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Steven Y. LiangGeorgia Institute of Technology,

George W. Woodruff School of MechanicalEngineering,

Atlanta, GA 30332-0405

Rogelio L. HeckerFacultad the Ingenieria.

Universidad Nacional de La Pampa,General Pico, LP, 6360,

Argentinae-mail: [email protected]

Robert G. LandersDepartment of Mechanical and Aerospace

Engineering,University of Missouri–Rolla,

Rolla, MO 65409-0050e-mail: [email protected]

Machining Process Monitoringand Control: The State-of-the-ArtResearch in automating the process level of machining operations has been conducboth academia and industry, over the past few decades. This work is motivated by abelief that research in this area will provide increased productivity, improved part quareduced costs, and relaxed machine design constraints. The basis for this belief isfold. First, machining process automation can be applied to both large batch producenvironments and small batch jobs. Second, process automation can autonomousmachine parameters (feed, speed, depth of cut, etc.) on-line and off-line to substaincrease the machine tool’s performance in terms of part tolerances and surface fioperation cycle time, etc. Process automation holds the promise of bridging thebetween product design and process planning, while reaching beyond the capabilithuman operator. The success of manufacturing process automation hinges primarthe effectiveness of the process monitoring and control systems. This paper discusevolution of machining process monitoring and control technologies and conductin-depth review of the state-of-the-art of these technologies over the past decaderesearch in each area is highlighted with experimental and simulation examples. Oarchitecture software platforms that provide the means to implement process monitand control systems are also reviewed. The impact, industrial realization, and futrends of machining process monitoring and control technologies arediscussed.@DOI: 10.1115/1.1707035#

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IntroductionManufacturing enterprises face the growing demands of

creased product quality, greater product variability, shorter pruct lifecycles, reduced cost, and global competition. A shortagexpert manufacturing equipment operators over the past dehas exasperated the pressure these demands are placing on tmanufacturing industries. Manufacturers are increasingly turnto automation as an effective means to meet these demandsmaintaining, or increasing, their overall competitiveness andducing their reliance on expert operators.

Given the importance of machining to most industries, machtools have often led the way in the development of automatechnology. Machine tool automation began in the 1950s withintroduction of Numerical Controlled~NC! machines where thecontroller was hardcoded in electronic circuitry and part prograwere prepared with punch tapes. A significant developmenmachine tool automation was the introduction of Computer Nmerical Control~CNC! in the early 1970s where a dedicated coputer replaced most of the electronic hardware and punch cardthe NC machines. Greater reliability, decreased floor space,increased flexibility were provided by CNCs. Increased processpeed, user-friendly programming tools, and increased seresolution have all contributed to the great strides in the areaservomechanism control and interpolation. Servomechanismtrol loops regulate the position, velocity, and acceleration of aand spindles and the interpolator loops coordinate the motiomultiple axes to achieve a desired tool path and orientation.next level in the machine tool hierarchy is the process level. Tmachining process level refers to the phenomena that occur dthe interaction of the cutting tool and part~e.g., forces, temperature, chip formation, chatter!. During the past few decades, a trmendous amount of research has focused on process monitand control.

Process monitoring is the measurement and estimation of

Contributed by the Manufacturing Engineering Division for publication in tJOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript receivedSeptember 2002; Revised December 2003. Associate Editor: K. Danai.

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cess variables. A broad spectrum of on-line sensors has bimplemented that use acoustic, optical, electrical, thermal, mnetic, etc. sensing systems. Signal processing and anaschemes in different transform domains and various model-bacalculations have been proposed to retrieve information relevto the condition of the machining process. With the use of thsensors, critical process variables are either gauged directlinferred based on indirect measurements. Given that machiprocesses are very complex~i.e., nonlinear, nonstationary, etc!and many process variables cannot be directly measured, promonitoring is a challenging problem. The advent of signal pcessing and analysis technologies has brought the utility of sesystems closer to industrial realization. Examples of applicatareas include tool condition monitoring, part surface/subsurfintegrity, and dimensional accuracy. Time or frequency domprocessing with deterministic or probabilistic modeling has bethe common approach. Heuristic rule based systems, inclufuzzy logic, expert systems, and artificial neutral networks, haalso gained maturity in recent years.

