A comprehensive tool-wear/tool-life performance model in the ...

ARTICLE IN PRESS
International Journal of Machine Tools & Manufacture 48 (2008) 878–886
www.elsevier.com/locate/ijmactool
A comprehensive tool-wear/tool-life performance model in the
evaluation of NDM (near dry machining) for sustainable manufacturing
P.W. Marksberry a, , I.S. Jawahir b,1
a Center for Manufacturing, College of Engineering, University of Kentucky, UK Center for Manufacturing, 414G Robotics Building (Office 414L),
Lexington, KY 40506-0108, USA
b Department of Mechanical Engineering, College of Engineering, University of Kentucky, 414C Center for Robotics and Manufacturing Systems 0108,
414C Robotics Building (Office 414C), Lexington, KY 40506-0108, USA
Received 23 January 2007; received in revised form 14 November 2007; accepted 19 November 2007
Available online 31 January 2008
Abstract
Traditionally, metal working fluids (MWF) are known to improve machining performance despite poor ecological and health side
effects. A new sustainable process that has minimized the use and application of MWFs is NDM (near dry machining). Although there is
much controversy on the effectiveness of NDM, it is agreed that a lack of science-based modeling prevents its widespread use. This paper
presents a new method to predict tool-wear/tool-life performance in NDM by extending a Taylor speed-based dry machining equation.
Experimental work and validation of the model was performed in an automotive production environment in the machining of steel wheel
rims. Machining experiments and validation of the new equation reveal that tool-wear can be predicted within 10% when the effect of
NDM is statistically different than dry machining. Tool-wear measurements obtained during the validation of the model showed that
NDM can improve tool-wear/tool-life over four times compared to dry machining which underlines the need to develop sustainable
models to match current practices.
r 2007 Elsevier Ltd. All rights reserved.
Keywords: NDM (near dry machining); Automotive; Sustainable manufacturing; Tool-wear/tool-life; Predictive modeling
1. Industry background and relevance
The US utilization of metal working fluids (MWFs) is
estimated at 100 million gallons annually and the current
worldwide consumption is 640 million gallons. It is estimated
that 52% is used for machining purposes and 31% is applied
to stamping processes [1]. In machining operations, MWFs
reduce friction between the cutting tool and the workpiece,
prevent galling, protect surface characteristics, reduce surface
adhesion or welding, balance heat generation effects and
flush away swarfs, chips, fines and residue [2].
However, there are serious drawbacks with the use of
MWFs: cost and negative health effects. The costs for
Corresponding author. Tel.: +1 859 257 6262x409.
E-mail addresses: marksberry@mfg.uky.edu (P.W. Marksberry),
jawahir@engr.uky.edu (I.S. Jawahir).
URL: http://www.engr.uky.edu (P.W. Marksberry).
1 Tel.: +1 859 257 6262x207
purchase, care and disposal of MWF are two times higher
than machining cost and can represent up to 17% per part
in the manufacturing of automotive components [1]. Itis
estimated that 1.2 million workers are potentially exposed
to the hazardous/chronic toxicology affects of MWFs [3].
MWF impact on health is significant in five main areas:
known occupational health effects, suspect occupational
health effects, occupational health trend, global environmental
performance and machining economics [4–9]. Thus,
operations concerned with sustainability need to look for
alternatives.
Near dry machining (NDM) is a sustainable manufacturing
technique that is safe for the environment [10–12],
the worker [13] and is cost effective [14]. The minimization
of MWFs is a direct indicator of sustainable manufacturing.
The goal of NDM is to machine parts using a minimal
amount of MWF so that the workpiece, chips and
environment remain dry after cutting. In NDM, new
MWF is applied to the cutting zone in a precise and
0890-6955/$ - see front matter r 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ijmachtools.2007.11.006

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controlled manner in a flow of compressed air that
is completely vaporized by the heat generated in the
cutting process. MWF can be reduced up to 10,000 times
compared to traditional flood cooling providing a
significant environmental and occupationally impact.
Unfortunately, the application of NDM is very difficult
due to several factors. First, NDM can be applied
in a variety of applications and offers a broad operating
range. Users of NDM have the option to select a
wide range of pressures, volumetric flow rates, nozzle
configurations and MWF types. Depending on the
parameter selection, various results (positive and negative)
have been reported which is summarized in
Table 1. In all cases, the range of operation in NDM
has a tremendous effect on the application and offers
a much smaller process window than traditional flood
cooling.
