chapter 2 literature review of hard...
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CHAPTER 2
LITERATURE REVIEW OF HARD TURNING
The process of hard turning has been studied theoretically and
experimentally for over 40 years (Tonshoff et al 2000). In the late 60s itself,
Shaw and Nakayama (1967) studied the machining characteristics of high
strength materials such as titanium alloy, beryllium, work hardened steel and
refractory materials. While machining these materials, a lower feed and speed
were recommended, since they imposed a large cutting force and high cutting
temperature. Moreover it was identified that the machining of a hard material
required high rigidity and high stability machine tools as they operate at high
energy levels.
In this chapter, the state of research in hard turning and its
monitoring is presented, under three broad headings, namely (i) The science
and technology of hard turning (ii) The various monitoring techniques that
were developed for the process monitoring of hard turning and lastly (iii)
Certain specific acoustic emission based monitoring techniques and signal
processing, employed to monitor the conventional turning operations, as it is
intended to use these techniques for monitoring hard turning.
2.1 HARD TURNING PROCESS
The various studies conducted to technically establish the
technology of hard turning and to understand the hard turning process, include
studies on developing suitable tool materials, tool geometry and tool edge
geometry, and coatings for enhanced tool life, identifying optimal cutting
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conditions, understanding the process of chip formation and identifying the
various tool wear mechanisms.
2.1.1 Development of Suitable Tool Material
Cutting tools used for hard turning require high hardness, high
compressive strength, high resistance to abrasive wear, thermal resistance and
chemical stability at elevated temperatures. Cubic Boron Nitride (CBN) is the
preferred cutting tool material rather than poly crystalline diamond (PCD) for
machining hardened steel since it has very high hardness and hot hardness and
is chemically inert particularly in the presence of carbon absorbing materials.
Ceramic inserts are also being used for hard turning. Hodgson and Trendler
(1981) carried out hard turning experiments to identify a suitable cutting tool
material for machining hardened steel. In this study, they performed the
turning tests on three different varieties of hardened steels, such as cold work
steels (D2 and D6), HSS steel (M2) using CBN and ceramic cutting tools. The
ceramic cutting tool did not perform satisfactorily, while machining cold
worked steel (D6) resulting in gross fracture and chipping of the cutting edge.
On the other hand, CBN inserts performed better while machining hardened
steel, in terms of extended tool life even at higher cutting speeds.
Konig et al (1990) conducted hard machining experiments in
turning, milling and drilling using CBN tool inserts. In the turning of hard
materials, such as high speed steel and bearing steel, they used both the CBN
and mixed ceramics, to study the wear behavior and tool life. Even though the
CBN and ceramics were tool materials frequently used for machining
hardened steel, CBN had emerged a preferred tool material due to the slow
and uniform tool wear characteristics, compared to ceramic tool inserts
(Figure 2.1). From the surface finish point of view also, the CBN tools were
found to perform better.
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Figure 2.1 Tool Wear in Hard Turning for CBN and Mixed Ceramic
Tool Inserts (Konig et al., 1990)
The performance of the CBN tools depends on the CBN content,
grain size, bonding material, and microstructure. In general, CBN tools can be
classified into two types. The first type contains very high CBN content with
more than 90% by volume of CBN, in which the CBN grains are directly
bonded by themselves or with a metallic binder. The second type contains a
lower CBN content, in the range of 50 to 70% by volume of CBN in which
the CBN grains are bonded by a ceramic binder such as TiC or TiN.
Kevin Chou et al (2002) experimentally investigated the wear
behavior of low and high CBN tool material. The authors performed hard
turning experiments on AISI 52100 steel material using high and low CBN
cutting tool material. The tool wear examination showed that low CBN
content tools generate a better surface finish and have lower flank wear rates
than their high CBN counterparts, even though low CBN tool material had
inferior mechanical properties compared to a high CBN tool. The
performances of the low CBN content tools were found to be better at higher
cutting speeds. The depth of cut was also found to have a minor effect on the
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tool wear. The performance of the CBN tool was found to depend not just on
the gross mechanical properties of the tool material but the combined effect of
gross mechanical property and the microstructure properties of CBN.
