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ORIGINAL ARTICLE
Robot machining: recent development and future
research issues
Yonghua Chen &Fenghua Dong
Received: 5 March 2012 /Accepted: 29 July 2012 /Published online: 12 August 2012
#The Author(s) 2012. This article is published with open access at Springerlink.com
Abstract Early studies on robot machining were reported
in the 1990s. Even though there are continuous worldwide
researches on robot machining ever since, the potential of
robot applications in machining has yet to be realized. In
this paper, the authors will first look into recent develop-
ment of robot machining. Such development can be roughly
categorized into researches on robot machining system de-
velopment, robot machining path planning, vibration/chatter
analysis including path tracking and compensation, dynam-
ic, or stiffness modeling. These researches will obviously
improve the accuracy and efficiency of robot machining and
provide useful references for developing robot machining
systems for tasks once thought to only be capable by CNC
machines. In order to advance the technology of robot
machining to the next level so that more practical and
competitive systems could be developed, the authors sug-
gest that future researches on robot machining should also
focus on robot machining efficiency analysis, stiffness map-
based path planning, robotic arm link optimization, plan-
ning, and scheduling for a line of machining robots.
Keywords Robot machining .NC path planning .
Machining efficiency .Industrial robots .Joint stiffness
1 Introduction
Modern industries are heavily dependent on robots that have
a wide range of applications such as material transfer, pre-
cision assembly, welding, and machining [1–4]. Statistical
data from International Federation of Robotics has shown
that there is a steady increase in annual robotic sales except
2009 when the financial tsunami had seriously hit the world
economy [1]. In 2011, the annual industrial robot sale was
estimated to reach 139,300 units, making the worldwide
population of operational industrial robots to reach
1,035,000 units. However, this data is dwarfed by the recent
reports about ambitious plans in Asian industrial companies
to rapidly increase their industrial robots population. For
example, there were many media reports in 2011 about an
Asian company Foxconn [2] who is going to install one
million industrial robots in the next 3 years. Foxconn Tech-
nology [2] is a subcontractor for world’s leading electronics
product companies such as Apple, Microsoft, etc. It employs
over one million production workers in China. Due to
dozens of suicidal death in a single year in 2010 inside its
factories (most were bored due to routine assembly line
work), the company announced an ambitious plan to devel-
op and install over one million robotic arms in the next
3 years. Even though most of the robots will be used in
operations traditionally performed by robots, some
machining-related operations are also expected. This will
definitely boost the applications of robots in machining.
This instance reflects that the future growth of industrial
robots will be even more dramatic when emerging countries
start to automate their factories.
According to a white paper published by The Robotic
Industries Association in 2009 [3], robotic machining prod-
ucts and services constitute less than 5 % of existing robotic
sales, but was seen as a growth application segment over the
next 3–5 years. Applications involve the pre-machining of
parts made from harder materials, with robots performing
various processes at lower tolerances. It was believed that
robotic machining could not replace computer numerical
control (CNC) machining for three to four axis applications,
but is currently viewed as an immediate viable alternative
Y. Chen (*):F. Dong
Department of Mechanical Engineering,
The University of Hong Kong,
Pokfulam, Hong Kong, China
e-mail: yhchen@hku.hk
Int J Adv Manuf Technol (2013) 66:1489–1497
DOI 10.1007/s00170-012-4433-4
tool for non-metallic materials and for metals depending on
the degree of hardness, required surface finish, and part
complexity.
The white paper also revealed that the barrier to more
widespread adoption of robot machining was a general lack
of knowledge by the end-user community regarding the
capabilities and advantages of robots in machining applica-
tions. A significant effort is therefore required to educate
end-users on the capabilities of robot machining before
significant increases in robotic machining applications are
realized. Realizing the potential of robotic machining, the
world’s leading industrial robot manufacturers are starting to
provide machining robots together with relevant software
packages [5–8]. Even traditional computer-aided design
(CAD)/computer-aided manufacturing (CAM) software
developers such as Delcam [9] have incorporated offline
CAD-based robot machining capabilities into their tradition-
al CAD/CAM software packages.
