radius vs. flank wear. (d) Tool corner radius vs. cutting force.
standardization of the covariance obtained using the standard deviations of the variables. Correlation coefficient definition is shown in Eq. (3) where x and y are the two examined parameters, ??x and ??y are their standard deviations, S(xx) and S(yy) respectively are x and y variances (Eqs. (4) and (5)), while S(xy) is their covariance (Eq. (6)). In those equations n is the sample size (i.e. the number of observations for each examined parameter). Pearson coefficient so defined allows to quantify the correlation between x and y variables with no dependence on the measurement scales. The r value is usually calculated by applying Eq. (7), obtained from an elaboration of the previous equations that allows to find out an operative formula for correlation coefficient evaluation.
Table 10 Critical values of correlation coefficient [30]. Sample size (n)
Critical value (r)
5 10 15 20 25 30 50 100
0.88 0.63 0.51 0.44 0.39 0.36 0.28 0.20
5.5. Results discussion
r= A statistical analysis of the above presented data was conducted. In order to evaluate the correlation between tool wear and force, roughness, and tool corner radius, the Pearson correlation coefficient r [30] was calculated. In particular, r coefficient represents the
S (xy ) = σx σy
S (xx ) =
1 n−1
S (xy ) S (xx ) S (yy ) n
∑ (xi − x¯)2 = i=1
− 1 ≤ r ≤ +1 (3) n
n (∑i = 1 x i )2 1 (∑ x i2 − ) n n − 1 i=1
(4)
Table 11 Correlation coefficient of force, roughness and tool corner radius in relation with tool wear. Parameters
Sample size (n)
Critical value (interpolated data)
Correlation coefficient (r)
Force vs. Wear Roughness vs. Wear Tool Radius vs. Wear
129 62 21
0.15 0.26 0.43
0.45 −0.29 0.68
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S (yy ) =
S (xy ) =
n
n
n (∑i = 1 yi )2 1 (∑ yi2 − ) n n − 1 i=1
1 n−1
∑ (yi − y¯)2 =
1 n−1
∑ (xi − x¯)(yi − y¯)
i=1 n i=1 n
n
n
(∑i = 1 x i )(∑i = 1 yi ) 1 (∑ x i y − ) = n n − 1 i=1 i
r=
1 n−1
n
∑( i=1
because the tool is more smoothed, and cutting force increases, because the ratio of edge radius to chip thickness becomes higher. These results mean that tool deterioration (and tool life), could be described using flank wear, as ISO 8688-2 recommends for standard milling. In fact, this quantity seems to have the features to be used in a tool-life criterion, also in micromilling. The results give also some clear information about the influence of the helix and rake angles. In fact, increasing the helix angle allows to reduce the cutting force improving the chip evacuation. The same influence on cutting force was observed for the rake angle, while increasing this angle the tool wear increases. Further tests and studies should concentrate on some specific aspects. With regard surface quality, it will be important to design tests that consider the need to measure roughness on the vertical surface, in order to put it in relation with flank wear. Other important aspects are related to abrupt breakages and run out. It will be interesting to determine the odds of the cases in which the tool incurs in an abrupt breakage or becomes completely worn in a very short time, and to determine if these events are completely accidental or if they are related to specific causes. Moreover, it will be important to consider the need to analyse the influence of the variables on the odds to incur in a consistent run out.
(5)
x i − x¯ yi − y¯ )( ) σx σy
(6)
(7)
In particular, Pearson coefficient allows to quantify the correlation between the considered parameters by a comparison between the absolute value of r (calculated with Eq. (7)) and an r critical value [30]. Correlation is present when the r absolute value is between 1 and the r critical value, while there is no correlation between the examined parameters if the r absolute value is minor than the critical value. Furthermore, when correlation is observed, it is positive or negative respectively when the calculated r (Eq. (7)) is positive or negative. The r critical value depends on the desired confidence level. The confidence level value usually adopted is the 95%. Once fixed the desired confidence level, tables reporting the r critical value depending on the considered sample size are available. Table 10 gives the 95% critical points for the absolute value of the correlation coefficient for different sample size [30]. Applying the above described procedure, experimental results were analysed and the correlation between the examined parameters was evaluated. Table 11 shows the results about correlation between tool wear and force, roughness, and tool corner radius. Regarding the correlation between force and wear, the value of r is higher than the critical value, and this means that the two parameters are actually related. The positive correlation between force and wear is explained because the tool surfaces are no longer optimal when tool wear occurs, and this fact leads to an increase in friction between tool and workpiece, and therefore an increase in force. The value of the correlation coefficient for roughness in relation to wear is higher than the critical value, so the correlation between these two parameters, that was not exactly clear from the graphs shown in Fig. 18, is confirmed from this analysis. An aspect that can be underlined is the negative value of the correlation coefficient. The negative sign denotes that there is a negative correlation between roughness and tool wear. In fact, as can be seen from Fig. 18c, roughness decreases as the tool wear increases. Finally, the r value shows the correlation between tool corner radius and tool wear. This phenomenon is explained because all the cutting edge is engaged in this cutting operation, so the decrease in volume that occurs during the process (as consequence of tool wear) results in an increase in the radius of the tool itself.