Second, models to predict machining performance
through parameter selection do not exist for NDM. The
wide spread use and application of NDM has been slowed
mainly due to three unanswered delivery parameter
questions:
1. How much MWF should be applied?
2. Where and how do I apply the MWF?
3. What type of MWF should I apply?
Thus, there is a need to develop a scientific approach in
selecting the optimal delivery parameters and cutting
conditions for machining performance that considers both
the cutting mechanics and the mist spray delivery. It is the
goal of this work to develop, validate and apply a
performance-based model in NDM that answers those
delivery questions. The use of a model will also serve to
Table 1
Literature review in NDM detailing varying results associated with operating range
Area Description of previous work Process range Comment
Health and
safety
Brinksmeier and Brockhoff [10] observed in oscillating
grinding that NDM should only be used with an air suction
due to aerosol concentration levels.
The work of Gressel [15] reinforced earlier findings
quantitatively by completing experiments while turning and
drilling steel. His work demonstrated that concentrations
exceeded NOISH REL (recommended exposure limits)
limits.
Marksberry [13] indicated that air pressure, MWF
volumetric flow rates and MWF type have a profound
effect on concentrations. MWF volumetric flow rates
should stay below 200 ml/h with pressures below 0.034 MPa
(5 psi) to prevent total mass particulate from reaching the
5 mg/m 3 REL when using oil-based MWFs. Water soluble
MWFs can employ a larger process window at double to
triple the MWF rates and pressures.
Pressure: 0.6 MPa (87 psi),
MWF rate: 30 ml/h
Pressure: not cited, MWF
rate: 660 ml/h
Pressure: 0.034 MPa (5 psi),
MWF rate: 200 ml/h
Need air suction due to aerosol
concentration levels when using NDM.
NDM exceeded NOISH REL limits.
NDM is safe if pressures are low and
MWF rates are low.
Process
Heisel et al. [11] stated that nozzle location in NDM has less
importance on tool-life/tool-wear when machining steel.
Wakabayashi et al. [12] concluded that NDM provided
almost equivalent advantages compared to flood cooling
when machining steel using dual nozzles at the rank face
and flank face.
Marksberry [14] indicated that nozzle position has a direct
effect on the process and greatly depends on the chip flow
path and the nozzle to cutting tool to workpiece
relationship.
Nozzle position: flank, rank,
chip
Nozzle position: flank, rank
Nozzle position: flank, rank,
chip
Nozzle position is not a sensitive control
variable.
Multiple nozzle positions aimed at the
rank and flank face provide benefit.
Obstructions from the workpiece, chip
flow path and cutting geometry greatly
affect NDM effectiveness.
Machining
performance
Scandiffio [16] observed that NDM did not offer any
improvement over conventional flood cooling when turning
steel at high cutting speeds.
Rahman et al. [17] demonstrated that NDM could provide
comparable results to flood when milling at low feed rates,
low speeds and depths of cut.
Chen et al. [18] observed that tool-wear could be reduced
over dry machining in turning stainless.
Marksberry [14] completed work that shows that tool-wear/
tool-life can be greatly improved by the use of NDM up to
four times compared to dry machining when machining
steel when directed at the dominate tool-wear pattern.
Process: turning steel
Process: milling steel
Process: turning stainless
Process: turning steel
Tool-wear did not improved compared
to flood cooling using NDM.
Tool-wear did not improved compared
to flood cooling using NDM.
Tool-wear improved over dry machining
using NDM.
Tool-wear improvement greatly depends
on the cutting geometry and machining
application.

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reduce machining cost associated in NDM and lesson the
environmental impact of manufacturing operations.
2. Machining modeling background
Several attempts have been made to develop methods
[19–23] for accurately predicting the effects of machining
operations over the past several decades. A common
approach for assessing machining performance is toolwear/tool-life.
Tool-wear/tool-life is one of the most
significant and necessary parameters required for process
planning and total machining economics. A review of
numerous theoretical and experimental techniques for
predictive assessment of tool-wear and tool-life reveals
that eight different types of tool-wear/tool-life relationships
are commonly being used for dry machining, as
shown in Table 2.
The trend in tool-wear/tool-wife modeling has been to
extend Taylor’s basic equation. This is mainly due to the
direct relationship between cutting speed and tool-wear/
tool-life. This relationship holds true for all machining
operations and is considered as a basis for more advanced
models. Currently, a model does not exist for NDM and no
attempts have been made to extend dry machining toolwear/tool-life
models. It is the author’s goal in this work to
select the most appropriate dry machining model and
extend it for NDM.
