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Int J Adv Manuf Technol (2010) 47:237–257DOI 10.1007/s00170-009-2191-8

ORIGINAL ARTICLE

A review of machining monitoring systems basedon articial intelligence process models

Jose Vicente Abellan-Nebot ·

Fernando Romero Subirón

Received: 12 October 2008 / Accepted: 24 June 2009 / Published online: 29 July 2009© Springer-Verlag London Limited 2009

Abstract Many machining monitoring systems basedon articial intelligence (AI) process models have beensuccessfully developed in the past for optimising, pre-dicting or controlling machining processes. In general,these monitoring systems present important differencesamong them, and there are no clear guidelines fortheir implementation. In order to present a genericview of machining monitoring systems and facilitatetheir implementation, this paper reviews six key issuesinvolved in the development of intelligent machiningsystems: (1) the different sensor systems applied tomonitor machining processes, (2) the most effectivesignal processing techniques, (3) the most frequent sen-sory features applied in modelling machining processes,(4) the sensory feature selection and extraction meth-ods for using relevant sensory information, (5) thedesign of experiments required to model a machiningoperation with the minimum amount of experimentaldata and (6) the main characteristics of several arti-cial intelligence techniques to facilitate their applica-tion/selection.

Keywords Machining monitoring systems · Articialintelligence · Sensor systems · Sensory features ·

Design of experiments

J. V. Abellan-Nebot ( B ) · F. Romero SubirónDepartment of Industrial Systems Engineering and Design,Universitat Jaume I, Av. de Vicent Sos Baynat s/n.,12071 Castellón, Spaine-mail: [email protected]

F. Romero Subiróne-mail: [email protected]

1 Introduction

Manufacturing enterprises currently have to cope withgrowing demands for increased product quality, greaterproduct variability, shorter product life-cycles, reducedcost, and global competition [ 1]. In the eld of ma-chining, manufacturers are turning increasingly moreoften to automation as an effective way to meet thesedemands. A key issue for an unattended and automatedmachining system is the development of reliable androbust monitoring systems. Research issues in monitor-ing machining systems based on articial intelligence(AI) process models cover several topics, such as sensorsystem selection [ 1, 2], multi-sensor and sensor-fusionsystems [1, 3, 4], signal processing and sensory featureselection/extraction [5, 6], design of experiments [ 7, 8]and AI techniques to model the process [ 1, 9]. Inspite of the intensive research being carried out in thiseld, there is still no clear methodology for developingmachining monitoring systems that allows machiningprocesses to be optimised, predicted or controlled. Fur-thermore, many of the research studies presented inthe literature seem to be contradictory. For example,Haber [10] reported that acoustic emission (AE) sen-sors attached in the soft vice jaws were more sensi-tive than those attached in the spindle, whereas Lan[11] reported a higher sensitivity of AE sensors in thespindle quill. These contradictions may be explained bythe fact that designing machining monitoring systems isa process-oriented problem where the selection of thesensor system, sensory features and the modelling ap-proach are closely related to the specic characteristicsof the machining process.



238 Int J Adv Manuf Technol (2010) 47:237–257

In order to overcome the lack of a global view inthe development of intelligent monitoring systems formachining, this paper reviews the main parts of thesemonitoring systems and includes the most relevant as-pects from previous research works presented in theliterature. To limit the scope of the study, the review isrestricted to monitoring systems applied to the predic-tion of part accuracy and cutting-tool wear diagnosis.The paper is divided as follows: First, a generic meth-odology is described, which covers all the main stepsinvolved in developing an intelligent monitoring systemfor machining. Then, the different sensors applied inmachining monitoring systems are reviewed, with spe-cial attention to their use in both single and sensor-fusion systems in the literature and their advantagesand drawbacks. Signal processing concepts allowing thesignal to be acquired correctly are then examined, and asensory-feature generation module is also presented todescribe the signal while taking into account most of thesensory features presented in the literature. In the nextstep, the paper outlines the possible methods that canbe employed to select or extract the most meaningfulfeatures which will be used for modelling the machiningprocess. Then, the common design of experiments car-ried out in the literature to acquire data for modellingpurposes is reviewed, and a generic methodology toconduct the experimentation with a minimal numberof runs is described. Finally, the last section discussesthe AI techniques applied in machining monitoringsystems and their advantages and drawbacks as regardsfacilitating their selection according to the monitoringpurpose.

2 Methodology overview

According to the literature, a generic methodology fordeveloping an intelligent monitoring system for ma-chining is composed of six key issues:

1. Sensors: Which sensors are to be applied? The cut-ting process can be characterised by a variety of

physical quantities. Appropriate sensors such asdynamometers, AE sensors, accelerometers, cur-rent/power sensors, thermistors, etc., transform aphysical quantity into the corresponding electricalsignals. It is important to take into account thereliability of each sensor, the cost, its intrusive na-ture, and its application in order to select the mostappropriate sensor system for a given monitoringpurpose.

2. Signal processing: How to acquire and process sensor signals? Signal processing can be more orless complex, consisting in amplifying and ltering(with analogical low-pass, band-pass, or high-passlters) the signals. Sample frequency limitations of acquisition boards must be taken into account toavoid aliasing. In addition, digital signal processingthrough digital lters and signal segmentation op-erations has to be considered to be able to acquirethe part of the signal which is of interest.

3. Feature generation: Which features could describethe signal adequately? The sensor signal has to betransformed into features that could describe thesignal adequately. Many different features fromthe time domain, frequency domain and waveletdomain can be used for this purpose.

4. Feature selection/extraction: Which of the gener-ated features are the most meaningful? In order todevelop robust and reliable models for monitoring,it is necessary to use the most meaningful featureswhich best describe the machining process. Featureselection and feature extraction are two methodswhich allow the most useful sensory features to bedened.

5. Process knowledge model

(a) Design of experiments: Which design of experiments is required to model the processaccurately enough? Experimental runs in ma-chining for modelling purposes are both eco-nomically costly and time-consuming, so an

effective design of experiments is mandatoryto enable monitoring systems to be applied inindustry.

(b) AI technique: Which AI technique has tobe applied to model the process? Monitor-ing systems require reliable models which areable to learn complex non-linear relation-ships between process performance variablesand process variables in machining. An ad-equate selection of the AI technique is cru-cial to develop reliable machining models.This selection depends mainly on the num-

ber of experimental samples, the stochasticnature of the process, the desired model ac-curacy, the explicit or implicit nature of themodel and the previous knowledge of theprocess.

Figure 1 shows the generic methodology to de-velop an intelligent monitoring system for machiningprocesses.



Int J Adv Manuf Technol (2010) 47:237–257 239

FeaturesGenerated

Feature generation

Diagnosis / Prediction / Prognostics

Process Knowledge Model

Design of Exp.-Experimental limitations-Model knowledge-DoE designs (full factorial,fractional, Taguchi’s array,Surface Response Surface)

AI technique-Experimental limitations-Model knowledge-AI techniques (ANN,Fuzzy,Neuro-fuzzy,Bayesian,HMM, etc..):advantages and drawbacks

Physicalquantities

Sensors

Signals

Signal Processing

Amplified andfiltered Signals

Feature selection/ extraction

Features

Selected/extracted

-Dynamometer-Accelerometer-Acoustic Emission-Current sensor-Others

-Amplification-Conditioning-Analogical filtering-Digital filtering-Segmentation

-Time Domain: RMS,Average,…

-Frequency domain: Single harmonic, PDS,…

-Wavelet domain: Wavelets,...

-Variable ranking-Subset features selection-PCA

TOOL W O R K P I E C E

How to acquireand process

sensor signals?

Which featurescould describe

the signal?

Which are themost meaningful

features?

DoE and AI

Selected

Which design ofexperiments is

required to model theprocess accurately

enough?

Which artificialintelligence technique

has to be applied tomodel the process?

Which sensors areto be applied?