Machine tool controls can be generally classified into three lels according to their scope of operations: servomechanismtrol loop, interpolation loop, and adaptive~or simply process! con-trol loop. The objective of the servomechanism control loop isregulate the position and velocity of axes and spindles in the fof adverse disturbances such as friction, stiction, backlash,chining forces, etc. The objective of the interpolator loop iscoordinate multiple axes to maintain a specified tool path aorientation. Process control, which is not commonly integrainto today’s machine tools, is the automatic adjustment of procparameters~e.g., feeds, speeds! in order to increase operation productivity and part quality. In addition to effective process montoring, critical elements for machine tool process control includynamic process models, control algorithms, software opennactuator response, etc.

Presently, research and development in the general area ofchining process monitoring and control has not met the requ

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ments necessary for industrial adoption. Commercial realizaof these technologies is still quite limited after some thirty yeof research efforts. This paper describes a number of key reathat have impeded the widespread utilization of process moning and control technologies and offers a viewpoint of howsearch directions may evolve over the next several years.following sections present a review of several issues that arcore importance to the automation of machining processes:

• Sensors and sensing techniques. The sensors are dividedfive main categories: surface texture, surface integrity, dimsional accuracy, tool condition, and chatter detection. Both digauging and indirect measurements are discussed in this pencompassing traditional sensors for force, power, and acoemission, as well as recent developments on transducers baselectromagnetic, fiber optics, etc.

• Process control strategies. The major strategies include Ative Control with Optimization~ACO!, Adaptive Control withConstraints~ACC!, and Geometric Adaptive Control~GAC!. Re-search in these areas has spawned a tremendous amountsearch in the regulation of individual process variables, mosttably force control and chatter suppression. Thus, this paperreview the main strategies, machining force control and chasuppression, and the control of other process variables.

• Open architecture software systems. Definition and baground of open architecture systems are given, as well as a reof the research and commercially available systems. Also, acussion is presented that attempts to explain the gap betweeresearch efforts and the industrial applications in the field of mchining process sensors and controls.

This paper provides an in-depth review of the developmentsprocess monitoring and control technologies for machine toover the past decade. The current technical advances in tfields are outlined, citing example applications that highlightresearch results.

Machining Process MonitoringHigh-level machine tool controls for process automation

intended to maximize material removal while at the same timinimizing tool wear or failure to maintain part quality specifictions. To this end, reliable sensors are required to identifybehaviors of the machine, tool, and work.

Various machine tool sensors have been developed formonitoring of tool wear and failure, part dimensions, surfaroughness, surface burn, chatter onset, etc., as reviewed insection. The sensed signal depends on the particular problewell as on the type of machine. Therefore, a large variety of ssors and signals have been investigated as Fig. 1 shows focase of grinding processes@1#.

Generally speaking, sensing techniques can be classifieddirect or indirect measurements. In indirect techniques the inmation is obtained after signal processing and model-based ipretation, whereas in a direct measurement the attribute istained directly from the signals. Typical examples for direct aindirect techniques are vision and acoustic emission, respectifor tool wear monitoring. The sensor can also be categorizeeither in-process or in-cycle. An in-process sensor monitorsing the machining process, whereas an in-cycle sensor examthe attributes periodically, such as between parts. By and laindirect sensing systems are in-process while direct sensingtems are in-cycle.

The following sections are arranged according to importanttributes in machining such as surface texture, dimensional aracy, tool condition and chatter detection, and the special topisensor fusion at the end. This information is also arranged in T1 according to the type of signal analyzed.

Surface Texture. Several quantities can be used to descrsurface texture, including flaws and defects, lay and directiona

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waviness and surface roughness. Surface roughness is thewidely used parameter to describe surface texture due to its dinfluence on friction, fatigue, electrical and thermal contact restance, and appearance.

The profilometry is a direct measurement of surface roughnbut it is limited to off-line applications. Vision systems@2#, on theother hand, allow faster, more reliable, and direct, on-line msurements. The advances in image processing software capties have made artificial vision an effective measurement sysfor automation. Based on the measurement of light reflectiontensity, the surface roughness can also be inferred@3#.

