Assessment of Machining Models : Progress Report

R.W. Ivester (2), M. Kennedy, M. Davies (1)\
National Institute of Standards and Technology[^1]

Gaithersburg, MD 20899, USA

Tel: 301/975-8324, Fax: 301/975-8058, e-mail: <ivester@nist.gov>

R. Stevenson, General Motors, Warren, MI, USA

J. Thiele, Caterpillar Incorporated, Peoria, IL, USA

R. Furness, S. Athavale, Ford Motor Company, Dearborn, MI, USA

##### 

##### Abstract

Machining involves extremely localized and nonlinear physical phenomena
that occur over a wide range of temperatures, pressures, and strains.
The complexity of the system has hindered progress in predictive
modeling of machining processes. Many different types of models ranging
from theoretical to empirical have been developed, but the wide variety
of the models makes performance assessment difficult. The difficulty in
assessing the performance of machining models has been cited by industry
as the major factor that limits the use of modern machining models in
industry. Thus, the current practice in industry is either to use
conservative machining settings and tool-change policies, or to conduct
costly empirical studies for a limited selection of tools and coolants.
Either practice may lead to sub-optimal process performance. The goal of
the *Assessment of Machining Models* project is to assess the ability of
modern machining models to predict the outputs of machining processes
based upon data typically available on the shop floor. In order to
achieve this goal, the participating laboratories plan to develop and
provide a clear, consistent, well-measured and relevant data set, and
use that data set to benchmark the predictive capability of machining
models in *blind tests*. This paper presents the project motivation,
goals, and some representative results.

Keywords: machining, tool wear, temperature, force, modeling, validation

# 1 MOTIVATIOn

In 1998, Merchant estimated that 15 % of the value of all mechanical
components manufactured worldwide is derived from machining operations
\[1\]. Other studies have found that total U.S. expenditures on
machining are between 3 % and 10 % of the annual U.S. gross domestic
product (GDP): between \$240 to \$850 billion dollars for 1998 \[2\].
However, despite its obvious economic and technical importance,
machining remains poorly understood. Parameters are chosen through
empirical testing and the experience of machine operators and
programmers. This process is expensive and time-consuming. Furthermore,
while large empirical databases have been compiled \[3\]\[4\]\[5\] to
aid in process design, these databases lose relevance as new tool
materials, machines, and workpiece materials are developed. For example,
in the development of high-speed machining centers over the past ten
years, speeds and feed rates have increased by an order of magnitude,
rendering databases and handbook tables essentially useless.

A number of recent industrial studies have illustrated the shortcomings
in the current empirical methods of designing machining processes. For
example, in an internal study, Kennametal gathered data over a one-year
period (1992-1993) on the global use of cutting tools. This study found
that in the United States: (1) the incorrect cutting tool is specified
more than 50 % of the time; (2) tools are not used at the rated cutting
speed in 42 % of applications; and (3) tools are not used to the end of
life in 62 % of applications. An internal study at a major automotive
manufacturer tracked the downtime on 111 different machine tools. The
results of this study, detailed in Figure 1, indicate that 35 % of this
downtime can be attributed to deficiencies in the performance of the
machining process. If these studies are truly representative of
machining production in the United States the financial impact of the
use of sub-optimal tooling selection, process parameters, and
tool-change policies is staggering.

![](media/image1.wmf)An alternative approach to empirical testing and
experience is the *development of predictive models* that are based upon
the fundamental physics of the machining process. The advantage of this
approach is that predictions are made from the basic physical properties
of the tool and workpiece materials together with the kinematics and
dynamics of the process. Thus, after the appropriate physical data is
determined, the effect of changes in cutting conditions (e.g., tool
geometry, cutting parameters, etc.) on industrially relevant decision
criteria (e.g., wear rate, geometric conformance, surface quality, etc.)
can be predicted without the need for new experiments. If robust
predictive models can be developed, this approach would substantially
reduce the cost of gathering empirical data and would provide a platform
for *a priori* optimization of machining process parameters based upon
the physics of the system.

The difficulties in realizing true predictive models for machining arise
from the extreme physical phenomena inherent in the system. Machining
generates a highly inhomogeneous plastic flow where local stresses
generate high rates of plastic deformation (up to 10^6^ s^-1^) that give
rise to inhomogeneous thermal fields, high temperatures (1200 ºC in
machining steel), and high pressures (10 MPa). This type of complex
plastic flow is difficult to predict even with sophisticated numerical
software, and the basic data on material behavior under such conditions
is nonexistent for most materials of practical interest \[6\]\[7\].
These difficulties have forced model development to rely on various
levels of empirical input data taken from machining tests (Figure 2) in
order to model process variables of industrial interest. The limitations
imposed on the applicability of machining models by their reliance on
empirical input data has limited their industrial use, particularly in
smaller operations which are unable or unwilling to perform extensive
validation testing.

The goal of the Assessment of Machining Models effort is to provide an
unbiased and anonymous assessment of the ability of current machining
models to predict the practical behavior of machining processes. We make
no attempt to restrict the definition of a model, other than to state
that the models should be clearly defined in terms of the input data
needed to make a prediction. For the purposes of the effort, we define a
correct prediction as one that agrees with an experimental result to
within a well-quantified experimental uncertainty. Thus to define an
accurate prediction, the uncertainty inherent in machining systems must
first be assessed by conducting experiments at multiple labs on
different machines. The intent of the effort is to generate an
experimental data set encompassing the inherent uncertainties associated
with multiple laboratories and machining centers, provide an unbiased
report of current capabilities for predicting the practical behavior of
machining operations, and develop a roadmap for future directions in
machining modeling research.

# 2 BACKGROUND

Machining research is driven by both ardent scientific curiosity and
tremendous practical and financial utility. This dichotomy of
motivations pervades the published literature. However, even though
these two motivations are often at odds, neither can truly advance
without the other. Without the practical utility, scientific studies are
simply curiosities, and yet without a systematic scientific approach,
the repeatability and overall utility of empirical machining studies is
severely limited.

