Key Engineering Materials Vol. 625 (2015) pp 213-218© (2015) Trans Tech Publications, Switzerland
doi:10.4028/www.scientific.net/KEM.625.213

Monitoring of End-Mill Process Based on Infrared Imagery
with a High Speed Thermography
Masatoshi SHINDOU1,a *, Hiroyuki KODAMA2,b,Toshiki HIROGAKI2,c and Eiichi AOYAMA2,d1

Research and Development Group, Yamamoto Metal Technos Co., Ltd.
4-7 Setoguchi, 2-chome, Hirano-ku, Osaka 547-0034, Japan2

Department of Mechanical Engineering, Doshisha University
1-3 Miyakodani, Tatara, Kyotanabe-shi, Kyoto 610-0321, Japanacshindou@yama-kin.co.jp, b etl1302@mail4.doshisha.ac.jp,
thirogak@mail.doshisha.ac.jp,d eaoyama@mail.doshisha.ac.jp

Keywords: end-mill process, tool temperature, infrared imagery, thermography, process efficiency,process monitoring.

Abstract. In this study, we perform the end-mill process of a difficult-to-cut material (JIS SUS310
stainless steel) and observe it with high performance infrared thermography. Considering the rotating
angle of end-mill tool, a pixel temperature in each frame is investigated to obtain the tool temperature
variation after cutting of each tooth in end-mill process. The tool temperature distribution can beanalyzed at each rotating tool position in end-mill process from imageries, considering therelationship between the time duration of each frame and the rotating speed of an end-mill tool.
Moreover, the tool/holder shape and the number of cutting teeth can be seen to affect the cutting
temperature because the tool heat capacity and the heat input are different. The examination andanalytical results show this method to be effective to estimate the tool temperature in the end-millprocess sufficiently.
IntroductionNowadays, manufacturing companies have been requested to develop new technologies todetermine the machining conditions in agile and trial manufacturing fields. The cutting force and thecutting temperature must be monitored to estimate machining conditions sufficiently. The cutting
force has recently become easy to measure with a piezoelectric sensor on the machine tool table,
whereas the cutting temperature is difficult to measure because a thermocouple sensor is considered
the only effective method to obtain it practically [1] [2].Few recent reports have dealt with measuring
the tool temperature with an infrared temperature sensor [3]. However, insufficient temperature
distribution was obtained because these methods are based on a point measuring method.
Additionally, an infrared thermography is considered to be an effective method to obtain the tool
temperature distribution during the drilling process [4]. We therefore attempt to develop anestimating method for end-mill process from an infrared imagery to measure the cutting temperatureeasily. We especially focus on a high response and high speed monitoring thermography, whichmakes it possible to obtain 30 frames per second and continuously forward them to an analyzing
computer. In the present report, we perform the end-mill process of a difficult-to-cut material (JISSUS310 stainless steel) and observe it with this high performance infrared thermography.
Considering the rotating angle of end-mill tool, a pixel temperature in each frame is investigated toobtain the tool temperature variation after cutting of each tooth in end-mill process. The tool
temperature distribution can be analyzed in images of each rotating tool position in the end-millprocess by considering the relationship between the time duration of each frame and the rotating
speed of an end-mill tool. In addition, we perform a temperature response analysis based on a Finite
Element Method (FEM) model. By comparing experimental and analytical results, the tool/holder
shape and the number of cutting teeth can be seen to affect the cutting temperature because the tool
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Precision Engineering and Nanotechnology Vheat capacity and the heat input are different. These results show this method to be effective toestimate the tool temperature in the end-mill process sufficiently.
Setup for monitoring of end-mill process
Figure 1 shows the setup for monitoring the end-mill process. Thermography used in the experimentis “Infrared Ray Technology H2640” made by Nippon Avionics Co. The minimum space resolution is
0.18mm at 0.3m working distance, which means the spatial resolution 0.6mrad, temperature
resolution is 0.03C, and the maximum measureable temperature is equal to or lower than 500C. The
thermography can transfer continuous images to a computer. Depending on the function, thethermography can also obtain frame imagery images at 30 images per second. As shown in Fig.1, thethermography is set up next to machining centers. We monitored the end-mill process at an angle of150° in direction of tool movement. The distance between end-mill and thermography is 1500mm.
The workpiece was SUS310, which is a difficult-to-cut material. The tool diameter was 10mm(Type:WXL-EMS,WXL-2D-DE, OSG Corporation). Twist angle is 30 degree. The numbers of cutting teeth
are two and four. The emissivity of end-mill surface is 0.4. The machining conditions are a 1450rpm
rotating speed, a 0.05mm per tooth feed amount, a 0.6mm width of cutting, and a 12mm depth ofcutting. The coolant is dry-air.
Figure 2 shows an infrared imagery of the end-mill process. As shown in Fig.2, you can easily andfinely obtain the temperatures of the holder, end-mill, workpiece, and machining chips by observing
infrared imagery during temperature distribution. Figure 2 shows that the monitoring method
effectively estimates the end-mill process by using high response infrared thermography equippedwith a function for continuously transmitting frame imagery to a personal computer.
Top view

θ=150°
Workpieceωθ

Spindle1500

End-mill

Tool holder

Thermography

Machining center

Endmill toolEnd-mill tool

Machined
chips
Workpiece

Thermography

Fig. 1 Setup for monitoring of end-mill process

Fig. 2 Infrared imagery of end-mill process

Results and discussions
Influence of tool rotational angle on temperature distribution. Figure 3 shows the definition oftool rotational angle and its influence on the imagery of the end-mill process. Machining conditions
are listed above. Temperature distribution changes slightly if tool rotational angle on end-mill process

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changes. We consider the relationship between frame time in the imagery and tool rotating angle. InFig. 3, angle α, which shows a phase of the cutting edge, is defined as the tool rotating angle whencutting edge is 0° in the normal direction relative to the shoot direction through the central axis.
If frame rate is defined by f(fps) and tool rotating speed by N(rpm), tool rotating speed per frame n isequation (1)n=(N/60)･(1/f).

