Accepted ManuscriptReviewMonitoring And Processing Signal Applied In Machining Processes A ReviewC.H. Lauro, L.C. Brando, D. Baldo, R.A. Reis, J.P. DavimPII: S0263-2241(14)00354-6DOI: http://dx.doi.org/10.1016/j.measurement.2014.08.035Reference: MEASUR 2962To appear in: MeasurementReceived Date: 13 May 2014Revised Date: 17 July 2014Accepted Date: 14 August 2014

Please cite this article as: C.H. Lauro, L.C. Brando, D. Baldo, R.A. Reis, J.P. Davim, Monitoring And ProcessingSignal Applied In Machining Processes A Review, Measurement (2014), doi: http://dx.doi.org/10.1016/j.measurement.2014.08.035

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MONITORING AND PROCESSING SIGNAL APPLIED IN MACHINING PROCESSES A Review

C.H. Lauro1*; L.C. Brando2; D. Baldo2; R.A. Reis2; J.P. Davim1

1 - Department of Mechanical Engineering, University of Aveiro, Campus Universitrio de

Santiago, 3810-193 Aveiro, Portugal.

2 - Department of Mechanical Engineering, Federal University of So Joo del Rei (UFSJ), Praa Frei Orlando n 170, Centro, 36.307-352, So Joo Del Rei, Brazil.

* - Corresponding author. E-mail address: carlos.lauro@ua.pt

Abstract: In machining processes several phenomena occur during material cutting. These phenomena can affect the production through the reduction of quality or accuracy, or by increasing costs (tools, materials, time). Thus, an understanding of machining phenomena is needed not only to define the cutting parameters for maximizing production, but also to ensure worker safety. An easy way to identify these phenomena is by monitoring machining processes, such as the measurement of cutting force, temperature and vibration. The acquired signal can have information about tool life, quality of cutting and defects in the workpiece. This review paper discusses the first steps involved in choosing and defining various techniques that may be used to monitor machining processes. Furthermore, this paper also outlines the techniques to acquire and process the signals of the monitoring processes. Hence, the objective of this paper is to help the reader understand the procedures for monitoring machining processes, and define methods, parameters, targets and other factors involved in doing so.

Key-works: Monitoring; Signal processing; Machining.

Nomenclature

AE Acoustic Emission

C/N Coulomb per Newton

CNC Computer Numerical Control

CQF Conjugate Quadratic Filters CWD Choi-Williams distribution CWT Continuous Wavelet Transforms DCT Discrete Cosine Transform

DFT Discrete Fourier Transform

DWT Discrete Wavelet Transform

Fc Cutting force Ff Feed force FFT Fast Fourier Transform

Fr Radial force FRF Frequency Response Function

FT Fourier Transform

HHT Hilbert-Huang Transform

HMM Hidden Markov model

HT Hilbert Transform IMF Intrinsic Mode Function MQL Minimum Quantity Lubrication PCA Principal Component Analysis SEA Synchronous Envelope Analysis STFT Short-time Fourier Transform SVD Singular value decomposition

TCM Tool Condition Monitoring WPTs Wavelet Packet Transforms

WT Wavelet Transform

WTMM Wavelet Transform Modulus

Maxima

ZAMD Zhao-Atlas-Marks distribution

1. INTRODUCTION

Machining processes are widely used to manufacture components that require great accuracy

and/or high surface quality finish. Furthermore, machining is a process that can provide low

costs to a certain number of parts. However, to obtain these advantages it is necessary to

ensure that the set up (machine, tool, cooling, etc) is in perfect order. An option for

controlling this is to use signal monitoring of various parameters including force, noise,

temperature and vibrations.

The monitoring of machining processes can represent economy and practicality due to it

helping to identify tool wear, surface roughness, and anomalies during metal cutting that can

cause waste, damage, and other impairing factors in this process. According to Dimla [1], the

nature of tool wear monitoring is complex and diverse and should provide an indication of

when the cutting tool should be changed without varying the workpiece surface finish, the

machine integrity, and the manufactured component tolerances.

Tool Condition Monitoring (TCM) is needed to obtain not only higher productivity and better

product quality, but also to identify the risks of severe damage to workpieces or machine-tool

components. This is because the operator reaction time has become insufficient during an

emergency, and the use of high speeds can cause serious damages [2].

Machining monitoring applied to hard or brittle materials can also meet the increasing

demands in precision and quality because it provides the characterization, control, and

improvement of processes. Valuable information about the manufacturing process can supply

the dual purpose of process control and quality monitoring, and it can be the first step

towards an automated manufacturing environment [3].

Lee et al. [4] mentioned in their article that the on-line monitoring of tool wear and the

prediction of its failure is reliable and able to respond quickly to tool failure. Even though the

personal computer used in their experiments (a CPU of 10 MHz 80286) did not offer a good

response time, the on-line real-time monitoring (based on cutting forces, sound and vibration,

laser scanners, vision systems, and computer tomography), was still able to supply surface

quality feedback to CNC machines focused on the on-line adjustment of cutting parameters.

However there are some drawbacks, such as signals that may be redundant and measurement

errors that are not easy to be avoided resulting in inaccurate prediction together with a

relatively high measurement cost, despite being able to supply surface quality feedback to

CNC machines focused on the online adjustment of cutting parameters [5].

This paper discusses the usage of monitoring in machining processes. In Section 2 a brief

review of the monitoring techniques is presented. Section 3 presents some parameters that

improve signal acquisition in machining monitoring. Section 4 presents signal processing

methods commonly used in machining monitoring. Section 5 gives the main conclusions.

2. MONITORING TECHNIQUES

The monitoring of machining processes can represent economy and practicality due to it

helping to identify tool wear, surface roughness, and anomalies during metal cutting that can

cause waste, damage and other impairing factors. According to Teti et al. [6], the measuring

techniques for the monitoring of machining operations have traditionally been categorised

into two approaches:

Direct measurement: where the actual value of the variable being measured gives a

high degree of accuracy. This method has been employed extensively in research

laboratories (due to the practical limitations of access during machining, illumination

and the use of cutting fluid) to support the investigations of fundamental measurable

phenomena during machining processes.

Indirect measurement: where the actual value is subsequently deduced using

empirically determined correlations. It is less accurate than the direct method but is

also less complex and more suitable for practical applications.

The indirect measurement applied to the monitoring of tool condition uses an estimate from

the measurable signal feature that is extracted through signal processing steps, as can be seen

in Fig. (1). It is used for a sensitive and robust representation of its corresponding state.

Examples of this measurement are the cutting forces, vibrations, acoustic emission, and

motor/feed current [7].

Figure 1. The framework of TCM [7].

Current control techniques are based on post-process measurement applied to finished

products, which leads to extensive quality control inspection times and also manufacture of

defective products with increase of production costs. System optimization involves three

basic elements: the correct choice of sensors for recording the signal in the monitoring

system, accurate signal processing and characterization, and reliable predictive models with

minor/low prediction of errors [8].

2.2. Cutting Forces

The analysis and prediction of cutting forces is very important in the research of metal cutting

processes and the design of cutting tools. These researches can develop a crucial role in

thermal analysis, tool wear estimation, chatter prediction, chip form categorization, surface

roughness prediction, monitoring of tool condition and others. Furthermore, a large cutting

force means more energy consumption. This has led to the study of the reduction of cutting

force through appropriate choices of parameters and tools [9]. According to Kim & Kim [10],

the cutting force comprises:

Static force: this is an average value and focuses mostly on measuring the cutting

force.

Dynamic force: this is the superimposed fluctuation and can satisfy the needs of

higher machining accuracy, because it has very useful information about the cutting

mechanism.

Gok et al. [11] investigated optimum cutting parameter values of the cutting force in the

milling of AISI H13 (50-54 HRC) on convex and concave inclined surfaces using a ball nose

tool. They observed that an absolute difference in percentile of measured and calculated

values was lower than 3.57 in both inclined surface types.

Turchetta [12] used the measurement of cutting force components in the tangential and

normal directions to analyse the influence of different conditions of tool wear in the milling

of Coreno Perlato Royal marble using a diamond tool.

According to Childs et al. [13], the cutting forces can be measured in:

Direct measurements: these are used when the forces need to be accurately known

both in magnitude and direction. This technique involves mounting a tool or the set of

tool/workpiece on a dynamometer, which responds to the forces by using electrical

signals proportional to the applied forces.

Indirect measurements: these are less accurate than direct methods, but can be

sufficient for monitoring purposes that involve deductions from the machine tool

behaviour.

Aggarwal et al. [14] monitored the electrical current and torque of a spindle motor from the

machine controller in the milling of Certal (AlZnMgCu 0.5) using a dynamometer, in order to

develop a holistic model that allowed the correct calculation of total spindle power to be

defined. According to Kim et al. [15], the cutting force can be measured indirectly by using

the current signal of the servo motor in the range of frequencies below 70 Hz.

The measurement of motor current is commonly used in indirect methods due to the input

phase current waveform variation when feeding during cutting. Furthermore, the phase

current signal is a perfect sine wave in the case of feeding without cutting. In this method, the

bandwidth of the Hall Effect current sensor should be higher than the frequency of the

applied cutting force. They used a sensor of bandwidth of 1 kHz to eliminate the high-

frequency noise and the feed motor current should be measured through an analogue filter

having a 50 Hz cut-off frequency [16].

