Applied Mathematics, 2013, 4, 1333-1339
http://dx.doi.org/10.4236/am.2013.49180 Published Online September 2013 (http://www.scirp.org/journal/am)
Application of Response Surfaces in Evaluating
Tool Performance in Metalcutting
Michael R. Delozier
State College, Pennsylvania, USA
Received May 15, 2013; revised June 15, 2013; accepted June 22, 2013
Copyright © 2013 Michael R. Delozier. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
This paper advances the collection of statistical methods known as response surface methods as an effective experi-
mental approach for describing and comparing the tool life performance capabilities of metalcutting tools. Example
applications presented demonstrate the versatility of the power family of transformations considered by Box and Cox
(1964) in modeling tool life behavior as revealed using simple response surface designs. A comparative analysis illus-
trates a method to gauge the statistical significance of differences in tool life estimates computed from response surface
models. Routine use of these methods in experimental tool testing is supported by their ability to produce reliable rela-
tive performance representations of competing tools in field applications.
Keywords: Response Surfaces; Metalcutting; Tool Life; Power Transformations
1. Introduction
Experimental, laboratory-based, tool performance testing
in machining operations is an integral part of the product
development cycle of metalcutting tools. Ideally, such
testing provides information-rich data as a basis for
screening various product designs and evaluating poten-
tial improvements in existing cutting tool materials, or
grades. In an iterative product development process,
testing often ultimately focuses on comparing the per-
formance qualities of candidate designs to those of com-
peting grades over a specified application range of cut-
ting, or operating, conditions. Laboratory testing is thus
an essential performance review to assure that product
design and manufacturing process concepts are correct
prior to field trials in actual applications.
The need to compare performance qualities over a
range of operating conditions suggests the use of expe-
rimental design to efficiently produce the required test
data. The purpose of data analysis is to provide insight to
potentially important performance differences influenc-
ing decisions to pursue further development or field-
testing.
A performance quality measure frequently analyzed in
metalcutting applications is tool life. As varied as the
field applications themselves, so are the criteria used to
determine the end of life of cutting tools in these applica-
tions. In the laboratory, a definition of tool life providing
a common basis for comparing tool performance capa-
bilities is required for useful analysis to proceed. The
definition ordinarily used is the cutting time until the
wear on the tool reaches a pre-specified level, or the tool
catastrophically fails. Depending on test objectives, other
quality measures such as the surface finish of the mate-
rial being machined (the “workpiece” material), the abil-
ity to control chip formation during machining, power
consumed, and the magnitude of various cutting forces
may be analyzed as well.
This paper presents a statistical approach to describe
and compare the tool life capabilities of metalcutting
tools using response surface methods, a coordinated sys-
tem of experimental design, regression analysis, and
graphic presentation. A mathematical approximation mo-
del, often termed a response surface model, expressing
tool life in terms of selected machining variables, whose
settings define the range of cutting conditions of interest,
is developed using test data from a designed experiment.
Examining the model using contour plots provides a
simple analysis of tool performance capabilities with
respect to changes in these variables.
2. Empirical Models of Tool Life Behavior
Consideration of the dominant features of tool life data is
basic to proper model formulation. Over sufficiently
wide ranges of cutting conditions, tool life tends to ex-
C
opyright © 2013 SciRes. AM