Before being applied to direct machining, robot arms
have been used to machining-related jobs. Some studies
have shown that robots can perform well in polishing
[10–12], grinding [13–16], and deburring [17,18]. The
major purpose of polishing is to generate a glossy or smooth
surface, not to modify a part’s dimensions. Polishing tools
are often soft or flexible, thus positional accuracy require-
ment is not very high. This has created an excellent oppor-
tunity for robots to excel in polishing operations because an
articulated robot arm can easily position the polishing tool to
any positions that are needed. It was argued that robot
grinding and polishing could produce surface quality better
than that from three axis CNC milling machines [18,19].
The better surface roughness from robot grinding/polishing
(0.52 μm against 1.30 μm in three-axis milling) is mainly
due to the ability of the robot to easily change the orientation
of the tool, therefore, it can always keep the tool normal to
the polished surface.
As for milling operations, many studies have reported
mixed results showing that many improvements must be
made before robot milling could be readily applied to mill-
ing operations. It is interesting to notice that articulated
robots have some problems such as low repeatability, yet
the robots were first successfully applied to the finishing
operations of machining (polishing and grinding) where part
surface quality requirements are high. This may be partly
explained by the material removal rate. At the rough cutting
(or milling) and finish cutting stages, a large amount of
material must be removed. This will subject the robot arm
to a large load yet the rigidity of current robot design is not
big enough to withstand such a large load in machining.
Thus large error in machining may occur.
To overcome the drawbacks of articulated robots in ma-
chining, in 2009, European Commission had funded a proj-
ect called COMET (plug-and-produce components and
methods for adaptive control of industrial robots enabling
cost-effective, high-precision manufacturing in factory of
the future) [19]. This project was aimed at overcoming
challenges facing European manufacturing industries by
developing innovative machining systems that are flexible,
reliable, and predictable with an average cost efficiency
savings in comparison to machine tools. Industrial robot
technology was chosen as the backbone of the project. The
project investigators are aware of the inherent weakness of
industrial robots, that is, low positioning accuracy, vibration
due to process force, and lack of reliable programming tool.
It is widely anticipated that this project will greatly progress
the technology in robot machining.
For articulated robots, the repeatability is inherently de-
pendant on its reach distance. The larger the reach distance
is, the lower the repeatability will be. This inherent charac-
teristic can be easily explained as when the robot is fully
stretched, it is a cantilever beam. The compliance of a
cantilever is heavily dependent on the cantilever length. In
fact, this characteristic has been manifested by the data
provided by commercial robot suppliers (for examples,
ABB, Motoman, Fanuc, Kuka). Table 1shows some data
for three selected ABB robot models [7]. It can be seen that
as the reach distance increases, the repeatability error is
increased too. This table also shows that the repeatability
of today’s industrial articulated robot can be as high as
±0.01 mm which is sufficient for many low- to medium-
accuracy part machining jobs. In fact, most machining jobs
do not need a large reach distance. If the reach distance of
robot design is further reduced, it will be possible to im-
prove the repeatability even further.
The following will classify recent research on robotic
machining into three areas, namely rapid prototyping, vibra-
tion/chattering analysis, path planning, and automatic robot
programming.
2 Robot machining for rapid prototyping
Articulated industrial robots are flexible, cost-effective, and
normally have a large working envelope. However, they
have low positional accuracy and rigidity which confine
early robotic machining researches be aimed at making large
prototypes that are difficult to be made by both CNC
machines and layer-based rapid prototyping machines.
Table 1 A robot’s reach distance and repeatability
Robot model Reach distance (mm) Repeatability (mm)
ABB IRB 120 580 ±0.01
ABB IRB 140 810 ±0.03
ABB IRB 1410 1,440 ±0.05
1490 Int J Adv Manuf Technol (2013) 66:1489–1497
2.1 Rapid prototyping based on machining
Early robot machining research was aimed at making parts
with complicated geometry and limited accuracy (say
1.00 mm). Vergeest and Tangelder had first reported a
robotic machining system that was consisted of an articulat-
ed industrial robot, a rotatable horizontal platform for stock
material fixation and a milling device mounted on the end
effector of the robot [20]. The system was capable of mak-
ing parts within an 80-cm cube. Offline robot programming
capability was mentioned yet no detail was reported. Almost
at the same time, Chen and Tse also reported a robotic
system for rapid prototyping purpose [20]. Apart from the
major components as reported in Vergeest and Tangelder’s
system, the robot arm in Chen and Tse’ssystemwas
mounted on a 3-m-long linear track which could significant-
ly extend the robot system’s machining capability. A de-
tailed robot machining path planning method was reported
as well. Further refinement of the robotic machining system
was documented in their subsequent publications [21–23].