Acknowledgments The Authors wish to thank Simitecno and Seco Tools which provided respectively the PF-60 non-contact profilometer and the tools used in these tests. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References [1] Malekian M, Park SS, Jun MBG. Tool wear monitoring of micro-milling operations. J Mater Process Technol 2009;209:4903–14. [2] Tansel I, Rodriguez O, Trujillo M, Paz E, Li W. Micro-end-milling–I. Wear and breakage. Int J Mach Tool Manuf 1998;38:1419–36. [3] Wiendahl HP, Elmaraghy HA, Nyhuis P, Zäh MF, Wiendahl HH, Duffie N, Brieke M. Changeable manufacturing – classification, design, and operation. CIRP Ann Manuf Technol 2007;56(2):783–839. [4] Zhou ZD, Chen YP, Fuh JYH, Nee AYC. Integrated condition monitoring and fault diagnosis for modern manufacturing systems. CIRP Ann - Manuf Technol 2000;49(1):387–90. [5] Kurada S, Bradley C. A review of machine vision sensors for tool condition monitoring. Comput Ind 1997;34:55–72. [6] Haapala KR, Zhao F, Camelio J, Sutherland JW, Skerlos SJ, Dornfeld DA, Jawahir IS, Clarens AF, Rickli JL. A review of engineering research in sustainable manufacturing. J Man Sci Eng 2013;135(4). [7] Altintas Y, Yellowley I, Tlusty J. The detection of tool breakage in milling operations. J Eng Ind Trans ASME 1988;110:271–7. [8] Attanasio A. Tool run-out measurement in micro milling. Micromachines 2017;8(7). [9] Attanasio A, Gelfi M, Pola A, Ceretti E, Giardini C. Influence of material microstructures in micromilling of Ti6Al4V alloy. Materials 2013;6(9):4268–83. [10] Gelfi M, Attanasio A, Ceretti E, Garbellini A, Pola A. Micromilling of lamellar Ti6Al4V: cutting force analysis. Mater Manuf Process 2016;31(7):919–25. [11] Venkatesh V, Swain N, Srinivas G, Kumar P, Barshilia HC. Review on the machining characteristics and research prospects of conventional microscale machining operations. Mater Manuf Process 2017;32(3):235–62. [12] Sutherland JW, DeVor RE. An improved method for cutting force and surface error prediction in flexible end milling systems. J Eng Ind Trans ASME 1986;108: [269]–79]. [13] Gygax PE. Cutting dynamics and process–structure interactions applied to milling. Wear 1980;64:161–84. [14] Takata S, Ogawa M, Bertok P, Ootsuka J, Matushima K, Sata T. Real-time monitoring system of tool breakage using kalman filtering. Rob Comp-Integr Manuf 1985;2(1):[33]–40]. [15] Attanasio A, Garbellini A, Ceretti E, Giardini C. Force modelling in micromilling of channels. Int J Nanomanuf 2015;11:275–96. [16] Oliaei S, Karpat Y. Influence of tool wear on machining forces and tool deflections during micro milling. Int J Adv Manuf Technol 2016;84. [17] Lucca DA, Seo YW. Effect of tool edge geometry in energy dissipation in ultraprecision machining. CIRP Ann 1993;42:83–6. [18] Kim CJ, Bono M, Ni J. Experimental analysis of chip formation in micro-milling. Trans NAMRI/SME 2002;30:1–8. [19] Gandarias E. Micromilling technology: global review. ed. Semantic scholar online 2009. [20] Vogler MP, Devor RE, Kapoor SG. On the modeling and analysis of machining performance in micro end milling, Part I: surface generation. J Manuf Sci Eng
6. Conclusions The aim of this work was to provide results that could be a basis for the development of a standard in the field of tool wear in micromilling. The research presented makes some aspects of the problem more evident. The experimental results give many interesting information about tool deterioration mechanism in micromilling. Firstly, the graphs that describe the evolution of flank wear in the micromilling process used in these experiments are very similar to the graphs that describe the evolution of flank wear in conventional milling processes. In particular, the three characteristic sections with decreasing, constant and increasing slope of tool wear curve are clearly recognizable. Moreover, flank wear is conditioned by the feed rate and the mill type. Cutting force, roughness and tool corner radius evolution are coherent with flank wear evolution. In fact, while the tool wears, tool corner radius increases, because the tool loses sharpness, roughness decreases, 93
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