M. R. DELOZIER
1334
hibit non-linearity of relationship and changing variabil-
ity as illustrated in Figure 1 adapted from Fung [1].
Power transformations of the response are often effective
in modeling such behavior. Therefore, we tentatively
assume that the relationship between tool life, y, and k
independent machining variables, 12
,,,
k
ξξ ξ
, can be
adequately represented by
()
()
,
ii
yg
λ
ε = ξθ
i
+, (1)
where if , if , i
()
yy
λλ
=0 λ ≠
()
()
lnyy
λ
=0 λ =
ξ
is a vector of independent variable settings determining
the ith experimental test condition, is a vector of pa-
rameters to be estimated from data, g is a low-order po-
lynomial in the
θ
ξ
’s, and the i are statistical errors
that follow, at least approximately, the usual linear model
assumptions.
ε
As noted in Balakrishnan and DeVries [2], many re-
searchers have advocated the use of a linearized tool life
model of the form
() ()
0
ln ln
ijji
j
y θθ
i
ξ
ε =+ +
, (2)
or an extension including second-order terms in the loga-
rithms of the
ξ
’s. The model in (2) is a linearized gen-
eralization of the tool life-cutting speed relationship
proposed by F. W. Taylor for high-speed steel cutting
tool materials in the early 1900s. While in some applica-
tions these models may be useful, there does not seem to
be any particular reason why model adequacy and sim-
plicity of relationship will always be achieved in the lo-
garithms. The form given in (1) thus provides a flexible
alternative starting point for the modeling process.
Figure 1. Schematic of tool life variability at three cutting
conditions with an end of life wear c riterion w0.
3. Applications
3.1. Application 1: A Power Family Model of
Tool Life
3.1.1. Description of Application and Experimental
Design
Metalcutting tools are designed and manufactured in
various sizes and shapes to service a wide range of ma-
chining applications. In addition, various geometric “chip
control” designs may be shaped on the tool surface in
manufacturing to control workpiece chip formation dur-
ing cutting operations, and to enhance tool life. This ap-
plication is part of a larger evaluation aimed at charac-
terizing the effects of three variables, cutting speed, feed
rate, and depth of cut, on the tool life performance of
tools produced with certain chip control designs.
The tool life data given in Table 1 are the results of a
completely randomized experiment for a cutting tool
grade manufactured with a commonly used chip control
design. The machining operation was turning a medium-
carbon steel workpiece material on a lathe (Figure 2). A
central composite test design with the axial points lo-
cated at the center of each face of the unit cube (Figure 3 )
was used to produce the required tool life data. A desir-
able characteristic of this design is its ability to estimate
higher-order effects with only three levels for each factor.
Also, this design provides protection from having fea-
tures of fitted models strongly influenced by one or a few
test results remotely positioned in the factor space—as
may occur say in using a rotatable central composite de-
sign having unreplicated axial points located outside the
unit cube. Replications at locations other than the design
center were run to reveal the approximate variance pro-
file across the operating range, and so obtain more realis-
tic estimates of tool life uncertainty. As is common in
practice, the levels of the three factors are coded giving
Figure 2. Schematic of a turning operation in machining.

M. R. DELOZIER 1335
Table 1. Cutting conditions and test results.
Speed (sfm) Feed (ipr) Depth of Cut (in) Tool Life (min)
650 0.010 0.050 49.4
650 0.010 0.050 31.5
650 0.010 0.050 50.0
800 0.010 0.050 22.0
800 0.010 0.050 30.0
800 0.010 0.050 31.9
650 0.026 0.050 10.0
650 0.026 0.050 8.1
650 0.026 0.050 6.5
800 0.026 0.050 3.2
800 0.026 0.050 1.4
800 0.026 0.050 2.3
650 0.010 0.200 32.2
650 0.010 0.200 29.1
650 0.010 0.200 40.2
800 0.010 0.200 13.5
800 0.010 0.200 14.1
800 0.010 0.200 14.6
650 0.026 0.200 1.4
650 0.026 0.200 1.5
650 0.026 0.200 1.5
800 0.026 0.200 0.5
800 0.026 0.200 1.1
800 0.026 0.200 0.6
650 0.018 0.125 9.1
800 0.018 0.125 3.0
725 0.010 0.125 27.5
725 0.026 0.125 2.9
725 0.018 0.050 12.3
725 0.018 0.200 4.2
725 0.018 0.125 7.1
725 0.018 0.125 8.6
725 0.018 0.125 4.2
Note: Speed is in units of surface feet per minute (sfm), feed in inches per
revolution (ipr), depth of cut in inches, and tool life in minutes. Tool life is
determined by the first occurrence of 0.015 inch uniform flank wear, 0.004
inch crater depth, 0.030 inch localized wear, or catastrophic failure.
Figure 3. Central composite design used in Application 1.
as predictors x1 = (speed − 725)/75, x2 = (feed − 0.018)/
0.008, and x3 = (depth − 0.125)/0.075.
3.1.2. Response Surface Model Selection
The adequacy of the model
 0.2
12
2
232 123
1.44790.12700.38230.1308
0.04160.1143 0.0431
yxx
3
x
x
xxxx
=− −−
−++x
)
(3)
is easily established. The “hat” notation is used to denote
predicted values determined by the estimated regression
equation.
A normal probability plot of the residuals and a plot of
the residuals against the fitted values show the success of
transformation as a remedy for error pattern inadequacies
characteristic of fits in the original response scale. The
estimated coefficients are highly significant (maximum
p-value for the individual t-tests of significance is 0.007)
and a large proportion of the total variation in the trans-
formed tool life values is explained by the fit (R2 is
0.975). Lack of fit is not indicated by either the pure er-
ror test or the Minitab Statistical Software data subsetting
test [3].
In this application, an initial model screening was car-
ried out using stepwise variable selection for chosen
over the interval −1 to +1. Subsequent ranking of various
models terminating the stepwise algorithm was based on
computing
λ
(
2
ˆ PRESS ii
i
yy
−
=−
. (4)
Here, is the predicted value of yi from a fit using
all of the data except the ith case (the ith tool life observa-
tion and its associated independent variable settings).
Better models have relatively small values of PRESS.
Allen [4] gives a computational form for PRESS easily
adapted to the class of models considered in (1).
ˆi
y−
From this heuristic evaluation, several candidate mod-
els were selected for further analysis to evaluate ade-