Figure 1a shows the hardware system setup. Robot machin-
ing path simulation and verification are shown in Fig.1b.
In order to further increase the efficiency of robot ma-
chining, Huang and Lin reported a dual robot machining
system [24]. In their system, the stock is installed on a fixed
working table, and two robotic arms are used to collabora-
tively machine a 3D part. A similar robot machining system
with two arms could be seen from the research conducted by
Owen et al. [25,26]. They used two robotic arms with one
serving as the stock fixtures and the other as a machining
tool. This system has more degrees of freedom allowing
parts with more complicated geometry be made. However,
the system is more compliant thus needs more careful mon-
itoring of the machining force. Forces acting on the end
effectors were monitored to identify the onset of a distur-
bance so that the system could be slowed down before
saturation actually occurred. In response to disturbances, a
time-scaling method could reduce the tool speed, thereby
reducing the demand on the joint torques and allowing the
pre-computed path to be followed more accurately.
The merits of robot machining were further extended by
Lee and Tsai et al. [27] who had tested Internet-based robot
machining scheduling and collaboration. This system is
good for resource sharing, autonomous repairing, and re-
placement of damaged parts in hazardous environment or
space. To machine a part with better surface quality, Zielin-
ski et al. developed a robotic system that was capable of
both milling and polishing [28]. It was believed that large
parts machined by a robot machining system could have
good surface finish.
Even though articulated robot arms have very good ac-
cessibility compared to traditional CNC machines, yet some
intricate geometric features such as cavities or internal holes
could not be made by articulated robots. Figure 2a shows a
sectioned view of a toilet flush model. The internal channels
of the model could not be machined by any existing CNC
machines. Chen et al. [29,30] had developed a layer-based
method using robot machining that could build parts with
complicated internal features such as the ones shown in
Fig. 2b and c. The layers do not have uniform thickness.
Instead, layer thickness is adaptive to curvature change and
accessibility analysis along the build orientation.
2.2 Robot calibration
In order to develop a robot machining system with better
accuracy, various calibration methods have been reported.
CCD cameras were frequently used to identify the kinematic
parameters of the actual machining setup so that positional
accuracy of the robot could be improved [23,24]. Figure 3
shows a vision-based robot calibration method where a
gauge cylinder was placed in pre-defined locations on the
rotary table [23]. Positional errors Δx,Δy, and Δzwere
measured using the two cameras installed in orthogonal
positions.
Morris et al. reported a robot calibration method using
coordinate measuring machine (CMM). Experimental meas-
urements of some robot poses are taken using a CMM.
Based on the measurements, a kinematic model is developed
(a)
The system set-up
(b)
Machining path simulation and verification
Fig. 1 A robotic machining
system. aThe system setup; b
machining path simulation and
verification
Int J Adv Manuf Technol (2013) 66:1489–1497 1491
for the robot arm. Its relationship to the world coordinate
frame and the tool is also established [31].
Andres et al. has reported a novel method for the cali-
bration of a complex robotic workcell with eight joints
devoted to milling tasks [32]. A planar calibration method
is developed to estimate the external joint configuration
parameters by means of a laser displacement sensor, thus
avoiding direct contact with the calibration pattern. A re-
dundancy resolution scheme on the joint rate level is inte-
grated with a CAM system for the complete control of the
robotic workcell during the path tracking of a milling task.
In general, a calibration method should serve two purposes.
First, it should establish a relationship between the robot
coordinate system and the workpiece coordinate system.
Second, it should take some measurements so that the kine-
matic parameters of the robot can be modified to accurately
describe the actual position and orientation of the robotic
links.
3 Vibration or chattering analysis
Articulated robot arms are very agile and flexible with good
accessibility. When used for machining, there is always a
tradeoff between low dynamic accuracy and good accessi-
bility. This is why early researches on robot machining were
targeted at making prototypes with large size and compli-
cated geometry [20–23,33]. Accuracy of part making was
not a major concern. When used in machining hard or metal
materials, the low stiffness of robot machining systems
presents a bigger problem. To make things worse, a robot
arm’s stiffness varies significantly in different directions.
