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11th International Conference on Micro Manufacturing Orange County, California, USA, March 2016 Paper# 71

Advances in Observing Micro End Mill Wear Brodan Richter1, Justin D. Morrow2, Frank E. Pfefferkorn3 1 2

Brodan Richter; Mechanical Engr., University of Wisconsin – Madison; email: [email protected] Justin D. Morrow; Mechanical Engr., University of Wisconsin – Madison; email: [email protected] 3 Frank E. Pfefferkorn; Mechanical Engr., University of Wisconsin – Madison; email: [email protected]

Abstract The objective of this work is to observe the break-in wear of square, two-flute, uncoated tungsten-carbide, 304.8µm-diameter micro end mills in terms of diameter and cross-sectional area change using optical metrology and a novel image processing algorithm. This will aid in the understanding of the progression of tool wear in micro end mills during the first seconds of machining. Full-width slots were milled in H13 tool steel with cutting times ranging from 0.0152 seconds (a channel length of half the tool diameter) to 15.0 seconds. It is shown that the tool wear in these micro end mills follows a power-law relationship at short machining times with a rapid wear rate in the initial 0.1 seconds of cutting followed by a continuous reduction in wear rate up to 15 seconds. These results provide insight into the early wear rate of micro end mills and the potential for this method of measuring tool wear on these tools. Keywords: Micro end milling, tool wear, tool life, break-in wear 1. Introduction Micro machining allows for the production of complex, three-dimensional, high aspect ratio, and micro-scale features that are difficult to produce through other manufacturing processes. However, advances in micro machining have been difficult due to the challenges in measuring tool wear and the prediction of tool life. The international standard for end milling [1] defines the end of tool life as when the average flank wear across all teeth reaches 0.3 mm and recommends measuring this wear through the use of a micrometer or caliper. This poses two major problems for application to micro-scale tools: the tool diameter is on the same order of magnitude as the recommended wear cut-off, and the measurement technique risks breaking the tool. Due to the high tool cost and slow processing speed of micro machining, it is desirable to machine as long and as aggressively as possible. Attempts have been made to develop real-time tool wear measurement systems through force measurement

[2,3] and acoustic emission measurements [4]. Other research has focused on improving the tool life through diamond coatings [5,6], alternative testing techniques [7], and laser assistance [8,9]. One additional area of focus has been on improvements to actual wear measurement systems. Prior work [10] has looked at tool wear of 304.8 µm diameter, two-flute, tungsten carbide, micro end mills during full-width cutting of channels in H13 tool steel with cutting times of 0.1, 0.2, 0.3, and 1.5 seconds using the same binary image measurement technique as in this work. However, those results were not able to capture the initial rapid break-in period of the tool wear. Additionally, this work extends the maximum cutting time from 1.5 seconds to 15.0 seconds to try to better capture the progression of wear. Tool wear of micro mills and the effect of cutting parameters has also been studied [11] by measuring the width of cut slots during machining with 0.50 mm diameter tools. Saedon et al. [11] found that cutting speed was the largest contributor to tool wear, followed by axial depth-ofcut and feed per tooth. However, their work did not 1

show a clearly defined break-in period. This work attempts to capture the initial break-in wear associated with macro-scale tools [1] as well as the progression of wear at longer cutting times. Additionally, this paper utilizes a rapid and objective computer algorithm to measure the actual change in diameter and area of micro tools.

dominated by abrasive edge rounding and flank wear mechanisms. All cutting parameters are assumed constant and all tools identical except for slight variations in the initial diameter. The workpiece is assumed to not change over the three days the tests were performed. 2.2. Tools

2. Experimental Method The complete list of experimental parameters can be found in Table 1. Table 1. Experimental parameters Cutting Parameters Type of Cut Repetitions Spindle Speed Cutting Speed Feed Rate Feed Chip Load Axial Depth-of-Cut Lubrication

Full-width slot 10 tools 60,000 RPM 57.3 m/min 600 mm/min 10 µm/rev 5 µm/tooth 30.1 ± 1.2 µm CRC Sulphur-free Cutting Fluid

End Mill Characteristics Material

Tungsten Carbide with 68% Carbide 304.8 µm (0.012 in) 2 450 µm 30° 1 µm ± 0.29 µm