![](media/image2.wmf)The research literature on machining problems is
vast (see for example Komanduri \[8\], Shaw \[6\]). Probably the
earliest scientific report on the formation of a chip was presented in
1881 by A. Mallock \[9\] in the Proceedings of the Royal Society of
London. Nearly coincident with the publication of Mallock's article, F.
W. Taylor was appointed foreman of the machine shop at the Midvale Steel
Company, and over the next 25 years produced his now famous study of
machining \[3\]. Thus, before the beginning of the 20^th^ century,
notable empirical and theoretical studies of machining were already
underway.

The next period of development occurred in the 1930's and 1940's. The
study of machining mechanics was for the first time placed on a solid
physical and mathematical foundation by the work of Piispanen \[10\],
Ernst \[11\], and Merchant \[12\]-\[15\]. Since then, four formal
categories of cutting models have emerged: (1) analytic models; (2)
slip-line models; (3) mechanistic models; and (4) finite element models.
Each approach has certain advantages and shortcomings. The choice of a
particular cutting model depends on the information desired, the
required accuracy of this information, and the available resources.
Availability of laboratory equipment enabling accurate process
measurements is of paramount importance to the development of accurate
models. The literature on experimental measurements in machining is
extremely vast and therefore even a description of the most important
results is beyond the scope of this paper. However, comprehensive review
articles and textbooks can be consulted for further information and
references \[6\]\[7\]\[8\].

Analytic models \[16-22\] establish relations between force components
(e.g., between cutting and thrust forces and normal and tangential
forces) based on the cutting geometry. These models are easy to use, but
require prior knowledge of the shear angle, mean-friction angle, and
chip-flow angle. These quantities must be determined experimentally,
which limits the applicability and the accuracy of these models.

Slip-line models \[23-33\] depend solely on material properties, rather
than on experimental data. These models predict mechanical response and
temperature distributions and are compatible with strain, strain-rate,
and temperature-dependent models. However, the geometry of the slip-line
field in the shear zone must be assumed.

Mechanistic modeling is a semi-empirical method capable of accurate
prediction of cutting forces in a wide range of complex machining
operations \[34-47\]. This approach is based on the assumption that
cutting forces are proportional to the uncut chip area. The constant of
proportionality, called the *specific cutting energy*, depends on the
workpiece material, the cutting conditions, and the cutting geometry.
The form of the function relating the specific cutting energy to the
cutting geometry and conditions is assumed. The actual function is then
determined by fitting experimental data in a process called calibration.
Calibration can be based on simple orthogonal or oblique machining
set-ups; geometric transformations can then be applied to predict
cutting forces for a complex, three-dimensional machining process
\[43\]. This simplifies the calibration set-up, but the need for testing
is not eliminated and other important quantities such as tool chip
interface temperatures are not predicted.

Finite element models for machining processes were introduced in the
early seventies \[48,49\]. Stevenson et al. conducted pioneering work in
thermal finite element analysis of machining \[50,51\]. Lajczok \[52\]
proposed a cutting model based on plane strain assumptions. Natarajan
and Jeelani used a viscoplastic model to predict chip geometry \[53\].
Usui et. al. employed an incremental, elastic-plastic finite element
model, starting from a steady-state solution \[54,55\]. Strenkowski and
Carroll \[56,57,58\] also did pioneering work in finite element
simulation of machining processes. More recently Marusich et al. \[59\]
and Ceretti et al. \[60,61,62\] have developed more sophisticated
schemes with adaptive remeshing that are capable of predicting
non-steady chip formation. Today, the use of numerical cutting models is
no longer limited to research; industry is starting to adapt these
methods to practical design applications. For example, Wayne et. al.
make use of a commercial finite element code to predict chip flow
\[63\]. Currently a major limit to the use of finite element software is
the lack of fundamental materials data relevant to the conditions that
occur in machining \[64\].

There are two main approaches to formulating finite element models for
machining, Lagrangian models and Eulerian models. Lagrangian models
track a fixed body of material as it approaches and passes the tool,
while Eulerian models trace the flow of material through a fixed spatial
domain in the vicinity of the tool.

Lagrangian methods are well suited to simulating the entry and exit
phases of chip formation as well as intermittent and discontinuous
machining processes. They are compatible with elastoplastic material
models \[65,66\]. On the other hand, Lagrangian models suffer a number
of disadvantages. The large plastic deformations that occur in machining
can generate unacceptable mesh distortion. It is difficult to construct
a Lagrangian model for *continuous* tool advance through the workpiece;
node-splitting methods \[56-58,63,66\] or continual remeshing \[65,
59,60,62\] are required to address this problem. Artificial parting
criteria are required in node-splitting methods to determine when to
advance the tool through the material \[56-58,63,66\]. It is also
difficult to maintain grid alignment across slip interfaces, a problem
that can lead to errors in predictions of thermal response. Lagrangian
models are incompatible with direct steady-state solutions and
relatively small time steps are required in transient solutions to
capture the rapid changes that occur in the vicinity of the cutting
tool. These features make Lagrangian finite element models
computationally expensive.

Eulerian models avoid problems of mesh distortion, since the mesh is
constructed on a fixed domain. Relative material motion across a contact
interface can be accommodated without sacrificing mesh compatibility
\[56-58,67-75\]. Eulerian models capture the continuous flow of material
around the tool, enabling a physically realistic model of machining of
ductile materials, without remeshing or node-splitting. Direct
steady-state solutions can be computed with Eulerian models, so they are
computationally far less expensive than Lagrangian models for this class
of problems. In transient problems, the solution at a fixed point in an
Eulerian frame evolves more gradually than the solution at a fixed
particle in a Lagrangian model. Therefore, larger time steps can be used
without loss of accuracy in transient Eulerian models. On the other
hand, Eulerian models require custom software development and are less
well suited to modeling intermittent machining, entry and exit phases,
and discontinuous chip formation (combined Eulerian-Lagrangian (ALE)
approaches \[76\] might address these problems).