M. R. DELOZIER
1336
quacy of fit. There appears to be little difference between
response scales regarding simplicity of relationship for
these data. The need to include interactions and, in some
cases, a quadratic effect in models to describe tool life
behavior was evident for all transformations tried. The
model in (3) has a PRESS of 649, which is slightly larger
than the smallest found (a model with the square root of
tool life as the response with a PRESS of 641 was found).
The model given in (3) was ultimately chosen over that
with the slightly smaller PRESS as a result of its some-
what more pleasing residual patterns.
Contour plots (not included) displayed in the original
units of the response show the loci of cutting conditions
giving specified estimates of the median of the predictive
distribution of tool life. Overlaying such contour plots
generated from the approximation models of several chip
control designs provides a simple means to simultane-
ously compare performance characteristics.
3.2. Application 2: A Comparative Tool Life
Analysis
3.2.1. Description of Application and Experimental
Design
This application is part of a larger metalcutting product
performance evaluation. The test objective was to obtain
a tool life comparison of two competing cutting tool
grades, Grade A and Grade B, in turning a medium-car-
bon steel workpiece material on a lathe. Tests for each
grade were set up using a 22 factorial plus center point
design with the factors cutting speed and feed rate. The
test runs were collectively randomized and the tool life
for each grade recorded as shown in Table 2. In this ap-
plication, the levels of the factors are coded giving as
predictors x1 = (speed − 650)/150 and x2 = (feed −
0.021)/0.006.
3.2.2. Prel i m inary Rem arks
Inspection of the test data suggests that statistically (and
likely practically as well) meaningful differences in tool
life level favoring Grade A may exist at and about the
center of the factor space. Without formal statistical
treatment of the data, a benefit of using the experimental
design is immediately realized. That is, important relative
performance information may have remained concealed
if testing was limited exclusively to say near the center,
or near an extreme, of the factor space.
3.2.3. Response Surface Model Selection
For each respective tool grade, ranking of the first-order
and first-order plus interaction fits based on PRESS for
chosen over the interval −1 to +1 was carried out to
provide guidance in model selection. A complete listing
of computational results is not given. To summarize, the
models found to have the smallest PRESS for Grade A
λ
Table 2. Cutting conditions and test results.
Tool Life (min)
Speed (sfm) Feed (ipr)
Grade A Grade B
500 0.015 67.0 84.4
500 0.015 101.9 91.2
500 0.015 63.6 66.7
800 0.015 23.5 16.0
800 0.015 17.6 15.2
800 0.015 21.3 17.6
500 0.027 17.9 24.6
500 0.027 25.3 15.3
500 0.027 25.4 30.4
800 0.027 0.4 1.1
800 0.027 0.6 0.5
800 0.027 0.5 0.9
650 0.021 21.4 11.8
650 0.021 19.2 8.9
650 0.021 22.6 10.6
Note: Speed is in units of surface feet per minute (sfm), feed in inches per
revolution (ipr), and tool life in minutes. Tool life is determined by the first
occurrence of 0.015 inch uniform flank wear, 0.004 inch crater depth, 0.030
inch localized wear, or catastrophic failure.
and Grade B respectively are the first-order fit with
of 0.5 (PRESS of 1607) and the first-order plus interac-
tion fit with of −0.1 (PRESS of 983). However, the
PRESS value for the first-order plus interaction fit for
Grade B with of 0 is not very different from the
smallest found. Thus, the suitability of this fit as a model
of the tool life performance for Grade B is examined.
λ
λ
λ
A likelihood-based procedure for estimating from
the data is given by Box and Cox [5]. Its application via a
Minitab Statistical Software macro for the first-order and
first-order plus interaction model forms for Grades A and
B respectively indicates general agreement with the
rankings based on PRESS. Moreover, in the case of
Grade B, tests for lack of fit suggest that the interaction
term is not removable by transformation.
λ
A normal probability plot of the residuals and a plot of
the residuals against the fitted values for the first-order
plus interaction fit with the logarithm of Grade B tool life
as the response indicate no model inadequacy. These
diagnostic plots for the first-order fit with the square root
of Grade A tool life as the response are not as satisfying,
seemingly due in part to the effect of the largest observa-
tion. However, to avoid suppressing variation at a condi-
tion expected to show sizable response variation, we re-