Li et al. [17] studied the milling of AISI 1045 with HSS tools monitoring the three-phase

currents of a feed motor using Hall Effect sensors and a low-pass filter with a cut-off

frequency of 50Hz. They observed that the permutation entropy associated with tool breakage

can be revealed by information hidden in the motor current signals. This method can

effectively work for different cutting conditions, including the entry/exit cut, the variation of

cutting parameters, and the beat in the feed-motor current signals.

According to Lee et al. [18], the spindle current signal components are as follows:

Cutting force from the machining conditions;

Additional force from the tool wear;

Force variation by non-homogeneity of the workpiece;

Electrical noise that is eliminated by using low-pass filtering.

In the monitoring of spindle current signals the linear independence of the signal components

and a hybrid approach to cutting force regulation should be considered. This procedure is

applied to successively remove the influence of each parameter not related to tool wear from

the measured signals in order to isolate the tool wear data. This technique allows the

monitoring of gradual tool wear, being indispensable for automated and unmanned CNC

machining [18].

The usage of a dynamometer is the most popular method for the measurement of cutting

forces. The torque/force transducer can be constructed using piezoelectric or classical strain

gauges. The piezoelectric effect relates to charge separation that occurs in certain materials

when subjected to mechanical force. Each force component is detected by a separate crystal

oriented relative to the force in its piezoelectric sensitive direction. Quartz is usually chosen

as the piezoelectric material because of its good dynamic mechanical properties, low loss and

piezoelectric constant of approximately 2x10-12 C/N. A charge amplifier that must have a

high input impedance is therefore necessary to create a useful output. Commercial machining

dynamometers are available with natural frequencies from 2 to 5 kHz, depending on size

[13].

Yaldz and nsaar [19] developed an analogue dynamometer consisting of strain gauges in a

Wheatstone bridge circuit configuration. They were mounted on four elastic octagonal rings

to measure the three-force components, feed force (Ff), radial force (Fr), and main cutting

force (Fc) in the turning of AISI 4140. They found the output error percentages to be -0.8%

(Ff), -0.16% (Fr) and -0.12% (Fc) with a cross-sensitivity error in the range of 0.170.92%. A

similar construction was used by Yaldz et al. [20] for milling AISI 4140 steel. The static

calibration curves showed a very high linearity, with errors of 1.3% (Ff), 1.4% (Fc) and 1.2%

(Fr) and a range of 0.61.7%. for cross-sensitivity errors. The advantage of the strain gage-

based dynamometer compared to piezoelectric methods is the cost, the former being around

20 times less expensive.

In the study of a combined-type tool dynamometer used to measure the static and dynamic

cutting forces in an ultra-precision lathe, Kim and Kim [10] used a high-pass filter to

eliminate the 60 Hz electrical component in the dynamic component measurement in the

strain gauge dynamometer.

To understand the cutting force induced errors in CNC turning, Topal and oun [21] used a

strain gauge dynamometer that was designed and constructed using a full Wheatstone bridge

configuration in order to give efficient temperature compensation. They successfully

developed an empirical model for the estimation of diametric error which, by using their

method, could be reduced by approximately 90%.

Panzera et al. [22] developed a dynamometer to independently measure deflection caused by

the three components of the turning force. They used a circular hollow bar of AISI 4340 steel

as an elastic element that was designed to withstand a maximum force of 1.5 kN and a

maximum torque of 7.5 Nm. The experiments were carried out for the turning of normalized

medium carbon steel with a maximum feed rate of 0.4 mm/rot and depth of 2 mm. Strain

gauges were connected in a full Wheatstone bridge configuration, with a voltage of 2V

applied. The output voltage had to be amplified 1,000 times using an operational amplifier

with passive filtering (low pass) and a sampling rate of 120 Hz.

2.3. Vibration

Vibration is a common phenomenon in the finishing machining of a flexible workpiece due to

its low rigidity [23]. The great industrial interest is to avoid the vibrations that produce bad

surface finish and may cause damage to the machining components [24].

According to Dimla [1], vibration signatures satisfy the conditions of robustness, reliability,

and applicability requiring fewer peripheral instruments than acoustic emission. In addition,

their signals have the necessary quick response time needed to indicate changes for on-line

monitoring. He used an analytical on-line system for TCM based on vibration signature

features in the three principal axes to correlate the tool wear and observed that it is possible to

distinguish different wear modes from an analysis of the trends in the vibration signals.

Furthermore, they confirmed that the time domain features were deemed to be more sensitive

to cutting condition than tool wear, whereas frequency based features correlated well with

tool wear.

Accelerometers are more commonly used to measure vibration. However, Devillez and

Dudzinski [24] mentioned that despite being very easy to use, accelerometers present a major

drawback in that the acceleration signal can only be used to analyse the vibration frequency

and amplitude. They proposed a method which used a non-contact displacement measuring

system based on the eddy current principle with a sensitivity of 30 V/mm, because their

interest was to directly measure tool movement in real time in order to correlate it with the

surface finish obtained. This method presented an efficient way to determine the dynamic

parameters of the tool system and to obtain the cutting tool displacement signals.

Torabi et al. [25] investigated the on-line TCM in the high speed milling of Inconel-718

using a 3-flute micro-grain tungsten carbide ball-nose tool. To acquire the vibration signal,

which is a preferred choice for signal processing regarding the lower price of the sensor and

its easy installation, they used a ceramic shear three axes accelerometer. Zhong et al. [26]

used the vibration during cutting to study the milling on AL 7050-T7451 aluminium alloy

using dry cutting and Minimum Quantity Lubrication (MQL), 150 ml/min and 300 ml/min.

They observed that the cutting fluid can speed up the attenuation of cutting vibration, and in

the finishing process it is appropriate to apply MQL, which can assure a high quality of

workpiece and reduce the usage of cutting fluid.

In the monitoring of the vibration in the ultraprecision face turning of Al 6061 aluminium

alloy, with accelerometers fixed on the spindle and tool holders, Meyer et al. [27] observed

that waviness errors caused by relative tool/workpiece vibration are a significant source of

inaccuracy. They also found that the surface finish lobes provide a systematic framework for

describing how broadband relative tool/workpiece vibrations manifest themselves on the

workpiece surface.

Salgado et al. [28] measured the cutting vibrations at a rate of 10 kHz on the turning of AISI

8620 using a triaxial accelerometer. They developed a method for determining the accuracy

of a surface roughness prediction system based on cutting vibrations.

According to Zeng et al. [23], some studies are focused on the vibration control of a flexible

workpiece, but the fixture has importance on the machining, since it has the ability to

suppress the excessive machining vibration on the workpiece and balance the cutting forces.

It presents the following advantages:

It can target the nature of the problem of machining vibration suppression on a

flexible workpiece because of its clear physical meanings;

Vibration reduction of a flexible workpiece can easily be achieved with an appropriate

fixture layout, and the capability of disturbance rejection of the workpiecefixture

cutter system can be improved using this method;

The location, the applied forces and the number of fixture elements can be

simultaneously optimized.

In their paper about the vibration signal on the milling, Bisu et al. [29] studied the

decomposition of vibration sources generally and directly related it to the degree of failure of

a mechanical component from a number of measures in various configurations achieved by

accelerometers. They highlighted that the vibration signals are the result of a mixture of

different sources that correspond to components of machines, making it difficult to identify

the state of damage to a particular component.

Lamraoui et al. [30] investigated the chatter and tool wear monitoring on the basis of the

stationary and cycle stationary tools in high speed milling. Cycle stationarity is a property

that characterizes stochastic processes whose statistical properties periodically vary with

respect to some generic variables. They used the cycle stationarity character of accelerometer

signals coming from those that were acquired simultaneously with encoder information and

the signals are resampled in the angular domain. It offered an indisputable advantage in

industrial rotating machining operations that opened up ways for using it for monitoring

machining.

Rao et al. [31] analysed the vibration signal in the turning of AISI1040 tube with length of 90

mm, outer diameter of 100 mm and inner diameter of 56 mm using a tool with nose radii of

0.8 and 0.4 mm under dry conditions. They used a Laser Doppler Vibrometer (LDV) to

obtain online data acquisition. Among the several observations, vibration amplitudes found

were to increase with the progression of tool wear.

2.4. Temperatures

The power consumed in cutting is converted into heat near the cutting edge of the cutting tool

and many of the economic and technical problems are caused directly or indirectly by this

heating action [32]. The two goals of temperature measurement in machining are, mainly, to

quantitatively measure the temperature distribution throughout the cutting region (commonly

over 700C) and the average temperature at the chip/tool point of contact [13].

According to Byrne [33], the temperature is fundamental to the process of chip removal and

perhaps it is the single most important factor influencing the efficiency of the process

showing specific characteristic factors:

The degree of plastic deformation;

The extent of tool wear;

The degree of diffusion and corrosion;

The fatigue properties;

Compositional changes in the workpiece material.

According to Sivasakthivel and Sudhakaran [34], measuring the cutting temperatures is

difficult because the temperature is a scalar field which varies throughout the system and

cannot be uniquely described by values at a point. Thermocouples are the most widely used

sensors. They can be embedded in the tool or workpiece to measure the temperature

accurately with less effort, besides being conductive, they also operate over a wide

temperature range, are rugged and inexpensive.