Fig. 1 Generic methodology to develop intelligent monitoring system for machining processes

3 Selection of the sensor system

Integration of sensors for process monitoring and con-trol has become a technology which is expected to havea major impact on manufacturing in the coming years[2]. There are two different methods for monitoring themachining process in order to predict part accuracy anddiagnose cutting-tool state: direct and indirect methods.Direct methods consist of laser, optical, and ultra-sonicsensors which provide a direct measurement [12, 13].These methods are still very expensive and difcult

to apply in the machining process environment [3]. Incontrast, indirect methods are more economical sys-tems for monitoring machining processes and are basedon sensors which infer the machining state by sensingcutting forces, vibrations, temperatures, current con-sumption, etc. Basically, four sensors have been widelyapplied to monitor machining systems: dynamome-ters, accelerometers, AE sensors and current sensors.Figure 2, adapted from [ 3], shows the relative frequencyof usage of these sensors in machining monitoringsystems. The sub-sections that follow review their use




100

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40

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51

29

0

10

20

30 21

R e l a t i v e

F r e q u e n c y o

f u s a g e

Dynamometer Accelerometer AcousticEmission sensor

Cur rent s enso r Other s

Fig. 2 Frequency of sensor usage related to machining monitor-ing systems adapted from [3]

to monitor machining systems, with special attentionpaid to monitoring part accuracy (surface roughnessand geometric quality) and cutting-tool condition (toolwear and tool breakage).

3.1 Dynamometers

In the world of machining, cutting force is considered tobe the variable that best describes the cutting process[14]. The information gleaned from the force patterncan be used to evaluate the quality and geometricprole of the cutting surface [ 15]. Hence, cutting-forcemonitoring is frequently used to diagnose/predict bothtool condition [ 5, 16–18] and part accuracy [ 7, 8, 19, 20].Tool wear can be easily detected as it occurs becausefriction forces increase considerably when the cuttingtool edge loses its ability to cut. However, the in-crease in cutting force due to tool wear is stronglydependent on other cutting conditions together withthe type of wear, cutting material, work material, etc.Experimentally, Jemielniak [ 21] showed that, when thedominant form of tool wear is ank wear, the increasein feed force and passive force is higher than whenthe dominant form is crater wear. Furthermore, craterwear changes the cutting-tool geometry, thus varyingthe components of the cutting forces [ 5]. In his reviewof the state-of-the-art of monitoring, Liang [ 1] foundthat feed and radial forces are more sensitive to toolwear than the cutting force. On the other hand, whena substantial breakage of the insert occurs, there isan instant increase followed by a drop in the cuttingforces. The magnitude of this decrement is dependenton a reduction in the cross-section of the uncut chipdue to the partial breakage. Oscillatory responses andpeaks in the pattern of the cutting force pattern are alsocommonly used as features to detect overloads and thedanger of tool breakage or damage to the workpiece.

Cutting forces are also monitored to improve part ac-curacy. In fact, cutting forces can also be related to sur-face roughness since phenomena such as un-deformedchip formation and the built-up edge may be dependenton cutting forces. Additionally, cutting-tool deectionsdue to cutting forces reduce part accuracy in the sensethat there is a deviation of the actual cutting-tool pathaway from the nominal one. Dynamometers can also beapplied for chatter detection, since chatter frequencyis often much higher than cutting-force frequency. Incases where the bandwidth of dynamometers may notbe able to cover chatter frequencies or where the inertiaof the workpiece and xtures may signicantly reducethe bandwidth of the measurement, other sensors likeAE sensors may be more effective in chatter detection[2]. However, the high cost of multi-axis dynamome-ters, their intrusive nature in production environments,their lack of overload protection in case of collision andtheir limited frequency response make it very difcultto apply them in industry [21–23].

3.2 Accelerometers

Vibration monitoring is mainly applied for predictionof surface roughness since surface roughness is the su-perpositionof the feed per tooth mark and the displace-ments of the cutting-tool due to vibrations apart fromother factors [ 24, 25]. This fact has been particularlyproved in turning applications [ 24, 26–28], whereas, inmilling operations, vibrations are less correlated withsurface roughness due to the non-continuous cuttingnature of the process and the runout effects [ 29]. Di-rection of the vibration measurements has to be takeninto account for part accuracy prediction since vibra-tion generates undesired displacements of the cuttingtool. Chen [ 25] studied the effective sensor location of the accelerometers in turning operations and reportedthat the strongest vibration signal is not necessarilythe most useful for determining surface roughness. Inhis experimentation, the Y direction (cutting speeddirection) received fewer vibration signals but it wasfound to be the most signicant; the Z direction (feedforce direction), on the other hand, received the mostvibrations, but they were less signicant.

Vibration monitoring has also been applied in thediagnosis of cutting-tool wear since vibration ampli-tude usually varies during machining as a result of theprogressive ank wear. When machining is conductedwith a new cutting tool, the contact between tool andworkpiece is restricted to almost a line of contact. Ittherefore offers a small amount of resistance to theoscillations of the tool and the workpiece and gener-ates vibrations. However, as ank wear-land progresses




with machining time, the larger contact area increasesthe amount of workpiece material being deformed elas-tically, and thus increases the frictional damping andreduces vibrations. If ank wear exceeds a certainthreshold, the stronger excitation caused by the largercutting force becomes dominant, with a consequentincrease in vibration [ 5]. Dimla [ 30] reported a correla-tion between vibration signal features and cutting-toolwear, and also identied a correlation between hightool wear and certain resonant peak frequencies.

Vibration signal analysis has also been applied to thedetection of cutting-tool breakage, as this is reected incutting forces and therefore in cutting vibrations. Chen[23] implemented a real-time method of tool breakagedetection with a four-ute end mill sensing vibrationsignals. The same author also reported how the ratiobetween the rst and second harmonics of the cuttingfrequency varies when a cutting insert is broken. Afterbeing validated experimentally, it was then possible todene a threshold to detect tool breakage. Comparedto other in-process methods, such as those that em-ploy dynamometers and AE sensors, this system wasreliable, low-cost, and easy to set up, although it waslimited to non-chatter processes.

Although the use of vibration sensors has been suc-cessfully applied to the monitoring of machining sys-tems, in fact, there are a number of practical problemsinvolved in monitoring the vibration level to assess toolcondition or part accuracy [ 4]:

– The machining speed should remain within a spe-cic range.

– The amplitude of the signal decreases with an in-crease in the distance of the sensor from the cuttingedge.

– Mounting the sensor close to the cutting locationincreases the variability of the signal.

– Chip formation could strike the accelerometer anddamage it or cause it to misread the vibration.

Furthermore, vibration signals may not be as accu-rate or reliable as other signals such as dynamometeror AE signals [4]. For example, Ertekin [ 5] testedthe robustness of different sensor signal features fromdynamometers, AE sensors and accelerometers usingthree different types of material. His results revealedthat dynamometer and AE signal features are morerobust than vibration signals for surface roughness pre-diction and dimensional accuracy estimation. However,monitoring systems based on accelerometers have theadvantages of simplicity and low cost.

3.3 Acoustic emission sensors

AE is the energy released in the form of mechanicalvibration from a material (tool, workpiece, machinebody) as it undergoes stress. Such stress may be gener-ated by chip deformation and fracture, friction betweenchip, workpiece and tool, tool breakage, exible defor-mation of machine structures and thermal reactions of materials [2]. AE derived from metal cutting consists of continuous and transient signals, which have distinctlydifferent characteristics. Continuous signals are asso-ciated with shearing in the primary zone and wear onthe tool face and ank, while burst or transient signalsresult from either tool fracture or chip breakage [ 31].Common sources of AE in metal-cutting processes are[5]: (a) deformation in the shear zone, (b) deformationand sliding friction at the chip-tool interface, (c) slidingfriction at the tool ank–workpiece interface and (d)the breaking of chips and their impact on the cuttingtool or workpiece.

The frequency spectrum of AE typically spans thekilohertz to megahertz range (usually 10 kHz–10 MHz),and AE sensors usually work in these ranges [2]. Themain advantage of AE is that the frequency range of itssignals is much higher than that of machine vibrationsand environmental noises, and that it is not intrusive incutting operations [31]. However, the lack of physicalunderstanding of the AE signal and its sensitivity tosensor location and cutting parameters remain difcultissues that continue to hinder the application of thistechnology in machining monitoring systems [1]. Sensi-tivity to sensor location was studied by Haber [ 10] andLan [ 11]. Haber proved that an AE sensor attachedin the soft vice jaws was more sensitive than a similarone attached to the main spindle for cutting-tool weardiagnosis, whereas Lan found higher sensitivity in thespindle quill. Therefore, choosing a suitable positionin which to place the AE sensors in order to sensesufcient AE signals is a necessary but contentiousmatter, and it requires an understanding of the AEtransmission path [ 30]. AE sensors are inexpensive andeasy to install, but they have to be carefully calibrated,and the range of cutting operations has to be testedto tune the gain in the buffer amplier so as to avoidsensor overload, which greatly distorts the signal [ 32].