Diameter Flutes Flute Length Helix Angle Edge Radius

Workpiece Material Dimensions Initial Sa

H13 Tool Steel 60 x 53.5 x 7.5 mm 60 nm ± 10 nm

Surface Preparation

Face milled, shoulder milled, cleaned with methanol, manually lubricated with oil

Channel Lengths

0.152, 1, 5, 25, 150 mm


~23° C

2.1. Assumptions The analysis presented in this paper assumes that tool wear is a progressive, abrasive process that is primarily dependent on the forces and contact area at the cutting edge. It also assumes that tool wear is

All tests used 304-µm-diameter (0.012 in.), two-flute, uncoated tungsten carbide, square end mills with flute length of 1.5 times the diameter (Performance Micro Tool, TS-2-0120-S) to cut fullwidth slots of variable length. Square end mills were chosen due to requirements of the rapid image processing algorithm. The tools were uncoated in order to maximize wear rate for the purpose of this study. The tools were imaged before and after machining to ensure that no cutting tips were fractured prior to cutting and to measure absolute and relative change in area and diameter for each tool. The cutting conditions were chosen based upon previous experimental work seeking to induce rapid wear without built-up-edge or gross tool fracture. Prior work studying the cutting edge radius on a set of 10 two-flute 304-µm-diameter micro end mills (20 cutting edges) [12] gave a mean cutting edge radius value of 1±0.29 µm on this tool type. 2.3. Workpiece All machining tests were done in the same rectangular block of H13 tool steel with dimensions of 58.4 mm x 52.9 mm x 6.5 mm. The tests took place over three days, with approximately one third of the tests performed on each day. Prior to each day’s experimental tests, the surface was prepared through face and shoulder milling to a surface roughness of 60 nm Sa with a standard deviation of ±10 nm, which was measured with a white light interferometer (Zygo NewView 6300) using a cutoff wavelength of 80 µm. The surface was then cleaned with methanol and the tool was zeroed to the surface with a manual touch-off. The depth-of-cut was measured at the beginning of each channel and was found to be 30.1±1.2 µm. Five different exponentially increasing lengths were machined in order to capture the initial rapid wear and the progression of the wear at longer times. The distances machined were 0.152 mm (half of the tool diameter), 1 mm, 5 mm, 25 mm, and 150 mm. Due to the block length of 50 mm, the 150 mm length required entering the block three times to cut three 50 mm passes. For channel lengths shorter than the block length, the tool was disengaged from the work piece by a quick upwards and backwards motion at 45° to minimize non-cutting dwell time inside the channel.


2.4. Equipment A 3-axis vertical machining center (HAAS TM1) was used for all machining tests. Prior to surface preparation, the work-piece was mounted to a milling vise, which was then mounted to the mill table. A high-speed spindle (NSK HES-810) was mounted to the HAAS main spindle to achieve spindle speeds appropriate for micro-machining tests (10,000-80,000 RPM). The dynamic runout of the tools was measured (Optech-RI-V) using a precision ground reference sample prior to machining each day ensure a runout of less than 3 µm. The machining center underwent a table warmup procedure lasting approximately 30 minutes prior to machining. The machining center’s horizontal repeatability (x and y axes) during long cuts was measured to be within ±50 µm and vertical repeatability (z axis) during long cuts was measured to be within ±3 µm. The high-speed spindle also underwent a warmup procedure consisting of incrementally ramping up the spindle speed through its full range over the course of approximately 35 minutes prior to performing the cutting tests. 2.5. Tool Imaging and Analysis The end of every tool was imaged before and after machining with a focus variation optical metrology system (Alicona InfiniteFocus G4) while being held in a precision tool holder (produced inhouse) to minimize angular variation. All tools were cleaned using the same procedure and then imaged with the same brightness, magnification, and contrast levels. The tools were cleaned prior to imaging by ultra-sonication in a bath of methanol for 15 minutes. In addition, non-stick adhesive putty was used to remove adhered material from the tool tip and was found to leave no visual residue and cause no tool damage. The tool images were analysed through a computer algorithm (MATLAB R2015a) that converts the tip image from a RGB image into a binary image. The algorithm calculates the crosssectional area of the tool and the smallest enclosing circle that would fit around the tool. The process for calculating tool cross-sectional area from optical images is: 1. 2. 3.

4. 5.

The RGB image is imported. A standard RGB to Intensity relationship is used to convert from RGB to grayscale. A threshold value was chosen to convert the grayscale image into a binary image (Fig. 1). This threshold was held constant for the entire investigation. Any white pixels outside of the tool (noise) are removed. The number of white (tool) pixels are counted.