Several experimental methods, such as thermocouple and radiation techniques, can be

employed to measure the temperature and the prediction of heat distribution in the cutting

zone. However, due to a narrow shear band, chip obstacles and the nature of the contact

phenomena where the two bodies, tool and chip, are in continuous contact and moving with

respect to each other these measurements become extreme difficulty [35]. Table 1 shows

some researches that used these methods.

Table 1. Temperature Measurements Type

To improve the understanding of work done by a cutting tool in removing metal, OSullivan

and Cotterell [46] monitored the temperature in the turning of Al 6082-T6 aluminium alloy

tube. They used two thermocouples on the inside of the tube and an infrared thermal camera

placed 0.5 m from the workpiece on the opposite side to the cutting tool. This technique

allowed the authors to observe that the amount of heat energy flowing into the tool at the

cutting edge increased due to increase in tool wear. The net result of this was to increase the

amount of energy required to perform the cutting process.

Davoodi and Hosseinzadeh [35] used an infrared sensor to monitor the temperature in high

speed machining as it is suitable for dry conditions due to its high response rate. It has the

ability to provide temperature measurements based on the distance from the cutting zone and

it is not necessary to make a hole in the tool or workpiece to install the instruments.

Moreover, it can be used for all types of materials, but the sensor should be installed as close

as possible to the desired surface, because the distance between target surface and sensor is

very important and can affect the results. Finally, it is not possible to use liquid cooling and

the chip may come between the surface and the sensor thereby causing an error.

2.5. Sound

Takate et al. [49] maintained that the operational sound contains a lot of information. The

sounds of the machine are perceived by the operator who can use them to identify the

operation (table movements, tool changes, and machining with various cutting tools), failures

of the machine or occurrences of abnormal machining conditions. They observed in their

results that there is a high rate of recognition for the movement of the machine and various

machining operations including machining with a broken tool. The chip formation process

emits an acoustic energy at high frequencies that can be used to determine events such as

TCM. The audio signal, although at a frequency higher than that of the vibrations, can be

detected by a microphone and is useful for characterising machining dynamics [50].

Microphones are very suitable for chatter detection in milling, as their sensitivity to chatter

onset is comparable to that of expensive sensors such as plate dynamometers, displacement

probes and accelerometers. This method is affected by some limitations (directional

considerations, low-frequency response and environmental sensitivity) and for successful

application, the suppression of environmental noise is mandatory [51]. However, the

microphone is a low cost solution for detecting chatter [50].

According to Weller et al. [52], some analysis and data reduction techniques to determine the

internal condition of machinery by listening to its sound, could also be applied to metal

cutting operations. They developed experiments for the construction and operation of a

detector of tool wear that observed the development of cutting edge flank wear-land, the

increase of cutting forces, and signal amplitude for the increased sound level produced.

In their paper, Lu and Kannatey-Asibu [53] monitored the audible sound signals, force,

vibration and acoustic emission on the turning of AISI 8620 steel using different tool wear

states. They used a inch microphone (B&K 4165) mounted at a distance of 7 inches (177.8

mm) from the cutting zone, at the end of a fixture supported by a magnetic base on the turret.

They used a 12 bit data acquisition board, with a 100 kHz sampling rate. The sound

monitoring was developed as a means of enhancing process monitoring capabilities. The

results agree qualitatively with the frequency characteristics of experimental data obtained

with sharp and worn tools.

Tekner and Yelyurt [54] used the sound signal to assess machinability of AISI 304

stainless steel, analysing the flank wear, built up edge, radii of chip curl, surface roughness

and sound pressure. They measured and recorded pressure levels of sound in the machining

process using a microphone connected to a computer and observed that levels of pressure

during the cutting levels decreased in parallel with positive results occurring in chip removal.

They affirmed also that measuring the cutting sound pressure level is a suitable method for

developing an alarm system.

Weingaertner et al. [55] used a inch free field microphone (PCB 377A02) positioned in the

machine working area close to the workpiece to monitor the milling of Al 7075- T6

aluminium alloy, using a 12 mm diameter cemented carbide end-mill under dry conditions.

The microphone was chosen as a sensor to detect vibrations during the process due to its

suitable frequency bandwidth and its ability to detect vibration signals from the tool. They

observed that for roughing operations, the results of analytical and time-domain simulations

were practically the same and for finishing operations the results obtained using the two

methods showed small differences. Furthermore, the greatest differences occurred for

conditions close to resonance, which related to a low depth of cut limit in the cutting tests.

Salgado and Alonso [56] highlighted that sound signal analysis during a cutting process has

been used for a long time. In their study, they used Singular Spectrum Analysis (SSA) to

extract valuable information correlated with tool wear in the turning of AISI 1040 steel. They

chose a condenser microphone (type 40AE, G.R.A.S. Skelstedet 10B with preamplifier

PRE12H) to amplify and log their data using a sampling rate of 50 kHz over a period of 100

ms. Their conclusion was that the sound signal emitted during turning and the feed motor

current have acceptable cost-performance ratios for their industrial application in relation to

other methods proposed in the literature.

Samraj et al. [57] proposed an on-line measurement system, using Singular Value

Decomposition (SVD) of the emitted sound during the turning process, to estimate the flank

wear of a tool. They used a microphone of 0.25" diameter with a dynamic range up to 122 dB

being more suitable to record a frequency response range of 20Hz to 20 kHz at a noise

accuracy of 0.5dB. They found an increase in the SVD features as the tool flank wear

increased, i.e., the condition monitoring of tool flank wear by emitted sound was proven

possible and is a relatively simple process.

Lu and Wan [58] developed a method for tool wear monitoring in the micromilling of SK2

Steel using a microphone with bandwidth of up to 80 kHz (higher than the traditional

microphone with bandwidth of up to 20 kHz). The collected signal was transformed to the

frequency domain using Fast Fourier Transform (FFT) and applied to a Hidden Markov

model (HMM) to process the signal and determine the tool condition. This method showed

that a classification rate of 100% can be obtained by normalizing the sound signal before

conducting the features selection process.

A disadvantage of this method is that in the region between 0 and 2 kHz the influence of the

surroundings and of the noise from adjacent machines, motors, conveyors, or processes can

influence the signals [56]. Moreover, frequencies below 100 Hz cannot be measured easily

and the microphone tends to pick up high levels of background noise [50].

Rafezi et al. [59] investigated the drilling on the Al 7075 aluminium alloy using HSS without

coolant implementation. They observed that frequency components less than 10 kHz are

affected by tool wear and other regions of the frequency spectrum are similar for both sharp

and worn tools. They also commented that the environmental noises (machine tools, fans,

human voice, and others) should be eliminated. They calculated that the frequency spectrum

of the noise in the proximity of the machine under study only has frequency components of

less than 2 kHz.

2.6. Acoustic Emission

Acoustic Emission (AE) is defined as the class of phenomena whereby transient elastic waves

are generated by the rapid release of energy from localized sources within a material. It can

be found in the primary (due to chip formation); secondary (due to friction between cutting

tool and chip); and tertiary (due to friction between cutting tool flank and workpiece) cutting

zones [5]. The usage of AE sensors to monitor machining processes (turning, milling, and

grinding) is quite effective, and detecting malfunctions due to the sensor is very sensitive to

the process and more reliable. One of the ways to take full advantage of high sensitivity is the

fusion with other types of sensor such as, for example, the force sensor [60].

The AE is due to the dynamic deformation of materials accompanied by the emission of

elastic stress waves, which occur over a wide frequency range but typically from 100 kHz to

1 MHz [13]. The AE technique is considered one of the most accurate monitoring methods in

machining that some researchers used it to identify damage mechanisms, furthermore, it is

also considered to be one of the most powerful methods in the composite drilling process

[61].

The AE technique shows a great advantage over the conventional load cell, it has a relatively

superior signal-to-noise ratio and sensitivity at the ultraprecision scale, even at extremely low

depths of cut [3].

The chip formation process emits an acoustic energy at high frequencies that can be used to

determine events such as TCM. This lower frequency audio signal, although its frequency is

higher than the vibrations, can be detected by a microphone and is useful for characterising

machining dynamics. However, frequencies below 100 Hz cannot be measured easily and the

microphone tends to pick up high levels of background noise [50]. Marinescu and Axinte

[62] used the AE sensory measures efficiently for monitoring both tool malfunctions and

workpiece surface anomalies in milling of Inconel 718 and when compared with the more

traditional sensory approaches (force/acceleration) limits their applications to TCM.

Hase et al. [63] studied the correlation between the AE signals and the cutting phenomena in

the turning of AISI O1. They used a lead zirconate titanate piezoelectric ceramic sensor with

resonance frequency of 1 MHz and a frequency band of 50 kHz to 2 MHz. They applied a

500-kHz high-pass filter to eliminate noise and signals caused by phenomena such as

collisions and twining of chips that are not directly related to the cutting phenomenon. They

observed that the amplitude of the signal formed by serrate-type chips is larger than that for

flow-type chips due to the amplitude of AE waves caused by the non-uniform discontinuous

fracture being larger than that caused by uniform ductile fracture. Furthermore, they also

observed a negative correlation between the mean value and the shear angle identifying the

process of formation of chips (the cutting state) that changes with the wear of the tool and

affects the quality of the machined surface.

To detect anomalous events in abrasive water jet machining, Axinte and Kong [64] employed

multiple point AE sensing (the nozzle, workpiece fixture, and a dummy plate) using a sample

rate of 1 MHz to collect the input, utilised and idle energy-related signals. They observed that

this technique is highly versatile without disturbing its setup while coping with the harshness

of the working environment characteristic of this process.

