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On the tool-life equation of TiN-coated high speed steel tools
267
Wear, 143 (1991) 267-275
On the tool-life equation of TiN-coated high speed steel tools M. V. Kowstubhan
and P. K, PhiIip*
Department of Mechanical Engineering, (India)
Indian Institute of Technology, Madras 600 036
(Received January 2, 1990; revised August 28, 1990; accepted September 11, 1990)
Abstract The introduction of computer numerical control and direct numerical control has demanded the precise prediction of tool-life under known constant cutting conditions. The recent developments in TiN coating technology have made conventional high speed steel (HSS) tools more versatile. Efforts are made to understand the behaviow of TiN coats 5 pm thick on the performance of turning too&. Two types of cutting tools were used to turn Cl5 low carbon steel workpieces. They are conventional M42 HSS and the other TiNcoated M42 HSS tools. The coating was accomplished by physical vapour deposition (FVD) using the plasma arc evaporation technique. A conventional HSS tool was used as a benchmark. A stepwise multiple regression analysis was used to investigate the combined effect of speed and feed on tool-life. This is found to have more effect on the tool-life equation of TiN-coated HSS tools. The tool-life of TiN-coated tools was found to be from 3 to 10 times higher than the conventions HSS tool under all cutting conditions. The results revealed that a second-order model is more approp~ate for the mathematical modelling of cutting data for W-coated
tools.
1. Introduction
The conventional
Taylor
tool-tie
equation
is often
found
inadequate
when tool-life pre~~tions are made over a wide range of mach~g conditions [ 1 J. Recently efforts have been made to use response surface methodology to predict tool-life using a first-order tool-life model [2]. When a wide range of cutting speeds and feeds were considered, it was found that second-order logarithmic equations are more effective [3] because of the problem of variability and scatter of tool-life data. Although the problem of variabihty and scatter of tool-life data has been known for a long time [ 1 I, a statistical measure of tool-life variance did not come into use until 1960 f4 J. It has been suggested that tool-life vahzes commonly follow a normal or log-normal distribution. The nature of the cutting response, i.e. tool wear, surface finish, etc. is claimed to be probabilistic rather than deterministic [ 51. It was shown that statistically planned ma*Author to whom correspondence should be addressed.
268
~h~ab~i~ tests have a studying one parameter With the increased other models have been [6, 31.
definite advantage over the classical approach of at a time. use of computers, newer versions of tool-life and developed with an increasing number of variables
1.2. Mathematical model Cutting parameters such as cutting speed, feed and the combined effect of speed and feed have an important effect on tool-life. It becomes necessary to use second-order tool-life models when dealing with composite tools such as TiN-coated high speed steel (HSS) tools. The tool-life equation ln the following general form can be adopted to account for the variability and scatter of the tool-life data. ln T=a,,fai
ln V+az lns+a,
In d
+all ln V2+a22 In s2+as3 ln d2
where 2’ is the tool life (min), V is the cutting speed (m ntin-‘), s is the feed (mm rev-‘), d is the depth of cut (mm) and a,, al, u2, a3, a12, . . . , etc. represent appropriate coefficients. The goals set in the present statistical evaluation are as follows: (1) The final equation should explain more than 90% of the variation (R2=0.9). (2) The standard error of the estimate should be less than 7%. (3) All estimated coefficients should statistically have 90% significance. (4) There should be no discernable patterns in residuals. 2. Experimental
details
A series of statistically designed tool-life tests were conducted to obtain tool-life data for both TiN-coated and uncoated tools while turning Cl5 low carbon steel in semi-orthogonal cutting with the objective of obtaining reliable and applicable tool-life data for roughing and finishing operations. Tables 1 and 2 give the tool geometry and cutting conditions. Table 3 gives material composition of both the tool and work materials used in the experiment. The feed conditions were chosen with reference to CIRP recommendations. The upper value of 0.26 mm rev-’ corresponds to a roughing operation and the selected lower value corresponds to a finishing operation. An 18 kW heavy duty precision lathe was used. Plank wear of tools was measured at regular time intervals using a toolmaker’s microscope with an accuracy of 0.001 mm. Tool-life criteria of average flank wear width VB= 0.3 mm was used. The variation of tool-life at various ranges is explained using chip roots obtained during deformation studies using a quick-stop device [9].