The range and complexity of machining models across these four formal
categories of analytic, slip-line, mechanistic and numerical models
makes it extremely difficult to compare their utility and robustness.
The quality of the output of models is uncertain, and there are no
universal definitions of model inputs and outputs (e.g., some models may
*predict* specific cutting energy from basic materials properties while
other models *use* specific cutting energy to predict the forces on a
tool with complex shape). Unfortunately, these complexities and
uncertainties often represent an unacceptable risk for the potential
end-users of such models, who currently resort to heavily empirical (and
costly) but reliable models such as Taylor tool-life curves.

# 3 OBJECTIVES

In an attempt to focus and better define the current state of machining
research the following question was raised at the *1^st^ International
CIRP Workshop on Modeling of Machining Operations:*

> "After 100 years of research in machining, why does industry still
> rely on 100 year old models to make predictions of what will be seen
> on the shop floor?"
>
> \- Rich Furness, Ford Motor Company.

The Assessment of Machining Models (AMM) effort arose from discussions
motivated by this question. *The goal of the project is to assess the
ability of state-of-the-art machining models to make accurate
predictions of the behavior of practical machining operations based upon
the knowledge of machining parameters typically available on a modern
industrial shop floor.* It is important to note that from the industrial
point-of-view, approximate predictions that can be made from machining
parameters typically available on the shop floor would be more useful
than more precise predictions based on less readily available model
parameters.

Critical assessment of model performance and robustness is necessary
before such models can see widespread industrial use. In addition to
assessing current prediction capabilities, the Assessment of Machining
Models effort will assist in evaluating how close the modeling community
is to the goal of practical predictive modeling of machining and where
future research efforts in this area should be focused. In designing the
effort it was asserted that any meaningful evaluation of model
performance must have two characteristics. First, it must be based upon
an accurate assessment of the uncertainty inherent in machining
operations. Second, predictions must be conducted blindly (i.e., the
predictor must have no *a priori* knowledge of the test results).

To achieve the goal of the project, the following plan was developed by
the project participants:

1.  Provide a clear, consistent, well-measured, and relevant data set
    consisting of two components: a *calibration data set* that is fully
    disclosed; and a *validation data set* that is not disclosed until
    predictions have been submitted. Assess the uncertainties in the
    measurements.

2.  Release the calibration data set and the parameters for the
    validation data set.

3.  Solicit and collect voluntary blind predictions of the validation
    data set.

4.  Provide an unbiased and anonymous reporting of the results. Organize
    a workshop to present and discuss the results of the project, and
    develop a roadmap for future research in predictive modeling of
    machining.

These steps are discussed in more detail below.

*Data Generation:* All data will be generated concurrently at the four
different labs (NIST, Ford Motor Company, General Motors, and
Caterpillar) and carefully compared prior to the solicitation of
predictions. Lab-to-lab consistency provides a filter for identifying
and correcting inconsistent or spurious experimental data and thereby
ensuring that the data is accurate. In addition, the mean and standard
deviation of the data will be calculated and used to define an accurate
model, i.e., a model is considered accurate if it can consistently
predict the standard deviations and the means of the validation data
set.

*Data Release:* Once consistent calibration and validation data sets are
obtained, the calibration data set and the parameters for the validation
data set will be publicly released via web site presentation, Internet
downloads, and data storage media (Zip[^2] Disks, CD's etc.).

*Solicitation and Collection of Blind Predictions:* Solicitations for
predictions will be sent to all parties who have expressed interest, and
a general solicitation will be posted on the web. Predictions will be
submitted on a voluntary basis. The due date for submission of
predictions will be six months following the release of the data.

*Reporting of Results:* An unbiased report of the results will be
developed based on comparison of the predictions and the experimental
data. This report will provide the focus for a workshop aimed at
developing a roadmap for future work in the modeling of machining
operations. The full calibration and validation data sets will be made
available over the Internet for use in future modeling efforts.

Currently, we are nearing the end of the data generation phase of the
work. Approximately 30 international research groups have already
expressed interest in participating in this study based upon responses
to presentations of the planned activities at the International
Mechanical Engineering Congress and Exhibition (IMECE) 1998 in Anaheim,
the second CIRP modeling meeting in Nantes, France, January 1999, the
North American Manufacturing Research Conference (NAMRC) 1999 in
Berkeley, California, and IMECE 1999 in Nashville, Tennessee. Release of
the data is scheduled for the end of the summer, 2000. During and after
each of these presentations, the plan was strengthened in response to
the comments and questions that arose. The remainder of the paper
describes the design of the experiments for the effort and presents some
representative samples of the experimental results.

# 4 DESIGN OF THE EXPERIMENTS

As stated above, the first step of the plan is to develop an accurate
data set relevant to a common machining process used in industry. The
choice of the process was primarily based upon input from the three
industrial participants in the effort. All experiments will be done
without coolant and will be repeated twice at each lab in order to
obtain a statistical sampling of behavior.

## 4.1 Choice of Process

The process chosen was turning of American Iron and Steel Institute
(AISI) 1045 steel using a general purpose tungsten carbide / cobalt
(WC/Co) unalloyed carbide grade insert. The simplest grade of carbide
was chosen to simplify tool-material modeling. Both uncoated and
titanium nitride (TiN) coated inserts are used. The merits of this
process are: (1) the machining of AISI 1045 steel has significant
relevance in the automotive and heavy equipment industries; (2) the
material properties of AISI 1045 steel and general grade carbide are
well known; and (3) the workpiece and tool materials are easily
obtainable in the configurations necessary for the tests.

## 4.2 Test Configuration and Conditions

Three types of experiments are being performed: (1) orthogonal machining
using coated and uncoated inserts to generate a "calibration" data set;
(2) orthogonal machining using machining parameters that fall both
between and outside the parameters chosen for the calibration data set
as part one of a validation data set; and (3) turning tests with
uncoated inserts and with coated grooved (chip-breaker) inserts as part
two of a validation data set. These experiments were chosen first to
provide data to calibrate any models as necessary and second to provide
a validation data set that tests model capabilities against problems of
increasing difficulty.