2.1.2 Tool Geometry and Tool Edge Geometry
In 1980’s investigations were conducted for developing a suitable
tool geometry and tool edge geometry, to improve the tool life of the CBN
inserts. Hodgson and Trendler (1981) conducted experiments in hard turning,
to study the effect of rake angle and edge preparation of the CBN tools on
tool wear. The study showed that the negative rake angle performed better
than the positive or zero rake angle from the tool life point of view.
Providing a negative rake angle also increased the wedge strength,
thereby preventing the chipping of the cutting edge. In hard turning, as the un-
deformed chip thickness, which is the feed, is very small, the actual cutting is
carried out mostly at the chamfered region of the cutting edge. However, a
larger negative rake increases the thrust force drastically, and this was shown
in the experimental results of Nakayama et al (1988).
The influence of the tool nose radius on the surface roughness,
cutting forces, tool wear and white layer formation had been systematically
investigated by Kevin Chou and Husi song (2004). The authors conducted
finish hard turning experiments on hardened AISI 52100 steel, using mixed
ceramic tool inserts. Inserts with nose radii of 0.8, 1.6 and 2.4 mm were used
in the experiments. The experiments revealed that the larger nose radius to
contributed to a better surface finish, but resulted in increased cutting forces
and increased specific cutting energy. Along with the lower nose radius, worn
out tool wear and higher feed rates were also found to have a strong impact on
the higher depth of the white layer formation.
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Since hard turning involves higher cutting forces and bigger shock
loads, the chamfered cutting edge of CBN insert was used. The chamfered
cutting edges had shown much higher tool life than the sharp cutting edge,
though they led to an increase in the main cutting forces. Tugur Ozel et al
(2005) performed the hard turning experiments on hardened AISI H13 steel,
using CBN inserts to study the effect of tool edge geometry, workpiece
hardness, feed rate and cutting speed on surface roughness and cutting force.
Their objective was to identify suitable edge geometry, for a better surface
finish and improved wedge strength. In the study sharp, chamfered and honed
edges were used. The honed edges were found to perform much better than
the sharp and chamfered edges in terms of surface finish, radial cutting force
and tangential cutting force.
Lalwani et al (2008) presented the effects of cutting conditions on
surface roughness. They have carried out hard turning experiments and
processed the data using response surface methodology and face-centered
central composite design approach. Their non-linear quadratic model surface
roughness showed the feed rate was the primary contributor and interactional
effect of feed and depth of cut, cutting speed and depth of cut to be the
secondary contributor for affecting the surface roughness of workpiece.
Tugrul Ozel et al (2008) introduced a ‘variable edge design’ on the
cutting edge of CBN insert and experimentally studied its influence on cutting
force, temperature distribution and tool wear. The results of experimentally
obtained cutting forces were used to validate an FEA model. The
investigation of the experiments concluded that variable edge tool inserts are
better than the normally prepared edge in terms of reduced tool wear, less heat
generation and less induced plastic strain on the work material during hard
turning.
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2.1.3 Tool Coating
Reginaldo et al (2007) undertook studies to report the wear
characteristics of a coated PCBN insert while machining hardened steel.
Three different TiN and TiC-based coatings were used on the PCBN, to study
the wear behavior and surface finish of the work piece. Finite element
simulation model was used to estimate chip-tool interface temperature for
studying thermal resistance of the coatings. The results show that the coatings
acted as a thermal barrier between the work piece and PCBN tool, resulting in
a small reduction at the substrate temperature.
Youngsik Choi and Richard Liu (2009) used micro / nano CBN
particle coated tungsten carbide tools in hard turning. The coated tools with a
CBN grain size of less than 0.5 m were observed to produce more uniform
residual compressive stresses on the machined workpiece. This resulted in an
enhanced fatigue life of the component.
2.1.4 Identifying Optimal Cutting Conditions
Hard turning is usually carried out at low feeds and depth of cuts
with normal cutting speeds without applying any coolant. Tugrul Ozel and
Karpat (2005) attempted to predict the surface roughness and tool flank wear
using regression and artificial neural network models. The predictive models
so developed suggested the surface roughness of the work piece to depend
upon primarily, the hardness of work material, edge preparation of insert, feed
and cutting speed. The surface finish of the work piece was found to improve
by the use of chamfered and honed edges, and with the reduction in cutting
speed and feed and with increased hardness in work material. But the tool
wear was found to increase with the hardness of the work material.