269
TABLE 1 Experimental details: cutting conditions Cutting
speed (m mir-‘)
3.15
Feed (mm rev-‘) Depth of cut (mm)
5
8
10
12.5
16
20
50
60
80
120
-
-
-
-
25
31.5
40
0.11 2.5
0.18 -
0.26
TABLE 2 Experimental details: tool geometry Side rake Y
Side relief a
Side cutting edge angle A
Approach angle K
Wedge angle 5
Nose radius r (mm)
Edge radius (mm)
10"
8”
0”
90”
72”
0.5
0.03
TABLE 3 Chemical composition of tool and work materials C
W
MO
Cr
V
Co
Mn
Si
P
S
Fe
H42 HSS (tool)
1.05
1.5
9.5
4.0
1.0
8.0
-
-
-
-
Balance
CIS steel (work)
0.15
-
-
-
-
-
0.2
0.01
0.03
0.03
Balance
Stepwise multiple regression analysis was carried out on data obtained by actual machining of Cl5 low carbon steel using both coated and uncoated HSS tools by making use of suitable programs on a Siemens main frame computer. Since the regression analysis and analysis of variance for the modified Taylor tool-life equation gave rise to a lower coefficient of determination and systematic lack of fit, the second-order logarithmic model is considered for further analysis. 2. I. Stepwise regression procedure This method involves the reexamination at every stage of regression of the variables incorporated into the model in previous stages. A variable which may have been the best single variable to enter at an early stage may at a later stage be superfluous because of its interactions with other variables now in the regression. To check on this, the partial “F” criterion for each variable in the regression at any stage of calculation is evaluated and compared
270
with a pre-selected percentage point of the appropriate “P distribution. This provides a judgement on the contribution made by each variable although it has been the most recent variable entered, irrespective of its actual point of entry into the model. Any variable which provides a non-significant contribution is removed from the model. This process is continued until no more variables are admitted to the equation and no more are rejected.
3. Results and discussion The data were subjected to an initial regression to obtain the correlation matrix for all transformed independent and dependent variables. Table 4 shows the correlation coefficients of all the independent and dependent variables. It can be seen that all the transformed variables play a significant role in the tool-life. Table 5 shows results from multiple regression of toollife data. To assess the strength of dependence or the amount of variation in toollife that can be explained by non-linear dependence upon two dependent variants operating together, the coefficient of determination R2 is preferred because of its straightforward interpretation. From Table 5 it can be seen that in the case of coated tools R'=0.99644, indicating that 99.64% of variation in tool-life is explained by cutting speed, feed and a combination of both.
TABLE4 Correlation
coefficients
A
D
K
G
C
Coated tool A 1 .ooooo G 0.02036 c -0.70771 D 0.97770 E - 0.03132 H -0.84811 K 0.75916
0.02038 1 .ooooo - 0.28486 0.02570 - 0.99752 0.48773 - 0.55349
- 0.70771 - 0.28486 1 .ooooo - 0.82965 0.30200 0.45658 - 0.46577
0.97770 0.02570 - 0.82965 1 .ooooo - 0.03949 - 0.82488 0.76795
-0.03132 -0.99752 0.30200 - 0.03949 1 .ooooo - 0.47736 0.54487
-0.84811 0.48773 0.45656 - 0.82488 - 0.47736 1.00000 - 0.97422
0.75916 - 0.55349 - 0.46577 0.76795 0.54487 -0.97422 1 .ooooo
Uncoated tool A 1.00000 G 0.01922 C - 0.65930 D 0.98984 E - 0.01977 H - 0.83412 K 0.71780
0.01922 1 .ooooo - 0.30189 0.00933 -0.99767 0.50834 - 0.606 11
- 0.65930 -0.30189 1 .ooooo - 0.72958 0.31463 0.35786 - 0.26349
0.98984 0.00933 - 0.72958 1 .ooooo - 0.00960 -0.83115 0.73192
-0.01977 - 0.99767 0.31463 -0.00960 1 .ooooo - 0.50652 0.60724
-0.83412 0.50834 0.35786 -0.83115 - 0.50652 1 .ooooo - 0.97390
0.71780 - 0.60611 - 0.26349 0.73192 0.60724 - 0.97390 1 .ooooo
A value of 99.00000 is printed if a coefficient r; D=log vxlog v; E=log sxlog s; H=log
E
H
cannot be computed. A =i log V; G = log s; C= log vxlog s and K=(log v)2X(log s)‘.