AE signals have been mainly applied to the di-agnosis of cutting-tool wear [ 33] and tool breakagedetection [ 34]. Although some research works haverecommended the use of AE sensors instead of dyna-mometers for tool-wear diagnosis [ 35], other researchesargue that the use of AE sensors as an indicator of toolwear is inappropriate because they are more sensitiveto noise and variations in cutting conditions than to the




condition of the tool itself [ 36], and that they are onlysuitable as an additional sensor to increase reliability.On the other hand, AE sensors are well suited to thedetection of cutting-tool breakage, since a large amountof AE is generated during tool breakage and fracture[34]. An interesting review of various issues in AEmonitoring can be found in [ 31], where AE generation,classication and signal correction, signal processingmethodologies and tool-wear estimation methods usingAE sensors are all reviewed.

3.4 Current and power sensors

Motor armature current is proportional to the torquea DC motor produces, which is, in turn, proportionalto the cutting forces. Therefore, current measurementcan be used to sense the machining forces indirectly.Current sensors are nonlinear sensors which requirecompensation or complicated calibration [ 2]. Thesesensors have a limited sensing bandwidth due to theinertia of the motor rotor, which acts as a low-passlter. Therefore, high-frequency components of cuttingforce are difcult to observe, and they are suitable onlyfor detecting slow events or when a fast response isnot essential [ 22]. On the other hand, power sensorsmeasure the spindle or axis drive power, which is pro-portional to current consumption, and its limitationsare basically similar to those of current sensors. Steinand Wang [ 37] studied the characteristics of an ACinduction-drive spindle system. By doing so, their aimwas to establish the capability of monitoring spindlepower consumption as a way of providing an indicationof cutting torque and therefore, indirectly, of tool con-dition. A concern identied by the authors was that thebandwidth of the spindle motor servo imposes limitingfactors to the combination of spindle speed and number

of cutting edges on the tool. A tool monitoring systemdeveloped by Jones and Wu [38] continually measuredthe spindle power consumption. The method requiredthe execution of a series of preliminary cuts, the resultsfrom which were then used to establish thresholds. Thepower consumed during the subsequent cutting processwas then simply compared with the thresholds and atool monitoring alarm set if one was exceeded. A majorlimitation of this type of system is that the thresholdswill vary greatly as the cutting conditions vary. Evena mere change in the radial or axial depth of cutwould require the trial cuts to be repeated. Althoughthese sensors are not as accurate as dynamometers,they are economical, easy to install (as they require nosignicant wiring), and are both good to be applied ascomplementary information for diagnosing tool wear[39] and detecting tool breakage [ 3, 15, 40, 41].

Table 1 summarises the different sensors applied insingle sensor machining monitoring systems and themain application of each one. Some references and theadvantages and drawbacks of each sensor in terms of cost, intrusive nature and reliability are also provided.

3.5 Other sensors

Other sensors for monitoring machining processes aretemperature sensors, optical sensors and ultrasonic sen-sors, among others. Temperature sensors applied inmachining are thermocouples, thermal resistant ele-ments, semiconductor elements, thermopiles and othertypes of thermal elements [ 2]. The measurement of thetemperature in the cutting zone can be a good indicatorof the cutting process since the temperature varies asthe tool wears due to changes in the tool geometry andits ability to cut [ 42]. Furthermore, cutting temperatureinuences chip formation and the generation of surface

Table 1 Typical single-sensor machining monitoring systems

Sensor Cost Intrusive nature Signal reliability a Main application ReferencesDynamometer Tool wear diagnosis [14, 17]

Tool breakage detection [ 18]

Surface roughness prediction [ 7, 8, 19]Dimensional part accuracy prediction [ 19, 20]

Accelerometer Surface roughness prediction [24, 26–29] Tool wear diagnosis [ 30]

AE Tool breakage detection [34] Tool wear diagnosis [ 33]

Current/Power sensor Tool wear diagnosis [40, 41] Tool breakage detection [ 52]

Most common applications highlighted in boldaSignal reliability for machining modelling purposes




roughness [ 42, 43] and accelerates tool wear, increasingdiffusion and weakening the tool. However, accuratecutting temperature monitoring is rather complicated,and it is usually monitored by average temperaturesaround the cutting tool with an important loss of infor-mation. Common optical sensors are based on a beamof light that is reected from the machined surface inthe specular direction and whose intensity can be corre-lated with the surface quality [ 44]. Other optical sensorssuch as machine vision systems are based on a lightsource to illuminate the surface and to acquire an imageby a digital system. The digitised data are then usedwith a correlation chart to monitor the actual roughnessvalues [ 45] or the cutting-tool condition [ 4]. Opticaltechniques present some limitations to their in-processuse since optical measurements can be affected bythe harsh machining environment (cutting uids, chips,etc.). Ultrasonic sensors have been applied to provide asurface prole measurement [ 46]. The ultrasonic sensoris positioned with a non-normal incidence angle abovethe surface, and the sensor sends out an ultrasonicpulse to the surface and measures the amplitude of thereturned signal. The data are then correlated with datafrom a stylus prolometer, and a model is obtained.

3.6 Sensor fusion concepts

Single signals have been used extensively to monitormachining processes. However, the sensitivity and thenoise rejection of the sensed signals can change withcutting conditions such as machining parameters, toolwear, machine stiffness, workpiece material properties,etc. Therefore, in order to increase the reliability of

sensor information under varying conditions and toavoid uncertainty, it is useful to use several sensorsignals instead of just one single sensor signal. Sensorfusion refers to the use of more than one sensor signalin a complementary manner to provide a more robustprediction of one or more machining attributes [ 2].Therefore, multiple sensors with non-complementarymeasurements dene a multi-sensor monitoring systembut not a sensor fusion system, and several featuresextracted from a single signal are not considered tobe multi-sensing or sensor fusion [ 47]. The success of sensor fusion depends on which types of signals aregood candidates for a given machining outcome andwhich extracted features and in which way they must becomplemented [1]. For example, using a dynamometerand a current sensor cannot be considered a sensorfusion system since a current sensor provides the sameinformation as the dynamometer but with less accu-racy [39]. Cutting forces and vibration measurementsat tooth-pass frequency are also closely related, andthey may not be adequate for sensor fusion, whereascutting forces and AE are less correlated and can beused effectively as complementary information [ 3, 5].Sensor fusion systems require algorithms to combineand fuse the sensor information. In general, two mainapproaches are usually applied for sensor fusion [ 16]:(1) statistical approaches and (2) AI approaches. Sta-tistical approaches relate sensor data to machiningprocess variables through multi-variable regressions,thus dening a statistical process model. AI approachesuse complex non-linear models such as neural networks(NN) or Bayesian networks (BN) to relate sensor datato machining process variables, thus dening an AI

Table 2 Sensor fusionsystems applied in machiningprocesses

Typical sensor fusion systemsSensors Fusion methodology Application ReferencesCurrent sensor, AE BN Tool wear diagnosis [ 22]Current sensor, accelerometer BN Tool wear diagnosis [ 48]

BN Surface roughness prediction [ 48]Accelerometer, AE NN Tool wear diagnosis [ 49]Accelerometer, vision system NN Tool wear diagnosis [ 4]Dynamometer, AE – Tool breakage detection [ 53]

NN Tool wear diagnosis [ 9]Dynamometer, AE, NN Surface roughness prediction [ 19]

accelerometer NN Tool wear diagnosis [ 10, 50]Dynamometer, accelerometer NN Tool wear diagnosis [ 16, 51]

NN Surface roughness prediction [ 54]NN Prediction of dimensional [ 54]

part accuracyDynamometer, thermistors NN Prediction of dimensional [ 20]

part accuracyDynamometer, accelerometer, NN Tool wear diagnosis [ 39]

spindle current, voltage sensor,sound pressure level




process model. Interesting research works on sensorfusion systems based on AI approaches can be foundfor different purposes, such as tool wear diagnosis[4, 9, 10, 16, 22, 39, 48–52], tool breakage detection[53], surface roughness prediction [19, 48, 54] and pre-diction of dimensional part accuracy [20, 54]. Table 2summarises these works, paying special attention to thesensors applied and the fusion methodology.