Given the current understanding of the physics of machining, it is
likely that prediction of changes from orthogonal to non-orthogonal or
non-orthogonal with a grooved insert is possible. However, because of
changes in the tool-chip interface conditions, even if the properties of
the coating are available, extrapolation from uncoated to coated
conditions is much more difficult. The coated chip breaker cutting tests
are most difficult of all to predict, yet they reflect current
industrial practice.

### 4.2.1 Workpiece Material and Geometry

The workpiece material was AISI 1045 steel bar obtained from a single
batch/heat. The material was machined to produce bars and tubes at NIST
(National Institute of Standards and Technology) and then distributed to
the industrial laboratories. The dimensions of test workpieces were:

Tubes - 152.4 mm overhang, 101.6 mm diameter, 1.6 mm wall thickness

> Bars - 101.6 mm overhang, 101.6 mm diameter.

The material was subjected to chemical, mechanical, and metallurgical
analysis as detailed in the next section.

### 4.2.2 Tools and Toolholders

Kennametal CTAPR 123 B toolholders were chosen for the experiments.
These toolholders have three major advantages: (1) they are commercially
available and so allow others to easily repeat the experiments in the
future; (2) they have a simple geometric configuration with a 5º rake
angle and a 0º included angle; and (3) they could be used for each of
the cutting configurations. The inserts used were Kennametal grade K68.
These inserts have the advantages: (1) they are commercially available;
(2) they consist of a very simple WC/Co material; and (3) they come in
configurations compatible with the toolholder and suitable for the
desired cutting conditions. Custom ground angle plates were manufactured
to mate with the toolholder and distributed by Kennametal to establish
the desired rake angles of +5º, 0º and -7º without changing the
toolholder. Observations of the initial tool edge radii for each of the
types of inserts provide insight into the effect of tool coating on edge
radius. Tool replications produced using a dental replicant (Dentsply
Hydrosil) were sliced and examined under an optical microscope. Pictures
of the sliced tool replications will be made available as part of the
data release.

### 4.2.3 Machine type and characterization

The experiments were carried out simultaneously in the four test
laboratories using CNC turret lathes as detailed in Table 1. While the
results of several internal round robin tests done at one of the
participating laboratories indicate that consistent machining behavior
can be

  ---------------- ------------------ ------------ -----------------------
  Lab              Machine            Force        Tool Wear

  Industrial Lab 1 OKUMA Cadet-L1420  Kistler      Wild Heerbrug MPS 11
                   X650 CNC (3500                  Optical Microscope
                   rpm, 22 kW)                     

  Industrial Lab 2 Lelond Makino      Kistler      Optical Microscope
                   Baron 60 Lathe (37 9257B and    
                   kW)                9403 mount   

  Industrial Lab 3 Okuma LC-20 Lathe  Kistler 9257 Wyko white-light
                   (3000 rpm, 15 kW)  B and 9403   Interferometer, SEM
                                      mount        

  NIST             Hardinge           Kistler 9257 Wyko white-light
                   Superslant (4000   B and 9403   Interferometer, SEM,
                   rpm, 7.5 kW)       mount        Air Bearing LVDT
  ---------------- ------------------ ------------ -----------------------

Table 1: Experimental equipment at each of the four labs.

  ------------ ------------------------- ------------------ --------------
  **Test No.** Cutting Speed (m/min)     Feed (µm/rev)      Rake Angle

  **1**        200                       150                -7

  **2**        200                       150                +5

  **3**        200                       300                -7

  **4**        200                       300                +5

  **5\*\***    300                       150                -7

  **6**        300                       150                +5

  **7**        300                       300                -7

  **8**        300                       300                +5
  ------------ ------------------------- ------------------ --------------

Table 2: Parameters for orthogonal cutting tests for model calibration.

obtained on different equipment, it has been reported elsewhere that
variations in dynamic behavior of machine tools can affect results.
Particularly, rates of tool wear have been reported to be affected by
the dynamics of the structural loop even under stable (non-chattering)
cutting conditions. Thus, the dynamics of each machine tool will be
characterized by NIST (with the dynamometer in the structural loop)
using tool-tip frequency response functions measured with a
piezoelectric hammer, signal analyzer, and accelerometer.

### 4.2.4 Calibration Data Set Parameters

A total of 16 orthogonal cutting tests were done to generate the
calibration data set. The parameter values for both the coated and
uncoated tools are shown in Table 2. Test 5 is an accelerated wear test
in which tool wear was measured as described below. The wear test is
intended to provide data that can be used by modelers to "calibrate" the
parameters in diffusion wear models.

The parameter values were chosen to span a range of behaviors. According
to data in Trent \[7\], all of these conditions will be free of a
built-up edge, which would lead to unpredictable behavior and be
impractical. According to Trent \[7\], machining of a low carbon steel
with uncoated carbide under most of these conditions will lead to slow
steady crater growth. Only the conditions for test 8 in Table 2 are
likely to lead to rapid crater growth.

### 4.2.5 Validation Data Set Parameters

The validation data set consist of two major portions, orthogonal
cutting and turning tests. The orthogonal tests were conducted with the
parameter values given in Table 3. These parameters will test both the
interpolation and extrapolation capability of the models. The turning
tests are also chosen to require interpolation and extrapolation of the
orthogonal cutting data. The cutting conditions for these tests are
summarized in Table 4. Each experiment will be conducted using uncoated
flat-faced inserts and TiN coated grooved inserts. As mentioned above,
this battery of experiments was chosen to provide a progressively
increasing level of modeling difficulty.

## 4.3 Measured Quantities

Forces, temperatures and wear were measured using the methods detailed
below for the conditions detailed in Tables 2-4. For all of the
calibration tests in Table 2 except test 5 and all of the validation
orthogonal tests (Table 3) no wear results were measured. Chip
morphology measurements were made for the orthogonal cutting tests in
the calibration data set. These include measurements of the chip
thickness and width after machining. Because measurements of contact
length can be ambiguous, contact length was not measured; however, it
could be inferred for some of the tests from the wear patterns that
occur (see section 5.5).