271 TABLE 5 Statistics
from multiple
regression
~~tiple R R’! Standard error Dependent Variables
variable:
of tool-life
data
Coated tool
Uncoated
0.99739 0.99479 0.06969
0.99822 0.99644 0.06978
tool
log T.
in the equation
Variable
B”
D H A K G E
- 1.09279 2.59143 6.90073 0.16886 - 2.64032 -0.12765
constant
- 1.84006
- 7.75820 7.34938 9.46504 5.16126 - 1.23186 -0.21320
Variable
BB
Pb
D A G K H E
- 0.47300 2.63095 - 1.98445 0.02172 0.41093 - 0.24265
- 3.56280 3.13540 - 0.68891 0.6393 1 0.99109 0.99109
Constant
I.12575
W&es of coefficients ue, a,, u2, ur2, . . . , etc. in the proposed tool-life equation. variables), y =ln T and T is the b&(m/s& = bfX(x-2_)2E(y -g-@}‘“, where x= log (~dependent toof-life in minutes.
The coefficients a,, a,, u2, a3, u12, . . . , etc. in the general tool-life equation are given by the values of B given in Table 5 against various variables indicated for both TiN-coated and conventional HSS tools. The tool-life equation can be arrived at by comparing the value of “p”. This is done while computing. Those coefficients whose values are not significant are removed and only those which are significant are included in the “@” column. The corresponding values of the coefficients along with the variables are shown in the “variable” and “B” columns of Table 5. Based on this the prediction equation can be written as (1) For coated tools lnT=1.2575+2.63095lnV-1.98445lns+4.1093lnVlns -0.473(ln
V)” - 024265(1n s)~
(2) For uncoated tools ln T= - 1.84006+6.90073
ln V-2.64032 lns
+2.59143@I
V)(ln s) - 1.09279&I V)Z
+O.l6886(ln
V)2(lns)2-0.12765(lns)2
Figure 1 shows the tool-life surface generated using the present ematical model. This gives the variation of tool-life with the combined of cutting speed and feed. There is a marked increase from 3 to 10 in the tool-life of TiN-coated HSS in all the speed and feed ranges
matheffect times when
272
/
2 Fig.
---
1.
Tool-lie , uncoated
/
3
///j/l/
‘56
II
10
/
20
30 40
m
120
2m
surface showing the combined effect of speed and feed. -, Coated tool; tool; q, s-O.26 mm rev-‘; A, s=O.185 mm rev-‘; 0, s==O.ll mm rev-‘.
compared with that of conventional HSS tools. Table 6 shows very little difference in the observed and calculated values, indicating that the empirical values of the coefficients of variables in the proposed equation can be accepted in the selected ranges of speed and feed. Table 7 shows the comparison of a number of results obtained from ref. 10 and the results of the proposed model. It can be seen that the results are comparable. Hence the proposed model can be used for predicting toollife data. Initially there is a steady increase in tool-life from 3.15 to 20 m min ‘. This is on account of a change in mode of deformation from Type 1 to Type 3 chip formation as shown in Figs. 2(a)-2(c). The proposed tool-life model is based on flank wear as criterion. While cutting using uncoated HSS tools built up edge (BUE) is associated with increased flank wear. This is due to the overhang of the BUE below the cutting edge and the periodic wandering of the strain hardened debris onto the finish surface, abrading the flank of the tool in the process. However, in the present case the TM coating on the tool, with its low adhesion for steel, does not promote any sizeable build-up at the cutting edge. The thin, embryonic BUE, as observed
273 TAEILE 6 Tool-life Test No:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
results Speed (m min-‘)
Feed (mm rev-‘)
3.15 3.15 3.15 8.00 8.00 8.00 20.00 20.00 20.00 30.00 30.00 30.00 40.00 40.00 40.00 50.00 50.00 50.00 60.00 60.00 60.00 80.00 80.00 80.00 120.00 120.00 120.00 180.00 180.00 180.00
T * = Tool-life
0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260
Tii-coated
M42 HSS
Uncoated
Observed In T
Predicted
Observed
Predicted
IItT
ln T*
ln T*
5.828945 5.480639 5.192956 5.991464 5.703782 5.438079 6.086774 5.857933 5.598421 5.886104 5.560681 5.298317 5.783825 5.370638 5.164785
5.818398 5.465259 5.171418 6.028278 5.799092 5.521460 6.192785 5.897792 5.690701 5.900717 5.574024 5.364796
5.323009 4.367534 4.700480 4.744932 4.382026 4.158883 4.158883 3.688879 3.332264 2.708050 2.703050 2.502649
5.226435 4.823701 4.595565 4.827002 4.378455 4.135864 4.160764 3.635363 3.366096 2.757080 2.757080 2.513280
4.382026 4.317488 4.382026 4.700480 4.553876 4.499809 4.442651 4.3 17488 4.605170 3.688879 3.912023 4.094344 3.401197 3.401197 3.806662 2.708050 2.708050 3.688879 2.302585 2.302585 2.128312
4.365119 4.346625 4.362228 4.605170 4.532590 4.444265 4.479181 4.332582 4.642879 3.689014 3.889338 4.046163 3.249226 3.362841 3.851541 2.775335 2.789973 3.672556 2.342902 2.263788 2.130280
5.686313 5.335671 5.121605
M42 HSS
in minutes.