In the face turning studies of carbon/phenolic composite, Sreejith et al. [65] mounted an AE

sensor on the tool holder configured with a pre-amplifier (160 B Model; gain = 60 db) and

filters (30 kHz to 2 MHz, 125 kHz to 250 MHz and 500 kHz to 2 MHz). Through the

frequency domain, the authors observed that the cutting tool exhibits a mixed mode of

emission consisting of low frequency burst emission and high frequency deformation modes.

Furthermore, it is also influenced by the cutting speed, which indicates that the cutting tool

cuts stably up to 200 m/min above which, degradation of the tool sets in resulting in

deteriorated performance.

2.7. Other techniques

Young et al. [66] published a paper about the in-process and on-line surface texture

measurement using the Optical Surface Measurement Techniques (Specular Reflectance,

Diffuseness, Ellipsometry and Speckle). Galante et al. [67] proposed a technique to estimate

the Ra in the turning of AISI 1040 steel with a WC tool and cutting speed of 200 m/min.

using tool on-line monitoring employing a CCD camera. They observed that this technique

offers results similar to those detected by a profilometer. Thus, this technique could be

utilized in production to maintain the integrity within assigned production specifications by

varying the feed with increasing tool wear.

Morala-Argello et al. [68] performed a quality test for the surface roughness on turned parts

using a computer vision system. It showed advantages of having the possibility to perform in

machine measuring and the chance to carry out an exhaustive control of surface finishing.

The error values are affordable in an industrial environment and the measuring is less time

consuming and therefore more economical.

Another method of monitoring was used by Gok et al. [11]. They measured the tool

deflection using an inductive sensor mounted on the cutting tool at a length of 1.5 times the

tool diameter. The sensor operated in a voltage range of 0-10 V, the output voltage value was

set to remain constant at around 5 V.

Abu-Zahra and Lange [69] investigated the ultrasonic waves in the turning of AISI 4140 (55

HRC). They described the measures as mechanical waves that propagate at a frequency above

the human audible range, i.e. 20 kHz or higher They are determined by the physical and

mechanical properties of the transmittance media, such as, temperature, pressure, density,

stiffness, and acoustic impedance. They are also similar to light waves in terms of their

propagation mechanisms such as reflection, refraction, distortion, and absorption.

3. SIGNAL ACQUISITION

To obtain success in the monitoring process, the choice of devices is very important. The user

should check if the measurement resolution and range will satisfy the process requirements.

The resolution refers to the number of binary levels of ADC that can be used to represent a

signal. The smallest detectable change in this signal determines the resolution that is required

of the device, as can be seen in Fig. (2). The BPS (bits per sample) will be kept constant at its

optimum value because during the test period it is not possible to know if the acquired signal

is in the audible range, between 20Hz and 20kHz [70].

Figure 2. Influence of bits per sample [70].

The sample frequency is another important aspect because this factor, generally, is related to

the resolution of the signal. The sample frequency should be clearly defined, because an

incorrect value can mask important values that will influence the integrity of the signal. Shaw

[71] suggests that the sample frequency should be four times the natural frequency, a

minimum, in the use of a dynamometer. Some researchers applied the Nyquist Theorem of

sampling at twice the maximum frequency, whilst others suggest sampling at least 10 times

the maximum frequency, Fig. (3) [70].

Figure 3. Example of sample rate [70].

3.2. Domain Analysis

The time domain is based on estimating the signal period and subsequent equidistant

sampling of one signal period, an integer number of signal periods, or on using measurement

time covering many signal periods that are needed to decrease sufficiently the uncertainty of

measurement caused by the energy leakage due to non-coherent sampling [72].

The time domain signal when processed by the application of the Fourier Transform, see

forward, transforms the signal data into the frequency domain, which refers to the display or

analysis of data based on frequency. In vibration analysis, the principal advantage is the

repetitive nature of the signal and are clearly displaced as peaks in the frequency spectrum at

the frequency where the repetition takes place [73]. The use of frequency analysis can verify

the monitoring of the tool rotational speed or can detect if the tool is cutting with one or

multiple blades [74].

Analysing the accelerator signal, Bisu et al. [29] realized that the convolution in the time

domain is equal to the product in the frequency domain, so the fault signal becomes a

modulated signal. They also affirmed that the fault signal can be enlarged more than ten times

at the resonant frequency. However, the fault signal is relatively diminished in the other

frequency range due to the character of the wide frequency range of impulsive signal that the

average noise signal always contains in the low-frequency range and this situation also

applies to the sensor.

Analysing the microphone and accelerometer power spectrum of signals for sharp and worn

tools, Lu and Kannatey-Asibu [53] observed that the energy distribution for sharp and worn

tools are easily discernible from the sound and vibration signals. In this regard, ignoring the

sound signals below 0.5 kHz, similar peaks were observed in both the sound and vibration

signals in the feed and cutting directions.

According to Lamraoui et al. [30], the frequency domain has a drawback due to not providing

information in the time domain. A time-frequency analysis is better as it characterises the

signal in both the time and frequency domains. In vibration analysis, it manifests in either the

frequency or the time domain and thereby gives a compromise between the frequency

resolution and the temporal resolution. They acquired the data using the angular-domain

because it is more convenient to sample the signal with respect to an angular variable , so

that the cycle stationary characteristic is preserved.

Zhang and Chen [75] developed a study of TCM in an CNC end-milling machine based on

the vibration signal collected through a microcontroller-based data acquisition system. They

observed that displaying vibration signals of the X, Y and Z directions in the time domain is

helpful in understanding the cutting condition. The vibration amplitudes in the time domain

and the frequency peaks at harmonic frequency bands of the X and Y directions can be used

as the key featured signals for monitoring the tool condition.

Sometimes the vibration and sound analysis can be analysed using frequency spectra. More

advanced signal-processing techniques needed for system, source, and path identification

problems require the computation of frequency spectra. Furthermore, if the data are random

in character, a frequency analysis in terms of power quantities per hertz greatly facilitates the

desired evaluations of the data signals. For deterministic signals that are periodic, a frequency

decomposition or spectrum is directly obtained by computing the Fourier series coefficients

of the signal over at least one period of the signal using an Fast Fourier Transform (FFT)

algorithm. Line Spectrum or Discrete-frequency Spectrum is represented by the Fourier

component magnitudes versus frequency. However, the phase information is generally

retained only in those applications where there may be a need to reconstruct the signal time

history or determine peak values. The Power Spectral Density function or Auto Spectral

Density function provides a convenient and consistent measure of the frequency composition

of random data signals. The power spectrum is most easily visualized as the mean-square

value of the signal passed through a narrow-band pass filter divided by the filter bandwidth

[76].

It is interesting to highlight however, that although the time and frequency domain are more

utilized, other domain analysis can also be applied in machining monitoring. In their paper,

Ritou et al. [2] studied the radial eccentricity of a new end mill. To study the contribution of

each tooth to the cutting force, they applied an angular approach. They acquired the force

signals in the time domain associated with the tool angular position. They observed that for

every tool revolution, a force peak is extracted for each tooth that passes and hence the cutter

eccentricity is estimated.

Lamraoui et al. [77] developed a chatter indicator method to diagnose chatter in high speed

milling of Al 7075-T6 aluminium alloy using an angular-frequency domain. They affirmed

that analysis in the angular domain is useful for observing the behaviour of cutting forces

during each revolution and provides information about the system stability. They acquired the

AC motor integrated rotational encoder data using a system with an angular sampling device.

4. SIGNAL PROCESSING

Sometimes the acquired signal can be influenced by a frequency range that is not of interest

in the analysis, and so causes the monitoring be totally impractical. An alternative is to use a

non-periodic excitation and statistical signal processing technique that requires a number of

operator decisions: the frequency range, the number of test averages, and choice of

windowing procedure [50].

Kalvoda and Hwang [78] affirmed that selecting the right data processing technique is one of

the most important items for a cutting process that is assumed to be nonlinear and non-

stationary. Kuljanic et al. [51] mentioned some analysis techniques for the signal processing

Time domain analysis (once per revolution sampling, Poincar sections);

Frequency domain analysis (Fast Fourier Transform, power spectral density);

Time-frequency domain analysis (Wavelet Transform);

Other (entropy, coarse-grained entropy rate, normalized coarse-grained information

rate).

4.1. Fourier Transform

The Fourier Transform (FT) is commonly applied in signal processing. The principle of the

Fourier Transform is to extract the fundamental frequency component of the fringe pattern in

the 1D or 2D frequency domain and its inverse transform of the filtered frequency domain

signal which then provides the modulo 2pi phase of the fringe pattern [79].

To study the high-precision machining, Kono et al. [80] applied the Fourier series in the

frequency domain to analyse geometric errors from other errors using an artefact and a laser

displacement sensor. In their study of the analysis using a multi-sensor in high speed

machining, Kang et al. [81] monitored the spindle vibration and analysed the rotation

frequency and tooth frequency of the acceleration signal transformed by a Fourier Transform.

In the literature it is possible to find several variations of the Fourier Transform applied to

machining signal processing. The Discrete Fourier Transform (DFT) and Discrete Cosine

Transform (DCT) are efficient forms of the Fourier Transform often used in various

applications including tool condition monitoring [82].