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Experimental work carried out by Neo et al (2003) and Mohamed
Athmane Yallese et al (2009) showed the surface roughness generated in
finish hard turning to be mainly influenced by the cutting parameters such as
cutting speed, cutting feed and depth of cut. Among these cutting parameters,
cutting feed is the dominant factor contributing to the surface roughness. In
their experimental study the authors had shown the cutting forces to increase
rapidly with increase in feed rate which in turn affected the stability of the
machine tool. The study also showed that cutting speed beyond 280 m/min to
produce sparks which resulted in very poor surface finish and very rapid tool
wear. The authors pointed out that the use of recommended range of cutting
speed and cutting feed for better surface quality and for extended tool life.
In the hard-turning process, tool geometry and cutting conditions
determine the time and cost of production, which ultimately affect the quality
of the final product. Optimization of the machining process not only improved
the overall machining economics, but also the product quality to a great
extent. In this context, Dibag singh et al (2007a) made an effort to estimate
the optimum tool geometry and machining conditions using genetic
algorithms to get the best possible surface finish within the constraints. A
surface roughness model was developed using response surface methodology
by incorporating nose radius, effective negative rake angle, cutting speed and
feed on machining of bearing steel. The typical optimum machining
conditions obtained were 200 m/min cutting speed, 0.1 mm feed, 6° effective
rake angle, and 1.2 mm nose radius, leading to a minimum surface roughness
value.
The cost of manufacturing finished component is directly related
machining time and how long the tool can be used for continuous machining
with the pre-requisite surface quality. Hence the deeper understanding of tool
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wear behavior, tool life, and surface finish by selecting appropriate cutting
condition is the important criteria for the machining of hard material.
Therefore the machinability characteristics of work-tool combination are
required for all possible combinations and this is truer for the hard machining.
Paulo Davim and Luis Figueria (2007) used the orthogonal array experiments
to determine the influence of cutting speed, feed and cutting time on the
specific pressure and surface roughness and tool flank wear. The authors were
carrying out experiments on cold work tool steel D2 (AISI) using ceramic
cutting tool. The study showed the tool wear to be primarily influenced by
cutting speed and the surface roughness to be influenced by feed and cutting
time which is an indirect measure of tool wear.
Hard turning is usually performed without any cutting fluid as dry
turning. However Vikram Kumar and Ramamoorthy (2007) had conducted
experiments with a minimum fluid application technique to result in a
sustainable hard turning process. The authors had reported a reduction in
cutting force, temperature and surface roughness.
2.1.5 Cutting Force and Temperature
When turning a soft steel material by feeding the cutting tool along
the workpiece axial direction, the principal (tangential) cutting force is the
largest among the three force components. However, because of the small
depth of cut and large nose radius commonly used in hard turning, chip
formation takes place only near the nose radius, and on the chamfer or hone
of the cutting tool. As a result, the thrust (radial) force component can
increase drastically and become the largest force component as the tool wear
progresses.
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Figure 2.2 Cutting Geometry and Cutting Forces in Hard Turning
(Tonshoff at al 2000)
Figure 2.2 shows the typical results of cutting forces during hard
turning (Tonshoff 2000). The experimental results clearly show the radial
force component (FP) to be the largest, especially after a long distance of cut.
A relatively steep rise in the radial force (FP) can be observed, compared with
the other two force components, due to a progressive increase in the tool flank
wear. Young-woo park (2002) had also reported the radial force during hard
machining to be the largest among the cutting forces, regardless of cutting
conditions and tool material.
The wear behavior of the CBN in machining hardened steel is
determined by the workpiece hardness and cutting temperature. During hard
machining, the energy consumed by the process is largely converted into heat.
The generation of heat during machining will increase the temperature in the
cutting zone. A higher cutting temperature creates a softening of the work
material, but it promotes the accelerated diffusion wear rates on tool material.