4 Signal processing

An appropriate signal processing strategy is manda-tory before selecting/extracting sensory features due tothe high levels of mechanical, electrical and acousticnoises in industrial environments [39]. In general, in-telligent monitoring systems for machining apply thesignal processing scheme shown in Fig. 3. The generalsignal processing scheme is made up of ve steps:

1. Analogical ltering and signal sampling. The sen-sor signal is ltered to keep the signal within therange of the frequency response of the sensor,suppress high frequency noise or continuous bi-ases and prevent signal distortion during signalacquisition due to aliasing. The Nyquist–Shannonsampling theorem asserts that the uniformly spaceddiscrete samples are a complete representation of the signal if its bandwidth is less than half thesampling rate. Then, the sufcient condition forthe ability for exact reconstruction from samplesat a uniform sampling rate is f s > 2 B , where B isthe signal bandwidth. If the sampling condition isnot satised, then frequencies will overlap, that is,frequencies above half the sampling rate will bereconstructed and appear as frequencies below half the sampling rate. The resulting distortion is calledaliasing. The reconstructed signal is said to be analias of the original signal, in the sense that it hasthe same set of sample values. In order to prevent

Step 1: Analogical filtering-Frequency sensor response-Sample frequency (anti-aliasing)-Amplification and conditioning

Step 2: Digital filtering

-Frequency range of interest

Step 3: Segmentation-Signal range of interest-Only time domain analysis

Step 4: Feature generation-Time domain: RMS, peak, mean, etc..-Frequency domain: harmonics, PSD, etc..-Wavelet domain: wavelets, etc..

Step 5: Feature selection/extraction-Feature selection: Variable ranking,forward/backward elimination, GA-Feature extraction: PCA

SIGNALPROCESSING

SCHEME

Fig. 3 Generic signal processing scheme

or reduce aliasing, sampling rate can be increasedor an anti-aliasing lter may be applied to restrictthe bandwidth of the signal.

2. Digital ltering. After sampling and with the signalin a digital format, digital ltering is performed tokeep the sensor information which best correlateswith the performance process variable of interest.For example, this may involve ltering the cutting-force signal so as to be able to study only the sig-nals at the tooth-pass frequency, which are closelyrelated to the cutting-tool wear and cutting mecha-nism [10]. In many applications, it can also be inter-esting to lter the acquired signal in order to pre-vent high frequency noises and signal oscillationsdue to transient mechanical events, like breakingof a built-up edge, local variation in hardness overthe workpiece, etc. Ghosh [ 39] tested an importantimprovement in the tool-wear estimation modelusing a third-order Butterworth low-pass lter onthe acquired cutting-force signals. Jemielniak [21]also applied a digital ltering process on the cuttingforces in order to improve the reliability of the tool-wear diagnosis model.

3. Signal segmentation. As a third step, an optionalsignal segmentation may be performed to extractthe signal information when the cutting-tool is actu-ally removing metal in a steady state, since only thispart of the signal contains information about tool-wear condition and surface roughness generation.However, this segmentation prevents the resultingsignal from being analysed in the frequency andwavelet domain. Segmentation is thus limited to aposterior analysis in the time domain such as peakvalue, root mean square, mean value, etc. Someconsiderations should be taken into account duringsegmentation. For example, as pointed outby Dong[14], it is preferable to analyse the signal withinone spindle rotation instead of one tooth period, inorder to reduce the inuence of runout. Figure 4shows an example of a signal segmentation appliedin [39].

4. Feature generation. The fourth step consists of afeature generation module, which transforms thedigital signal into several signal features calleddescriptors or features. Different methods havebeen applied for feature generation in the time,frequency and wavelet domains. Section 5 brieydescribes this step.

5. Feature selection/extraction. After generating thefeatures, one can obtain many different descriptorsfrom different sensor signals, and so, a featureselection or extraction procedure is required. Theselected or extracted features should be the most




F o r c e

( N )

F o r c e

( N )

F o r c e

( N )

No of samples

No of samples No of samplesx 10 4

Fig. 4 Example of a segmentation signal process adapted from[39]. Segmentation performed every tooth period

relevant descriptors, which may lead to simplerprediction models and more reliable monitoringsystems. In general, most of the research studiespublished in machining monitoring systems usu-ally present a direct feature selection, mainly in-volving root mean squares features [ 32, 39, 55],mean values [ 10, 32, 50] or harmonic signal values[9, 16, 27]. However, a complete methodology todesign monitoring systems requires a feature selec-tion/extraction algorithm in order to use the mostrelevant features [14]. Section 6 briey reviews sev-eral methods designed for this purpose.

5 Sensor signal features generation

The subsections that follow briey describe the mostusual descriptors applied in monitoring machining sys-tems according to the sensor system used.

5.1 Descriptors for cutting-force signals

Average and root mean square (RMS) values of cuttingforces are simple and effective features that have beentested in the past for both tool wear and part accuracymonitoring systems [ 5, 10, 39, 50]. Force ratios canalso be used to predict tool wear since they present acertain pattern as the tool wears. For instance, the feed-force to cutting-force ratio was found to be sensitiveto ank wear in [ 56]. Dontamsetti and Fischer [57]reported that RMS values of the vertical componentof the cutting force ( F z ) remain fairly constant dur-ing milling, and therefore, the RMS values of cutting-force components in the X and Y directions ( F x, F y)or a ratio of both components is a better descrip-

tor for modelling machining processes. Benardos [8]also reported similar conclusions, and it was conrmedexperimentally that the X components of cutting forceswere the most signicant descriptors for surface rough-ness modelling. Haber [10] reported that the best per-formance for tool-condition monitoring was obtainedexperimentally through the mean and peak descriptorsfrom cutting-force and vibration signals. Ertekin [5]analysed average and RMS values of cutting forces inthe X and Y directions and discarded average valuesdue to their being meaningless for tool-wear diagnosis.Furthermore, RMS values were shown to be closelyrelated to the wear process. In his research, he provedthat the increase in F y was mostly due to the ank wearof the tool, whereas the decrease in F x was mainly dueto built-up edge generation and crater wear on the rakeface of the tool.

Power spectral amplitudes at the cutter-tooth fre-quency of cutting-force signals were found to be verysensitive to progressive tool wear, and in [ 5, 10], theywere recommended as suitable sensory features formonitoring progressive tool wear. Dong [ 14] presentedan extensive study of several features for monitoringthe cutting-tool condition based on cutting forces. Allthe features examined had been applied previously inseveral research studies, in both the time (average,RMS, skew, kurtosis, standard deviation, peak, ratios)and the frequency domains (total harmonic power).

5.2 Descriptors for vibration signals

An increase in cutting energy generated due to ankwear should give rise to an increase in vibration mag-nitude, which can be detected by the RMS or themean of the signal [ 4, 29]. Haber [ 10] applied bothmean and peak values of vibration signals for tool-condition monitoring. Dimla [30] analysed the corre-lation of vibration signal features to cutting-tool wearin both the time and the frequency domains duringturning operations. Time domain featureswere deemedto be more sensitive to the cutting condition than toolwear, whereas certain peak values in the frequencydomain correlated well with the measured wear val-ues. Abouelatta [ 27] studied the inuence of singleharmonics and the power spectral density (PSD) forsurface roughness prediction, and several empiricalmodels based on multivariate regression were devel-oped. Chen [ 23] applied three sensor-signal featuresto model tool condition, namely: the average, vari-ance and single harmonic from vibration signals. Otherfeatures resulting from sensory feature combinations(summation, product, division, etc.) were also appliedfor tool-wear modelling. Ertekin [ 5] analysed the RMS




of X and Y vibrations and their single harmonics. Al-Habaibeh [49] analysed both workpiece and spindlevibrations in the X , Y and Z directions using differentfeatures such as wavelet analysis, average value, stan-dard deviation, power value, kurtosis value, harmon-ics frequency and skew value. Kuo [50] analysed veharmonic values of vibration signals in the frequencydomain and the signal average in the time domain.