The Gabor Transform, also called the short-time Fourier Transform (STFT), is a time-

frequency technique used to deal with non-stationary signals that have a short data window

centred on time. Its implementation for AE signal processing is efficient when it is used to

locate and characterise events with well-defined frequency patterns, not overlapping and long

relative to the window function [83].

According to Gu et al. [84], from among the several approaches available, the short-time fast

Fourier Transform (STFFT) is often used for non-stationary signal analysis, but it is a trade-

off between time and frequency resolutions. They proposed an approach based on the Choi-

Williams time-frequency distribution analysis and singular value decomposition that gives

satisfactory results and can be used for on-line condition monitoring and diagnosis of

machines.

Zhu et al. [7], affirm that although the Fast Fourier Transform (FFT) is the standard method

for observing signals in the frequency domain and has been widely studied, it has certain

serious theoretical drawbacks in processing machining signals. Liu et al. [85] used FFT to

avoid excessive vibrations noise in the machining signal and to filter the undesired frequency

components in the interpolation points, which guaranteed the exact trajectory generation and

shock free motion simultaneously.

4.2. Wavelet Transform

Wavelet Transform (WT) decomposes a single signal series in the time domain into a two-

dimensional function, where each of the decomposed signals is a mixture of source signals. It

can be considered as a series of band pass filters, whose results could be regarded as different

mixtures of independent source signals [86].

According to Zhu et al. [7], the Wavelet Transform was developed in the late 1980s to meet

the needs of adaptive time-frequency analysis in applied mathematics, physics, and

engineering. It has also been used for machinery fault diagnostics and TCM. Its great

potential in detecting abrupt changes of tool conditions can be explained by:

Sparse representation of signal, the wavelet expansion coefficients cj,k and dj,k drop

rapidly with increase in j and k, and only a few large coefficients exist while the

others are small.

Setting a suitable threshold, the undesired noise is filtered, the real essence of wavelet

denoising, and compression.

The localization of the time and frequency description of the signal that reveals the

signal behaviour in real time and its corresponding frequency property.

Liao et al. [82] highlighted the properties because the Wavelet Transforms are more powerful

and versatile than the Fourier Transform:

Some WTs have compact support, thus are able to capture local time-dependent

properties of data, whereas Fourier Transforms can only capture global properties.

WTs are more efficient even when compared with the FFT.

The WT is hierarchical and allows much fine tuning for a variety of applications.

Unlike the Fourier Transform, Wavelet Transform has an infinite set of possible basis

functions.

The Wavelet Transform has been applied to many engineering studies with great success and

can equally be applied for monitoring machining processes. It occurs due to the wavelet

exhibiting a natural shape, which is more descriptive of most natural processes than the sine

function used in Fourier analysis. Furthermore, the Wavelet Transform is capable of

revealing aspects of data that other signal analysis techniques miss, like trends, breakdown

points, discontinuities in higher derivatives, and self-similarity [87].

Different Wavelet features are used in tool condition monitoring. Continuous Wavelet

Transforms (CWT) are recognized as being effective tools for both stationary and non-

stationary signals, but they involve much redundant information and are computationally very

slow. The Discrete Wavelet Transform (DWT) has a fast algorithm based on Conjugate

Quadratic Filters (CQF) [7].

Grzesik and Brol [88] used the CWT (Mexican hat and Morlet) which is also capable of

detecting the fractal or partial fractal (multifractal) properties of the roughness profile on the

hard turning using optionally standard and wiper ceramic inserts. Kasashima et al. [89] used

the DWT to investigate the cutting forces in the milling of stainless steel 304.

Zhong et al. [26] adopted the Wavelet Packet Transforms (WPTs), which is often used to

analyse the frequency of a signal and provide a possibility of time-frequency localization, to

analyse vibration signals in order to show the effect of cutting fluid on different frequency

bands.

Wang and Liang [90] developed a non-dimensional chatter index based on the Wavelet

Transform Modulus Maxima (WTMM) and statistical analysis which included as advantages

the random and statistical nature of the metal cutting process. The sensitivity to chatters is

well known to be effective in detecting singularities; less susceptible to process changes; it

varies between 0 and 1 independent of cutting processes and hence can be used in different

machining processes.

Torabi et al. [25] applied CWT analysis of force and vibration in various scales to search for

the appropriate number of peaks according to cutting conditions, sampling rate and time

duration. They observed that wavelet features of force and vibration can be extracted for use

in a clustering method. It filters out the signal before feature extraction and makes the model

robust against the effects of noise.

Xu et al. [91] studied a TCM method using the vibration signal of the tool wear, which was

identified by pattern recognition technology, thus the conditions were identified. They

observed that calculating and classifying the speed of neural network and wavelet packet

analysis is very fast and a fault diagnosing system can be utilized to build up practical on-line

fault diagnosis, so it has broad prospects in many application areas. Table 2 shows some

researches that used WT to process monitoring signals.

Table 2. Some machining researches using Wavelet.

4.3. Hilbert and Hilbert-Huang Transform

The usage of the Hilbert Transform (HT), or its derivations, may be found in the literature for

machining research. The HT is one of the integral transforms like Laplace and Fourier, Eq.

(1). It was first introduced to solve a special case of the integral equations. Investigators of

digital algorithms for the realization of the HT made a major contribution when the digital

revolution started, and digital computers and digital signal procedures appeared everywhere.

In 1985, the HT was included as a typical signal procedure for the Brel and Kjr two

channel analyser. HT application to the initial signal provides some additional information

about an amplitude, instantaneous phase and frequency of vibrations. It also can be employed

for solving an inverse problem. In the frequency response function (FRF) of a linear structure

the original FRF is reproduced, and any departure from this, i.e. a distortion, can be attributed

to nonlinear effects [98].

[ ] ( )

== dtx

pi

1x(t)H(t)x Eq. (1)

Bisu et al. [29] used HT to develop a method based on synchronous envelope analysis (SEA),

which is a very useful tool for monitoring and also used to create special signals called

analytic signals. It is especially important in simulation and in this case a model was

developed with the purpose of using simulation to determine the teeth evolution in the

machining process. Furthermore, they affirmed that HT can be interpreted in the frequency

domain to highlight the different dynamic phenomena. This form of analysis is a very useful

tool for monitoring and creating special signals called analytic signals which are especially

important in simulation. They affirmed that the method to compute the HT of a function is a

frequency-domain approach, Eq. (2).

( )

= dty

pi

1(t)y Eq. (3.2)

Cao et al. [99] decomposed Acoustic Emission (AE) signals via a lifting scheme and

extracted features from the wavelet coefficients using HT to identify salient features of

different tool states (normal conditions, slight breakage and serious breakage) in the milling

of AISI 1045. They observed that tool breakage can be detected successfully through the

recognition of these features.

A derivation of the Hilbert Transform (HT), the Hilbert-Huang Transform (HHT), is also

used to process the signals. According to Cao et al. [100], the HHT consists of the following:

Empirical Mode Decomposition , when a complicated signal is decomposed into a

series of simple oscillatory modes, designated the intrinsic mode function (IMF), and

a residue;

The Hilbert Transform is then invoked for each intrinsic mode function (IMF) to

obtain the instantaneous frequencies and the instantaneous magnitudes, which

comprise the Hilbert-Huang spectrum of the signal.

Cao et al. [100] monitored the vibration signals from the milling of aluminium 7050 with a

carbide end mill cutter (two flutes) using a spindle speed of 8.5 krpm (1.5 m/min) and a

sampling frequency of 6.4 kHz. They used the HHT to analyse the reconstructed signals and

obtain the Hilbert-Huang spectrum, from which their mean value and standard deviation were

used to calculate the chatter indices.

Kalvoda and Hwang [78] used the HHT to analyse the cutting forces and vibration from the

milling of aluminium alloy using an end-mill with diameter of 12 mm and four flutes. They

used a sampling frequency of 9 kHz for both acquisition signals with a cutting speed of

204m/min (5.4 krpm). They used the HHT to correlate the tool wear/breakage by observing

the change in the frequency peak with the change in cutting geometry of a cutter tool. They

considered the shift of the main frequency peak into lower frequency together with higher

frequency fluctuations to be a cutter tool wear indicator. Although the HHT method is a new

method, it has shown good results and it has attracted other researchers to use it. Table 3

shows some researchers who have used this method.

Table 3. Some machining researches using HHT.

4.4. Other signal processing methods

Kim et al. [15] applied a real-time Kalman filter in the motor current signals in order to filter

out the undesired current component due to the acceleration or deceleration from the

measured servo motor current signal. A Kalman filter is a linear and minimum error variance

recursive algorithm for optimally estimating the unknown state of a dynamic system from a

noisy environment.

In their paper, Marinescu and Axinte [104] developed a monitoring method based on

combinations of time-frequency domain analysis of the AE by use of advanced processing

techniques, Choi-Williams distribution (CWD), Zhao-Atlas-Marks distribution (ZAMD) and

formant analysis methods. They mentioned that ZAMD produces good resolution in time and

frequency domains and reduces the interference obtained in the cross-terms present in multi-

component signals. It is useful in resolving small spectral peaks and capturing non-stationary

and multi-component signals.