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Liu et al (2002) studied the performance of poly crystalline Cubic
Boron Nitride (PCBN) tool in finish hard turning of GCr15 bearing steel with
different hardness values. They examined the influence of work material
hardness and cutting conditions on the cutting temperature and tool wear. A
natural thermocouple was used to measure tool temperature during
machining. The cutting temperature was found to increase along with the
workpiece hardness till the workpiece hardness reached a value of 50 HRC
and then as the hardness was further increased the temperature was found to
reduce. This was attributed to material softening at elevated temperatures. The
increase in cutting speed and feed was also found to increase the tool
temperature.
Ren et al (2004) had conducted experiments by hard turning the
hard-faced high chromium layers using PCBN tool material. The temperature
at the tool - shim interface was measured using thermocouple and the
temperature at the wear zone was estimated using finite element analysis. The
cutting temperature was found increase with increasing cutting speed and
feed.
2.1.6 Mechanism of Chip Formation
In hard turning a localized plastic deformation was found to occur at
the primary shear zone (Nakayama et al 1988). Since the work material didn’t
have enough ductility, the crack was initiated at the surface where there was
no hydrostatic pressure. This was the primary reason identified for the
formation of saw-toothed chips, observed in the cutting experiments of
hardened 70-30 brass materials.
The high brittleness of the work material prevented the plastic flow
under the high compressive stress and led to the formation of a crack. The
crack enabled the release of the pent up energy and provided a sliding surface
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for the segmented material. Simultaneously the plastic deformation and the
heating of work material occurred at the vicinity of the cutting edge of the
tool, facilitating the sliding of chip segment. This cycle repeated continuously
leading to the severely segmented chip formation (Konig et al 1990).
Through experimental investigations Shaw et al (1993, 1998)
showed that saw toothed, wavy and segmented chips were formed in
machining of hardened steel, due to catastrophic shear and plastic flow. Rapid
tool wear is the basic difficulty in hard turning and wear significantly affects
on the quality of surface finish.
Davies et al (1997) conducted the experiments to study the
mechanism of segmented chip formation in AISI 52100 bearing steel and
electro plated nickel-phosphorous on copper substrates using CBN tool with
relatively low cutting speed. The average spacing of shear band in the chip
during machining was found to increase with the increasing cutting speed and
reached a limiting value determined by the cutting feed and depth of cut and
work material properties. The authors used a simple one dimensional thermo
mechanical model of a continuous homogeneous material getting sheared by
an impinging rigid wedge to explain this behavior.
2.1.7 Tool Wear Mechanism
Since rapid tool wear is the basic problem in hard turning, efforts
are being made to understand the wear mechanism operating between the tool
and work material, in conditions prevailing during hard turning. One major
difficulty in turning of hardened steels is the tool-wear caused by the hardness
of the material. The wear behavior of the CBN is influenced by many factors,
such as the composition of the CBN material, work piece hardness, cutting
condition and nature of cutting operation etc. The experimental investigation
of Kevin Chou et al (1997) showed the wear rate of the CBN tool to depend
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upon the carbide particle in the work material and size of the CBN grain in
tool. The results of the experiments clearly indicated the wear rate to be
directly proportional to the size of the carbide particle in the work material
and inversely proportional to the size of the CBN grains.
Finish hard turning experiments were carried out by Penalva et al
(2002) to understand the effect of tool wear on surface roughness. Finish
turning was performed on AISI 52100 hardened steel using CBN insert. The
examination of tool wear profile and surface roughness profiles showed the
replication of worn-out cutting edge on the surface of the work piece.
The tool wear of a cutting tool is also dependent on the type of
hardened steel being machined, namely the physical and chemical interactions
between the workpiece material and the tool. Tool and die steels typically
contain a variety of carbide particles based on alloying elements like tungsten,
molybdenum, vanadium and chromium that can greatly accelerate abrasive
wear.