For example, the static stiffness of a robot machining system
was reported to be 83.65 μm/N in Xdirection, 20.35 μm/N
in Ydirection, and 68.76 μm/N in Zdirection [34,35]. Due
to the difference of stiffness in different directions, cutting
accuracy was also found different in different cutting direc-
tions. In order to improve robot cutting accuracy, correlation
between vibration/chatter and machining parameters must
be established through experiments. For example, Zaghbani
et al. had collected vibrations and cutting force signals with
analysis in order to find a reliable dynamic stability machin-
ing domain with respect to spindle speed [36]. Feedrate is
also an important machining parameter. It has a large impact
on the machining accuracy as well [35]. Therefore it is
desirable to plan an optimal feedrate with a compromise
between machining efficiency and machining quality. It
was found that a constant feedrate was always preferred if
possible throughout the machining process [37].
Given the fact that the low stiffness of a robot arm may
cause machining errors, researchers have developed some
methods to compensate the errors. Zhang and Pan had
reported a method to control the machining error based on
deflection compensation and adaptive material removal rate
(MRR) [38–40]. The deflection compensation was based on
a stiffness matrix in Cartesian space that the researchers had
developed based on experiments. The MRR was adaptive to
the cutting forces which were measured real-time in the
machining process. Based on both deflection compensation
and the controlled MRR, it was reported that the machining
accuracy of foundry parts could be improved from 0.9 to
0.3 mm.
For automatic offline robot machining programming,
Abele et al. developed an offline error compensation model
for the machining path [41]. The model can be used for the
prediction of the cutting force so that the anticipated cutting
deviation could be compensated based on the robot’s com-
pliance matrix during actually cutting. Since the cutting tool
path could be controlled, the accuracy of an industrial robot
for machining could be increased [42].
Because an articulated robot has heterogeneous stiffness
within its working envelope, it will be best for a robot to
perform a machining job within its possible range of best
stiffness. Vosniakos and Matsas had presented a method that
the robot milling operation could be performed in regions of
the robot’s workspace where manipulability, both kinematic
and dynamic was the highest, thereby exhausting the robot’s
potential to cope with the process. By selecting the most
suitable initial pose of the robot with respect to the work-
piece, a reduction in the range of necessary joint torques
(a) CAD model (b) A toilet flush model (c)A castle model
Fig. 2 Large parts with internal
features made by layer-based
robot machining. aCAD mod-
el; ba toilet flush model; ca
castle model
1492 Int J Adv Manuf Technol (2013) 66:1489–1497
could be reached, to the extent of alleviating the heavy
requirements on the robot. Genetic algorithm was used to
minimize the joint torque loads given the milling forces.
[43]. Similar to Vosniakos and Matsas’s work, Lopes and
Pires proposed an approach to optimize the workpiece loca-
tion based on machining trajectory and machining forces.
Again, a genetic algorithm was used for the optimization of
initial workpiece location [44].
Realizing that the accuracy of robot machining is affected
by many factors, Andrisano et al. proposed an integrated
approach for robot machining accuracy enhancement based
on robotic process simulation, tailored design of mechanical
apparatus for the machining system, and software modules
for robot control and programming [45]. They also high-
lighted the importance of machining strategy validation,
automatic robot path generation, workcell calibration, and
robot code commissioning.
In order to accurately define the dynamic behavior of a
machining robot, both experimental method and analytical
method have been reported. Bisu et al. have used a frequen-
cy method to measure the dynamic response when milling at
designated points [46]. Since only very few points could be
measured, this method is not directly useful for robot tra-
jectory planning in machining. A more useful method for
robot joint stiffness identification is reported by Dumas et al.
[47]. They evaluate joint stiffness values with consideration
of both translational and rotational displacement of the robot
end effector for a given applied wrench (force plus torque).
Based on the joint stiffness values, they have also developed
the robot’s Cartesian stiffness matrix which is more useful
for robot machining path planning.
4 Robot machining path planning
There are many literature reports about automatic CNC
machining path planning based on CAD models [48]. In
the past 30 years, the authors estimate that at least tens of
thousands articles on CNC machining path planning have
been published by international journals and conferences.