To process the signal, Simeone et al. [105] used Principal Component Analysis (PCA), also

known as the Karhunen Loeve Transformation, the purpose of which is to reduce the high

dimensionality of sensor signal data, consisting of a large number of interrelated variables, by

extracting significant signal features. The usage of PCA consists of a signal pre-processing of

mean centring which needs to be performed by calculating the mean of each variable and

subtracting it from the original data to generate a zero-mean distribution. Signals oscillating

around zero are obtained to ensure that the first principal component describes the direction

of maximum variance. They observed a success rate that varied between 88% to 100%

confirming the capability of PCA to extract valuable sensory features for on-line residual

stress monitoring.

5. CONCLUSION

Monitoring applied to machining processes can improve the process through an increase in

tool life and surface quality with a simultaneous decrease of electric energy and waste

material. However, the choice of the monitoring method requires great care due to

implementation cost and requirements, besides the objectives to be analysed, i.e., in certain

cases, the tool wear monitoring using a dynamometer can be as efficient as the accelerometer,

which is more inexpensive. Furthermore, signal interpretation is fundamental. The user

should match the best method of analysis to the objective, i.e., if the process uses variable

revolution, the vibration or sound monitoring in the frequency domain can be unsuitable if

the user does not revise the revolution/frequency ratio. Although machining monitoring

requires great care, its usage brings excellent results for both industrial and academic

research.

6. ACKNOWLEDGEMENT

The authors would like to thank the Ministry of Educations Coordination for the

Improvement of Higher Education Personnel (CAPES), the University of Aveiro and the

Department of Mechanical Engineering of the Federal University of So Joo del Rei.

Additional thanks go to Dr. Juan Carlos Campos Rubio, from the Federal University of Minas

Gerais, Brazil.

7. REFERENCES

[1] D. E. Dimla, The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation, Int. J. Adv. Manuf. Technol., vol. 19, no. 10, pp. 705713, Jun. 2002.

[2] M. Ritou, S. Garnier, B. Furet, and J. Y. Hascoet, Angular approach combined to mechanical model for tool breakage detection by eddy current sensors, Mech. Syst. Signal Process., vol. 44, no. 12, pp. 211220, Feb. 2014.

[3] X. Han and T. Wu, Analysis of acoustic emission in precision and high-efficiency grinding technology, Int. J. Adv. Manuf. Technol., vol. 67, no. 912, pp. 19972006, Nov. 2012.

[4] K. S. Lee, L. C. Lee, and S. C. Teo, On-line tool-wear monitoring using a PC, J. Mater. Process. Technol., vol. 29, no. 13, pp. 313, Jan. 1992.

[5] C. Lu, Study on prediction of surface quality in machining process, J. Mater. Process. Technol., vol. 205, no. 13, pp. 439450, Aug. 2008.

[6] R. Teti, K. Jemielniak, G. O. Donnell, and D. Dornfeld, Advanced monitoring of machining operations, CIRP Ann. - Manuf. Technol., vol. 59, no. 2, pp. 717739, Jan. 2010.

[7] K. Zhu, Y. S. Wong, and G. S. Hong, Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results, Int. J. Mach. Tools Manuf., vol. 49, no. 78, pp. 537553, Jun. 2009.

[8] E. Garca-Plaza, P. J. Nez, D. R. Salgado, I. Cambero, J. M. H. Olivenza, and J. G. Sanz-Calcedo, Surface Finish Monitoring in Taper Turning CNC Using Artificial Neural Network and Multiple Regression Methods, Procedia Eng., vol. 63, pp. 599607, Jan. 2013.

[9] W. J. Deng, Q. Li, B. L. Li, Y. T. He, W. Xia, and Y. Tang, Study on the cutting force of cylindrical turning with novel restricted contact tools, Int. J. Adv. Manuf. Technol., Jun. 2013.

[10] J. Kim and D. Kim, Development of a combined-type tool piezo-film accelerometer for an ultra-precision lathe, J. Mater. Process. Technol., vol. 71, no. 3, pp. 360366, 1997.

[11] A. Gok, C. Gologlu, and H. I. Demirci, Cutting parameter and tool path style effects on cutting force and tool deflection in machining of convex and concave inclined surfaces, Int. J. Adv. Manuf. Technol., no. 3, Jun. 2013.

[12] S. Turchetta, Cutting force and diamond tool wear in stone machining, Int. J. Adv. Manuf. Technol., vol. 61, no. 58, pp. 441448, Nov. 2011.

[13] T. H. C. Childs, K. Maekawa, T. Obikawa, and Y. Yamane, Metal Machining Theory and Applications. London: Arnold, 2000, p. 400.

[14] S. Aggarwal, N. Nei, and P. Xirouchakis, Cutting torque and tangential cutting force coefficient identification from spindle motor current, Int. J. Adv. Manuf. Technol., vol. 65, no. 14, pp. 8195, May 2012.

[15] T.-Y. Kim, J. Woo, D. Shin, and J. Kim, Indirect cutting force measurement in multi-axis simultaneous NC milling processes, Int. J. Mach. Tools Manuf., vol. 39, no. 11, pp. 17171731, Nov. 1999.

[16] G. D. Kim and C. N. Chu, Indirect Cutting Force Measurement Considering Frictional Behaviour in a Machining Centre Using Feed Motor Current, Int. J. Adv. Manuf. Technol., vol. 15, no. 7, pp. 478484, Jul. 1999.

[17] X. Li, G. Ouyang, and Z. Liang, Complexity measure of motor current signals for tool flute breakage detection in end milling, Int. J. Mach. Tools Manuf., vol. 48, no. 34, pp. 371379, Mar. 2008.

[18] K. J. Lee, T. M. Lee, and M. Y. Yang, Tool wear monitoring system for CNC end milling using a hybrid approach to cutting force regulation, Int. J. Adv. Manuf. Technol., vol. 32, no. 12, pp. 817, Mar. 2006.

[19] S. Yaldz and F. nsaar, A dynamometer design for measurement the cutting forces on turning, Measurement, vol. 39, no. 1, pp. 8089, Jan. 2006.

[20] S. Yaldz, F. nsaar, H. Salam, and H. Ik, Design, development and testing of a four-component milling dynamometer for the measurement of cutting force and torque, Mech. Syst. Signal Process., vol. 21, no. 3, pp. 14991511, Apr. 2007.

[21] E. S. Topal and C. oun, A cutting force induced error elimination method for turning operations, J. Mater. Process. Technol., vol. 170, no. 12, pp. 192203, Dec. 2005.

[22] T. H. Panzera, P. R. Souza, J. C. C. Rubio, A. M. Abro, and T. R. Mansur, Development of a three-component dynamometer to measure turning force, Int. J. Adv. Manuf. Technol., vol. 62, no. 912, pp. 913922, Dec. 2011.

[23] S. Zeng, X. Wan, W. Li, Z. Yin, and Y. Xiong, A novel approach to fixture design on suppressing machining vibration of flexible workpiece, Int. J. Mach. Tools Manuf., vol. 58, pp. 2943, Jul. 2012.

[24] A. Devillez and D. Dudzinski, Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers, Mech. Syst. Signal Process., vol. 21, no. 1, pp. 441456, Jan. 2007.

[25] A. J. Torabi, Er Meng Joo, Li Xiang, Lim Beng Siong, Zhai Lianyin, San Linn, Gan Oon Peen, and Ching Chuen Teck, Application of classical clustering methods for online tool condition monitoring in high speed milling processes, in 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2012, pp. 12491254.

[26] W. Zhong, D. Zhao, and X. Wang, A comparative study on dry milling and little quantity lubricant milling based on vibration signals, Int. J. Mach. Tools Manuf., vol. 50, no. 12, pp. 10571064, Dec. 2010.

[27] P. A. Meyer, S. C. Veldhuis, and M. A. Elbestawi, Predicting the effect of vibration on ultraprecision machining surface finish as described by surface finish lobes, Int. J. Mach. Tools Manuf., vol. 49, no. 15, pp. 11651174, Dec. 2009.

[28] D. R. Salgado, F. J. Alonso, I. Cambero, and a. Marcelo, In-process surface roughness prediction system using cutting vibrations in turning, Int. J. Adv. Manuf. Technol., vol. 43, no. 12, pp. 4051, Sep. 2008.

[29] C. F. Bisu, M. Zapciu, O. Cahuc, A. Grard, and M. Anica, Envelope dynamic analysis: a new approach for milling process monitoring, Int. J. Adv. Manuf. Technol., vol. 62, no. 58, pp. 471486, Dec. 2011.

[30] M. Lamraoui, M. Thomas, and M. El Badaoui, Cyclostationarity approach for monitoring chatter and tool wear in high speed milling, Mech. Syst. Signal Process., vol. 44, no. 1, pp. 177198, 2014.

[31] K. V. Rao, B. S. N. Murthy, and N. M. Rao, Cutting tool condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring, Measurement, vol. 46, no. 10, pp. 40754084, 2013.

[32] E. Trent and P. Wright, Metal Cutting, 4th ed. Woburn: ButterworthHeinemann, 2000, p. 446.

[33] G. Byrne, Thermoelectric signal characteristics and average interfacial temperatures in the machining of metals under geometrically defined conditions, Int. J. Mach. Tools Manuf., vol. 27, no. 2, pp. 215224, 1987.

[34] P. S. Sivasakthivel and R. Sudhakaran, Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm, Int. J. Adv. Manuf. Technol., vol. 67, no. 912, pp. 23132323, Dec. 2012.

[35] B. Davoodi and H. Hosseinzadeh, A new method for heat measurement during high speed machining, Measurement, vol. 45, no. 8, pp. 21352140, Oct. 2012.