Gerard Poulachon et al (2003) carried out hard turning experiments
on specimens of hardened steel having different material composition but
having same hardness to understand wear behavior of CBN. Dry turning
experiments were conducted on four different hardened steels namely
X155CrMoV12 cold work steel (AISI D2), X38CrMoV5 (AISI H11) hot
work steel, 35NiMo16 hot work steel and 100Cr6 bearing steel (AISI
52100).Their results suggested the various carbides present in steel such as
primary carbides M7C3 and secondary carbides M3C in the microstructure to
influence the tool wear. Higher tool wear rates were attributed to the presence
of high quantities of primary carbides in steels.
An extensive literature review was presented by Cora Lahiff et al
(2007) on the wear modes and wear mechanism of various grades of CBN
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inserts. Tool wear mechanism is a complex phenomena and no single wear
mechanism can satisfactorily provide a full explanation. The authors
identified two body abrasion caused by hard carbide particles and martensite
in the workpiece, three body abrasion caused by the loosened CBN grains,
diffusion wear and adhesion because of high temperature to be the
predominant wear modes. They also observed the tool wear to depend on the
composition CBN content and binder, tool edge geometry and machine tool
stability.
Yong Huang et al (2005) had modeled the depth of crater wear as a
function of cutting temperature, stress and other cutting conditions taking in
to consideration tool work material properties. The model predicted the crater
depth and this predicted crater depth was validated experimentally. According
to the authors modeling of crater depth is important as increased crater depth
will weaken the cutting edge leading to micro chipping and catastrophic
fracture of the cutting edge. The authors claim this model to help the user to
select the cutting conditions and tool manufacturer to optimize the tool
geometry for improved wear resistance.
Lin et al (2008) in their experimental study on hard turning
machined AISI 4340 alloy steel using cubic boron nitride tool. They used
CBN inserts having 45- 50 % of CBN and TiC based binder. The experiments
were conducted at various cutting speeds in the range of 58 to 180 m/min. In
all the experiments the feed rate and depth of cut remained constant at 0.1
mm/min and 0.2 mm respectively. An infrared photography unit was mounted
on side of the carriage adjacent to tool tip to measure the temperature of the
back side chip. In their experiments cutting forces were measured and tool
wear was studied using SEM analysis. During the experiments the authors
observed the average cutting temperature to be low at very low cutting speeds
and it gradually increased with increase in cutting speed. But the Principal
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cutting force decreased as the cutting speed was increased. In low speed
cutting, the binder of the hard particles of the cutting tool was observed to be
removed from the substrate due to high cutting force resulting from low
cutting temperature.
2.1.8 Surface Roughness Model
Since hard turning is the finishing process, the quality of the surface
finish is the most expected outcome of the process. The surface finish in hard
turning is influenced by the cutting conditions, tool geometry, edge
preparation, tool wear and rigidity of the machine tool. The generation of the
surface roughness in turning process has been modeled for several decades.
Many researchers had developed theoretical and empirical models to predict
the surface roughness (Tugrul Ozel and Karpet 2005 and Lalwani et al 2008).
Knuefermann and McKeown (2004) attempted to develop a
numerical model of surface roughness in hard turning based on variables such
as machine tool vibration, tool geometry, material deformation, chip removal
and tool path. In their model, the authors employed the material partition
equation for defining chip removal. Cutting experiments were carried out in
an ultra precision lathe using CBN tool to compare measured surface
roughness with simulated roughness. The analysis of results found the
simulated surface roughness to be fairly comparable with measured
roughness.
2.2 MONITORING HARD TURNING
As hard turning is an advanced manufacturing process close
monitoring and control of the process is essential. Tool wear is a complex
phenomenon occurring due to different mechanisms and is stochastic. In
general, a worn tool affects the surface quality of the work material, and
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therefore there is a need to alert the operator to stop further machining to
avoid undesirable consequences. The modern machine tool has many sensing
systems as an integral part of it. Using the data from these sensors to obtain
useful information about the process is an important domain of current
research. One of the major issues that need to be addressed is evolving an
online, reliable and robust monitoring methodology, is to predict the process
condition in hard turning.
2.2.1 Monitoring Strategies
The basic strategy of process monitoring can be classified into (i)
signal based method (ii) Model based method and (iii) classifying method,
depending on the complexity of manufacturing process (Tonshoff et al.,
2000a)
Figure 2.3 Classification of Monitoring methods (Tonshoff et al 2000a)
The three approaches are schematically explained in Figure 2.3.