### 4.3.1 Forces

Forces were measured using Kistler 3-axis piezoelectric dynamometers as
detailed in Table 1. The tools were mounted to the dynamometer using
Kistler toolholder mounts as detailed in Table 1. The rake angles were
established using custom ground angle brackets from Kennametal. An
example of the dynamometer toolholder set-up is shown in Figure 3.

4.3.2 Temperatures

Average temperatures were measured using two methods. All tests were
measured using an intrinsic thermocouple at industrial lab 3 and NIST,
and selected measurements are being made at NIST using IR microscopy.

  ------------- ----------------- --------------------- ------------------
  **Test No.**  Cut Speed (m/min) Feed (µm/rev)         Rake Angle

  **1**         225               200                   0

  **2**         275               250                   -7

  **3**         400               400                   +5

  **4**         400               100                   -7
  ------------- ----------------- --------------------- ------------------

Table 3: Parameters for the orthogonal cutting portion of the validation
tests.

  ---------- ---------------- ---------------- ------------- -------------
  **Test     Cutting Speed    Feed (µm/rev)    Rake Angle    Depth of Cut
  No.**      (m/min)                                         (mm)

  **1**      180              125              0             1

  **2**      250              225              -7            2

  **3**      350              375              +5            1

  **4**      350              125              -7            3
  ---------- ---------------- ---------------- ------------- -------------

Table 4: Parameters for turning portion of the validation tests.

![](media/image3.wmf)Figure 3 : Photograph of the tool, dynamometer, and
the intrinsic thermocouple.

The intrinsic thermocouple uses the bi-conducting tool-chip interface as
a thermocouple junction. First the toolholder was electrically isolated
from the remainder of the machine using a Bakelite spacer between the
dynamometer and the Kistler toolholder mount. Bakelite washers were also
placed between the screw heads and the toolholder. One wire was then
mounted to a slip ring on the back of the spindle and the other to the
tool holder. The calibration procedure for the intrinsic thermocouples
involves placing the Kennametal toolholder in contact with a workpiece
and observing the output voltage while heating the toolholder and
workpiece at the contact point and measuring the temperature with a
conventional thermocouple. The intrinsic thermocouple systems have not
yet been calibrated. As such, only comparisons of the voltages measured
by NIST and industrial lab 3 can be made until the systems have been
calibrated. While this method has been used for some time in
experimental research \[77,78,79,80\] it does have the following
documented shortcomings: (1) it reports some weighted integration of the
temperatures across the tool-chip interface surface; (2) it is affected
by other bi-conductor interfaces in the measurement loop; and (3) it is
affected by (unknown) fluctuations in tool-chip contact area. However,
this method remains one of the most robust and reliable methods for
assessing changes in mean tool-chip interface temperatures. Therefore,
it was chosen as the main method of temperature measurement for this
effort.

As part of a separate project at NIST a thermal imaging micro-pyrometer
(MIPY) was constructed using a commercial 128 by 128 indium antimony
(InSb) focal plane array (FPA) with an all-reflective 0.5 numerical
aperture (NA) and 15x microscope objective that directly focused the
image of the object on the FPA. The InSb detector was used with a
broad-band spectral filter, which transmits from 3 μm to 5 μm in
wavelength, and the spatial resolution of the system was estimated to be
approximately equal to the wavelength of the detected light from using
the Rayleigh criterion. The individual pixels in the FPA were deposited
50 μm apart resulting in a spatial resolution of at best \< 5 μm per
pixel, and the spatial resolution was further verified using a
chrome-on-glass USAF 1951 resolution target. The system was calibrated
using a NIST miniature blackbody with a 2 mm aperture. The emissivity of
the steel was measured as a function of temperature using a reflectance
technique. Due to the nonlinear calibration curve of the detector and
the nonlinear temperature variation of the emmissivity, an iterative
procedure was required to obtain material temperature from the measured
signals. To reduce error motions and maintain focus the thermal
microscopy system was mounted on a high-load capacity air-bearing
spindle configured to conduct orthogonal cutting as shown in Figure 4a.
This system will be used to provide some limited thermal maps of
machining AISI 1045 steel for the AMM project.

### ![](media/image4.wmf)4.3.3 Wear

Wear measurements were conducted according to ISO 3685. The following
methods of measuring wear patterns were investigated and compared: (1)
air bearing linearly variable differential transducer (LVDT) (crater
depth); (2) white-light interferometer (500 micrometer vertical travel
with 10 nm resolution and 5 mm by 5 mm field of view using a 2.5x
objective); (3) replication techniques; (4) optical microscope; and (5)
scanning electron microscope. The first three methods were used to
quantitatively measure crater geometry. The optical microscope was used
to measure crater depth and flank wear shape. The SEM was used primarily
for qualitative imaging of worn tools.

### 4.3.4 Chip Morphology

For each of the tests the machined chips were collected and classified
according to type as specified in ISO 3685 Table G1. For the orthogonal
cutting tests, the thickness and width of the chips were measured after
machining. By combining these measurements and the forces a table (see
below) of relevant physical quantities (e.g., shear angle, shear and
friction forces, etc.) was populated using the classical Merchant model.

# 5 Generation of the Calibration Data set

This section details the generation of the calibration data set and
provides some sample results that demonstrate the approach and the level
of lab-to-lab consistency obtained.

## 5.1 Material Characterization

The material was ordered as a single batch of AISI 1045 steel, 30.5 m
(100 ft) of 101.6 mm (4 in) diameter rod in 5 rods, each 6.1 m (20 ft)
long. The 5 rods were cut into a combination of 152.4 mm (6 in) and
203.2 mm (8 in) long bars, with a 50.8 mm (2 in) thick circular sample
removed from each end. The ten samples were subjected to chemical and
metallurgical analysis using American Society for Testing and Materials
(ASTM) test procedures E3-95, E407-93, E112-95, E1019-94, and E415-95a.
Grain sizes were measured quantitatively using an optical microscope.
Additionally, Brinell hardness measurements and a series of ultrasonic
nondestructive evaluations were performed on a sampling of the bars.