TABLE 7 Type of tools
Coated Uncoated Coated Uncoated
Cutting speed (m min-‘)
40 40 70
Feed rate (mm rev-‘)
0.2 0.2 0.2
Tool-life
(min)
Ref. 10
Proposed equation
230 40 60
220 38 55
tool-life
274
(a)
cc>
Cd)
Fig. 2. Micrographs of chip roots showing three types steel with Tii-coated HSS tools at a feed of 0.26 mm m mir-‘; (b) quasi-discontinuous, V= 8 m min- *; (c) m mir-‘; (d) type 2 (continuous without BUEI), V=80
of chip formation while machining Cl5 rev-‘. (a) Discontinuous type, V= 3.15 type 3 (continuous with BUZZ),V= 20 m min-‘. Original magnification X50.
in Fig. 2(c), is confined to the rake face of the tool and the debris from its ~s~te~ation passes exclusively to the chip underside. Thus ploughing and adhesion wear in the flank of the tool is less in this case and, hence, the increase in tool-life. At lower speeds there is a reduction in the hydrostatic stress, thereby increasing deviotoric stress in the primary shear zone, resulting in discontinuous and quasi-discontinuous chip formation, As the speed is increased further, the temperature effect dominates, which results in flow layer formation and also softening of the tool substrate 171 leading to increased plastic deformation of the tool, causing a reduction
275
in tool-life. On account of the high temperature stability and hot hardness of these coatings, the coated tools perform better even at high speeds where conventional HSS tools cannot work. This results in an increase in the working range of TiN-coated HSS tools.
4. Conclusions (1) The equations are best suited for predicting the tool-life of TiNcoated and conventional HSS tools giving the combined effect of speed and feed. (2) Extrapolation and intrapolation of the tool-life can be done within reasonable limits using the tool-life surface. (3) There is an increase in the tool-life from 3 to 10 times over the whole of the selected speed and feed ranges. (4) The maximum tool-life for both coated and uncoated tools was found to be at a cutting speed of 20 m min- ‘. (5) TiN-coated tools perform better even at high cutting speeds and feeds where conventional tools fail.
References 1 V. A. Tipnis and A. L. Joseph, Testing for machinability, American Society for Metals, Materials/Metal Working Technology, Series No. 7, 1975, pp. l-8. 2 V. A. Tipnis, M. Feld and M. Y. Friedman, Development and use of machinabiity data for process planning optimization, SME Paper No. MS75-5I7, C! Programs, (1975) 1-12. 3 Ibrahim Mahmoud, The effect of some surface treatments on the Iife of high speed steel tools, Wear, 118 (1987) 27-31. 4 S. M. Wu, Tool Iife testing by response surface methodology, J. Eng. Ind. Trans. ASME, 86 (2) (1964) 215-220. 5 J. G. Wager, The use of statistical transformations in assessing the Iife of cutting tools, Ann. CIRP, 25 (1) (1976) 2931. 6 K. Iwata, Y. Murotsu, T. Iwatsubo and S. Fujii, A probabihstic approach to the determination of the optimum cutting conditions, J. Eng. Znd. !Prans. ASME, 94 (4) (1972) 1099-1105. 7 R. Wertheim, R. Sivan, R. Porat and h Ber, Characterisation of CVD coated carbide layers and their thermal properties, Ann. CLRP, 31 (1) (1982) 7-12. 8 E. J. Weller, C. Reitz, J. Montandouin, B. E. Hirsch, H. Zoelzer, W. H. Engelskirchen and E. W. Zimmers, Computerized machinability data systems, in N. R. Parsons (ed.), N/C Machinability Data Systems, SME, Dearborn, MI, 1971, pp. 63-128. 9 P. K. Philip, Study of performance characteristics of an explosive quick-stop device for freezing cutting action, Int. J. Mach. Tool Dee. Res., II (1971) 138-144. 10 F. A. SoIimsn and 0. A. Abu-Zeid, On the improvement of the performance of high speed steel turning tools by TiN coating, Wear, 219 (1987) 199-204.