Any monitoring system attempts to measure the condition of a machine tool
or of the process itself. In the signal based method, the measured signal values
are compared with the predefined signal or the ranges of signals. In model-
based monitoring, an empirical model of the process is built, and this is
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compared with actual experimentally measured quantities, to detect any
process deviation. In a classifier based monitoring system, a feature vector is
identified which is used for subsequent discrimination. Depending on the
actual system being monitored, a suitable strategy needs to be identified and
used.
2.2.2 Monitoring using Vibration Signal
Figure 2.4 On-Line Roughness Measuring Technique using Cutting
Vibration in Hard Turning (Dong Young et al 1996)
Dong Young Jang et al (1996) conducted hard turning experiments
to understand the correlation between the cutting tool vibration and the
surface roughness of the work material in hard turning. The experimental
setup is shown in Figure 2.4. The relative vibration between the tool and the
work piece was measured using an inductive pickup. This vibration signal
was used to predict the surface roughness. The predicted surface roughness
was found to correlate well with the actually measured roughness values.
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Thus, the authors had proposed an online surface roughness monitoring
system using an inductive vibration sensor.
2.2.3 Monitoring using Cutting Forces and Acoustic Emission
Scheffer et al (2003) proposed a tool wear monitoring system for
hard turning using a three-directional cutting force dynamometer, an AE
sensor and a temperature sensor. The authors formulated a monitoring
methodology using artificial intelligence (AI) techniques for monitoring tool
flank and crater wear of CBN insert. Self-Organizing Map analysis was used
to classify and isolate some of the common noises present during the process
such as floor vibration, work piece clamping and temperature conditions.
Zhou et al (2003) measured the cutting force in the radial direction
of the workpiece using a Kistler three component dynamometer and found it
to be sensitive to tool wear in hard turning process.
Figure 2.5 A Proposed ANN Model for Tool Wear Monitoring
(Zhou et al 2003)
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In hard turning the radial force is much higher than feed force and
the cutting force due to very small cutting depth, very small feed rate and
negative rake angle of the tool. From the experiments, the authors observed
both frequency and amplitude to show a tendency to increase as the flank
wear progressed The authors had developed an ANN based wear prediction
model using radial force, frequency energy and accumulated cutting time as
inputs The ANN architecture is shown in Figure 2.5. The predicted wear was
corrected using tool holder temperature. The predicted wear was observed to
be very close to the measured flank wear values.
Mehdi Remadna and Rigal (2006) had conducted turning
experiments on tempered steel of hardness 52 HRC using CBN inserts and
monitored the three component cutting forces. The authors observed the
specific cutting force measured along the radial direction of the workpiece to
correlate well with the tool wear and surface roughness.
An online process monitoring system for hard turning using three-
component force sensors was developed by Dongfeng Shi and Gindy (2007).
Experiments were carried out for machining Inconel 718 using ceramic tools.
The static and dynamic components of the forces were measured using
multiple sensors and a power sensor was used to trigger the data acquisition.
The acquired signal was subsequently processed using wavelet transforms.
This signal processing enabled to distinguish between static and dynamic
components, and to obtain features of tool malfunctions such as tool wear,
tool chipping and tool breakage. The analysis of force signals showed the
gradual wear of the cutting tool to be related to the static force components
and the transient occurrences like tool chipping got revealed in the dynamic
components.
Acoustic emission based online tool failure detection system waspresented by Liao et al (1995) in face milling of high chromium material
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using PCBN tool insert. The experiments were carried out to extract thefailure features from AE count rate, AE peak and AERMS. During the cuttingexperiments the authors observed the pattern of AERMS to change verysharply beyond the point of tool fracture. Further the authors proposed amoving range control chart for online tool failure monitoring in hard millingoperation.