The chemical analysis results are shown in Table 5. The chemical content
did not vary significantly among the ten samples as indicated by the
average and standard deviation of the measurements given in Table 5. The
material conforms to the military specification (milspec) for AISI 1045,
and satisfies the more stringent ISO C45 standard except for copper
content, which is slightly high. The grain size ranges were measured for
each sample. The lower and upper bounds of these ranges averaged 3.4 and
7.55, with standard deviations and repeatability uncertainties of 0.7
and 0.1, respectively, for both the upper and the lower bounds. The
resulting ±2s expanded uncertainty \[81\] on the upper and lower bounds
is ±0.71. Four representative pictures of longitudinal and transverse
cross-sections from two of the samples are shown in Figure 5.

Brinell Hardness measurements were performed on two of the 150-mm long
workpiece bars with a 29.4 kN load and 10-mm ball. For each of the bars,
six measurements were made on one face approximately 1 cm away from the
center of the face in a circular pattern around the center. The
diameters of the resulting impressions left on the bars were measured at
4.3 mm each with a measurement precision of ±0.025 mm. This equates to a
Brinell Hardness Number (BHN) with a ±2s expanded uncertainty of 196 ±5
\[81\].

Ultrasonic examinations of the workpiece material were also performed on
four of the 150-mm long bars before they were machined into tubes. Two
different types of pulse-echo ultrasonic contact tests were performed
with an orientation parallel to the axis of the cylinder. One using
longitudinally oriented waves, and the other using transverse waves. The
results of the two pulse-echo ultrasonic tests enable the speed of sound
through the material to be estimated, and thus the consistency of the
bulk elastic modulus and density can be characterized. On each of four
bars, measurements of length were taken with a caliper, and elapsed time
between ultrasonic pulses and echoes were recorded on an oscilloscope.
The measured values of the speed of longitudinal and transverse
ultrasonic waves in the bars were found to be in reasonable agreement
with the handbook values \[82\] for steel, 5,900 m/s and 3,230 m/s
respectively.

Additionally, the transducer was moved continuously in circular rings
around the center of the bars' circular ends. No distinguishing features
were observed among the four bars judging from the echo amplitude
pattern. Had there been any large differences in the density or
microstructure of the material a visible difference would have been
observed in the echo pattern.

Figure 5: Micrographs of polished and etched longitudinal and transverse
sections of bar caps.

+-------------------+----------+----------+----------+---------------+
| ![](media/i       | Mean     | Std      | Mil-spec | ISO           |
| mage5.wmf)Element |          |          |          |               |
|                   |          | Err      |          | C45 E4        |
+-------------------+----------+----------+----------+---------------+
| Carbon            | 0.445    | 0.005    | 0.450    | 0.42-0.50     |
+-------------------+----------+----------+----------+---------------+
| Manganese         | 0.677    | 0.009    | 0.700    | 0.50-0.80     |
+-------------------+----------+----------+----------+---------------+
| Phosphorus        | 0.010    | 0.001    | 0.009    | 0.035         |
+-------------------+----------+----------+----------+---------------+
| Sulfur            | 0.011    | 0.001    | 0.008    | 0.035         |
+-------------------+----------+----------+----------+---------------+
| Silicon           | 0.247    | 0.005    | 0.260    | 0.10-0.40     |
+-------------------+----------+----------+----------+---------------+
| Nickel            | 0.080    | 0.001    | n.a.     | 0.200         |
+-------------------+----------+----------+----------+---------------+
| Chromium          | 0.144    | 0.005    | n.a.     | 0.150         |
+-------------------+----------+----------+----------+---------------+
| Molybdenum        | 0.010    | 0.001    | n.a.     | 0.050         |
+-------------------+----------+----------+----------+---------------+
| Copper            | 0.234    | 0.005    | n.a.     | 0.200         |
+-------------------+----------+----------+----------+---------------+
| Aluminum          | 0.020    | 0.001    | n.a.     |               |
+-------------------+----------+----------+----------+---------------+
| Nitrogen          | n.a.     | n.a.     | n.a.     | 0.003-0.008   |
+-------------------+----------+----------+----------+---------------+

Table 5: Military specification (milspec), mean and standard error of
chemical concentrations and the specification given in ISO 3685 for
workpiece material AISI 1045.

The results of the chemical analysis, hardness tests, and ultrasonic
examinations establish that the original material was mechanically and
chemically suitable for use in the machining tests.

## 5.2 Preparation of workpieces

The material arrived at NIST in 5 bars each approximately 6 m in length.
The bars were rough-sawed into shorter bars 150 mm and 200 mm long for
use in the orthogonal and turning tests, respectively. The short bars
were then faced and numbered in the order they were cut from the long
bars. Parts were stamped with a 3 digit number, with the first digit for
the bar number out of the original 5 and the second and third digits for
the part number within the bar (01-36). In order to address the
possibility of variation among the tubes by location within the original
bar, the bars were distributed to the four labs in a staggered order.
The outer diameter of the 200 mm bars was finish turned to remove scale.

The 150 mm bars were machined into tubes for orthogonal cutting through
a series of operations including drilling, rough and finish boring of
the internal diameter, and finish turning of the outer diameter. The
cores of the 150 mm bars were drilled out to a depth of 100 mm on a
manual lathe using a 31.75 mm (1.25 in) diameter 118° included angle
tapered shank drill bit. The drilled hole was then rough bored at 245
rpm spindle speed, 22.4 mm/min feed rate, and 2.54 mm depth of cut.
Finish boring was performed at 165 rpm spindle speed, 38.1 mm/min feed
rate and 0.56 mm depth of cut. All boring was performed with a Carboloy
S16-SCLCL-3 boring bar and a SECO CCMT 32.52-F2 TP30 insert. Finish
turning of the outer diameter was performed with a Kennametal CN42 80°
Diamond K68 grade coated insert at 300 rpm spindle speed, 38.1 mm/min
feed rate, and 0.89 mm depth of cut. The length of cut for all
operations was 101.6 mm. Wall thickness measurements were performed to
ensure consistency. The ±2s expanded uncertainty \[81\] for the 1.6 mm
wall thickness was ±0.05 mm.