6. 7. 8.

The known pixel size of the optical measurement is used to convert the pixel count into a tool cross-sectional area (µm2). Each tool was measured before and after machining to get a direct measurement of area change due to wear. Tool diameter was found using an iterative smallest enclosing circle algorithm.

The difference in area and diameter of a tool before and after machining is defined as the areal and diametrical wear respectively. The sensitivity of input parameters on the MATLAB code (e.g., threshold) was found to be minimal near the values used, and the values of the smallest enclosing circle before machining was in close agreement with the manufacturer specification for the nominal tool diameter. A more detailed description of the image processing technique is presented by Taduvai [10]. An example result of the RGB-to-binary conversion of a tool image is shown in Figure 1(a) and an example outline of a new tool compared to the same tool after machining is shown in Figure 1(b). This initial work focused only on the change in tool area and diameter for simplicity, but future advancements in the measurement algorithm could allow the addition of other metrics such as flank wear and cutting edge radius from the profiles in Figure 1(b).


(b) Fig. 1. Tool image converted from (a) RGB to binary and (b) tool outline comparison


2.6. Exclusions Ten of the fifty tools used in this study experienced severe fracture, defined as a fracture extending far from the cutting area, as shown in Figure 2. Because the current study is focused on studying steady-state tool wear, these tools were not included in the tool wear analysis in order to prevent heavy skewing of the results. A representative example of an excluded tool is presented in Figure 2. The tools were excluded based on the observed fracture prior to performing any quantitative wear analysis to prevent bias. The cause of these large fractures has not yet been identified, but could be of interest in future work assessing catastrophic events during micro milling. The cutting conditions were chosen based upon prior experimental cutting conditions as well as preliminary assessment trials, and in those trials fractures were not observed. While the tools excluded showed sever fracture, those tools still successfully cut channels of acceptable quality in the material.

seconds, 7 repetitions at 0.5 seconds, 7 repetitions at 2.5 seconds, and 8 repetitions at 15.0 seconds of cutting time. 3. Results and Discussion 3.1. Areal Wear The areal wear, which is being defined as the change in area of the tip of the flute of the end mill, during this break-in period is well described by a power-law relationship with a rapid rise in wear at the start of machining followed by a more gradual wear rate as the machining progresses. The average and standard deviation of the areal wear can be seen plotted in Figure 3 (a) and (b) and shows that the tool break-in period is very rapid. This is consistent with foundational tool wear research for macro-scale machining that established the “S-curve”: A rapid tool “break-in” period followed by slower steadystate wear progression and then a rapid increase in wear rate leading to tool failure [13].

Fig. 2. Example of excluded fractured tool 2.7. Experimental Procedure The experiment used a one-way design where the five different cutting times were directly compared by the change in diameter and change in cross-sectional area at the tip of the tool. Each cutting tool was used to cut a single channel and then cleaned and measured. Full randomization and blocking (10 blocks, 5 tools in each) was used to prevent day and time biases, and in each block every cutting distance was performed once. The orders the cutting distances were performed in each block was randomized. The tests were conducted over the course of three days between 10am and 3pm. Analysis of variance (ANOVA) was used to measure the statistical significance of cutting distance, and analysis of covariance (ANCOVA) was used to detect any non-controlled significant variables, such as workpiece temperature, tool runout, tool ID, order of cutting tests, day test was performed, and depthof-cut. No significant covariate was found. Due to the tool exclusions stated in Section 2.6, there were 9 repetitions at 0.0152 seconds, 9 repetitions at 0.1


(b) Fig. 3. Percent change in area as a function of machining time The trendline of the mean areal wear plotted in Figure 3 is represented by Equation 1: WAreal,mean = 1.1277T 0.137

(1) 4

where WAreal,mean is the areal wear (% change) and T is the cutting time in seconds. This equation fits the mean areal wear of the tool. At each point there is variance in the wear, which is shown by the bars in the plots. A one-way analysis of variance (ANOVA) test showed statistical significance below an alpha level of 5% for the effect of cutting time on areal tool wear and is given in Table 2. Table 2. ANOVA table for areal wear FSource Df S.S. M.S. Stat Cutting 1 653.3 653.3 31.1 Time Residual 38 798.6 21.0 -

P Level