The appropriate selection of a dry machining model for
extension to NDM depends on various factors. First, it is
important that the model be robust to accommodate a wide
range of machining parameters and applications. The
model should be easily modified and practical for industry
applications where complete machining data is not available.
Lastly, the model should be accurate, repeatable and
suited for industrial use where experimental constants can
be generated without using sophisticated equipment; such
as tool dynameters to calculate forces, high-speed film to
understand complicated chip flow paths and electron
microscopes to determine residual stress patterns and
material structure.
Table 2
Summary of tool-wear and tool-life models for dry machining
No. Tool-life/tool-wear equation Determination of
constants
1 Taylor’s basic equation: VT n ¼ C C and n are
experimentally determined
and currently available
from many reference
sources
2 Taylor’s reference-speed based equation: V=V R ¼ðT R =TÞ n n is experimentally
determined and currently
available from many
reference sources
3 Taylor’s extended equation: T ¼ C 2 =ðV P f q d r Þ All constants (C 2 , p, q and
r) are experimentally
determined
4 Temperature-based tool-life equation: yT n ¼ C 3 n is found between 0.01
and 0.1 and C 3 is
experimentally determined
5 Taylor’s extended equation including cutting conditions and
tool geometry: C /½ðcot b tan aÞ n Fða; bÞ 1= 1
Š
6 Taylor’s extended equation including cutting conditions and
workpiece hardness: T ¼ C 4 V n f m d P r q s t i u j x
7 Taylor’s extended equation including cutting conditions and
workpiece hardness: V ¼ C 5 =ðT m f y d x ðBHN=200Þ n Þ
8 Taylor’s extended equation including chip groove effect factor
and a tool coating effect factor:
The influence of a and b
can be theoretically
determined as partial
contribution to Taylor’s
constant C
Requires excessive toollife
testing to determine all
constants (C 4 , n, m, p, q, t,
u and x)
All constants (C 5 , m, y, x
and n) are experimentally
determined
Comment
Most widely used
equation; however, C and
n apply to a particular
tool–workpiece
combinations
n applies only to
particular tool–workpiece
combinations
Gives better accuracy
than Taylor’s basic
equation, but more toollife
tests are required
Although the equation is
set only on an empirical
basis, it is not convenient
for practical use in the
shop floor environment
A complicated
relationship between toollife
and rake/clearance
angles
It is claimed that the data
for setting up the
equation are generated
from both laboratory and
industrial sources
It is claimed to be a good
approximation for toollife
ranges of 10–60 min
Ref.
Mills and Redford
[24], Schey [25]
Mills and Redford
[24]
Niebel et al. [26],
Hoffman [27]
Quinto [28], Oxley
[29]
Lau et al. [22]
Venkatesh [21]
Wang and Wysk
[30], Hoffman [27]
T ¼ T R W g ðV R =VÞ W cð1=nÞ ; where W c ¼ n=n c and W g ¼ km=f n 1
d n 2
Constants (k, n 1 , n 2 and n c ) are experimentally determined; m is the machining
operation effect factor (with m ¼ 1 considered for turning)Extends the Taylor-type equation to include two new factors: tool coating effect factor and
chip-groove effect factor. Equation also includes the effects of feed, depth of cut and cutting speed.Jawahir et al. [31], Li et al. [32]

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3. The new tool-wear/tool-life relationship for NDM
A recent advance in tool-wear/tool-life modeling for
dry machining is the work of Jawahir et al. [31] and Li
et al. [32]:
km V ð1=nc Þ
R
T ¼ T R
f n 1
d n , (1)
2
V
where T is tool-life or tool-wear, T R is the reference
tool-life or tool-wear for 1 min, V is the cutting speed,
V R is the reference cutting speed for 1 min of tool-life
or tool-wear, n c is the coating effect factor and W g is
the chip-groove effect factor, represented as km=f n 1
d n 2
. W g
is a function of feed (f), depth of cut (d), tool nose radius,
chip breaker configurations and the type of machining
operation (m) with m ¼ 1 for turning. n 1 , n 2 and k are
empirical constants. Outputs and constants of this model
can be easily generated using a series of machining trials
(o10) while varying depth of cut, feed, and cutting speed
while achieving accuracies on the order of 90%.
Modifications to the tool-coating effect factor, n c , is
possible by including NDM parameters to the series of
trial experiments.