(1)

If it assumes that there is no difference in the form of all teeth of the end-mill, the degree of changing
the phase of cutting edge at m frame is defined by ε, the number of teeth of the end-mill by Z, andgauss symbol by [ ].
ε can be shown equation (2).
ε=m･n･Z -[m･n･Z].

(2)Thus, tool rotating angle αm at m frame is equation (3)αm=ε･(360/Z).

(3)

Fig. 3 Definition of tool rotational angle and its influence on the imagery of end-mill process
Figure 4 shows the relationship between the rotational angle as defined in Fig.3, tool temperature, andprocessing time. Here, the 15 fps sampling time is set to prevent the high frequency noise in Fig.4 (b).
Change of the rotational angle and tool temperature is well in agreement. Therefore, we found that wecan estimate the influence of changing the position of the cutting edge by tool rotating from getting
the infrared imagery.

(a) Calculated results(b) Experimental value
Fig. 4 Relationship between the rotational angle, tool temperature, and processing time

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Influence of processing time on tool temperature. Figure 5 shows the influence of processing timeon the temperature distribution. The end-mill temperature rises if cutting time increases. The results
show that the heat storage action occurs.
Figure 6 shows the relationship between the tool temperature and the processing time. This figure
shows that the converged temperature is different when the number of teeth is different. This isthought to be because only half as much heat is input to the tool of the end-mill per unit time when
there are half as many teeth.

Fig. 5 Influence of processing timeon the temperature distribution

Fig. 6 Relationship between tool temperatureand processing time

Temperature response model based on time delay of first order and FEM. Figure 7 shows atemperature response model based on time delay of first order. Heat storage action of the tip of thetool explains the model of the first order lag system [5]. Figure 7 shows the definition of heat capacity
in the tip of the tool. We consider a column of infinite length. The density is defined by ρ, specific heatby C, thermal conductivity concerning the outside by α, primary temperature of the column by T0, andstep temperature outside of the step reply, assuming it changes rapidly, by Ts.
.

Fig. 7 Modeling by time delay of first order(4)

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The left side shows the changing of thermal energy per unit time, and the right side shows the amountof heat transferred from surface per unit length. The end-mill process is intermittent cutting.
However, the end-mill rotating speed is much faster than the phenomenon of transient heat transfer ofenergy. Therefore, an end-mill process is considered that can approach the model of perimeter.
Solve a differential equation (4), as a primary condition, if T=T0 when t=0, as the left side isdimensionless temperature.
.

(5)

Therefore, if Tm=ρCd/4α,.

(6)

Tm is damping time constant of time delay of the first order. Tm is proportional to heat capacity.
Figure 8 shows a temperature response based on time delay of the first order. The results in Fig.8 arealmost the same as those in Fig 6.
Figure 9 shows a FEM end-mill model considering the decrease in heat capacity in the tooth part. We
use the model of axial symmetry, and consider its element size is almost same as image element ofthermography. In reality, the volume of tooth part is smaller than the shank part of the end-mill.
Therefore, we use the model for changing the average diameter of the tooth part. Thus, we use themodel in which the average diameter of the tooth part is changed. We charged the heat source to thedepth of the cutting part that was distributed all-round. The rest of the boundary is insulation. The
material of a holder part is steel, and that of a shank and an equivalent cutting part is cemented
carbide. Density of 14800kg/m3, specific heat of 0.21kJ/(kg ･ K), and thermal conductivity of80W/(m･K) were used for the physical-property values of the tool part. Density of 7860kg/m3,
specific heat of 0.50kJ/(kg ･ K), and thermal conductivity of 42W/(m ･ K) were used for thephysical-property values of the holder part.

Fig. 9 FEM model withan equivalent diameter

Fig. 10 Influence of equivalent diameter

Figure 10 shows the influence of equivalent diameter on the time constant. These are the results of a
10-mm-diameter tool and four cutting teeth. We found that responsiveness is improved as theequivalent diameter decreases. From Fig.10, the calculated results do not agree well with theexperimental ones when a two-step model is used that has a constant equivalent diameter in the flutepart. Thus, we used a five-step model with a multi diameter in the flute part as shown in Fig.9, and its

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calculated results agree well with the experimental ones in Fig.6. The amount of heat input in thefive-step model was set to 15.5W.
Figure 11 shows the influence of the number of teeth on tool temperature. Two-flute is the results
when heat input is half of four-flute. They are almost the same as the results in Fig.6. Thus, theproposed monitoring method can enable estimation of the end-mill temperature.

Fig. 11 Influence of number of teeth on tool temperature

Summary
We proposed a novel temperature monitoring method for end-mill process from infrared imagery witha high response and resolution infrared thermography to improve the processing of difficult-to-cut
material. By comparing experimental results and analytical ones, these temperatures were confirmed
to agree well. As a result, this method is found to be effective to estimate the tool temperature in theend-mill process sufficiently.
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