5.3 Descriptors for acoustic emission signals

Themajority of AE monitoring applications have reliedon the RMS value of AE signals [ 31]. Strong correla-tion of the AE RMS to tool wear was presented byMoriwaki and Tobito [ 58], where features such as meanvariance and the coefcient of RMS were related totool wear. Several features of AE signals were stud-

ied in [32] for tool-wear monitoring, including averagesignal value; the standard deviation of the absolutevalue of the raw signal; the power of the AE signal inspecic frequency ranges and over its entire spectrum;the average value and the standard deviation of theRMS signal; the burst rate, which is the number of times the RMS signal exceeds pre-set thresholds persecond; the burst width, which is the percentage of timeit remains above each threshold and the pulse rate,which is equivalent to the burst rate but is applied tothe raw signal. Of all these features, the ones that weremost correlated with tool wear were the average of theRMS signal, the power of the signal in a specic highfrequency range, the average of the signal value, theburst rate and the pulse rate. As all of them except theburst rate were highly correlated, just one of them wasenough to describe tool wear.

Table 3 Signicant descriptors applied for modelling machining operationsSignal Ref. Time domain Frequency domain Wavelet domain

Rms Peak Mean Std Skew Kurt. Var. AR TDA H ratios Single H PSD RMS PeakCutting [ 39]

forces [ 10] [14] [16] [7][5] [9][49] [50] [8]

Vibrations [ 10] [4][27] [16] [23][30] [5] [29][49] [50]

Current [ 39]power [ 34]

[41][40][55]

Acoustic [ 5]emission [ 10]

[9] [49] [50] [58][32] [36] [60][34]




Table 4 Common descriptors and equations

Descriptor Equation Descriptor Equation

Average ¯ x =N j = 1 x j N RMS 1

nni= 1 x2

i

VarianceN j = 1 ( x j − x) 2

N − 1 Standard deviation 1

N − 1N i= 1 ( xi − x)2

Skewness1

N N i= 1 ( xi− x)3

( 1

N N i= 1 ( xi− x) 2 )3 / 2 Kurtosis

1

N N i= 1 ( xi− x)4

( 1

N N i= 1 ( xi− x) 2 )2 − 3

Peak max ( xi), i = 1 , . . . , N AR models a1 xi− 1 + · · · + a p xi− p, i = p + 1 , . . . , N Single harmonic FFT analysis: f i ith harmonic Harmonic ratio f i

f j i = j , i, j = 1 , . . . , n harmonicsTime-domain averaging “See [ 41]” Wavelet “See [ 34]”

xi , i = , 1 , . . . , N sampled sensor data, p regressors

Haber [ 10] reported in his research that the meanand peak values of neither of the AE signals revealed aclear transition from a new to a worn tool. Ravindraet al. [36] used a time series modelling technique toextract parameters from AE signals acquired duringturning. Autoregressive (AR) parameters and powerdensity were taken as features for successfully mon-itoring the tool wear condition. Kamarthi et al. [59]dealt with the representational and analysis issues of AE signals in turning processes. The effectiveness of the wavelet representation of AE signals was studiedin the context of the ank-wear estimation problem inturning processes. Accurate ank-wear estimation indi-cated that the wavelet transform representation of AEsignals was very effective in extracting the AE featuresthat were sensitive to gradually increasing ank wear.Li [60] applied a wavelet transform to decompose AE

signals into different frequency bands in the time do-main. The RMS values extracted from the decomposedsignal for each frequency band were used as monitoringfeatures for tool wear. Li extended his work in [ 34],where the wavelet domain was analysed in both AEand spindle current signals for tool breakage detection.Tool breakage detection was dened by comparingthe signal wavelet coefcients with wavelet coefcientthresholds.

5.4 Descriptors for current or power signals

Current and power signals have been analysed mainlyin the time domain due to their limited sensing band-width. Ghosh et al. [ 39] only selected the simplestfeatures in the time domain to ensure feasibility of real-time implementation. Features such as peak val-ues, standard deviation, mean and RMS values wereanalysed for tool-wear estimation in milling. Kim [55]developed an in-process tool-fracture monitoring sys-tem based on the spindle current signal. The dynamiccutting-force variation in a face milling process was

measured indirectly using a spindle motor current sen-sor. The cutting force was correctly represented bythe spindle current RMS value in rough face millingoperations and the tool fracture was well distinguishedfrom cutter run-out and transient cutting. Li [ 41] alsopresented a system for detecting tool ute breakagein end milling using feed-motor current signatures.Li applied the time-domain averaging (TDA) methodto decompose the compound repeated current signalinto a periodic signal and a residual component. Itwas found that the amplitude of the residual compo-nent uctuates severely when ute breakage occurs.Therefore, the mean of the residual components of thefeed-motor current was used as a feature of tool utebreakage detection. Lee [40] monitored the AC motorcurrents of the feed-drive system and applied a rst-order AR model, which provided a good indication

of tool breakage using an analysis of the differencebetween the predicted and the actual motor currents.The wavelet domain has also been analysed in currentsignals for tool-breakage detection. Li [ 34] developeda tool breakage monitoring system using both AE andcurrent signals in the wavelet domain. Tool break-age detection was performed by comparing the signalwavelet coefcients with wavelet coefcient thresholds.

5.5 Summary of descriptors

Table 3 summarises the relevant descriptors applied indifferent machining monitoring systems. The equationsof some of these descriptors are also shown in Table 4.

6 Feature selection/extraction

The large number of sensor features reported in theliterature for modelling and monitoring machining sys-tems makes it necessary to nd out which feature com-binations are the most signicant and reliable for a




specic monitoring purpose. Intuitively, it is difcultto estimate which features are more sensitive to part-accuracy prediction or tool-wear diagnosis, as the effec-tiveness of the features is affected by various factors,such us machine-tool characteristics, material proper-ties, lubrication, workholding, location of the sensors,signal-to-noise ratios of the data acquisition system, etc.Therefore, a systematic approach to reducing the num-ber of features to be used provides valuable guidancefor the successful development of reliable and robustmachining monitoring systems.

There are two methods for feature reduction: featureselection and feature extraction. The goal of featureselection methodologies is to nd k of the d featuresthat give the most information and to discard the other(d − k) features. On the other hand, feature extractionmethodologies try to nd a new set of k features thatare a combination of the original d features. Figure 5shows an example of feature selection/extraction pro-cedures. In general, the benets of applying a featureselection/extraction methodology to develop monitor-ing systems can be dened as follows [ 61]:

– Reduced feature inputs tend to yield easier processknowledge models by means of less complex learn-ing algorithms.

– Simpler models are more robust on small datasets.– Simpler models have less variance; in other words,

they vary less depending on the particulars of asample, including noise, outliers, and so forth.

– When data can be explained with fewer features,one gets a better idea about the process that under-lies the data, which allows knowledge extraction.

– When data can be represented in a few dimensionswithout loss of information, they can be plottedand analysed visually to determine structure andoutliers.

6.1 Feature selection

Feature selection algorithms typically fall into two cat-egories: feature ranking and subset selection. Featureranking uses a metric to rank the features and elimi-nates all features that do not achieve an adequate score.The advantages of this algorithm are its simplicity,scalability, and high degree of empirical success [ 6].Computationally, it is efcient since it requires onlythe computation of n scores, where n is the numberof features. Typically, sensor feature selection has beenconducted through a correlation index ranking, in orderto use the features that are most correlated with thevariable of interest. The application of this method canbe found in [ 49, 62].

On the other hand, subset selection searches theset of possible features for the optimal subset. Thecommon approaches applied for subset selection areforward selection, backward elimination and geneticalgorithms. In forward selection, the algorithm startswith no variables and adds them one by one, at eachstep adding the one that decreases the prediction errorof the model the most until further addition does notdecrease the error (or decreases it only slightly). Inbackward elimination, it starts with all the variablesand removes them one by one, at each step removingthe one that increases the error only slightly, untilany further removal increases the error signicantly. Ineither case, the error should be checked on a validationset other than the training set in order to test thegeneralisation accuracy. With more features, there isgenerally a lower training error, but not necessarilya lower validation error. Research works that haveapplied forward or backward algorithms can be foundin [5, 7]. Genetic algorithms (GA) have also been usedas a search algorithm to select the best feature subset.Due to the high computational cost, GA becomes a

Fig. 5 Example of featureselection/extraction. Featureselection only selects asubgroup of the initialfeatures, whereas featureextraction transforms a groupof features into another onethrough a linear combination

Descriptors

X=

RMSPeakMean

StdSkewKurt.Var.AR

TDAH ratiosSingle H

PSD...