M. R. DELOZIER 1337
tain this case and select a model otherwise fitting the data
well. The first-order model with the square root of tool
life as the response appears to fit well and is selected.
Soothing the decision to retain this observation is that a
later test at this condition resulted in a tool life of nearly
two hours.
A comparative performance analysis will thus be
based on the following models for Grade A and Grade B
respectively:
 0.5
1
4.67262.06531.9582
A
yx=−− 2 x
12
(5)
()

12
ln2.4778 1.23631.07240.4384.
B
y
xx=− −−xx (6)
3.2.4. A Comp ara ti ve Analysis Method
A simple comparative analysis may be carried out by
superimposing contours of the estimated response sur-
faces and observing the relative position of contours of
equal estimated median tool life. Figure 4 shows the
superimposed contours of 10, 30, and 50 minutes median
life. The apparent effect that Grade A is capable of oper-
ating at higher and more productive cutting conditions,
while yielding the same median life as Grade B, is most
prominent near the center of the factor space. That is,
meaningful performance differences favoring Grade A
are likely to exist in this region. However, without an
indication of the variability associated with these esti-
mates, differences that are statistically important, or sig-
nificant, are not discernible.
An ad hoc, but useful analysis approach that incorpo-
rates the variability of model estimates in comparing
performance differences is to form the surface that is the
difference of the two tool life models standardized by a
measure of uncertainty. A contour plot of this surface is
useful in identifying regions of test conditions where the
differences in estimated median response are large rela-
tive to uncertainty.
A slight complication occurs in cases such as this
where the models are fit using different response trans-
formations. Sensible choices of a common scale in which
to compare differences include that of either model, or
the original response scale. In any case, a first-order
propagation of error approximation can be used to esti-
mate variability in an alternate scale.
In this application, an analysis in the logarithmic scale
may be carried out by plotting contours of the surface
(Figure 5)
()

()
()

{
}
()
0.5
0.5
12
, 2lnln
ABA
xxyyV V δ

=− +

 B
, (7)
where

()


(
2
0.5 0.5
4Var
AA
Vy y

=

)
A
and

()

Var ln
B
V

=
B
y.

Var denotes the estimated variance
of the indicated argument.
Loosely speaking, we shall say statistically significant
Figure 4. Selected contours of the estimated tool life sur-
faces for the cutting tool grades in Application 2.
Figure 5. Contours of the estimated significance surface.
performance differences exist at test conditions where
this surface is sufficiently high or low. In practice, condi-
tions with 3 δ ≥ have been found to adequately ap-
proximate those exhibiting meaningful tool life differ-
ences in similar field applications. The egg-shaped re-
gion about the center of the factor space shown in Figure
6 has indicating a tool life advantage for Grade A.
It is reassuring to find that analysis in either the square
root or the original scale identifies essentially the same
region of tool life advantage for Grade A (Figure 7).
3 δ ≥
Extension of this analysis to more than two independ-
ent variables is straightforward though visualizing the
results would require multiple contour plots, each gener-
ated with the variables chosen to be “off-axis” fixed at
desired settings.
3.3. Application 3: Adaptability to a More
Complex Testing Situation
In some situations where appreciable systematic varia-
tion in the test environment is expected, test methods
may be suitably modified to yield useful performance