Surface roughness measurement may be possible throughcombination of more than one measured quantity by analyticaempirical relations. Jang et al.@4# estimated the part roughnesbased on the relative cutting vibration between the tool and woNowicki and Jarkiewicz@5# correlated part surface height toFringe Field Capacitive~FFC! measurement for Ra down to 0.mm at high revolving speeds with low sensitivity to material aworking liquid. Artificial neural network~ANN! has been a popular means for sensor fusion in surface measurements. Tsai e@6# trained a four input ANN based on accelerometer and proxity sensor signals to recognize the surface roughness produce96–99% success rate for a variety of cutting conditions. Azoand Guillot@7# reported similar accuracy with the use of the depof cut, feed, and radial force in an ANN to estimate surface finiThe ANN approach, however, suffers criticism from its lackphysical meaning in that any two strings of event measuremewith or without an actual relationship, can be correlated with vaous connectivity coefficients in an ANN.

Surface Integrity. The term surface integrity not only describes the topology of a surface but also its mechanical and mallurgical properties as related to fatigue strength, corrosion retance, and service life. The common industry practice for surfintegrity testing resorts to etching and visual inspection frowhich a quantitative assessment is not possible. High resolulaboratory techniques like X-ray diffraction, indentation hardnetesting, or metallographical inspection are time consumingcannot be used for real time process monitoring or even in-cy

A new way of testing metallic or ferromagnetic materials is

Fig. 1 Relevant grinding problems for monitoring †1‡

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Table 1 Signals and their feature analyzed used to monitor machining outcomes

Type of signal Analyzed feature Application Reference

Vision Light reflection intensity Surface roughness @3#Image using halogen lamp Tool wear @14#Image using laser Tool wear @13#Image with two-lightarrangement

Tool wear @16#

Stereo imaging Crater wear volume @17#Intensity of the reflected light Surface burn @3#

Surface roughness @2#Force Feed and radial force Tool wear, Flank wear @18–20#

Radial force Nose wear @20#Mean cutting force Tool breakage @40#, @41#Feed force/cutting force Flank wear @21#Standard deviation of thrustforces

Chatter @35#

RMS of axial force in milling Dimensional accuracyand surface roughness

@42#

AE Ring down count, rise time,event duration, frequency andevent rate

Tool wear and chipping @26#

Mean variance and thecoefficient of RMS

Tool wear @23#

Time series coefficients Tool wear @25#RMS Tool breakage @40#DC component of AE Dimensional accuracy

and surface roughness@42#

Power Intensity Surface Burn~grinding! @10#Standard deviation Chatter @35#

Torque Wear ~drilling! @27#Vibration Power spectral density Chatter @35#, @37#

Variances of the accelerometersignal

Chatter @36#

Relative signal between tool andworkpiece

Surface roughness @4#

Peak value of acceleration Tool breakage @41#Surface roughness @6#

Audio signal Sound pressure Chatter @33#, @34#Materialsmicromagneticproperties

Barkhausen noise and thecoercivity

Grinding burn, surfacehardness and depth ofhardness

@8#, @9#

Direct gauges LVDT Part diameter @11#, @12#Lineal scale Part diameter @3#

Temperature Thermocouple in the back faseof the tool

Tool wear @28#

Thermocouple formed betweentool and workpiece

Flank wear @29#

Thermocouple and thermalmodel

Temperature in thecutting zone

@30#

Infrared pyrometer and inversefinite element method


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measure the micromagnetic properties@8# since the magnitude othe Barkhausen noise and the coercivity is sensitive to resistresses, hardness and metallurgical properties in surface andsurface layers. Different micromagnetic signals can be correlato different quality parameters in grinding as Fig. 2 shows. T¨n-shoff @9# presented a non-destructive sensor that combines mmagnetic signals to detect grinding burn, surface hardnessdepth of hardness. This method also shows in-process potentimerit of its non-destructive nature. Other examples suited forprocess monitoring include the measurement of the change inintensity of the reflected light from the work surface for surfaburn detection in grinding@3#. Alternatively, Malkin @10# usedpower measurement to indirectly monitor the extent of work sface burn.

Dimensional Accuracy. An example of part dimensional accuracy measurement is the use of an in-process diameter g~Heidenhain-DMK 100! installed by Shunsheruddin and Kim@3#in a cylindrical grinding machine to monitor the process. Toutput was a square wave pulse train with duration of 1mm in thework diameter using a linear scale with a grating pitch of 20mm.