## 5.3 Characterization of Experimental Apparatus 

The tool holder and mount and dynamometer were assembled as shown in
Figure 3. The dynamic stiffness of both of the lathes to be used for the
experiments at NIST was measured. The frequency response function (FRF)
for the Hardinge Superslant is given in Figure 6. The ±2s expanded
uncertainties for the peak/valley heights and frequencies are ±10 % and
±5 %, respectively.

## 5.4 Forces

While the raw data for the calibration cutting experiments will be
released in its entirety, full presentation of the raw data would be
impractical in this paper. Instead, representative samples of the wear
progressions are given, and averages of the forces and intrinsic
thermocouple voltages are given for the full data set. The average
forces for the orthogonal cutting tests with coated and uncoated flat
inserts for experiments conducted at all four laboratories are shown in
Figure 7. In this graph, the cutting forces are negative and the thrust
forces are positive, as produced by the Kistler Dynamometer in the
cutting setup used. The lines represent ±2s~I~ expanded uncertainty for
the set of all force averages from the four labs for test condition i.
The consistency between the four laboratories is sufficient to allow
differences in forces due to parameter variations to be discerned.

## 5.5 Accelerated Wear Test

Figure 8 shows two wear progressions for experiments conducted at NIST.
Cutting was stopped every 5 mm of tube length and the insert was
examined with an optical microscope, an LVDT air bearing indicator, and
a white-light interferometer. Wear measurements were taken following
procedures detailed in ISO 3685. The two wear progressions shown are for
nominally identical cutting conditions and demonstrate a high degree of
repeatability in the wear progression. The squares and diamonds
represent LVDT measurements of peak crater depth. The triangles
represent measurements of peak crater depth from the interferometer. The
five interferograms used are shown inset on the figure adjacent to the
corresponding data points.

## 5.6 Chip Morphology

For each of the calibration tests in Table 2, the final chip thickness
and width was measured, and the shear angle was calculated based upon
the assumption that the Merchant model was valid. The shear angle, Φ,
was calculated as Φ=atan\[r\*cos(α)/(1-r\*sin(α))\], where α is the rake
angle, and r is the chip thickness ratio. It is important to note that
the chip width varies considerably, which violates the assumptions for
the Merchant model. The ±2s expanded uncertainty for NIST measurements
is less than ±100 µm. The averages of chip measurements are given in
Table 6.

## 5.7 Representative Temperature Data

The primary temperature measurement method was the intrinsic
thermocouple. The signals measured through the coated inserts were not
found to be sufficiently consistent to be a meaningful representation of
the average temperature at the tool-chip interface surface. Figure 9
shows the averages and ±2s expanded uncertainties of the intrinsic
work-tool thermocouple voltage outputs for each of the calibration data
set tests with uncoated inserts. The clearest trend in the changes of
thermocouple voltage with test number are between test 1-2 and 3-4,
where the rake angle changes from --7° to +5°, and between tests 1-4 and
tests 5-8, where the changes between the ordered pairs from these sets
(1 and 5, 2 and 6, etc.) are an increase in cutting speed from 200 m/min
to 300 m/min.

Figure 10 shows a representative thermal profile of machining with the
cutting conditions indicated in the caption. While these conditions are
not as aggressive as those used in the calibration data set, they do
serve to demonstrate the resolution and capability of the system. The
white lines on the figure represent the tool and workpiece surfaces in
the plane. The peak temperature is approximately 650 °C and occurs at a
point about 140 μm from the cutting edge of the tool. This is consistent
with other findings as well as tool wear patterns and is a result of the
highly localized plastic flow that occurs at the boundary of the tool
and chip. The ±2s expanded uncertainties in these measurements from all
sources are less than ±30 °C.

# 6 Future Work and Timetable

Currently, the calibration data set is nearly complete. The future steps
in the effort are described below.

## 6.1 Release of Calibration Data 

The calibration data will be released during the summer of 2000. The
primary method of release will be through downloads from the project's
web site, located at http://www.nist.gov/amm/. The web site is used for
reporting progress, disseminating the model reference data and input
parameters as described above, and collecting prediction reports. The
calibration data set will be provided in a spreadsheet format that will
include a template for submitting results. At the time of data release,
all groups who have expressed an interest in submitting predictions will
be contacted via email.

## 6.2 Solicitation and Collection of Predictions

All parties who have expressed interest in AMM at various workshops will
be invited to provide predictions. After the release of the calibration
data set, a six-month deadline for submission of predictions will be
established. By that deadline all prediction reports will be
electronically submitted to NIST via the project web site.

## 6.3 Evaluation and Reporting of Results

A committee of representatives from the four industrial participants
will meet to discuss the results. At this meeting the results will be
discussed and a final report will be generated. The report will
objectively and impartially document the results and make suggestions
for future work in the area of modeling of machining. Also at this time,
a workshop at NIST will be held to discuss future work. All of the
modeling participants will be invited to attend the workshop. Some of
the modeling participants may be invited to make presentations at this
workshop.

# 7 Summary

The project plan and experimental procedure were designed to result in
acceptable control of experimental consistency. Based on the results
presented in this paper, the lab-to-lab consistency is acceptably good.
This effort will result in an unbiased assessment of the current
capabilities of machining models.

# Acknowledgements

We would like to take this opportunity to thank the many people who have
contributed to the development and implementation of this effort,
including Ranajit Ghosh, Ming Leu, Vivek Chandrasekharan, Bob Polvani,
Tony Schmitz, Dieter Ebner, Wendy Shefelbine, and Gerry Blessing. This
work was supported in part by National Science Foundation Grant Number
9909130.