Extension to the dry machining model is shown
below:
km V ð1=nc Þð1=N NDM Þ
R
T ¼ T R
f n 1
d n , (2)
2
V
where N NDM is the NDM effect factor and is expressed as
N NDM ¼ n mist
n c
, (3)
where n mist is the modified coating factor for NDM mist
spray. n mist can be defined as the following:
log V 1 log V 2
n mist ¼
, (4)
logðG F;W;N;MZX ;M ZY
Þ log T 1
where G F;W;N;MZX ;N ZY
is the new modified tool-wear/tool-life
value in NDM, empirically derived while varying MWF type,
MWF volumetric flow rate and nozzle(s) position. Subscripts
of the G function: F, W, N, M ZX and M ZY each represent a
modified tool-wear value that is empirically derived. Table 3
explains each mapping function and how it is derived.
3.1. Derivations of the M ZX and M ZY effect factors
M ZX and M ZY effect factors can be solved by linearization
of actual tool-wear/tool-life results as shown in Fig. 1.
MWF rate (denoted X rate in units ml/h along the X-axis)
represents the actual volumetric flow rate of the MWF. The
performance and linkage of MWF type can be accomplished
by using a standard MWF evaluation method. In
this work, a standard tapping torque test method was used
(denoted as Y typ in units N cm along the Y-axis). Variations
of the standard method were also developed to characterize
cooling and lubrication behaviors of MWFs.
Having obtained M ZX and M ZY , G F;W;N;MZX ;M ZY
can be
solved using Eq. (5):
G F;W;N;MZX ;M ZY
¼ T NDM ½ðM ZX ÞðX rate b min Þ
þðY type a min ÞðM ZY ÞþZ 1 Š, ð5Þ
where G F;W;N;MZX ;M ZY
40, T NDM is the predominate toollife
of the cutting tool, Z 1 is the intercept of the effect
Table 3
G function explanation
Mapping
function
(subscript)
Definition Comment Categories Method to calculate
F Ideal MWF function MWFs are often classified as
‘‘coolants’’ or ‘‘lubricants’’
W
Dominant tool-wear
pattern
Tool-wear pattern responsible for
catastrophic failure or end of life
N Nozzle(s) position MWF source direction and
distance to cutting zone
M ZX MWF rate effect factor Linearization of tool-wear and
MWF vol. flow rate data
M ZY MWF type effect factor Linearization of tool-wear and
tapping torque data from MWF
G C , cooling (water
miscible); G L , lubrication
(non-water miscible)
W BL , length of groove
backwall wear
Single nozzle: R, rake
face; F, flank face; C, chip
M ZX
M ZY
Collect torque test data (N m) for
each MWF using the ASTM D 5619
tapping torque test standard with
reamed holes at 5.48 and 5.55 mm.
Measurements using new method for
assessing tool-wear.
Vector representation of nozzle to
cutting zone (include three
dimensional angle and distance to
cutting zone from nozzle tip)
Calculate slope of axis:
1. Tool-wear
2. MWF volume flow rate
Calculate slope of axis:
1. Tool-wear
2. Tapping torque results from
MWF

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factors, b min is the minimum MWF volumetric flow rate
used in the construction of the data set, and a min is
the minimum MWF tapping test torque value used in the
construction of the data set.
MWF Type
Effect Factor
(Slope) M ZY
Y-Axis
MWF
Type (N-cm)
Y type
X-Axis
MWF
Rate (ml/hr)
X rate
4. Experiment set-up
Experimental work was carried out on a CNC turning
center in a high volume automated environment for the
machining of steel wheel rims. The approach for assessing
tool-wear is shown in Fig. 2 [31] and is a comprehensive
method for evaluating grooved tools that goes beyond the
outdated ANSI/ASME B 94.55 M and ISO 3685 standard.
The predominant tool-wear pattern was determined to
be W BL (length of groove backwall wear). An example of
experimental set-up can be seen in Fig. 3, detailing the
nozzle to workpiece to cutting tool orientation.
A standard CNMG 432 ESA AC2000 tool holder was
used in the experimental work. The workpiece material was
HSLA (high strength low alloy) steel; SAE 070 Y
equivalent. Fixed machining and mist spray delivery
parameters can be seen in Table 4.
4.1. Experimental procedure
Z 1
Z-Axis
Tool-Wear
W BL or W BW
MWF Rate
Effect Factor
(Slope) M ZX
Fig. 1. Linearization of MWF rate and type effect factors.