Feature Selection (Ranking or Subset)

Feature Extraction (PCA)

RMSMeanStd

H ratiosSingle H

Xsignificant =

PeakSkewKurt.Var.AR

TDAPSD...

Xnon-significant =

New Descriptor =a x RMS + b x Mean

R M S

Mean




reasonably efcient method of feature selection whenthere is a high dimensional feature space [ 63]. Otherapproaches have been applied to feature selection butwithout a theoretical base to prove its effectiveness.For example, Azouzi [19] applied orthogonal arraysfrom Taguchi’s design of experiments to test differentsensor feature combinations for surface-roughness andpart-accuracy prediction. In these orthogonal arrays,different NN structures, sensory features and cuttingparameters were considered in order to select the op-timal combination of these factors for modelling pur-poses. Although this methodology chooses the bestfactor combination, it is not clear whether the selectedsubset of sensor features is the best or not, since notall interactions between sensor features are included inthe orthogonal array. Kuo [ 50] studied the use of force,vibration and AE sensors independently, and traineddifferent NN models to diagnose tool wear with xedsensor features in the time domain and the frequencydomain. The performance of the NN model was usedto select the sensor candidates and the domain to beused (time, frequency or both). This methodology canbe seen as a kind of variable ranking, and its main draw-back is that the interactions between sensor features,which might provide betterNN model performance, arenot studied. The feature selection approach offers someadvantages with respect to feature extraction [ 64]: (1)after a set of features has been selected, non-selectedfeatures will no longer be used; (2) to collect new data,only collection of the selected features is necessary,which may reduce computational costs and (3) thephysical meaning of each selected feature is retained.

6.2 Feature extraction

Compared with the feature selection approach, thefeature extraction approach has a higher degree of freedom in nding the set of the most signicant fea-tures [ 64]. The objective of feature extraction is topreserve as much of the relevant information as pos-sible by removing redundant or irrelevant informationin acquired sensory signals. The main feature extrac-tion technique is principal component analysis (PCA),which has been widely used in system identicationand dimensionality reduction in dynamic systems. It isalso an efcient approach for extracting features fromsensory signals acquired from multiple sensors [ 65].In general, multiple sensory signals can be viewed asa high-dimensional multivariate random matrix com-posed of several vectors formed by different sensorysignals. It is not feasible to input the above-mentionedmatrix to the prediction model without any feature ex-traction or dimensionality reduction procedure because

of the curse of dimensionality and the high degree of correlation between vectors. By implementing PCA,the complexity of modelling processes can be reducedand new feature vectors can be reconstructed. PCAtransforms a number of correlated sensory features intonew uncorrelated features (or principal components),thus reducing the complexity of modelling processes.In general, the multiple sensory signals SP × N may berepresented as a matrix with N samples acquired fromP sensors. In order to remove irrelevant or redundantinformation from acquired signals, an orthogonal lineartransformation is introduced to convert the originalmatrix S into a new space denoted by aT S, wherea = (a 1 , a 2 , · · · , a p) T . To preserve the greater part of the variation in data set S, the orthogonal vector a ischosen to maximise the variance of the projections aT S,estimated by Var (aT S) = aT

Sa, where S is the co-variance matrix of S. Hence, the optimisation problemoutlined above is dened as maximising aT

Sa with re-spect to the constraint aT a = 1 . The optimisation prob-lem can be solved by using Lagrange multipliers, andthe corresponding Lagrange function is constructed as

L (a, λ) = aT Sa − λ( aT a − 1 ), (1)

where λ is the Lagrange multiplier. The solution of this problem derives into a typical eigenvector problem,where a is the eigenvector of the symmetrical matrixS with corresponding eigenvalue λ . These eigenvectorsrefer to the principal components and represent thedirections of greatest variance in the multiple sensorysignals. In practice, some eigenvalues make little con-tribution to variance and may be discarded. The leadingk components that better explain the variance, up to acertain percentage, can then be taken into account [ 61].When λ i are sorted in descending order, the proportionof variance explained ( % VE) by the k principal compo-nents is

%VE =λ 1 + λ 2 + · · · + λ k

λ 1 + λ 2 + · · · + λ k + · · · + λ p(2)

If the dimensions are highly correlated, there will be asmall number of eigenvectors with large eigenvalues, kwill be much smaller than p and a large reduction indimensionality may be attained. If the dimensions arenot correlated, k will be as large as p and there is nogain through PCA. If the variances of the original sen-sor features vary considerably, they affect the directionof the principal components more than thecorrelations,and so a common normalisation procedure for pre-processing the data is required before using PCA. Thefeatures transformed by the principal components areapplied directly to model the process, with an impor-




Table 5 Several researchworks on monitoringmachining systems withfeature selection/extraction

Ra surface roughness,Dim dimensional deviation

Ref. Modelling technique Feature selection/ Monitoring Features selected/extraction technique application extracted

[5] Multiple regression Forward elimination Ra and Dim RMS forcesand AE signals

[62] Multiple regression Variable ranking Ra Average vibration[49] NN Variable ranking Tool wear Features from AE

and vibrations[7] Multiple regression Forward/backward elim. Ra Average forces[19] NN Orthogonal arrays Ra and Dim Feed and radial

average forces[65] Support vector machines PCA Tool wear Average forces

tant reduction in dimensionality that may improve therobustness and reliability of the monitoring system.However, the new features do not provide a physicalexplanation of the system. Table 5 shows several re-search works on monitoring machining systems wherefeature selection/extraction methods were applied.

7 Design of experiments

While machining experimentation is costly and time-consuming, it is important to carry out effective ex-periments with few runs in order to nd out the mostrelevant factors for a certain machining performancevariable. In thepast, various methods have been used toquantify the impact of machining parameters (cuttingspeed, depth of cut, feed, etc.) and process variables (vi-brations, tool wear, temperatures, etc.) on part qualityand cutting-tool state. All these methods include somekind of design of experiments (DoE) that quantiesthe effects of a nite number of parameters througha systematic methodology. In general, four methodolo-gies are applied in DoE: full factorial designs, fractionalfactorial designs, Taguchi’s orthogonal arrays, and re-sponse surface designs.

A DoE with every possible combination of all theinput factors is called a full factorial design. A commonfull factorial design is one with all input factors set attwo levels each, which dene a cube in k dimensions,where k is the number of factors being studied. Thistwo-level full factorial design requires 2 k experimentalruns and makes it possible to know all the main effectsand factor interactions. However, as the number of fac-tors increases, the number of runs required also growsrapidly, and this is unfeasible from a time and resourcepoint of view. In order to minimise experimentation, afractional factorial experiment can be conducted.

A fractional factorial design is a variationof the basicfull factorial design in which only a subset of the runs isperformed. These designs collect data from a specic

subset of all possible vertices of the cube and require2 k− q runs, where q is chosen according to the de-sired design resolution. Fractional factorial designs candetermine which factors and their combinations havesignicant effects on the response variable; however,they deal with confounding terms [66]. Confoundingoccurs when only the summation of several effects canbe estimated, not the effects separately. Which effectsare confounded is dened by the experimental designresolution. Resolution III designs confound the maineffects with two-factor interactions. Resolution IV de-signs confound no main effects with two-factor interac-tions, but two-factor interactions are aliased with eachother. For resolution V designs, no main effect or two-factor interaction is aliased with any other main effector two-factor interaction, but two-factor interactionsare aliased with three-factor interactions.

Taguchi’s orthogonal arrays are similar to fractionalfactorial designs, but they apply new concepts to pa-rameter design and tolerance design [ 67]. In Taguchi’sarrays, the factors are divided in two groups: controlfactors whose levels may be controlled in both the labo-ratory and in actual production and error factors whoselevels may be controlled in the laboratory but not inproduction. A Taguchi’s array can be used to determinewhich levels of the control factors have little impact onthe performance variable. On the other hand, Taguchi’sarrays can also be applied to determine, based on a lossor cost function, which variables have critical tolerancesthat need to be tightened for better performance.