[36] E. Bagci and B. Ozcelik, Analysis of temperature changes on the twist drill under different drilling conditions based on Taguchi method during dry drilling of Al 7075-T651, Int. J. Adv. Manuf. Technol., vol. 29, no. 78, pp. 629636, Jul. 2005.

[37] R. Li and A. J. Shih, Spiral point drill temperature and stress in high-throughput drilling of titanium, Int. J. Mach. Tools Manuf., vol. 47, no. 1213, pp. 20052017, Oct. 2007.

[38] L. C. Brando, R. T. Coelho, and C. H. Lauro, Contribution to dynamic characteristics of the cutting temperature in the drilling process considering one dimension heat flow, Appl. Therm. Eng., vol. 31, no. 1718, pp. 38063813, Dec. 2011.

[39] T. Ueda, H. Tanaka, A. Torii, and T. Matsuo, Measurement of Grinding Temperature of Active Grains Using Infrared Radiation Pyrometer with Optical Fiber, CIRP Ann. - Manuf. Technol., vol. 42, no. 1, pp. 405408, 1993.

[40] C. Wei, K. Xu, R. Li, and D. Hu, Temperature modeling in end grinding of coated workpieces, J. Shanghai Jiaotong Univ., vol. 15, no. 3, pp. 319322, May 2010.

[41] A.-M. O. Mohamed, A. Warkentin, and R. Bauer, Variable heat flux in numerical simulation of grinding temperatures, Int. J. Adv. Manuf. Technol., vol. 63, no. 58, pp. 549554, Feb. 2012.

[42] C. H. Lauro, L. C. Brando, and S. L. M. Ribeiro Filho, Monitoring the temperature of the milling process using infrared camera, Sci. Res. Essays, vol. 8, no. 23, pp. 11121120, 2013.

[43] L. C. Brando and R. T. Coelho, Temperature and heat flow when tapping of the hardened steel using different cooling systems, Rev. Chil. Ing., vol. 17, no. 2, pp. 267274, 2009.

[44] G. Fromentin, a. Bierla, C. Minfray, and G. Poulachon, An experimental study on the effects of lubrication in form tapping, Tribol. Int., vol. 43, no. 9, pp. 17261734, Sep. 2010.

[45] S. Bhowmick, M. J. Lukitsch, and a. T. Alpas, Tapping of AlSi alloys with diamond-like carbon coated tools and minimum quantity lubrication, J. Mater. Process. Technol., vol. 210, no. 15, pp. 21422153, Nov. 2010.

[46] D. OSullivan and M. Cotterell, Temperature measurement in single point turning, J. Mater. Process. Technol., vol. 118, no. 13, pp. 301308, Dec. 2001.

[47] F. M. Aneiro, R. T. Coelho, and L. C. Brando, Turning Hardened Steel Using Coated Carbide at High Cutting Speeds, J. Brazilian Soc. Mech. Sci. Eng., vol. XXX, no. 2, pp. 104109, 2008.

[48] M. Hadad and B. Sadeghi, Minimum quantity lubrication-MQL turning of AISI 4140 steel alloy, J. Clean. Prod., vol. 54, pp. 332343, Sep. 2013.

[49] S. Takate, J. H. Ahn, M. Miki, Y. Miyao, and T. Sata, A Sound Monitoring System for Fautt Detection of Machine and Machining States, in Annals of the ClRP, 1986, vol. 35, pp. 14.

[50] K. Cheng, Machining Dynamics - Fundamentals, Applications and Practices. London: Springer, 2009, p. 328.

[51] E. Kuljanic, G. Totis, and M. Sortino, Development of an intelligent multisensor chatter detection system in milling, Mech. Syst. Signal Process., vol. 23, no. 5, pp. 17041718, Jul. 2009.

[52] E. J. Weller, H. M. Schrier, and B. Weichbrodt, What sound can be expected from a worn tool?, J. Eng. Ind., vol. 91, no. 3, pp. 525534, 1969.

[53] M. C. Lu and E. Kannatey-Asibu, Analysis of Sound Signal Generation Due to Flank Wear in Turning, J. Manuf. Sci. Eng., vol. 124, no. 4, p. 799, 2002.

[54] Z. Tekner and S. Yelyurt, Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel, Mater. Des., vol. 25, no. 6, pp. 507513, Sep. 2004.

[55] W. L. Weingaertner, R. B. Schroeter, M. L. Polli, and J. de Oliveira Gomes, Evaluation of high-speed end-milling dynamic stability through audio signal measurements, J. Mater. Process. Technol., vol. 179, no. 13, pp. 133138, Oct. 2006.

[56] D. R. Salgado and F. J. Alonso, An approach based on current and sound signals for in-process tool wear monitoring, Int. J. Mach. Tools Manuf., vol. 47, no. 14, pp. 21402152, Nov. 2007.

[57] A. Samraj, S. Sayeed, J. E. Raja, J. Hossen, and A. Rahman, Dynamic Clustering Estimation of Tool Flank Wear in Turning Process using SVD Models of the Emitted

Sound Signals, in World Academy of Science, Engineering and Technology, 2011, vol. 56, pp. 11511155.

[58] M.-C. Lu and B.-S. Wan, Study of high-frequency sound signals for tool wear monitoring in micromilling, Int. J. Adv. Manuf. Technol., vol. 66, no. 912, pp. 17851792, Aug. 2012.

[59] H. Rafezi, J. Akbari, and M. Behzad, Tool Condition Monitoring based on sound and vibration analysis and wavelet packet decomposition, in 2012 8th International Symposium on Mechatronics and its Applications, 2012, pp. 14.

[60] I. Inasaki, Application of acoustic emission sensor for monitoring machining processes, Ultrasonics, vol. 36, no. 15, pp. 273281, Feb. 1998.

[61] N. Zarif Karimi, H. Heidary, G. Minak, and M. Ahmadi, Effect of the drilling process on the compression behavior of glass/epoxy laminates, Compos. Struct., vol. 98, pp. 5968, Apr. 2013.

[62] I. Marinescu and D. A. Axinte, A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations, Int. J. Mach. Tools Manuf., vol. 48, no. 10, pp. 11481160, Aug. 2008.

[63] A. Hase, M. Wada, T. Koga, and H. Mishina, The relationship between acoustic emission signals and cutting phenomena in turning process, Int. J. Adv. Manuf. Technol., vol. 70, no. 58, pp. 947955, Oct. 2013.

[64] D. A. Axinte and M. C. Kong, An integrated monitoring method to supervise waterjet machining, CIRP Ann. - Manuf. Technol., vol. 58, no. 1, pp. 303306, Jan. 2009.

[65] P. S. Sreejith, R. Krishnamurthy, and S. K. Malhotra, Effect of specific cutting pressure and temperature during machining of carbon/phenolic ablative composite using PCBN tools, J. Mater. Process. Technol., vol. 183, no. 1, pp. 8895, Mar. 2007.

[66] R. D. Young, T. V. Vorburger, and E. C. Teague, In-Process and On-Line Measurement of Surface Finish, CIRP Ann. - Manuf. Technol., vol. 29, no. 1, pp. 435440, Jan. 1980.

[67] G. Galante, M. Piacentini, and V. F. Ruisi, Surface roughness detection by tool image processing, Wear, vol. 148, no. 2, pp. 211220, Aug. 1991.

[68] P. Morala-Argello, J. Barreiro, and E. Alegre, A evaluation of surface roughness classes by computer vision using wavelet transform in the frequency domain, Int. J. Adv. Manuf. Technol., vol. 59, no. 14, pp. 213220, Jul. 2011.

[69] N. H. Abu-Zahra and J. H. Lange, Tool Chatter Monitoring in Turning Operations Using Wavelet Analysis of Ultrasound Waves, Int. J. Adv. Manuf. Technol., vol. 20, no. 4, pp. 248254, Aug. 2002.

[70] National Instruments, How to Choose the Right DAQ Hardware for Your Measurement System. pp. 15, 2012.

[71] M. C. Shaw, Metal Cutting Principles, 2a ed. USA: Oxford University Press., 2004, p. 672.

[72] M. Novotny and M. Sedlacek, Measurement of active power by time domain digital signal processing, Measurement, vol. 42, no. 8, pp. 11391152, Oct. 2009.

[73] A. R. Bhende, G. K. Awari, and S. P. Untawale, Assessment of bearing fault detection using vibration signal analysis, VSRD Tech. Non-Technical J., vol. 2, no. 5, pp. 249261, 2011.

[74] A. Morska, M. Matuszak, and P. Waszczuk, Zeszyty Naukowe Experimental sensor system implementation for selected micromilling-related parameters, Sci. Journals Marit. Univ. Szczecin, vol. 31, no. 103, pp. 134139, 2012.

[75] J. Z. Zhang and J. C. Chen, Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system, Int. J. Adv. Manuf. Technol., vol. 39, no. 12, pp. 118128, Sep. 2007.

[76] I. L. Vr and L. L. Beranek, Noise and vibration control engineering: principles and applications. John Wiley & Sons, 2006, p. 966.

[77] M. Lamraoui, M. Thomas, M. El Badaoui, and F. Girardin, Indicators for monitoring chatter in milling based on instantaneous angular speeds, Mech. Syst. Signal Process., vol. 44, no. 12, pp. 7285, Feb. 2014.