Over 400 experiments have been conducted to establish
the machining constants and to validate the new tool-wear/
tool-life predictive model for NDM. The factors under
study can be seen in Table 5 and were deliberately changed
in a controlled manner. Because there is skepticism about
the delivery of NDM for optimal tool-wear/tool-life
performance, a complete balance factorial experimental
pattern was performed.
NW2
N
A
NL2
BL
BW
A- A
5W
A
NW1
SD
KT
VB
NL1
[1] VB = Flank Wear Land
[2] BW = Width of Groove Backwall Wear
[3] BL = Length of Groove Backwall Wear
[4] KT = Crater Depth Wear
[5] SW = Width of Secondary Face Wear
[6] SD = Depth of Secondary Face Wear
[7] n = Nose Wear
[8] NL1 = Notch Wear Width on Main Cutting Edge
[9] NW1 = Notch Wear Width on Main Cutting Edge
[10] NL2 = Notch Wear Length on Secondary Cutting Edge
[11] NW2 = Notch Wear Width on Secondary Cutting Edge
Fig. 2. New approach to measuring combined tool-wear patterns for grooved tools [31].

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-Y (j)
4
80˚
+Y (j)
1
2
3
~50-mm
5
16°
+X (i)
+X (i)
4
1
2
5
3
-Z (k)
[1] Removable Hardened Steel Nozzle Holder
[2] Stainless Steel Adjustable Nozzle
[3] Tool Holder
[4] Indexable Cutting Tool Insert
[5] Tool Holder Carousel (4 qty)
Nozzle to Cutting Tool Relationship (vector notation)
i, j, k (+X, +Y, +Z) Vectors (+/- 2-mm)
i. = -48-mm j. = -8-mm k. = -14-mm
Fig. 3. Nozzle to cutting tool to workpiece orientation.
Table 4
Fixed machining and mist spray delivery parameters
Table 5
Design of experiments
Parameter
Description
Control factors Levels Description of levels
Cutting tool
material
Cutting tool
geometry
Cutting
parameters
Al 2 O 3 , aluminum oxide (supplier: Kennametal CNMG
432 ESA AC2000)
Type: grooved chip breaker
Relief angle: 01 (side), 01 (end)
Cutting edge angle: 51 (side), 51 (end)
Tool nose radius: 0.8 mm
Depth of cut: 3.2 mm
Cutting speed: 4 m/s
Feed: 0.3 mm/rev
Metal removal rate: 11,520 mm 3 /s
MWF type Straight oil, Cooluble 2210
Soluble—water miscible, Cimperial 1011
Semi-synthetic—water miscible, TRIM SC235
Synthetic—water miscible, Hocut 763
Atomization
system
Atomization type: co-axial air blast atomization
Machine: MSK-G 100—MicroJet
Air pressure: 5 psi
Nozzle: stainless steel
The 95% confidence interval was determined at 0.85 and
1.00-mm for the dominant tool-wear pattern (W BL ) for dry
cutting using a sample size of 20.
Modifications to the ASTM tapping torque procedure
were carried out to distinguish the primary MWF functions
using two different reamed hole diameters 5.55 and
5.48 mm to characterize cooling and lubricity, respectively.
Fig. 4 shows the ASTM tapping torque results for each of
MWF volumetric flow rate (ml/h) 4 (1) 20
(2) 40
(3) 80
(4) 160
MWF type 4 (1) Straight oil
(2) Soluble oil
(3) Semi-synthetic
(4) Synthetic oil
Nozzle position 3 (1) Rake face
(2) Flank face
(3) Chip
the MWFs used in this work while varying the reamed hole
diameter. Test results were collected from a Microtap
Megatap 2, Labtop II G8 machine using standard 1018
steel test bar. A forming tap type was used in the test at a
size of M6 1 pitch. Tapping speed was held constant at
1000 rpm or 18.8 m/min.
5. Results
A comprehensive study has been conducted to
validate the new tool-life/tool-wear relationship for
NDM. A summary of the study is presented in
Tables 6–8 and Fig. 5.

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ASTMD 5619 Tapping Torque (N-cm)
550
500
450
400
350
300
250
Straight Oil
5.55-mm Reamed Hole
(Lubricity)
5.48-mm Reamed Hole
(Cooling)
Soluble Oil Semisynthetic
Oil
Synthetic Oil
MWF Type
Fig. 4. ASTM tapping torque results varying MWF type and reamed
holes.
