Advanced Materials Research Vol. 565 (2012) pp 472-477
Online available since 2012/Sep/28 at www.scientific.net© (2012) Trans Tech Publications, Switzerland
doi:10.4028/www.scientific.net/AMR.565.472

An End-milling Condition Decision Support System Using Data-Mining
for Difficult-to-cut Materials
Hiroyuki Kodama1,a, Masatoshi Shindou2,b, Toshiki Hirogaki1,c,Eiichi Aoyama1,d and Keiji Ogawa3,e1

Department of Mechanical Engineering, Doshisha University

1-3 Miyakodani, Tatara, Kyotanabe-shi, Kyoto 610-0321, Japan
2

Research and Development Group, Yamamoto Metal Technos Co., Ltd.
4-7 Setoguchi, 2-chome, Hirano-ku, Osaka 547-0034, Japan3

Department of Mechanical Systems Engineering, University of Shiga Prefecture2500 Hassaka-cho, Hikone-shi, Shiga 522-8533, Japanaetl1302@mail4.doshisha.ac.jp, bshindou@yama-kin.co.jp, cthirogak@mail.doshisha.ac.jp,
deaoyama@mail.doshisha.ac.jp, eogawa@mech.usp.ac.jp

Keywords: End-milling, Catalog data, Data mining, Hierarchical and non-hierarchical clustering,Response surface method, Difficult-to-cut materials, JIS SUS310S

Abstract. We proposed the data-mining methods using hierarchical and non-hierarchical clustering
methods to help engineers decide appropriate end-milling conditions. The aim of our research is toconstruct a system that uses clustering techniques and tool catalog data to support the decision ofend-milling conditions for difficult-to-cut materials. We used variable cluster analysis and theK-means method to find tool shape parameters that had a linear relationship with the end-milling
conditions listed in the catalog. We used the response surface method and significant tool shapeparameters obtained by clustering to derive end-milling condition. Milling experiments using a square
end mill under two sets of end-milling conditions (conditions derived from the end-milling condition
decision support system and conditions suggested by expert engineers) for difficult-to-cut materials(austenite stainless steel) showed that catalog mining can be used to derive guidelines for deciding
end-milling conditions.
Introduction
The demand for high-speed, high-efficiency, high-accuracy processing continues to grow due to theincreasing need for quick machining and delivery of workpieces with a variety of complicated shapes.
However, the processing and end-milling methods commonly used are not appropriate for manydifficult-to-cut materials such as Ni-base superalloy, titanium alloy, and carbon fiber reinforced
plastic, which are used extensively in aerospace. Such materials generally have highstrength-to-weight ratios, high corrosion resistance, high strength retention ability at elevatedtemperatures, and low thermal conductivity. These characteristics can result in uneven tool wear andchatter vibration. Therefore, deciding the appropriate end-milling conditions is more difficult fordifficult-to-cut materials than for other materials. There has been much research on the high-speed
milling of these materials, and effective end-milling conditions [1], end-mill tool shapes [2], andprocessing methods have been reported [3]. Yamane and Sekiya [4] proposed a difficult-to-cut ratingthat can be calculated from the mechanical and thermal properties of the workpiece and used toestimate the difficulty of processing considering only the properties of the workpiece. However, it isstill difficult to determine the appropriate end-milling conditions. Since there have been few reportsof systematically obtained comprehensive information useful for determining appropriate end-milling
conditions and processing methods, it is difficult to present standard end-milling conditions fordifficult-to-cut materials. Even
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Advanced Materials Research Vol. 565