The intelligent fusion model using an ANN by Azouzi an

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Fig. 2 Relevant quantities for subsurface quality inspection†9‡

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Fig. 3 The use of 6th order autoregressive time series coefficients for turning tool wear moni-toring: AE signal, coefficient and its modeling error, orthogonal coefficients with respect to toolwearland size †25‡

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Guillot @7# not only predicted the surface roughness, but also pdicted the dimensional deviation. The error between the estimdata and the measure dimension was between 2 and 20mm forvarious cutting conditions.

A dual Linear Variable Displacement Transducer~LVDT ! gaugedevice was implemented in internal and external cylindrical griings to measure the work diameter on-line. Each individual LVsignal, at a certain number of samples per work revolution, wprocessed by a Fast Fourier Transform~FFT! and used to detecpart form errors@11,12#. These methods have been successfuimplemented on-line, however, the measurement speed is limby the mechanical cut-off frequency that varies depending ongauge configuration.

Tool Condition. Considerable research has been conductethe area of tool monitoring due to the fact that tool failure repsents about 20% of machine tool down-time and that tool wnegatively impacts the work quality in the context of dimensiofinish, and surface integrity.

Vision is a suitable technique to evaluate tool wear in a labotory setting although attempts have been made to use this tnique on shop floors as a direct measure system. Among thenology bottlenecks of this measurement is the illumination issDifferent light sources such as coherent light from a 0.8 mmameter laser@13# and halogen lamp@14# have been used alonwith various light configurations, including a two-light arrangment @15#, to record tool wear images. The sensor to be uon-line must survive the harsh machining environment unchips and atomized coolant@16#. Most of the image techniques arlimited to 2D; however, a 3D measurement system using a pastereo imaging was developed by Karthik et al.@17# to identifythe crater wear volume. However, this method is useful onlycrater wear, which does not occur for all cutting conditions.

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The forces developed in machining are affected by the tgeometry. As the tool gets worn the geometry changes, theimpacting the cutting forces. Early attempts found that the feand radial forces are more sensitive to tool wear than the cutforce @18,19#. The radial force component was reported to bemost sensitive to nose wear, with the feed force and the raforce components affected by flank wear@20#. Similarly, flankwear was observed to correlate with the feed and cutting focomponents@21#. Force ratios can also be used to predict towear since they present a certain pattern as the tool wears@22#.The feed force to cutting force ratio was found to be sensitiveflank wear@21#.

During cutting the workpiece undergoes considerable pladeformation associated with the generation of acoustic emis~AE!. Progressive tool wear affects the cutting zone with a chain the AE signal. Due to its high frequency feature~1 KHz to 1MHz, far beyond machine tool resonance!, AE is generally foundto be more sensitive to tool wear than cutting forces@23#. Strongcorrelation of the AE RMS to tool wear was presented by Mowaki and Tobito@24#, where statistical features like mean varianand the coefficient of RMS were related to tool wear. The variawas the most sensitive parameter, showing the largest amplituthe end of tool life. Other AE features such as ring-down, rtime, event duration, frequency, and event rate, as well as tseries coefficients@25#, as shown in Fig. 3, have also been corrlated to tool wear and chipping@26#. However, the lack of physi-cal understanding of the AE signal and its sensitivity to senlocation and cutting parameters remain difficult issues for theplication of this technology.

O’Donnell et al. @27# presented~Fig. 4! the sensors used todetect wear and breakage in drilling, reaming, and tapping op

Fig. 4 Applications of sensors for drilling, reaming and tapping †27‡


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tions. The torque signal has the most useful information to motor these processes; however, the use of a torque sensor ialways possible. Therefore, O’Donnell et al.@27# presented anexperimental research in drilling with multiple sensor types alocations that included power, vibration, and AE signals. Thebration and the AE signals presented high levels of noise relto the manufacturing environment. The power signal presentedgreatest sensitivity to variations in the tool performance andleast sensitivity to noise.