Wear, 143 (1991) 267-275
On the tool-life equation of TiN-coated high speed steel tools M. V. Kowstubhan
and P. K, PhiIip*
Department of Mechanical Engineering, (India)
Indian Institute of Technology, Madras 600 036
(Received January 2, 1990; revised August 28, 1990; accepted September 11, 1990)
Abstract The introduction of computer numerical control and direct numerical control has demanded the precise prediction of tool-life under known constant cutting conditions. The recent developments in TiN coating technology have made conventional high speed steel (HSS) tools more versatile. Efforts are made to understand the behaviow of TiN coats 5 pm thick on the performance of turning too&. Two types of cutting tools were used to turn Cl5 low carbon steel workpieces. They are conventional M42 HSS and the other TiNcoated M42 HSS tools. The coating was accomplished by physical vapour deposition (FVD) using the plasma arc evaporation technique. A conventional HSS tool was used as a benchmark. A stepwise multiple regression analysis was used to investigate the combined effect of speed and feed on tool-life. This is found to have more effect on the tool-life equation of TiN-coated HSS tools. The tool-life of TiN-coated tools was found to be from 3 to 10 times higher than the conventions HSS tool under all cutting conditions. The results revealed that a second-order model is more approp~ate for the mathematical modelling of cutting data for W-coated
tools.
1. Introduction
The conventional
Taylor
tool-tie
equation
is often
found
inadequate
when tool-life pre~~tions are made over a wide range of mach~g conditions [ 1 J. Recently efforts have been made to use response surface methodology to predict tool-life using a first-order tool-life model [2]. When a wide range of cutting speeds and feeds were considered, it was found that second-order logarithmic equations are more effective [3] because of the problem of variability and scatter of tool-life data. Although the problem of variabihty and scatter of tool-life data has been known for a long time [ 1 I, a statistical measure of tool-life variance did not come into use until 1960 f4 J. It has been suggested that tool-life vahzes commonly follow a normal or log-normal distribution. The nature of the cutting response, i.e. tool wear, surface finish, etc. is claimed to be probabilistic rather than deterministic [ 51. It was shown that statistically planned ma*Author to whom correspondence should be addressed.
268
~h~ab~i~ tests have a studying one parameter With the increased other models have been [6, 31.
definite advantage over the classical approach of at a time. use of computers, newer versions of tool-life and developed with an increasing number of variables
1.2. Mathematical model Cutting parameters such as cutting speed, feed and the combined effect of speed and feed have an important effect on tool-life. It becomes necessary to use second-order tool-life models when dealing with composite tools such as TiN-coated high speed steel (HSS) tools. The tool-life equation ln the following general form can be adopted to account for the variability and scatter of the tool-life data. ln T=a,,fai
ln V+az lns+a,
In d
+all ln V2+a22 In s2+as3 ln d2
where 2’ is the tool life (min), V is the cutting speed (m ntin-‘), s is the feed (mm rev-‘), d is the depth of cut (mm) and a,, al, u2, a3, a12, . . . , etc. represent appropriate coefficients. The goals set in the present statistical evaluation are as follows: (1) The final equation should explain more than 90% of the variation (R2=0.9). (2) The standard error of the estimate should be less than 7%. (3) All estimated coefficients should statistically have 90% significance. (4) There should be no discernable patterns in residuals. 2. Experimental
details
A series of statistically designed tool-life tests were conducted to obtain tool-life data for both TiN-coated and uncoated tools while turning Cl5 low carbon steel in semi-orthogonal cutting with the objective of obtaining reliable and applicable tool-life data for roughing and finishing operations. Tables 1 and 2 give the tool geometry and cutting conditions. Table 3 gives material composition of both the tool and work materials used in the experiment. The feed conditions were chosen with reference to CIRP recommendations. The upper value of 0.26 mm rev-’ corresponds to a roughing operation and the selected lower value corresponds to a finishing operation. An 18 kW heavy duty precision lathe was used. Plank wear of tools was measured at regular time intervals using a toolmaker’s microscope with an accuracy of 0.001 mm. Tool-life criteria of average flank wear width VB= 0.3 mm was used. The variation of tool-life at various ranges is explained using chip roots obtained during deformation studies using a quick-stop device [9].