473

though tool makers design tool shapes(d) Interpretation
Data-mining methods
in much the same way, the end-milling・ Cutting experiments
Hierarchical and non-hierarchical clustering
conditions they recommend in their(c) Statistic analysis
tool catalogs, which affect processing
New knowledge
・ Variable cluster analysis
・ Principal component regression (PCR)
efficiency and cost, differ among
・ Response surface method
Resultsmakers due to differences in the(b) Attribute extraction
coating base material coatings and・ K-means method
Preprocessed dataedge angles they develop. There are(a) Selection
Cemented carbide
thus no clear guidelines for deciding
square end-mill for
Target data
the end-milling conditions. A system
difficult-to-cut materials
for helping engineers decide theData baseend-milling
conditions
would
Feedback Loop
therefore speed up production and help
reduce manufacturing costs. We
Fig. 1 Catalog-mining process.
previously
proposed using data-mining of tool catalog data as part of a system supporting the decision ofend-milling conditions [5]. Using end-milling conditions derived from this system, we
experimentally validated the effect of combining hierarchical and non-hierarchical clustering
methods for milling JIS SKD61 die steel [6]. This system is targeted at deciding the conditions for die
machining. We have now developed a system using data mining that supports the decision ofend-milling conditions for difficult-to-cut materials. Testing using JIS SUS310S austenite stainlesssteel under end-milling conditions derived from the system showed that catalog mining can be used toderive guidelines for deciding end-milling condition.
Data mining
The data-mining process starts with data acquirement, selection, and cleansing. An analyst checks thedata directly and removes any noisy data on the basis of judgment and experience. This removal of
inaccurate or corrupt data is done in the data-cleansing step, which is said to account for the largest
portion (70 to 80%) of the effort involved in the data-mining process.
Catalog-mining process. In the work reported here, we used end mill catalog data as the database.
Such data are typically obtained on the basis of trial and error by cutting-tool makers during testing to
identify appropriate end-milling conditions. These data are thus both sophisticated and numericalmeaning that we could omit a large part of the data cleansing. The flow of the catalog-mining process
we used is shown in Fig. 1. The algorithms used for the clustering and statistical analysis aredescribed in detail elsewhere [5-6]. We used the K-means method, a non-hierarchical clustering
method, to make clusters and extract attributes from the viewpoint of end mill tool shapes. This wasdone using Visual Mining Studio software (Mathematical Systems, Inc.). We used variable cluster
analysis, a hierarchical clustering method, to visualize the data structure using tree diagrams, and weused principal component regression (PCR), to quantify the correlation between the objective andpredictor variables. We developed equations for deciding the end-milling conditions by using the
response surface method, which uses significant variables derived from variable cluster analysis and
PCR. Several end-milling experiments were conducted to validate the equations.
Analysis results and discussion
Acquisition of data. The tool used was square end mills listed in the catalog of the largest tool makerin Japan. The catalog has a total of 825 pieces of data on cemented carbide square end mills related totheir end-milling conditions for difficult-to-cut materials. For our database, we assumed a milldiameter of 0.1-25 mm. The catalog contains mill diameter D, cut length l, overall length L, shankdiameter Ds, number of flutes z, shape data such as helix angle θ, and the tool coating material.
Equivalent diameter De was calculated in terms of the number and weight of the flutes [5-6]. All the474

Advances in Abrasive Technology XV

l/De

Ds

De

shape variables were assumed to be predictor variables. These predictor variables were selected sothat engineers could quickly determine the appropriate end-milling conditions from the shape of thesquare end mills. The work
L/l
l/De Ds/De
Cluster size
1
172
3
4
1
materials were composed of
l
2
441
3
3
1
L
3
113
6
4
2
Ni-base superalloy (Inconel 718)
4
85
17
3
4
6
5
14
68
3
18
(17%), titanium alloy (Ti-6Al-4V)
5(43%), and austenite stainless steel
4
Cluster1
3
Cluster2(JIS SUS304 and SUS316) (40%)
2Cluster3
up to HRC35-45 hardness.
Cluster4
1Hardness was used as a predictor
Cluster5
00 20 40 60 80 100 120 140
variable. As criterion variables, we
L/lused the recommended end-millingFig. 2 Results of K-means method.
conditions [spindle revolution S(rpm), table feed speed F (mm/min), and depth of cut] listed in the catalog. The values for thesevariables were input into the NC program. We therefore made the criterion variables cutting speed V(m/min), feed rate f (mm/tooth), and axial depth of the cut Ad (mm). These are important processing
condition factors in side milling and slotting. V and f are defined as V=πDS/1000 and f =F/(S･z),
respectively. We further used the radius depth of the cut Rd (mm) as a criterion variable for side
milling.
Modeling of cutting tool shape. Figure 2 shows a diagram of an end mill, the results obtained with
the K-means method, the distribution map for each cluster for l/De against L/l, and the representative
shape of each cluster. We used three variables, L/l, l/De, and Ds/De, to visualize the shape of the end
mill. Once these variables were fixed, we could decide on the external shape of the square end mill.
We set the number of clusters to five. The larger the L/l and Ds/De, the smaller the outside diameter.
The smaller the l/De, the greater the number of flutes z. Clusters 4 and 5 consisted of small-diameter
shaped end mills, and Clusters 1, 2, and 3 consisted of general rod-shaped end mills. The amount ofdata for Cluster 2 accounted for 53% of the total data. End mills that can conduct high-speed cutting
are included in this cluster. These end mills have 4 or 6 flutes, more than other end mills. These results
are consistent with those previously reported [5-6]. Furthermore, each cluster could be divided into
ones of two types of processing: side milling and slotting.
-0.6 -0.4 -0.2 0.0