+---------+-----------+------------+----------+----------+----------+
| Test    | Uncut     | Cut Chip   | Shear    | Width    | Lab      |
| No.     | Chip      | Thick-ness | Angle    | (µm)     |          |
|         | T         | (µm)       | (deg)    |          |          |
|         | hick-ness |            |          |          |          |
|         | (µm)      |            |          |          |          |
+---------+-----------+------------+----------+----------+----------+
| 1       | 150       | 523        | 15.38    | 2050     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 513        | 15.65    | 2060     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 2       | 150       | 501        | 17.03    | 1956     | NIST     |
|         |           |            |          |          |          |
|         |           | 465        | 18.30    | 1949     | NIST     |
|         |           |            |          |          |          |
|         |           | 462        | 18.41    | 1839     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 3       | 300       | 889        | 17.83    | 2637     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 894        | 17.74    | 2611     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 4       | 300       | 671        | 24.86    | 1891     | NIST     |
|         |           |            |          |          |          |
|         |           | 682        | 24.50    | 1990     | NIST     |
|         |           |            |          |          |          |
|         |           | 721        | 23.27    | 2108     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 5       | 150       | 904        | 9.17     | 2626     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 437        | 18.11    | 1976     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 6       | 150       | 392        | 21.52    | 1778     | NIST     |
|         |           |            |          |          |          |
|         |           | 340        | 24.56    | 1742     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 503        | 19.96    | 1930     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 7       | 300       | n.a.       | n.a.     | n.a.     | n.a.     |
+---------+-----------+------------+----------+----------+----------+
| 8       | 300       | 787        | 21.44    | 2154     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 757        | 22.24    | 1986     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 9       | 150       | 460        | 17.29    | 2108     | NIST     |
|         |           |            |          |          |          |
|         |           | 454        | 17.50    | 2061     | NIST     |
|         |           |            |          |          |          |
|         |           | 472        | 16.89    | 2113     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 475        | 16.79    | 2098     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 10      | 150       | 441        | 19.25    | 1870     | NIST     |
|         |           |            |          |          |          |
|         |           | 416        | 20.35    | 1826     | NIST     |
|         |           |            |          |          |          |
|         |           | 417        | 20.30    | 1875     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 478        | 17.82    | 1890     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 11      | 300       | 803        | 19.53    | 2696     | NIST     |
|         |           |            |          |          |          |
|         |           | 819        | 19.19    | 2743     | NIST     |
|         |           |            |          |          |          |
|         |           | 772        | 20.22    | 2490     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 757        | 20.57    | 2576     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 12      | 300       | 683        | 24.46    | 1953     | NIST     |
|         |           |            |          |          |          |
|         |           | 620        | 26.71    | 2012     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 676        | 24.70    | 2037     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 13      | 150       | 435        | 18.18    | 2032     | NIST     |
|         |           |            |          |          |          |
|         |           | 425        | 18.56    | 2003     | NIST     |
|         |           |            |          |          |          |
|         |           | 478        | 16.70    | 2047     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 452        | 17.57    | 2063     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 14      | 150       | 375        | 22.43    | 1781     | NIST     |
|         |           |            |          |          |          |
|         |           | 371        | 22.66    | 1809     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 366        | 22.95    | 1758     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+
| 15      | 300       | 660        | 23.15    | 2464     | NIST     |
|         |           |            |          |          |          |
|         |           | 743        | 20.90    | 2499     | NIST     |
+---------+-----------+------------+----------+----------+----------+
| 16      | 300       | 600        | 27.51    | 1852     | NIST     |
|         |           |            |          |          |          |
|         |           | 640        | 25.96    | 1870     | NIST     |
|         |           |            |          |          |          |
|         |           | 666        | 25.04    | 1895     | Lab 2    |
|         |           |            |          |          |          |
|         |           | 671        | 24.86    | 1961     | Lab 2    |
+---------+-----------+------------+----------+----------+----------+

Table 6: Measurements of chip thickness and width. Shear angles are
calculated from a Merchant model assumption.

![](media/image6.wmf)

![](media/image7.wmf)Figure 6 : Real and imaginary components of the
frequency response measured at the tool tip on the NIST Hardinge lathe.
The x- y- and z- components are shown in solid, dashed, and dash-dotted
lines, respectively. The ±2s expanded uncertainties on the peak/valley
heights and frequencies are ±10 % and ±5 %, respectively.

![](media/image8.wmf)

Figure 7 : Force data for the calibration tests from all four labs. NIST
data is shown as diamonds, industrial labs 1, 2, and 3 are shown as
squares, circles, and crosses, respectively. The lines bounding the data
points represent ±2s expanded uncertainty on the data points for all
four labs. The upper trace is thrust force, the middle trace is the out
of plane force, and the lower (negative) force is the cutting force.

![](media/image10.png){width="0.6715277777777777in"
height="2.7527777777777778in"}

![](media/image10.png){width="0.6263888888888889in"
height="0.39652777777777776in"}

Figure 8: Measured tool wear progression in two orthogonal wear tests
showing white light interferometer measurements (triangles and overlays)
and LVDT measurements (squares and diamonds). Overlays are
three-dimensional crater depth plots, with the cutting edge at the top.
The depth scale for these overlays is shown on the right. Expanded
uncertainties (±2s) on LVDT measurements and interferometer measurements
are ±5µm.

![](media/image15.wmf)

Figure 9: Average thermocouple voltages for uncoated calibration tests.
Diamonds and crosses represent measurements performed at NIST and
industrial lab 3, respectively. Dashes represent ±2s expanded
uncertainties on data points from both labs.

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[^1]: Official contribution of the National Institute of Standards and
    Technology; not subject to copyright in the United States.

[^2]: Identification of specific commercial products is included in
    order to provide a complete description of the experimental work and
    does not imply endorsement by the National Institute of Standards
    and Technology, nor does it imply that the commercial products
    identified are necessarily the best for the purpose.