269
TABLE 1 Experimental details: cutting conditions Cutting
speed (m mir-‘)
3.15
Feed (mm rev-‘) Depth of cut (mm)
5
8
10
12.5
16
20
50
60
80
120
-
-
-
-
25
31.5
40
0.11 2.5
0.18 -
0.26
TABLE 2 Experimental details: tool geometry Side rake Y
Side relief a
Side cutting edge angle A
Approach angle K
Wedge angle 5
Nose radius r (mm)
Edge radius (mm)
10"
8”
0”
90”
72”
0.5
0.03
TABLE 3 Chemical composition of tool and work materials C
W
MO
Cr
V
Co
Mn
Si
P
S
Fe
H42 HSS (tool)
1.05
1.5
9.5
4.0
1.0
8.0
-
-
-
-
Balance
CIS steel (work)
0.15
-
-
-
-
-
0.2
0.01
0.03
0.03
Balance
Stepwise multiple regression analysis was carried out on data obtained by actual machining of Cl5 low carbon steel using both coated and uncoated HSS tools by making use of suitable programs on a Siemens main frame computer. Since the regression analysis and analysis of variance for the modified Taylor tool-life equation gave rise to a lower coefficient of determination and systematic lack of fit, the second-order logarithmic model is considered for further analysis. 2. I. Stepwise regression procedure This method involves the reexamination at every stage of regression of the variables incorporated into the model in previous stages. A variable which may have been the best single variable to enter at an early stage may at a later stage be superfluous because of its interactions with other variables now in the regression. To check on this, the partial “F” criterion for each variable in the regression at any stage of calculation is evaluated and compared
270
with a pre-selected percentage point of the appropriate “P distribution. This provides a judgement on the contribution made by each variable although it has been the most recent variable entered, irrespective of its actual point of entry into the model. Any variable which provides a non-significant contribution is removed from the model. This process is continued until no more variables are admitted to the equation and no more are rejected.
3. Results and discussion The data were subjected to an initial regression to obtain the correlation matrix for all transformed independent and dependent variables. Table 4 shows the correlation coefficients of all the independent and dependent variables. It can be seen that all the transformed variables play a significant role in the tool-life. Table 5 shows results from multiple regression of toollife data. To assess the strength of dependence or the amount of variation in toollife that can be explained by non-linear dependence upon two dependent variants operating together, the coefficient of determination R2 is preferred because of its straightforward interpretation. From Table 5 it can be seen that in the case of coated tools R'=0.99644, indicating that 99.64% of variation in tool-life is explained by cutting speed, feed and a combination of both.
TABLE4 Correlation
coefficients
A
D
K
G
C
Coated tool A 1 .ooooo G 0.02036 c -0.70771 D 0.97770 E - 0.03132 H -0.84811 K 0.75916
0.02038 1 .ooooo - 0.28486 0.02570 - 0.99752 0.48773 - 0.55349
- 0.70771 - 0.28486 1 .ooooo - 0.82965 0.30200 0.45658 - 0.46577
0.97770 0.02570 - 0.82965 1 .ooooo - 0.03949 - 0.82488 0.76795
-0.03132 -0.99752 0.30200 - 0.03949 1 .ooooo - 0.47736 0.54487
-0.84811 0.48773 0.45656 - 0.82488 - 0.47736 1.00000 - 0.97422
0.75916 - 0.55349 - 0.46577 0.76795 0.54487 -0.97422 1 .ooooo
Uncoated tool A 1.00000 G 0.01922 C - 0.65930 D 0.98984 E - 0.01977 H - 0.83412 K 0.71780
0.01922 1 .ooooo - 0.30189 0.00933 -0.99767 0.50834 - 0.606 11
- 0.65930 -0.30189 1 .ooooo - 0.72958 0.31463 0.35786 - 0.26349
0.98984 0.00933 - 0.72958 1 .ooooo - 0.00960 -0.83115 0.73192
-0.01977 - 0.99767 0.31463 -0.00960 1 .ooooo - 0.50652 0.60724
-0.83412 0.50834 0.35786 -0.83115 - 0.50652 1 .ooooo - 0.97390
0.71780 - 0.60611 - 0.26349 0.73192 0.60724 - 0.97390 1 .ooooo
A value of 99.00000 is printed if a coefficient r; D=log vxlog v; E=log sxlog s; H=log
E
H
cannot be computed. A =i log V; G = log s; C= log vxlog s and K=(log v)2X(log s)‘.