Guo and Ammula (2005) had attempted to monitor the hard turningprocess of AISI 52100 steel of about 62 HRC turned with CBN inserts usingacoustic emission monitoring. The authors had attempted to monitor primarilythe surface integrity which was measured in terms of (i) The thickness of thewhite layer formed and (ii) The surface roughness. The authors had attributedboth the increase in the thickness of the white layer formation and thedeterioration in the surface roughness to the increased flank wear and theassociated increased rubbing between the flank and the workpiece. Theauthors had measured the conventional Acoustic Emission parameters likeAERMS, frequency and count rate and observed them to correlate well with thethickness of the white layer and surface roughness. The sharp tool, because oflow contact area with the workpiece was observed to produce less dampingand increased AERMS. As the tool wears off the contact area between the toolflank and the workpiece was found to increase resulting in relativelyincreased damping between the two leading to lowering of AERMS. But whenthe tool flank wear land crossed the limit the increased rubbing was found toproduce highly brittle white layer. The brittle white layer was observed tocontribute low damping leading to increased AERMS values.
2.3 ACOUSTIC EMISSION BASED MONITORING INTURNING
Tool wear in conventional machining can be monitored usingvarious indirect variables that are sensitive to tool wear. The frequently usedindirect parameters are the three components of cutting force, Acoustic
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Emission (AE) signal, motor current and vibration Teti et al (2010). Unlikedirect methods these methods can be used for on-line tool wear monitoring
AE signals are transient elastic strain waves, generated duringplastic deformation, and by disturbances occurring at the level of the atomicstructure. Plastic deformation, fracture, wear and rubbing are all importantsources of acoustic emission signals.
2.3.1 Sources of AE in Metal Cutting
The AE monitoring is one of the most effective methods for processmonitoring the conventional metal cutting operations (Dornfeld 1992). Themajor advantage of using the AE to monitor the machining operation is that,the frequency range of the AE signal is much higher than environmental noisein a non intrusive process. The friction between the cutting tool andworkpiece generates a continuous AE signal, which gives rich information onthe cutting process. In process monitoring, using acoustic emission, if aspecific threshold and band width can be established for a particular processconfiguration, then the AE signal and AERMS may be effectively used formonitoring the event of tool wear (Dornfeld and Asibu 1980). The threemajor sources of the AE signal in metal cutting process are (Dornfeld 1992):
(i) Continuous deformation in the primary shear zone andfracture of work material in the secondary, tertiary shear zone.
(ii) Fracture of the cutting tool between chip-tool and tool-workpiece interfaces.
(iii) Collision, entangling and breakage of chips.
An AE signal can be classified into two types, namely continuousand burst AE signal. Continuous signals are generated with shearing in theprimary zone and wear on the tool flank. The burst types are observed duringcrack growth in the material, tool fracture and chip breakage. The importanceof AE signal processing is to eliminate unwanted noise and to extract feature
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signals, which can be used to correlate the process parameters. To effectivelydetect tool wear and fracture of the cutting tool one has to understand thethree basic elements of the monitoring system, they are compatibility of theAE signal, AE sensor and the pre amplifier (Krzysztof Jemielniak 2001).
2.3.2 AE Based Tool Condition Monitoring
A brief review on AE based tool wear monitoring methodology inturning was presented by Xiaoli Li (2002). In his review, the author haspresented the state of research on various AE signal processing techniquesand feature extraction. The various AE signal processing techniques widelyused include time domain analysis, Fast Fourier Transform and wavelettransform. For feature extraction and tool wear estimation, techniques likepattern classification, group method of data handling (GMDH) methodology,fuzzy classifier, and neural network with data fusion technology are beingused. Quantifiable characteristics such as (i) ring down count, (ii) AE event,(iii) rise time, (iv) peak amplitude, and (v) RMS voltage can also be used tocharacterize the wear of the tool insert. According to the author, careful signalprocessing and integrations with other sensors will be an effective approachfor AE-based tool condition monitoring.
Dornfeld and Asibu (1980) conducted the turning experiments usingHSS cutting tool. They have presented a brief report about the generation ofAE during metal cutting process. The objective of the study was to establishthe relationship between AE root mean square (AERMS) value with the metalcutting parameters such as strain rate, cutting speed and feed in turningoperation and to validate this relationship by experimental investigation. Theexperimental data was presented as plots between AERMS signal and cuttingparameters for two different tool rake angles. They observed the strain rate tobe influenced by the cutting speed and this in turn increased RMS voltage.They have also identified the RMS of the AE signal suitable for processmonitoring in turning.