[78] T. Kalvoda and Y.-R. Hwang, Analysis of signals for monitoring of nonlinear and non-stationary machining processes, Sensors Actuators A Phys., vol. 161, no. 12, pp. 3945, Jun. 2010.

[79] L. Huang, Q. Kemao, B. Pan, and A. K. Asundi, Comparison of Fourier transform, windowed Fourier transform, and wavelet transform methods for phase extraction from a single fringe pattern in fringe projection profilometry, Opt. Lasers Eng., vol. 48, no. 2, pp. 141148, Feb. 2010.

[80] D. Kono, A. Matsubara, I. Yamaji, and T. Fujita, High-precision machining by measurement and compensation of motion error, Int. J. Mach. Tools Manuf., vol. 48, no. 10, pp. 11031110, Aug. 2008.

[81] M. C. Kang, J. S. Kim, and J. H. Kim, A monitoring technique using a multi-sensor in high speed machining, J. Mater. Process. Technol., vol. 113, no. 13, pp. 331336, Jun. 2001.

[82] T. W. Liao, C.-F. Ting, J. Qu, and P. J. Blau, A wavelet-based methodology for grinding wheel condition monitoring, Int. J. Mach. Tools Manuf., vol. 47, no. 34, pp. 580592, Mar. 2007.

[83] E. M. Rubio, R. Teti, and I. L. Baciu, Advanced signal processing in acoustic emission monitoring systems for machining technology, in Intelligent Production Machines and Systems, 2006, pp. 16.

[84] S. Gu, J. Ni, and J. Yuan, Non-stationary signal analysis and transient machining process condition monitoring, Int. J. Mach. Tools Manuf., vol. 42, no. 1, pp. 4151, Jan. 2002.

[85] X. Liu, F. Ahmad, K. Yamazaki, and M. Mori, Adaptive interpolation scheme for NURBS curves with the integration of machining dynamics, Int. J. Mach. Tools Manuf., vol. 45, no. 45, pp. 433444, Apr. 2005.

[86] H. Shao, X. Shi, and L. Li, Power signal separation in milling process based on wavelet transform and independent component analysis, Int. J. Mach. Tools Manuf., vol. 51, no. 9, pp. 701710, Sep. 2011.

[87] C. Scheffer and P. S. Heyns, Wear monitoring in turning operations using vibration and strain measurements, Mech. Syst. Signal Process., vol. 15, no. 6, pp. 11851202, Nov. 2001.

[88] W. Grzesik and S. Brol, Wavelet and fractal approach to surface roughness characterization after finish turning of different workpiece materials, J. Mater. Process. Technol., vol. 209, no. 5, pp. 25222531, Mar. 2009.

[89] N. Kasashima, K. Mori, G. H. Ruiz, and N. Taniguchi, Online Failure Detection in Face Milling Using Discrete Wavelet Transform, CIRP Ann. - Manuf. Technol., vol. 44, no. 1, pp. 483487, Jan. 1995.

[90] L. Wang and M. Liang, Chatter detection based on probability distribution of wavelet modulus maxima, Robot. Comput. Integr. Manuf., vol. 25, no. 6, pp. 989998, Dec. 2009.

[91] C. Xu, Z. Liu, and W. Luo, A Frequency Band Energy Analysis of Vibration Signals for Tool Condition Monitoring, in 2009 International Conference on Measuring Technology and Mechatronics Automation, 2009, vol. 1, pp. 385388.

[92] Y. S. Tarng and B. Y. Lee, Amplitude demodulation of the induction motor current for the tool breakage detection in drilling operations, Robot. Comput. Integr. Manuf., vol. 15, no. 4, pp. 313318, Aug. 1999.

[93] K. Mori, N. Kasashima, J. C. Fu, and K. Muto, Prediction of small drill bit breakage by wavelet transforms and linear discriminant functions, Int. J. Mach. Tools Manuf., vol. 39, no. 9, pp. 14711484, Sep. 1999.

[94] a. Velayudham, R. Krishnamurthy, and T. Soundarapandian, Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform, Mater. Sci. Eng. A, vol. 412, no. 12, pp. 141145, Dec. 2005.

[95] Z. Yang and Z. Yu, Grinding wheel wear monitoring based on wavelet analysis and support vector machine, Int. J. Adv. Manuf. Technol., vol. 62, no. 14, pp. 107121, Dec. 2011.

[96] Y. Choi, R. Narayanaswami, and a. Chandra, Tool wear monitoring in ramp cuts in end milling using the wavelet transform, Int. J. Adv. Manuf. Technol., vol. 23, no. 56, pp. 419428, Mar. 2004.

[97] J. Bhaskaran, M. Murugan, N. Balashanmugam, and M. Chellamalai, Monitoring of hard turning using acoustic emission signal, J. Mech. Sci. Technol., vol. 26, no. 2, pp. 609615, Apr. 2012.

[98] M. Feldman, Hilbert transform in vibration analysis, Mech. Syst. Signal Process., vol. 25, no. 3, pp. 735802, Apr. 2011.

[99] H. Cao, X. Chen, Y. Zi, F. Ding, H. Chen, J. Tan, and Z. He, End milling tool breakage detection using lifting scheme and Mahalanobis distance, Int. J. Mach. Tools Manuf., vol. 48, no. 2, pp. 141151, Feb. 2008.

[100] H. Cao, Y. Lei, and Z. He, Chatter identification in end milling process using wavelet packets and HilbertHuang transform, Int. J. Mach. Tools Manuf., vol. 69, pp. 1119, Jun. 2013.

[101] T. Kalvoda and Y.-R. Hwang, A cutter tool monitoring in machining process using HilbertHuang transform, Int. J. Mach. Tools Manuf., vol. 50, no. 5, pp. 495501, May 2010.

[102] a. M. Bassiuny and X. Li, Flute breakage detection during end milling using HilbertHuang transform and smoothed nonlinear energy operator, Int. J. Mach. Tools Manuf., vol. 47, no. 6, pp. 10111020, May 2007.

[103] Z. Yang, Z. Yu, C. Xie, and Y. Huang, Application of HilbertHuang Transform to acoustic emission signal for burn feature extraction in surface grinding process, Measurement, vol. 47, pp. 1421, Jan. 2014.

[104] I. Marinescu and D. Axinte, A timefrequency acoustic emission-based monitoring technique to identify workpiece surface malfunctions in milling with multiple teeth cutting simultaneously, Int. J. Mach. Tools Manuf., vol. 49, no. 1, pp. 5365, Jan. 2009.

[105] A. Simeone, T. Segreto, and R. Teti, Residual Stress Condition Monitoring via Sensor Fusion in Turning of Inconel 718, Procedia CIRP, vol. 12, pp. 6772, Jan. 2013.

Figure 4. The framework of TCM [7].

Figure 5. Influence of bits per sample [70].

Figure 6. Example of sample rate [70].

Table 4. Temperature Measurements Type

Researcher Material Method Target

Dril

ling [36] Al 7075-T651 aluminium alloy Thermocouple Tool

[37] Ti-6Al-4V Thermocouple Tool [38] AISI H13 Thermocouple Workpiece

Grin

ding [39] AISI 1055 Annealed and Hardness Infrared Radiation Pyrometer Tool

[40] Steel with WC-Co coating Thermocouple Workpiece [41] AISI 4140 Infrared Thermal Camera Workpiece

Mill

ing [35] CuZn40Al12 Infrared Sensor Workpiece

[34] Al 6063 aluminium alloy Thermocouple Workpiece [42] Al 7050 aluminium alloy Infrared Thermal Camera Workpiece

Tapp

ing [43] AISI H13 Thermocouple Workpiece

[44] AISI 1070 Thermocouple Workpiece [45] Al 319 aluminium silicon alloy Infrared Thermometer Workpiece

Turn

ing [46] Al 6082-T6 aluminium alloy Thermocouples Workpiece Infrared Thermal Camera

[47] AISI 4340 Thermocouple Tool [48] AISI 4140 Thermocouples Tool

Table 5. Some machining researches using Wavelet.

Researcher Monitored Signal Material Spindle Speed (rpm) Sampling Frequency

Dril

ling [92] Electric Current S45C steel 1000, 1200 1 kHz

[93] Cutting Force Stainless steel 316 2000 1 kHz [94] Acoustic Emission Laminated composite 1762.95

Grin

ding

[82] Cutting Force

Ceramic 4500

4906 Hz Vibration 4906 Hz

Spindle Power 4906 Hz Acoustic Emission 1 MHz

[95] Acoustic Emission ASTM 1045 1 MHz

Mill

ing

[96] Cutting Force AISI 1018 1000, 2000 500 Hz [17] Electric Current AISI 1045 300, 450, 600, 900, 1200 1 kHz

[26] Vibration Al 7050-T7451 aluminium alloy 3000 - 18000 2 MHz

Turn

ing [69] Vibration AISI 4140 200 - 1600 100 MHz Ultrasound waves 10 MHz

[68] Image AISI 6150 [97] Acoustic Emission AISI-D3 171, 245, 318 8333 Hz

Table 6. Some machining researches using HHT.

Researcher Monitored Signal Material Spindle Speed (rpm) Sampling Frequency

Mill

ing [101] Vibration SAE 1045 773, 1345, 2004 2 kHz

[102] Current SAE 1045 300, 450, 600, 900, 1200 1 kHz

Gri

ndi ng

[103] Acoustic emission AISI 1045 24 2 MHz

Vibration 2 MHz Voltage, Current 10 kHz