The temperature in the cutting zone can change as thewears due to changes in the tool geometry and its ability totherefore, the use of temperature was suggested to monitotool state. As an early attempt, Moshref@28# predicted the toolwear area using an empirical relation of temperature differencwear difference ratio correlated with cutting time. However, ttemperature measured on the back of the cutting tool is nottrue temperature on the cutting zone. Recently, Choudhury e@29# experimentally correlated the relation between the flank wand cutting zone temperature in turning where the temperasensor was the natural formed thermocouple between the toothe worpkiece. In this case, only the average temperature incutting zone is sensed. Another possibility is to measure the tperature by thermoimages from the cutting zone. However, incase, the chip, which carries approximately 90% of the enedissipated during cutting, will dominate the intensity of radiatio

Embedded thermocouples and thermoimages are indirect msurements that can be used to predict the temperature distribif a proper heat conduction model is used. De´rrico @30# assumed asimple relation between the measured temperature with an emded thermocouple and the tool face temperature as a scaling fand a propagation delay. Lin@31# measured the temperature ouside the cutting zone in milling by an infrared pyrometer aestimated the interface temperature by an inverse finite elemmethod. This method is affected for the thermal properties ofworkpiece. Lima et al.@32# performed a numerical simulation testimate the cutting temperature using a three-dimensional invheat conduction technique. The heat flux generated in cuttingestimated using the inverse heat conduction technique basethe conjugate gradient method, and the temperature was meawith thermocouples at the bottom face of the tool. However, ttechnique was not implemented in a real cutting process. Theof embedded thermocouples and an inverse heat condumethod seems to be the best technique to predict the facetemperature; however, more research must be conducted tocount for the changes in the boundary conditions such asgeometry and the use of coolant.

Chatter Detection. The forces generated when the cuttintool and part come into contact produce significant dynamicflections of the tool structure~i.e., cutting tool, spindle, etc.! andthe part structure~i.e., part, fixture, etc.!. When this interactionbecomes unstable, it is known as regenerative chatter. Regetive chatter results in excessive machining forces and tool wtool failure, and scrap parts due to unacceptable surface finis

A significant amount of research has been devoted to automchatter detection. It is well known that the chatter frequencycurs near a dominant structural frequency. Thus, the most cmon approach to chatter detection is to investigate the spedensity of a process signal and develop a threshold valueindicates chatter. Delio et al.@33# and Altintas and Chan@34# in-vestigated sound pressure as the process signal. Tarng and L@35#created threshold values for the spectrum and the standard dtion of thrust forces and torque signals in machining operationshould be noted that the tooth-passing frequency contains sigcant energy and the process signal must be properly filtered itooth passing frequency is close to a dominant structuralquency.

These threshold algorithms require an empirically selecthreshold value that will not be valid over a wide range of cutticonditions. A more general signal was proposed by Bailey e


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@36#. An accelerometer mounted close to the cutting region pvides for the calculation of the variance ratioR5@ss /sn#2. Theparametersss andsn are the variances of the accelerometer snal in low and high frequency ranges, respectfully. A value ofR!1 indicates the presence of regenerative chatter.

Figure 5 shows the power spectral density of a force sigtaken during a single spindle revolution of a face milling opetion. Details regarding the experimental system may be foundLanders@37#. The dominant structural frequencies are 334, 4653, and 716 Hz. The presence of regenerative chatter is clevisible with a chatter frequency of 750 Hz. Figure 5 also cleashows the tooth passing frequency of 100 Hz.

Sensor Fusion. Single signals have been extensively usedquantify a machining outcome as was reviewed above. Howethe sensitivity and the noise rejection of the sensed signalchange with the cutting conditions such as machining parametool wear, machine stiffness, etc. Therefore, more than one sican be used in a complementary manner to provide a more roprediction of one or more machining attributes. This is referredas sensor fusion. The success of sensor fusion depends on wtype of signals are good candidates for a given machining ocome and which extracted features and in which way they muscomplemented.

In an early attempt, Lezanski and Rafalowicz@38# used normaland tangential forces, vibration, acoustic emission, and both dieter and out-of-roundness to characterize the state of a grinprocess. The methodology used can be seen as a multiserather than a sensor fusion, because the signals were used staneously but were not complementary to each other.

Bahr et al.@39# used vibration signal as an on-line techniquepredict tool wear and detect breakage, and they also used macvision between cuts to quantify the worn tool. In this way, a direand an indirect technique were complemented and the monitoreliability was increased because the machine vision can defalse signals from the vibration sensor.

Lou and Lin @40# claimed that force is easily affected by thcutting conditions and acoustic emission is interfered by the eronmental noise. Then, they combined both of them to enhathe robustness of monitoring tool breakage in a machining cenThe RMS of the AE and the mean cutting force were used to tra back Propagation Neural Network modified by a Kalman filtThis proposed self-learning techniques reduced the possibilitiefalse alarms and misalarm of tool breakage.