M. R. DELOZIER
1338
Figure 6. Significance region superimposed on the contours
of the estimated tool life surfaces.
Figure 7. Significance regions for comparisons in the origi-
nal, square root, and logarithmic scales.
information. For example, consider a situation where a
tool life comparison of two grades is desired. Expecting
substantial variation in workpiece material properties
(e.g., hardness) over the course of testing, consider gen-
erating tool life data as follows: a cutting edge of one of
the two grades is run for a predetermined “short” cutting
time, using a machining condition randomly selected
from those prescribed in the experimental design, and
tool wear measurements made; an edge of the competing
grade is run for the same time and wear measurements
made; the edge first run is run again for the same time
and wear measurements made; the competing edge is run
again for the same time and wear measurements made;
and so on until the end of life is reached for both grades.
This procedure is repeated at each respective condition.
Testing in this “back-to-back” manner creates a paired
data structure. Actual tool life data generated in this fa-
shion are given in Table 3. In this data set, the cutting
edges resulting in 89.1 and 29.6 minutes tool life were
run together, 56.9 and 20.5 were run together, 8.4 and 3.5
were run together, and so on.
Table 3. Cutting conditions and test results.
Tool Life (min)
Speed (sfm) Feed (ipr)
Grade C Grade D
400 0.015 89.1 29.6
400 0.015 56.9 20.5
700 0.015 8.4 3.5
700 0.015 10.2 4.3
700 0.015 5.1 2.5
400 0.025 34.0 19.0
400 0.025 24.0 20.1
400 0.025 22.0 18.2
400 0.025 24.5 15.6
700 0.025 1.9 1.3
700 0.025 1.6 2.0
700 0.025 1.5 1.1
550 0.020 10.8 7.6
550 0.020 11.2 2.7
550 0.020 8.7 5.6
550 0.020 12.0 6.6
Note: Speed is in units of surface feet per minute (sfm), feed in inches per
revolution (ipr), and tool life in minutes. Tool life is determined by the first
occurrence of 0.015 inch uniform flank wear, 0.004 inch crater depth, 0.030
inch localized wear, or catastrophic failure.
For brevity, an analysis of the data in Table 3 is not
pursued. An analysis approach that has generally pro-
vided satisfactory results in practice involves developing
an approximation model using the differences between
paired observations as the response values. Estimated
differences computed from this model, standardized by a
measure of uncertainty, can then be used to assess statis-
tical significance over the operating range covered by the
test.
4. Summary
This paper advances response surface methodology as an
effective experimental approach for describing and com-
paring the tool life performance capabilities of metalcut-
ting tools. Such an approach provides a means of identi-
fying important performance differences between the
tools tested over a specified range of operating condi-
tions. Several example applications demonstrate the ver-
satility of the power family of transformations in model-
ing tool life behavior as revealed using simple response
surface designs. A comparative analysis application il-
lustrates a method to gauge the statistical significance of
differences in tool life estimates computed from response

M. R. DELOZIER
1339
surface models. Therefore, test conditions producing sta-
tistically important differences may be identified thus
approximating operating regions of field performance
strength or weakness. Routine use of these methods in
experimental tool testing is supported by their ability to
produce reliable relative performance representations of
competing tools in field applications.
5. Acknowledgements
The tool performance modeling and analysis methodol-
ogy presented in this paper was contributed by this au-
thor, now retired, while a statistician supporting product
research and development, and process improvement, for
a leading manufacturer and supplier of metalworking
tools and tooling systems. The author gratefully ac-
knowledges this former employer for the opportunity to
develop and contribute this work, and as the source of the
schematic displayed in Figure 2.
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Manufacturing Research Conference Proceedings, Soci-
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[3] Minitab, Inc., “Minitab User’s Guide 2: Data Analysis
and Quality Tools—Release 12,” 1998.
[4] D. M. Allen, “The Prediction Sum of Squares as a Crite-
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Number 23, Department of Statistics, University of Ken-
tucky, 1971.
[5] G. E. P. Box and D. R. Cox, “An Analysis of Transfor-
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