D

D

Ds

Ds

L

0.2

0.4

0.6

L
l

l

z

z

θ

θ

HRC

HRC

Fig. 3 Dendrogram of Cluster 2 for side milling.

Regression Coefficients Cp
Radius depth of cut Rd
Axial depth of cut Ad
Feed rate f
Cutting speed V

Fig. 4 Calculation result of PCR

Predictor variable selection. We applied variable cluster analysis and PCR to each cluster classified
by the K-means method from the viewpoint of end mill shape. Example results of variable cluster
analysis and PCR for Cluster 2 for side milling (typical shape) are respectively shown in Figs. 3 and 4.
From Fig. 3 (a dendrogram), we can visualize the correlation between variables by focusing on the left
clusters, which are marked off with a dashed line (cutting line). The closer the clusters combine to theleft, the higher the correlation. The variables for side milling for Cluster 2 are divisible into threeclusters: (D, Ds, L, and l), z, and (θ, HRC). The (D, Ds, L, and l) cluster correlates with the end millAdvanced Materials Research Vol. 565

475

shape parameter. Helix angle θ is correlated with HRC. Number of flutes z is a sovereign variable.
From Fig. 4, we see that cutting speed V has a positive relationship with z (Cp, 0.45) and a negative
relationship with θ and HRC (Cp, -0.22). While the axial depth of cut Ad has a positive relationshipwith the end mill shape parameters (Cp, 0.38) and a negative relationship with θ and HRC (Cp, -0.35),
Rd has no relationship with any of the predictor variables. These results show that Rd does not dependon the tool shape parameter and HRC listed in the tool catalog. Using these figures, we selected thepredictor variables which used for the end-milling condition decision equations.
Derivation of end-milling condition decision equations. We derived ternary second-orderpolynomial response surface equations for deciding the end-milling conditions by using the variables
determined to be significant using the response surface method, a practical optimization method. The
ones for Cluster 2 for side milling are shown below.
Ad ( R 2 ad 0.93) = 3 z + l + 0.35Dz − 0.31Dl − 0.28 zl + 0.29 D 2 + 0.075l 2 − 15(1)
Rd ( R 2 ad 0.75) = 0.75 D − 0.24l + 1.17 HRC + 0.019 Dl − 0.17 DHRC + 0.0058lHRC − 0.017 D 2− 0.0052l 2 − 0.015 HRC 2 − 23
V ( R 2 ad 0.79) = 5 D + 129 z + 75 HRC − 0.6 Dz − 1.7 zHRC − 0.09 D 2 − 3 z 2 − HRC 2 − 1657
2

−5

−6

2

(2)
(3)

−6 2

f ( R ad 0.72) = 0.022 D − 0.0040l + 0.042 HRC − 22 × 10 DHRC − 34 × 10 D − 57 × 10 l