Even by now, articles in this field are still constantly
emerging [49]. Compared to the wealth of path planning
for CNC machining, very little publications on automatic
robot machining path planning could be found. There might
be a misconception that robot machining path planning is
the same or similar to NC path planning. This misconcep-
tion may have hindered the development of robot machin-
ing. It is true that there are some similarity between NC path
planning and robot machining path planning. Yet the differ-
ence is substantial. For example, the impact of stiffness on
robot machining path planning is significant yet a CNC
machine’s stiffness has much smaller impact on NC path
planning.
Apart from academic research on automatic path plan-
ning for robot machining, some commercial companies
are also actively engaged in developing software pack-
ages capable of generating robot trajectory automatically
from a CAD model, or from a tool path. Robotmaster ®
has reported a software solution for CAD/CAM-based
programming for robot milling and trimming [50].
Robotmaster can create accurate six-axis robot trajecto-
ries from tool path data. Singularity, collision, out of
reach, and joint extension errors are checked when the
robot trajectory is generated. The functionality of Robot-
master has more or less reflected achievement from early
research on automatic robot machining path planning
[21–23,51]. However, the dynamic features of a robot
are not considered in Robotmaster.
Recent research on robot machining has focused more
on the influence of robotic dynamics on machining accu-
racy and efficiency [46,47,52,53]. Olabi et al. have pro-
posed to optimize the tool-tip feedrate in Cartesian space
for a given tool path using a smooth jerk limited pattern
with consideration of the joints kinematics constraints
[52]. That is, the dynamic characteristics of the robot are
considered in determining one of the key machining
parameters “feedrate”.
Xiao et al. propose a robot trajectory planning method
based on cutter location (CL) data generated by convention-
al CAD/CAM [53]. When doing inverse kinematics, a re-
dundant mechanism is analyzed to avoid the singular
configurations and joint limits. A gap bridging strategy is
applied to reduce the jerk motion caused by tool retraction
and cutting paths connection.
Apart from the above-mentioned articles for path/trajec-
tory planning in robot machining, not much else could be
found. Almost all reported robot cutting trajectory planning
methods are based on CL data generated by either using an
existing method, or by an existing CAD/CAM software
package. Based on the authors’experiences, when generat-
ing CL data, robot dynamics should be considered. A gen-
eral principle about CL data generation should be to
minimize joint motion when the robot moves from one
cutter location to the next cutter location.
Camera 1
Gauge
cylinder
Calibration
tool
XO
Z
Camera 2
Xi
Zi
Rotary platform
Workpiece
coordinate
X'
O'
Z'
Base coordinate
Fig. 3 Calibration of a robot arm
Int J Adv Manuf Technol (2013) 66:1489–1497 1493
5 Future research issues
Progress in robot machining research is relatively slow in
recent years. This may have been the results of a variety of
factors. In order to advance the science and technology of
robot machining, the following issues are identified and
must be studied in the future.
1. Robot machining efficiency has never been investigat-
ed: In fact, this is one of the major issues in robot
machining that must be addressed in order to extend
robot machining to more applications. Normally, robot
machining has much bigger advantages when machin-
ing large components compared to CNC machining. Yet
when machining a large component, in general, more
material must be removed. However, due to limited
robot rigidity and payload, feed rate, depth of cut, and
cutter diameter must be kept to small values. This will
limit the material removal rate or machining efficiency.
It is desirable to develop some machining strategies
such as special cutting path patterns so that machining
efficiency could be increased. Figure 4a shows a 3D
model of a crane bird. If it is to be made by robot
machining based on a rectangular block raw material,
a lot of material must be removed. If the excess material
is removed bit by bit in a traditional zigzag pattern, it
will take a lot of time. Figure 4b shows the rough
machining of the part to near shape in the projection
plane X–Y. It may be followed by make the part to near
shape in Y–Zplane and X–Zplane. After these rough
cutting, the excess material is significantly reduced.
This will greatly increase the efficiency of robot ma-
chining.
It is also possible to use dual robots machining with
one robot for rough machining and the other for finish
machining. Robot machining can afford the luxury of
multi-robots due to its low cost. This demarcation of
machining job may greatly improve robot machining
accuracy as well as efficiency because some robot arms
are best designed for higher payload and others are
designed for greater precision.