6. Discussion
Tool-life/tool-wear modeling using mist spray applications
can be successfully and more accurately predicted
than using dry machining equations for NDM. Fig. 5
shows that predicted tool-wear values mostly fall within
actual 95% machining confidence intervals for actual
results while varying MWF type and rate. Table 6 indicates
that significant improvements in tool-wear (over 400%)
can be achieved using NDM compared to dry machining.
This study emphasis that models developed for dry
machining conditions cannot accurately predict NDM
performance. Equation accuracy over a broad range of
MWFs, nozzle position(s) and MWF volumetric flow rates
was observed to be less than 10% on the average.
Linearization of MWF rate and type effect factors
simplified model calculations, yet provided to be fairly
accurate for shop-floor use. Modification of the tool
coating effect factor allowed the Taylor equation to be
extended without deteriorating chip groove or tool coating
factor accuracy.
Table 6
MWF rate and type effect factors for MWFs primary used for lubrication
Dominant tool
wear pattern
Nozzle position
MWF type
MWF rate
( 10 4 ) M ZX ( 10 4 ) M ZY
effect factor effect factor
W BL R (rake face) 25.6 2.7
F (flank face) 1.3 6.2
C (chip) 5.1 18.9
Table 7
MWF rate and type effect factors for MWFs primary used for cooling
Dominant tool
wear pattern
Nozzle position
MWF type
MWF rate
( 10 4 ), M ZX ( 10 4 ), M ZY
effect factor effect factor
W BL R (rake face) 23.4 10.4
F (flank face) 6.6 7.5
C (chip) 5.7 8.3
7. Conclusion
It has been observed that the selection of ‘‘mist spray’’
delivery parameters represents an essential element in
minimizing tool-wear/tool-life. The following is a summary
of findings from the present work:
A new tool-wear/tool-life relationship has been developed
for NDM with coated grooved tools by extending
the Taylor-type equation to include mist spray
delivery parameters by modifying the tool coating effect
factor.
More accurate and consistent estimates of tool-wear are
made by using the new predictive model for NDM
compared to dry machining models.
The G mapping function allows users of the empiricalbased
model to customize the equation to consider a
wide variety of mist-spray delivery parameters, including
nozzle position, MWF volumetric flow rate and
MWF type.
Table 8
MWF rate and type effect factors for MWFs primary used for cooling
MWF
type
New tool coating for mist
factor (n mist )
NDM total effect factor
(NDM)
Predicted tool-wear
W BL (mm)
Actual test tool-wear
W BL (mm)
Error
%
Improvement % over dry
machining
Straight 0.078 0.140 0.88 0.91 3.30 102
Soluble 0.060 0.135 0.77 0.74 4.05 125
Semisynthetic
0.055 0.126 0.55 0.59 6.78 157
Synthetic 0.065 0.138 0.22 0.23 4.35 402
Dominant tool-wear pattern: W BL ; nozzle position: rake face; MWF volumetric flow rate: 180 ml/h; chip-groove effect factor (W g ): 0.89; tool coating effect
factor (n c ): 0.56; empirical constants: k ¼ 0.32, m ¼ 1 for turning, n 1 ¼ 0.230, n 2 ¼ 0.642.

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1.00
Predicted Tool-wear
Value using Dry Equation
0.90
Straight MWF
Width of Groove Backwall Wear (mm)
0.80
0.70
0.60
0.50
0.40
0.30
Predicted Tool-wear
Value using
New NDM Equation
Tool-wear Repeatability
Results Shown with
95% confidence interval
Soluble MWF
Semi-Syn. MWF
Synthetic MWF
0.20
0 50 100 150
200
MWF Volumetric Flow Rate (ml/hr)
Fig. 5. Tool-wear varying MWF type for rake face nozzle position.
Modification of the ASTM Tapping Torque Test can be
used as a technique to characterize cooling and lubricity
behaviors in MWFs while altering the reamed hole size.
The new approach in assessing tool-wear/tool-life
remains useful for characterizing tool-wear patterns in
NDM that cannot be described by traditional standards.
It is proposed to validate the new tool-wear/tool-life
equation for a wider range of conditions including
atomization type, chip form/flow interference with spray
mist field, cutting geometry and work materials.
Acknowledgments
The author would like to thank the two anonymous
reviewers for their comments. Very special thanks to Bob
Gregory for suggestions for improving the draft. I would
also like to thank the participants of the spring 2006,
Sustainability Seminar Series (coordinated by Dr. I.S.
Jawahir at the University of Kentucky, College of
Mechanical Engineering) for their comments.
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