Catalog value V m/min(4)− 53 × 10 − 5 HRC 2 − 0.82
160
To evaluate the accuracy of a prediction model,
JIS SUS304
we have to compare the residual per unit
Ti-6Al-4V
freedom. In general, adjusted R-squared (R2ad) is120
Inconel718
used for judging accuracy. We used a T-test of
the regression coefficient to determine the80significance of each coefficient. On the basis of
the results, the model was optimized by adding ordeleting
coefficients
through
stepwise40
elimination. Significant variables used in eachequation are D, l, z, θ, and HRC. These variables
0
are also significant in deciding the square
0
40
80
120
160
end-milling conditions for die machining [5-6].
Application of the adjusted R-squared results
Estimated value V m/min
made each equation more accurate than ones
Fig. 5 Relationship between catalog
derived from multiple regression analysis. Thisvalue and estimated value.
was especially true for V and f, which havevelocity dimensions and showed low determination coefficients inprevious studies [5-6]. The values from catalog mining are plotted against those estimated from thecatalog cutting speed in Fig. 5. The catalog-recommended V can be divided into two ranges: 20-40
m/min for Inconel718 milling and 30-150 m/min for JIS SUS304 and Ti-4Al-6V milling. As shown in
Fig. 5, the V given by Eq. (3) is 10-70 m/min for Inconel718 milling and 10-150m/min for JIS
SUS304 and Ti-4Al-6V milling. When the estimated value was ~80 m/min, the range and data
number of catalog recommended condition are widest and largest in other estimated value. Therefore,
we have to validate the utility of the estimated values in this range.
Experimental validation of equations
End-milling conditions and experimental set-up. The cutting force, cutting temperature,
workpiece ductility, and chip treatability are generally important as evaluation indicators of millingdifficulty [4]. Engineers often decide the end-milling conditions and processing method on the basisof cutting force and temperature. To validate our equations, we conducted milling experiments underconditions:

476

Advances in Abrasive Technology XVTable 1 End-milling conditions.
Spindle speed rpm
Table Feed mm/min
Cutting speed V m/min
Feed rate f mm/tooth
Axial depth of cut Ad mm
Radius depth of cut Rd mm
MRR cm3/min

Mined condition
Standard condition
1350 2700 5400 715 1430 2860
270 540 1080 140 280 560
43
85 170 23
45
90
0.05
0.05
15
20
1.0
1.0
4
8
16
3
6
12

φ10

φ10

L/l=3, l/De=3, Ds/De=12270
θ=30degree, Z=4, PRICE=7280 yen

Fig. 6 Tool shape parameter of square end mill.
derived from data mining (mined conditions) and under standard end-milling conditions suggested byengineers (standard conditions). The workpiece was a 100×100×50 mm sheet of heat-resistant steel(JIS SUS310S, 25Cr-20Ni, HRC35). We used a TiAlN-coated φ10 general-purpose square end millthat catalog recommended and appropriate end-milling conditions for JIS SUS310S are unknown.
The tool shape is shown in Fig. 6. This end mill belongs to Cluster 2. Table 1 lists the mined
conditions obtained by substituting the tool parameters into Eqs. (1)-(4) and the standard conditions.
The Ad under the mined conditions was 25% less than under the standard ones. The material removal
rate (MRR, cm3/min) is defined as MRR = F･Ad･Rd/1000. The experiments involved processing(down-cut in one pass direction) a flat surface. The machine tool was an ACCUMILL4000 (made by
Mori Seiki). The tool extension was 30 mm. The cuttings were made under dry air and minimal
quantities of lubricant (MQL) (Blube LB-1 Fujigiken Inc. 6cc/h) condition. An infrared radiation
thermometer with thermographic resolution of 0.03 K (H2640 NEC/Avio) was used to measure thecutting temperature. The emissivity of the measurement surface was set to 0.43 for dry air cutting andto 0.80 for MQL cutting. The cutting speed was varied by cutting it in half and doubling it for eachcondition (see Table 1).
Tool holder
Results and discussion. Fig. 7 shows athermal image of dry milling understandard conditions. From such images, we
Machined chips
End-mill tool
can photographically analyze not only theblade temperature of the end mill and thetemperature increase of the shank part andCoolant: Dry Air
workpiece but also the temperature and fly
Workpiece： JISSUS310S
Cutting speed V： 45m/min
appearance chip. Fig. 8 shows the
Workpiece
Feed rate f： 0.05mm/tooth
Axial depth of cut Ad： 20mm
maximum cutting temperature as shown in
Workpiece feed direction
Radius depth of cut Rd： 1mm
MRR: 6 cm /minFig. 7 for the different end-milling
conditions. We can see that maximum
Fig. 7 Thermal image of dry cutting
temperature can be reduced 30 to 50% by
under standard conditions.
using MQL under both conditions. Under the standard conditions, the maximum
temperature and thermal gradient due to saturation were higher than under the mined conditions.
Under the mined conditions for V set to 170 m/min, the maximum temperature did not rise rapidly andwas almost the same as for V set to 85 m/min. However, chatter vibration arose under the mined
3