271 TABLE 5 Statistics
from multiple
regression
~~tiple R R’! Standard error Dependent Variables
variable:
of tool-life
data
Coated tool
Uncoated
0.99739 0.99479 0.06969
0.99822 0.99644 0.06978
tool
log T.
in the equation
Variable
B”
D H A K G E
- 1.09279 2.59143 6.90073 0.16886 - 2.64032 -0.12765
constant
- 1.84006
- 7.75820 7.34938 9.46504 5.16126 - 1.23186 -0.21320
Variable
BB
Pb
D A G K H E
- 0.47300 2.63095 - 1.98445 0.02172 0.41093 - 0.24265
- 3.56280 3.13540 - 0.68891 0.6393 1 0.99109 0.99109
Constant
I.12575
W&es of coefficients ue, a,, u2, ur2, . . . , etc. in the proposed tool-life equation. variables), y =ln T and T is the b&(m/s& = bfX(x-2_)2E(y -g-@}‘“, where x= log (~dependent toof-life in minutes.
The coefficients a,, a,, u2, a3, u12, . . . , etc. in the general tool-life equation are given by the values of B given in Table 5 against various variables indicated for both TiN-coated and conventional HSS tools. The tool-life equation can be arrived at by comparing the value of “p”. This is done while computing. Those coefficients whose values are not significant are removed and only those which are significant are included in the “@” column. The corresponding values of the coefficients along with the variables are shown in the “variable” and “B” columns of Table 5. Based on this the prediction equation can be written as (1) For coated tools lnT=1.2575+2.63095lnV-1.98445lns+4.1093lnVlns -0.473(ln
V)” - 024265(1n s)~
(2) For uncoated tools ln T= - 1.84006+6.90073
ln V-2.64032 lns
+2.59143@I
V)(ln s) - 1.09279&I V)Z
+O.l6886(ln
V)2(lns)2-0.12765(lns)2
Figure 1 shows the tool-life surface generated using the present ematical model. This gives the variation of tool-life with the combined of cutting speed and feed. There is a marked increase from 3 to 10 in the tool-life of TiN-coated HSS in all the speed and feed ranges
matheffect times when
272
/
2 Fig.
---
1.
Tool-lie , uncoated
/
3
///j/l/
‘56
II
10
/
20
30 40
m
120
2m
surface showing the combined effect of speed and feed. -, Coated tool; tool; q, s-O.26 mm rev-‘; A, s=O.185 mm rev-‘; 0, s==O.ll mm rev-‘.
compared with that of conventional HSS tools. Table 6 shows very little difference in the observed and calculated values, indicating that the empirical values of the coefficients of variables in the proposed equation can be accepted in the selected ranges of speed and feed. Table 7 shows the comparison of a number of results obtained from ref. 10 and the results of the proposed model. It can be seen that the results are comparable. Hence the proposed model can be used for predicting toollife data. Initially there is a steady increase in tool-life from 3.15 to 20 m min ‘. This is on account of a change in mode of deformation from Type 1 to Type 3 chip formation as shown in Figs. 2(a)-2(c). The proposed tool-life model is based on flank wear as criterion. While cutting using uncoated HSS tools built up edge (BUE) is associated with increased flank wear. This is due to the overhang of the BUE below the cutting edge and the periodic wandering of the strain hardened debris onto the finish surface, abrading the flank of the tool in the process. However, in the present case the TM coating on the tool, with its low adhesion for steel, does not promote any sizeable build-up at the cutting edge. The thin, embryonic BUE, as observed
273 TAEILE 6 Tool-life Test No:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
results Speed (m min-‘)
Feed (mm rev-‘)
3.15 3.15 3.15 8.00 8.00 8.00 20.00 20.00 20.00 30.00 30.00 30.00 40.00 40.00 40.00 50.00 50.00 50.00 60.00 60.00 60.00 80.00 80.00 80.00 120.00 120.00 120.00 180.00 180.00 180.00
T * = Tool-life
0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260 0.110 0.185 0.260
Tii-coated
M42 HSS
Uncoated
Observed In T
Predicted
Observed
Predicted
IItT
ln T*
ln T*
5.828945 5.480639 5.192956 5.991464 5.703782 5.438079 6.086774 5.857933 5.598421 5.886104 5.560681 5.298317 5.783825 5.370638 5.164785
5.818398 5.465259 5.171418 6.028278 5.799092 5.521460 6.192785 5.897792 5.690701 5.900717 5.574024 5.364796
5.323009 4.367534 4.700480 4.744932 4.382026 4.158883 4.158883 3.688879 3.332264 2.708050 2.703050 2.502649
5.226435 4.823701 4.595565 4.827002 4.378455 4.135864 4.160764 3.635363 3.366096 2.757080 2.757080 2.513280
4.382026 4.317488 4.382026 4.700480 4.553876 4.499809 4.442651 4.3 17488 4.605170 3.688879 3.912023 4.094344 3.401197 3.401197 3.806662 2.708050 2.708050 3.688879 2.302585 2.302585 2.128312
4.365119 4.346625 4.362228 4.605170 4.532590 4.444265 4.479181 4.332582 4.642879 3.689014 3.889338 4.046163 3.249226 3.362841 3.851541 2.775335 2.789973 3.672556 2.342902 2.263788 2.130280
5.686313 5.335671 5.121605
M42 HSS
in minutes.