The previous DoE methods are basically proceduresto determine the signicance of each factor in a sys-tem, and they do not formulate any kind of model. Aresponse surface design is another DoE method whichinvestigates how important factors affect the responseof an experiment. It also leads to the development of rst- and second-order polynomial models that includethe parameters under consideration and their statisticalsignicance.

Table 6 shows some research works where a DoEwas conducted for modelling the machining process.




Table 6 Designs of experiments conducted in theliterature for modellingpurposes

s spindle speed, f feed speed,

d depth of cut, r nose radius,Co coolant, W wear,Le length, En engagement,Te temperature, Ra surfaceroughness

Reference Factors and levels per factor DoE Purpose[68] s: 4, f : 8, d : 3 Full factorial Ra prediction in end milling[29] s: 4, f : 4, d : 3 Full factorial Ra prediction in end milling[62] s: 3, f : 6, d : 2 Full factorial Ra prediction in turning[54] V c: 2, f : 2, d : 2, Co : 2, Le : 2 Full factorial Ra prediction in turning

Dimensional deviation in turning[69] s: 3, f : 3, W : 2 Full factorial Tool wear diagnosis in face milling[8] V c: 3, f : 3, d : 3, W : 2, Co : 2, En : 2 Taguchi’s array Ra prediction in face milling[70] s: 3, f : 3, d : 3, W : uncontrolled, Taguchi’s array Ra prediction in end milling

Te : uncontrolled[71] s: 3, f : 3, d : 3, r : 3 Response surface Ra prediction in turning

modelling

The table shows research works based on full facto-rial designs [29, 54, 62, 68, 69], Taguchi’s orthogonalarrays [ 8, 70] and response surface designs [71].

7.1 A generic methodology for DoE in machiningmonitoring systems

According to the literature review, a generic DoEmethodology for developing machining monitoring sys-tems can be proposed in order to minimise the exper-imental efforts and increase the effectiveness of DoEmethods. The methodology is presented in Fig. 6, and itis divided into six steps as follows:

– Step 1: First, the k factors that may affect the per-formance variable of interest that is to be modelled.With these factors, a screening DoE is performedto discard those factors which do not affect the

variable of interest. The screening design is a 2k− p

fractional factorial design of resolution IV ( 2 k− pIV ),

where k is the number of factors to be analysed and p is dened in order to get a IV resolution [ 66].This screening experiment allows the informationabout all the main effects to be captured, whereastwo-factor interactions are aliased with each other.As one can consider the sparsity-of-effects principlein machining operations, the system will usuallybe dominated by the main effects and low-orderinteractions. Three-factor interactions or higher areusually negligible.

– Step 2: After conducting the screening experiment,normal probability plots are drawn to assess thesignicance of the main effects, the n variableswhich are not relevant in the next experimentsbeing discarded.

– Step 3: After identifying the signicant variables,two paths can be followed according to the previousknowledge of the process.

– (a) If the presence of quadratic or higher ef-fects is assumed in the process, a Taguchi’s

orthogonal design of k − n factors is carriedout. The Taguchi’s design is selected accord-ing to the expected effects of each signicantvariable with respect to the variable of interest.

NO

YES

S T E P 1

S T E P 2

S T E P 3

S T E P 4

DoE for k factors

Screening:Fractional factorial design

2k-pIV

Normal Probability Effects Plot:n factors are non-significative

Are highlyprobably the

quadratic effects?

Taguchi’s orthogonal designk-n factors

Projection to 2k-n

Full factorial

Is n > p?

Projection to 2k-pH

H IV or V

Add center points

Lack of fit?

Add axial pointsCentral Composite Design

Training an AI model(75% experimental data)

YES

NO

S T E P 5

Successful AImodel

Is the AI modelaccurate enough?

Validating the AI model(25% experimental data)

Add experimentalreplicates

S T E P 6

YES

YES

NO

NO

Fig. 6 Generic methodology for DoE in machining monitoringsystems




Therefore, if a factor is expected to have aquadratic effect on the variable of interest, atleast three levels should be considered in theTaguchi’s design for this factor. To select themost adequate orthogonal array, it is necessaryto count the degrees of freedom (DOFs) of thefactors included in the study [ 67]. TheTaguchi’sarray must equal or exceed the DOFs requiredby the experiment. The DOFs are dened as:• DOF of a factor: (number of levels of the

factor)-1• DOF of a column in a Taguchi’s array:

(number of levels in the column)-1• DOF of an array: total DOFs of all columns

in the array• DOF of an experiment: (total number of

ex-periments)-1As an example, consider a system where fourtwo-level factors and one four-level factor areanalysed. This system requires a Taguchi’s ar-ray with seven or more DOFs, since four two-level factors have four DOFs, one DOF pertwo-level factor, and a four-level factor hasthree DOFs. An L-8 array is a Taguchi’s arraywith seven columns where each column is usedfor two-level factors. Therefore, an L-8 arrayhas seven DOFs and can be used to analyse thesystem with four two-level factors and one four-level factor.Once the array has been selected, the factorsand interactions are assigned to the columns of the orthogonal design. The assignment shouldtake into consideration which factors and in-teractions are interesting to analyse, and whichones can be confounded. Which columns rep-resent each factor and interaction and whichinteractions or factors are confounded is givenby the linear graphs of each Taguchi’s array. If it is not possible to assign all the desired factorsand interactions without confounding, then theexperimentation requires more DOFs and ahigher orthogonal array should be selected.

– (b) If the presence of cubic or higher effectsis negligible, the previous screening DoE isprojected from the initial 2 k− p

IV to a full factorial2 k− n or a fractional 2 k− p

H with H = IV or Vdepending on the initial number of insignicantfactors.

– Step 4: If, in the previous step, the DoE to beconducted is the projection of the initial screeningdesign, additional experimental runs in the centrepoints of the design have to be added in order

to ensure that quadratic or higher effects are notsignicant. If the central points that are added donot report a lack of t, the DoE conducted wouldbe appropriate. If this is not the case, axial pointson the DoE have to be added, so that the nalDoE would be equal to a central composite design(CCD). By conducting the runs for the CCD, thequadratic effects can be evaluated.

– Step 5: After conducting the nal DoE (Taguchi’sarray, fractional factorial design or CCD) with atleast three replicates for each experiment, the train-ing of the AI model is conducted according tothe AI technique selected. For this purpose, theexperimental data are divided into two sets: thetraining set with 75% of the experimental data andthe validating set with the other 25%, care beingtaken to ensure that at least one replication of eachexperimental run is included within the validatingdata set. During training, the trained model mayinform the user about the possible presence of spurious data that might be caused by errors indata collection. In order to discard such spuriousdata and to conduct a new replicate, normality testsshould be applied.

– Step 6: The trained AI model is validated with thevalidating data and the model error is analysed. If the accuracy of the model is sufcient, the AI modelto be used for monitoring purposes has been learnt.Otherwise, additional experimental replications areneeded to increase the training data set and theaccuracy of the AI model.

8 Selection of AI techniques

Conducting a correct DoE may make it possible tot a regression model relatively well using cutting pa-rameters as regressors. However, the use of indirectmeasurements such as cutting forces, vibrations or AEmeasurements can provide additional informationwhich can be used in a more complex and accuratemodel. In this context, several AI techniques have beenwidely used in the past for surface roughness predictionand the diagnosis of tool wear. The main AI tech-niques applied for modellingand monitoring machiningsystems are articial neural networks (ANN) [ 39, 54,72], expert systems called fuzzy logic systems [ 26] andthe AI technique that results from the hybridisationof these two techniques, called neuro-fuzzy inferencesystems [ 73, 74]. Other AI approaches such as BN[22, 48], hidden Markov models [ 69, 75], evolutionaryalgorithms [76, 77] or support vector machines [78]




4% 12%

59%15%

10%ANN ( 59%)

Fuzzy (15%)

Neuro −fuzzy (10%)

Bayesian Networks (4%)

Others (12%)

Fig. 7 Frequency of usage of AI approaches in intelligent ma-chining systems according to the references found in the researchplatform ISI-Web of knowledge from 2002 to 2007

have been less widely used, although they are gainingpopularity in recent works. Figure 7 shows the AIapproaches applied in machining monitoring systemsaccording to the references found in the research plat-form ISI-Web of knowledge from 2002 to 2007.