Kim and Choi@41# presented a sensor fusion with the deviatiof average cutting force, the peak value of acceleration, and

Fig. 5 Power spectral density of a force signal during aspindle revolution in a face milling operation †37‡

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relative displacement between tool and workpiece to detectbreakage. Values of these signals in excess of an adaptive thold were used to detect breakage. The system was testedresulted in 90% positive readings, 2% of false detection, andof nonbreakage detection.

Azouzi and Guillot@7# presented an exhaustive analysis to dtermine the most sensitive process parameters~feed, depth of cut,cutting velocity! and signals~AE, forces, vibration! to predict thesurface roughness and the final diameter error in machinBased on experimental data and statistical tools, the feed,depth of cut, and the radial and feed force components werelected as inputs to a neural network to predict the mentionedchining outcomes. The surface roughness was predicted witerror between 2% and 25% under different cutting conditiowhile an error ranging between 2mm and 20mm were observedfor the prediction of the dimensional deviation.

Recently, Etekin et al.@42# studied the signals of force, AE, anspindle quill vibration to predict dimensional accuracy and surfroughness in a CNC milling operation. They found that the RMof the axial force component and the DC component of thesignal are the candidate signals to be integrated and predicquality characteristics of the machined parts using three diffeworkpiece materials.

Machining Process ControlMachine tool controllers consist of a Programmable Logic C

troller ~PLC! that handles the sequencing and operator interfaand a microprocessor that coordinates the real-time control futions. The microprocessor architecture can be generally diviinto three levels: servomechanism control loop, interpolator loand process control loop as shown in Fig. 6. The servomechacontrollers regulate the velocity and position of individual axand spindles and interpolators generate the reference positionthe axes. These functions are found in all modern CNCs.process control loop, also referred to as adaptive control inchine tool terminology, is not commonly available in todayCNCs and has been the focus of a tremendous amount of resdue to its potential to significantly increase operation productivand quality.

The first subsection presents the general classification schfor machining process control. Machining force regulation aregenerative chatter suppression have received a vast amouresearch and, thus, both are reviewed in separate subsectionsother subsection is devoted to the control of burr and chip formtion, cutting temperature, and tool condition. Supervisory systethat intelligently coordinate the entire machining operationpresented next, followed by a summary of the trends in proccontrol and its impact to date.

ACO, ACC, and GAC Systems. Machining process controis generally classified as Adaptive Control with Constrai

Fig. 6 Machine tool control and monitoring—general scheme

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~ACC!, Adaptive Control with Optimization~ACO!, or GeometricAdaptive Control~GAC!. Process parameters are manipulatedreal time in ACC systems to maintain a specific process variasuch as force or power, at a constraint value. Typically, ACsystems are utilized in roughing operations where operationductivity is maximized by maintaining the process variable atmaximum value. In ACO systems, machine settings are seleto optimize a performance index~e.g., production time, unit cost!.The settings are derived from models and some process feedsignals. Finally, GAC systems seek to maximize the qualityfinishing operations in the face of structural deflections and twear.

In order to develop a successful ACC system, the controvariable must be related to productivity parameters. For examthe power consumed in Cylindrical Traverse Grinding~CTG! isrelated to wheel wear. When the power is greater than a givalue, known as the break-down value, the G-ratio drops sha@43#. In this regard, Hecker and Liang@44# applied a power con-troller in CTG to maintain the power constant at a value belowbreak down value to guarantee an acceptable G-ratio while mmizing the material removal rate as shown in Fig. 7. An adaptgain version of this controller was achieved in Hekman et al.@45#.For turning and milling processes, adaptive gain force controllwithin ACC systems, have been developed using model refereadaptive [email protected]., @46##, self tuning [email protected]., @47##,direct adaptive [email protected]., @48##, etc. Edge breakage and toolife is a function of the equivalent chip thickness. Therefore, Ardkani and Yellowley@49# implemented a multiple constraint controller in an open architecture turning machine, where force aequivalent chip thickness were the constraints. Figure 8(a) showsthe workpiece geometry used to test the controller with a mamum allowable force of 500 N and a maximum chip thickness0.1 mm. During the initial straight diameter cut the chip thickneconstraint was active and the force was well below the constrvalue, as shown in Fig. 8~b!. Due to the high taper angle in thsecond stage that produces a low relation between the chip thness and chip area, the force constraint became active. Thein ACC systems has spawned a tremendous amount of researmachining force control, which is discussed below.