TABLE 7 Type of tools
Coated Uncoated Coated Uncoated
Cutting speed (m min-‘)
40 40 70
Feed rate (mm rev-‘)
0.2 0.2 0.2
Tool-life
(min)
Ref. 10
Proposed equation
230 40 60
220 38 55
tool-life
274
(a)
cc>
Cd)
Fig. 2. Micrographs of chip roots showing three types steel with Tii-coated HSS tools at a feed of 0.26 mm m mir-‘; (b) quasi-discontinuous, V= 8 m min- *; (c) m mir-‘; (d) type 2 (continuous without BUEI), V=80
of chip formation while machining Cl5 rev-‘. (a) Discontinuous type, V= 3.15 type 3 (continuous with BUZZ),V= 20 m min-‘. Original magnification X50.
in Fig. 2(c), is confined to the rake face of the tool and the debris from its ~s~te~ation passes exclusively to the chip underside. Thus ploughing and adhesion wear in the flank of the tool is less in this case and, hence, the increase in tool-life. At lower speeds there is a reduction in the hydrostatic stress, thereby increasing deviotoric stress in the primary shear zone, resulting in discontinuous and quasi-discontinuous chip formation, As the speed is increased further, the temperature effect dominates, which results in flow layer formation and also softening of the tool substrate 171 leading to increased plastic deformation of the tool, causing a reduction
275
in tool-life. On account of the high temperature stability and hot hardness of these coatings, the coated tools perform better even at high speeds where conventional HSS tools cannot work. This results in an increase in the working range of TiN-coated HSS tools.
4. Conclusions (1) The equations are best suited for predicting the tool-life of TiNcoated and conventional HSS tools giving the combined effect of speed and feed. (2) Extrapolation and intrapolation of the tool-life can be done within reasonable limits using the tool-life surface. (3) There is an increase in the tool-life from 3 to 10 times over the whole of the selected speed and feed ranges. (4) The maximum tool-life for both coated and uncoated tools was found to be at a cutting speed of 20 m min- ‘. (5) TiN-coated tools perform better even at high cutting speeds and feeds where conventional tools fail.
References 1 V. A. Tipnis and A. L. Joseph, Testing for machinability, American Society for Metals, Materials/Metal Working Technology, Series No. 7, 1975, pp. l-8. 2 V. A. Tipnis, M. Feld and M. Y. Friedman, Development and use of machinabiity data for process planning optimization, SME Paper No. MS75-5I7, C! Programs, (1975) 1-12. 3 Ibrahim Mahmoud, The effect of some surface treatments on the Iife of high speed steel tools, Wear, 118 (1987) 27-31. 4 S. M. Wu, Tool Iife testing by response surface methodology, J. Eng. Ind. Trans. ASME, 86 (2) (1964) 215-220. 5 J. G. Wager, The use of statistical transformations in assessing the Iife of cutting tools, Ann. CIRP, 25 (1) (1976) 2931. 6 K. Iwata, Y. Murotsu, T. Iwatsubo and S. Fujii, A probabihstic approach to the determination of the optimum cutting conditions, J. Eng. Znd. !Prans. ASME, 94 (4) (1972) 1099-1105. 7 R. Wertheim, R. Sivan, R. Porat and h Ber, Characterisation of CVD coated carbide layers and their thermal properties, Ann. CLRP, 31 (1) (1982) 7-12. 8 E. J. Weller, C. Reitz, J. Montandouin, B. E. Hirsch, H. Zoelzer, W. H. Engelskirchen and E. W. Zimmers, Computerized machinability data systems, in N. R. Parsons (ed.), N/C Machinability Data Systems, SME, Dearborn, MI, 1971, pp. 63-128. 9 P. K. Philip, Study of performance characteristics of an explosive quick-stop device for freezing cutting action, Int. J. Mach. Tool Dee. Res., II (1971) 138-144. 10 F. A. SoIimsn and 0. A. Abu-Zeid, On the improvement of the performance of high speed steel turning tools by TiN coating, Wear, 219 (1987) 199-204.