2. Develop a rigidity map within a robot’s working enve-
lope: For a given point within the working envelope, it
can be reached with many possible robot joint config-
urations, joint configurations many have quite different
rigidity which will affect the machining quality. If the
rigidity map is known and easily available, optimal joint
configurations could be identified for a given machining
path. This will help improving the machining quality. In
previous robot machining research, almost all reported
systems used an existing industrial articulated robot arm
(a) the crane bird model (b) machine to near shape
Fig. 4 Machining of a crane bird. aThe crane bird model; bmachine
to near shape
wrist wrist
w
w
nn
wrist
w
n
spindle
spindle
spindle
L
E
E
(a)parallel attachement (b) vertical attachment (c) design into wrist
Fig. 5 Spindle attachments to a
robot’s end link (wrist)
1494 Int J Adv Manuf Technol (2013) 66:1489–1497
with a retrofitted tool spindle. The two popular spindle
attachments are shown in Fig. 5a and b. Both attach-
ment methods will weaken the already weak stiffness of
a robot arm and complicate the system calculation. A
suggestion here is to design the tool spindle into the
robot’s end link (or wrist) as shown in Fig. 5c so that
eccentric force could be avoided and computation
simplified.
3. Optimized robot machining system configuration: Ro-
bot machining were currently researched based on exist-
ing industrial robots that are best suited to material
transfer and welding applications. To get the best ma-
chining results, research on robot machining should not
be restricted to current industrial robot configurations.
Investigation on the proportion and design of the links
L1, L2, and L3 as shown in Fig. 6for optimal machin-
ing accessibility and rigidity should be conducted.
We all know that serial robots have accuracy prob-
lems mainly due to the error magnifying effect of the
arm design and the low arm stiffness. One approach is
to scale down the robot arm since this can reduce the
effect of error magnification and increase the robot
arm’s stiffness. The reduced reach range may be com-
pensated by introducing a linear stage for the position-
ing of the workpiece or mounting of a robot arm to a
precision XYZ stage as shown in Fig. 6. It is not neces-
sary to control the XYZ stage during machining. Instead,
positions of the robot arm on the XYZ stage can be pre-
computed so that optimum machining operations in
terms of accessibility and rigidity could be performed
for a given part. Since XYZ stage design and manufac-
turing is a mature technology which could provide
stages with good rigidity and sub-micron accuracy.
The addition of the XYZ stage will not have visible
impairment of the robot system’s accuracy and stiffness.
4. Robotic machining lines: The advantages of robotics are
best illustrated when a line of robots are used to perform
jobs automatically as those frequently seen in a factory’s
automatic assembly lines. There are researches on iso-
lated robot applications to machining, deburring, grind-
ing, or polishing. Yet no effort has been reported about
the development of an automatic machining line that has
all of the above functionalities. Figure 7shows a pro-
posed such robot machining line. The authors do not
agree with the concept of concurrent machining with
multiple robots since this may create a lot of extra
problems such as vibration and torsion. Instead, a ded-
icated robot for a dedicated operation will make parts
with best quality. For example, the robot machining line
shown in Fig. 7has a dedicated rough machining robot
and a finish machining robot. The robot for rough
machining may be designed for high stiffness, yet the
finishing robot might be designed for greater accuracy
as the material removal rate in finish machining is
normally very small thus the load capacity requirement
is low. Other finish machining operations such as grind-
ing or polishing may be added when necessary. If need-
ed, a painting robot may also be added so that a product
can be completely made in a single line. Of course, a lot
of research work must be done in order to make the
automatic robot machining line a reality.
6 Discussions and conclusions
This paper has provided a review of recent research and
development related to robot machining. It is found that
there is still a long way to go before robot machining
systems are widely used in practical applications. In current
researches, most researchers have chosen to use existing
industrial robots that are not designed or optimized for
machining operations. The inherent problems of low dy-
namic accuracy, vibration, and chattering could never be
resolved based on current research effort. This has hindered
the development of robot machining in recent years.
In this paper, the authors have suggested ways of im-
proving robot machining accuracy and efficiency so that
robot machining systems could be widely used in the future.
Four future research issues on robot machining are outlined.
L1
y
x
z
L2
L3
Fig. 6 A proposed robot machining cell
Fixture platform
loading
Rough
machining
Grinding/de-burring
Off loading
polishing
Finish
machining
Fig. 7 A proposed robot machining line
Int J Adv Manuf Technol (2013) 66:1489–1497 1495
It is hoped that research on these issues may eventual
advance the technology of robot machining.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution License which permits any use, distribution,
and reproduction in any medium, provided the original author(s) and
the source are credited.
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