The first implementation of an ACO system was the BenSystem@50#. The objective of the Bendix system was to maximimaterial removal rate by changing both feedrate and spinspeed. However, this system was hampered by the need foaccurate tool wear model. Amatay et al.@51# presented an ACOsystem for plunge grinding of steel where the objective wasmaximize the volumetric removal rate subject to constraintssurface roughness and work surface burn. The optimal locusproach@52# maps the objective function into the process variaspace~i.e., a locus of optimal points!. Machine and process constraints are mapped in the same space and the process varare selected at the intersection of the optimal locus and the mlimiting constraint. Real-time measurements may be utilizedvary constraint functions and, therefore, the optimal process vables. An extension to the optimal locus approach was preseby Ivester et al.@53#. In their work, Recursive Constraint Bounding ~RCB! is used to account for the model uncertainties ainherent noise. This methodology adjusts the constraints after epart is complete. Hekman and Liang@54# piloted the use of mul-tivariate feedrate and depth of cut control of surface grindingthe minimization of ground part form error as shown in Fig. 9.recent optimization strategy using artificial intelligence was aplied to grinding processes by Li et al.@55#. The optimizationobjective therein was the minimization of grinding time subjectquality constraints such as surface burning, surface roughnout-of-roundness, size tolerance, and power. Lundholm@56# inte-grated ACC, ACO, and failure diagnostics into a turning opetion. This system had five major functions: current monitorinprocess failures~worn tool, chippage, tool breakage, and colsion!, cutting force regulation, chatter, and tool wear. In compa


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son to ACC, ACO generally requires an additional layer of phycal understanding of the machining process. This is needeformulate the penalty functions and to relate these functionsmeasurable process attributes and adjustable process param

Geometric Adaptive Control~GAC! systems are typically usedin finishing operations with the objective of maintaining specifipart quality despite structural deflections and tool wear@57#.While simple empirical relationships are available~e.g., theBoothroyd model that relates surface finish to feed!, sensor feed-back is often employed in GAC systems. Surface roughnessdimensional quality are typically measured between parts andjustments, in terms of tool offsets and feed overrides, are madethe next part. Coker and Shin@58# utilized an ultrasonic sensor toestimate the surface roughness in a machining operation. Thewas adjusted manually between parts given a correlation betwthe feed and the measured surface roughness.

Machining Force Control. The implementation of ACC sys-tems requires the automatic control of a variable that directly

Fig. 7 „a… Power controlled cylindrical OD grinding: set up andcontrol scheme †44‡ „b… Power controlled cylindrical OD grind-ing: response to depth of cut variation †44‡


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fects the machining operation productivity. This has lead to a sstantial amount of research in the area of machining force~ortorque or power! control. It is well known that the force proceschanges dramatically during the course of the operation, evender normal operating conditions. Thus, the body of work ficoncentrated on adaptive control [email protected].,@46,59,60,61,48##. A Model Reference Adaptive Control~MRAC!extended by Zero Phase Error Tracking Control~ZPETC! wasimplemented by Rober and Shin@62# to control the cutting forcein an end milling process. This controller remained stable evethe presence of marginally stable and non-minimal phase prozeros. In Fig. 10, the measured force was maintained at thesired level of 1500 N by adjusting the feedrate against changean axial depth of cut of 10 mm. There have been many appltions over the past decade of fuzzy logic and neural networkthe machining force control problem. These techniques are cpared to adaptive control methods in Liu et al.@63#.

There has also been much interest in model-based controproaches for machining force regulation. Harder@64# linearizedthe force process and applied standard control techniques tosign a fixed gain controller. The linearization technique was amented by Landers and Ulsoy@65# to directly account for changein the depth-of-cut. Another methodology broke the nonlineforce process system into a linear system coupled with a torder static equation in the feed@66,67#. A deadbeat controller wasapplied to the linear system and the controller output was usecomplete the feed equation. The multiple feed solutions are cpared toa priori feed bounds and the solution that falls withthese bounds is selected as the command feed. Landers and@68# explicitly accounted for the force-feed and force-depth nolinearities. This approach used a change of variable to accounthe force-feed nonlinearity and adjusted the controller gains

Fig. 8 „a… Tool-workpiece interface geometry during a tapercut †49‡ „b… 70 degree taper cut with force and chip thicknessconstraints †49‡

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account for the force-depth nonlinearity. An application of thmethodology for a face milling operation is shown in Fig. 11.