8.1 AI recommended applications

Although AI techniques can be successfully applied tomonitoring machining systems, a specic AI techniquemay be recommended according to the monitoringpurpose, the experimental data set for modelling theprocess, the previous knowledge of the process andso forth. Hence, a general set of guidelines to selectthe most appropriate AI technique according to itsadvantages and drawbacks is proposed as follows:

– ANN: applications where the purpose is not toextract knowledge, and there is no previous knowl-edge of the process (or if there is previous knowl-edge, this knowledge is not intended to be added tothe model). Applications where high-accuracy pre-diction is required. Applications where there is noextrapolation and a good generalisation is required.Applications where the experimental data set iscomposed of a medium/high number of samples,since the data set is usually divided for training,testing andvalidating. Applications where only pre-diction or diagnosis is required and the inverseproblem, such as the selection of cutting parame-ters to ensure a certain value of machining per-formance, is not considered. In general, ANNs arewell-suited for accurate surface roughness predic-tion, cutting-tool ank wear prediction and cutting-tool state diagnosis.

– Fuzzy inference systems: applications where thereis enough knowledge from the process and thisknowledge is intended to be added into themodel. Applications where the understanding of the process prevails over the accuracy of the model.Applications where extrapolation can occur and

the general process behaviour is expected to besmooth. Applications where the experimental dataset consists of a low/medium number of samples,since part of the model is developed using previousknowledge. Applications where the inverse prob-lem has to be solved apart from specic variableprediction. In general, fuzzy inference systems areused for surface roughness prediction, cutting-toolank wear prediction and cutting parameter selec-tion for optimal surface roughness.

– Adaptive neuro-fuzzy inference systems: applica-tions where the aim is to add previous knowledgeand also to extract hidden knowledge from ex-perimental data in rule-form. Applications wherethe ability of extrapolation and generalisation isrequired. Applications with a moderate accuracyrequirement. Applications where the experimen-tal data set is composed of a medium number of samples. Applications where the inverse problemhas to be solved. Since neuro-fuzzy systems are ahybridisation of ANN and fuzzy systems, the rec-ommended applications are similar to both ANNand fuzzy applications.

– BN: applications where the aim is to add previousknowledge and also to extract hidden knowledgefrom experimental data in the form of causal rela-tionships and probabilities. Applications where lowaccuracy prediction but a high degree of reliabilityare required. Applications where the system that ismodelled has a highly stochastic behaviour and theprediction reported by the model is given with anexpected uncertainty level. Applications where theexperimental data set is composed of a large/verylarge number of samples depending on the variablediscretisation ranges and the expected accuracy.Applications where prediction/diagnosis and theinverse problem for cutting parameter selection arerequired. BN is recommended in highly stochasticmachining processes for cutting-tool diagnosis, pre-diction of part accuracy and the selection of cuttingparameters in order to meet part specications.

In addition to the recommended application of eachAI technique, several drawbacks and advantages whichmay facilitate the selection of a particular AI approachare also presented in Fig. 8.

8.2 Periodic model verication

A machining monitoring system should be periodicallyveried in order to detectchanges in the machining per-formance. Due to changes in machining operation, themachining model learnt by AI techniques may have to




Drawbacks: No clear guidelines on how to design neuralnets; Lack of physical meaning; Low extrapolationcapability; Trial and error procedures to find neural networkparameters.

Advantages: Model can be obtained without previousknowledge; NN can learn patterns in a noisy environmentor with incomplete data; Good generalisation capability

NEURAL NETWORKS

FUZZY LOGIC BAYESIAN NETWORKS

Drawbacks: Do not have much learning capability;Generalisation capability is poor compared with NN; Nostandard methods to transform human knowledge into fuzzymodels; Inputs limited.

Advantages: Tolerant of imprecise data; Easy to understandsince it is based on natural language; Models can be built ontop of the experience of experts; Good extrapolationcapability

ARTIFICIAL INTELLIGENCE APPROACHSELECTION

Drawbacks: High quantity of experimental datais required; High computational cost; Variable

discretisation is required and depends on networkreliability and accuracy.

Advantages: Adequate for modelling stochasticsystems; The model presents the causal relationshipsbetween variables; Let fuse prior knowledge by fixingwell-known causal relationships.

Drawbacks: Many parameters to be learnt ordefined by the user; Usual contradictory learntrules; Inputs limited.

Advantages: Combines fuzzy systems and NNIt can be applied with or without previousprocess knowledge; Tolerant of imprecise data;Good extrapolation and generalisation capability.

NEURO-FUZZY MODELS

Fig. 8 Summary of advantages and drawbacks of the main AI approaches applied in intelligent machining

be re-trained in order to be adapted to a new machiningprocess. Two statistical tests have to be conducted toinfer whether the present machining process conditionsare statistically equivalent to the previous ones wherethe monitoring system was implemented, i.e. the equalvariance test and the two-sample t test. The equal vari-ance test checks whether the present prediction errorhas a variance that is statistically equal to that from theprediction error obtained after the monitoring systemwas implemented. On the other hand, the two-sample t test checks whether the mean of the present predictionerror is statistically equal to that obtained after themonitoring system was set up [79]. The results of bothtests indicate whether, statistically speaking, there isany change in the machining process behaviour, andthen whether the monitoring machining system can stillbe applied or it requires a re-training process.

The null and alternative hypotheses for the equalvariance test are dened as follows:

H 0 : σ 21 = σ 22

H a : σ 21 = σ 22

and the equal variance test statistic is:

F 0 = s2

1

s22 (3)

which has a similar distribution to a F distribution withn 1 − 1 and n 2 − 1 DOFs in the numerator and denom-inator, respectively, where n 1 and n 2 are the samplesize. The variables s2

1 and s22 dene the variances of each

sample. hypothesis, which means the two samples havethe same variance, is rejected if:

F 0 < f (1 − α/ 2 , n1 − 1 , n2 − 1 ) or F 0 > f (α/ 2 , n1 − 1 ,n 2 − 1 ) , (4)

where f (α/ 2 , n1 − 1 ,n 2 − 1 ) is the value of the F distributionwith n 1 − 1 and n 2 − 1 DOFs at a signicance levelof α .

The null and alternative hypotheses for the two-sample t test are dened as:

H 0 : μ 1 = μ 2

H a : μ 1 = μ 2

and the two-sample t test statistic is:

T =Y 1 − Y 2

s21 / n 1 + s2

2 / n 2

(5)

where Y 1 and Y 2 are the sample means. The null hy-pothesis that the two means are equal is rejected if:

T < − t (α/ 2 ,v) or T > t (α/ 2 ,v) , (6)

where T > t (α/ 2 ,v) is the value of the t distribution withv DOFs at a signicance level of α , where:

v =( s2

1 / n 1 + s22 / n 2 ) 2

( s21 / n 1 ) 2 /( n 1 − 1 ) + ( s2

2 / n 2 ) 2 /( n 2 − 1 )(7)

9 Conclusions

Many machining monitoring systems based on AIprocess models have been developed in the past for op-timising, predicting or controlling machining processes.All research works present different methodologieswithout showing clear guidelines or key issues for thedevelopment of intelligent machining systems. In orderto overcome the lack of a global view on how to de-velop machining monitoring systems based on AI mod-




els, this paper has presented a generic methodologywhich reviews the main parts of a machining monitoringsystem and includes the most relevant aspects fromprevious machining monitoring systems presented inthe literature. The paper has reviewed: (1) the differentsensor systems applied to the monitoring of machin-ing processes, (2) the most effective signal processingtechniques, (3) the frequent sensory features applied inmodelling machining processes, (4) the sensory featureextraction methods for using the relevant sensory in-formation, (5) the DoE required to model a machiningoperation with minimum experimental data and (6)the main characteristics of several AI techniques tofacilitate their application/selection.

Acknowledgements This work has been partially supported byFundació Caixa-Castelló under the research project 07I006.44/1.

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