500
450
400
350
300
250
200
150
100
50
0

S2700 Dry
S1350 Dry

S5400 Dry

S2700 MQL
S1350 MQL
S5400 MQL

0

1

2

3

4

5 6
Time s78

9 10

Maximum temperature ℃

Maximum temperature ℃

Advanced Materials Research Vol. 565

500
450
400
350
300
250
200
150
100
50
0

477

S2860 Dry
S1430 Dry
S715 Dry

S2860 MQL
S1430 MQL
S715 MQL024(a) Mined conditions

6

8 10 12 14 16 18 20
Time s(b) Standard conditions

conditions for V set to 170 m/min for both the dry
air and MQL condition. Therefore, these
conditions are not practical. No wear was observedon the end mill blade under the mined conditions
except for V set to 170 m/min. Figure 9 shows therelationship between the saturated maximum tool
temperature in Fig. 8 and MRR for the different
end-milling
conditions.
Themaximum
temperature differed greatly between the minedand standard conditions at each MRR. The use of
MQL effectively reduced the cutting temperature
at each MRR. Near the crossover point of thedashed and solid MQL curves, the maximum
temperature for a specific MRR value can be
reduced by choosing more effective end-milling
conditions.

Maximum temperature ℃

Fig. 8 Maximum temperature for different end-milling conditions.
550
500
450
400
350
300
250
200
150
100
50
0

Mined conditions (Dry)Standard conditions (Dry)Mined conditions (MQL)
Standard conditions (MQL)

0

2

4

6

8 10 12 14 16 18
MRR cm3/min

Fig. 9 Thermal image of dry cutting
under standard conditions.

Conclusion
Catalog mining, which is an application of hierarchical and non-hierarchical clustering methods canbe used as part of a decision methodology for difficult-to-cut end-milling conditions. We found thatcatalog mining can be used to derive guidelines for deciding end-milling conditions. Measurement of
the milling temperature using an infrared radiation thermometer and examination of the analyticalmethod of the thermal imaging confirmed that using an MQL condition reduced the maximum tool
temperature during milling. The guidelines derived from catalog mining are found to be more
effective under a severe condition such as dry air coolant.
References[1] B. Ozcelik et al: Int. J. of Advanced Manufacturing Technology, Vol. 27, (2005), p. 234-241.
[2] M. Rahman et al: JSME International Journal, Series C, Vol. 49, No. 1 (2006), p. 11-20.
[3] P. Huang et al: Int. J. of Advanced Manufacturing Technology, Vol. 55, (2011), p. 153-160.
[4] Y. Yamane et al: J. of Precision Engineering, Vol. 70, No. 3, (2004), p. 407-411 (in Japanese).
[5] H. Kodama et al: Int. J. of Automation Technology, Vol. 6, No. 1 (2012), p. 61-74.
[6] H. Kodama et al: Advanced Materials Research, Vol. 325, (2011), p. 345-350.

Advances in Abrasive Technology XV
10.4028/www.scientific.net/AMR.565

An End-Milling Condition Decision Support System Using Data-Mining for Difficultto-Cut Materials
10.4028/www.scientific.net/AMR.565.472
DOI References[1] B. Ozcelik et al: Int. J. of Advanced Manufacturing Technology, Vol. 27, (2005), pp.234241.
doi:10.1007/s00170-004-2175-7[2] M. Rahman et al: JSME International Journal, Series C, Vol. 49, No. 1 (2006), pp.11-20.
doi:10.1299/jsmec.49.11[4] Y. Yamane et al: J. of Precision Engineering, Vol. 70, No. 3, (2004), pp.407-411 (inJapanese).
doi:10.2493/jspe.70.407