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2.3.3 Traditional quantitative measurement in AE signal
The most widely used signal measurement parameters are count,peak amplitude, duration and rise time. The figure2.5 illustrates theseparameter under filtered signal envelop. These parameters detected andcaptured through AE sensor and digital storage oscilloscope in terms ofapplied voltage with respect to time. The terminology of these terms asexplained as follows.
Peak amplitude - The peak voltage of AE signalDuration - The time from the first threshold crossing to
the end of the last threshold crossingCount - The number of AE signal exceeds thresholdCount rate - Number of count per unit timeRise time - The time from the first threshold crossing to
the maximum amplitude.
Figure 2.6 Characteristics of Acoustic emission signal (Robert and
Talebzadeh, 2003)
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2.3.4 Acoustic Emission Distribution Parameters
One of the methods of processing AE signals, is by using
parameters like the ring down count and total count (Teti and Micheletti,
1989), as metrics for monitoring tool wear. There are several techniques to
extract the features in the time domain AE signal. The commonly applied
techniques are (i) using measures of central tendency like the mean, median,
mode and root mean square value (ii) using measures of dispersion like
standard deviation or variance and (iii) using higher order moments, like the
Skew and Kurtosis.
In their study on tool wear monitoring in conventional turning,
Kannatey-Asibu and Dornfeld (1982) had assumed -distribution to
characterize the AERMS signals and evaluated the central moments and
distribution function parameters. The skew is the normalized third order
central moment and kurtosis is the fourth order central moment of the
assumed -distribution function.
The probability density function of the AERMS can be expressed as
in equation (2.1)
r 1 s 1x (1 x)f (x)(r,s) (2.1)
Where (r, s) can be expressed as
1 r 1 s 1
0(r,s) x (1 x) dx (2.2)
The distribution parameters of the distribution of ‘r’ and ‘s’ are
given by the equations 2.3 and 2.4.
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2 22r ( ) (2.3)
2 22
(1 )s ( ) (2.4)
The skew indicates the level of symmetry about its mean value and
the kurtosis indicates the sharpness of the peak of assumed -distribution. The
values of the skew (SB) and kurtosis (KB) can be obtained from the equations
2.5 and 2.6 given below.
12
B2(s r) r s 1)Sr s 2 rs (2.5)
2
B6{(r s) (r s 1) rs(r s 2)}K
rs(r s 2)(r s 3) (2.6)
In their study Kannatey-Asibu and Dornfeld (1982) had
demonstrated the possibility of using the distribution parameters of AERMS
signal, like skew and kurtosis, for effectively monitoring the tool wear status.
As can be seen in Figure 2.6, the skew was observed to decrease as the tool
flank wear increased. Also, the kurtosis was observed to increase as the tool
flank wear increased (Figure 2.7).
30
Figure 2.7 Skew of AERMS Vs Flank Wear in Conventional Turning
(Kannatey-Asibu and Dornfeld 1982)
Figure 2.8 Kurtosis of AERMS Vs Flank Wear in Conventional Turning
(Kannatey-Asibu and Dornfeld 1982)
31
Though various works had been carried out to monitor the process
of hard turning, the quest for a novel sensor monitoring system that is robust,
reconfigurable, reliable, intelligent and inexpensive, meeting the demands of
advanced manufacturing technology, continues.
2.4 OBJECTIVE AND SCOPE
The overall objective of this study is to develop a reliable process
monitoring technique for the hard turning process using acoustic emission
signals.
The hard turning of AISI – D3 steel using cubic boron nitride
inserts, is taken as the application domain.
2.5 CHAPTER SUMMARY
This chapter dealt with the literature review of hard turning process
and the various techniques adopted in monitoring it. Specific emphasis was
given in studying the influence of the various process parameters on hard
turning, mechanism of chip formation and tool wear. Section 2.2 of this
chapter dealt with the various monitoring strategies adopted in hard turning.
The application of acoustic emission monitoring in conventional turning was
also reviewed, as it is very relevant to the present work on monitoring hard
turning using acoustic emission signal. Section 2.4 indentified the objective
and scope of the study.