Some of the recent work in machining force control has focuson robust techniques. Carillo et al.@69# designed a delta approacrobust adaptive controller. In their work, a Linear Quadratic Gusian ~LQG! controller was designed to take advantage of LQGguaranteed robust properties for Single-Input Single-Out~SISO! linear systems. Hayes et al.@70# designed a robust controller in the continuous domain using Quantitative FeedbaTheory ~QFT!. Punyko and Bailey@71# and Nordgren and Nwo-kah @72# proposed QFT designs in the discrete domain utilizi

Fig. 9 „a… Feedrate and depth of cut control of surface grind-ing: optimal feedback control scheme †54‡ „b… Feedrate anddepth of cut control of surface grinding: resulting part heightand grinding force †54‡

Fig. 10 Cutting force control in end milling using extendedMRAC. Radial depth of cut 1.27 mm †62‡

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the delta transform. Their designs were based on a linear pwith uncertainty in pole and zero locations as well as the magtude of a gain factor that indirectly accounts for variationsdepth-of-cut and nonlinear process parameters. Rober et al.@73#augmented the discrete QFT design in Nordgren and Nwokah@72#and implemented the controller on-line. Once again, their deswas based on a linear plant, and the depth-of-cut and force-nonlinearity were accounted for indirectly as uncertainty in pozero locations and a gain factor in the transfer function oflinear plant. Kim et al.@74# extended the work in robust machining force control by applying process compensation therebylowing tighter performance bounds for a range of parameter vation than conventional techniques. Experimental results for a fmilling application of this robust force control approach ashown in Fig. 12.

Chatter Suppression. Regenerative chatter may be avoideand productivity may be maximized by selecting the spindle spto lie in one of the stability lobe pockets, as shown in Fig. 13.determine the so-called Stability Lobe Diagrams~SLDs!, regen-erative chatter analysis~i.e., the stability analysis of the closedloop system formed by the machining force process and the tpart structures! must be performed. Many researchers have uNyquist techniques in an iterative manner to generate [email protected].,@75,76##. Time domain simulation~TDS! is another technique thamay be used to generate [email protected]., @77–80##. The closed-loop

Fig. 11 Application of a nonlinear machining force controllerin a face milling operation †37‡

Fig. 12 Application of a robust machining force controller withprocess compensation to a face milling operation †74‡


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dynamic model of the machining operation is simulated for a pticular set of cutting conditions in an iterative manner until insbility is encountered. The strength of TDS is that all major aspeof the machining operation, including nonlinear characteristmay be incorporated into the chatter analysis; however, the inent drawback is the tremendous computational burden. Anochatter analysis technique that generates analytical SLDs watroduced by Altintas and Budak@81# and Budak and Altintas@82,83#. The utilization of SLDs suffers from modeling inaccurcies, and often modeling the structural characteristics of thechining operation is economically unfeasible.

A means of automatically suppressing regenerative chatteaccomplished by adjusting the spindle speed set point. Delio e@33# developed an automatic Spindle Speed Selection~SSS! meth-odology and applied it to milling operations. A chatter detectialgorithm using sound pressure determined the chatter frequeThe tooth passing frequency was adjusted to equal the chfrequency and this procedure was repeated until chatter waspressed. Shiraishi et al.@84,85# utilized state feedback and optmal, respectively, control to suppress chatter and demonstrtheir system via a simulation of a turning operation. These teniques required a second order Pade´ approximate of the delayterm, an observer to estimate states that could not be measand knowledge of the structural dynamics. The need for compknowledge of the structural dynamics will limit the robustnessthese techniques. Feed has also been shown to have a monoaffect on regenerative chatter@86# and is sometimes used by machine tool operators. The depth-of-cut may also be decreasesuppress chatter as there always exits a depth-of-cut below wchatter will not occur~see Fig. 13!.

Early investigations showed that Spindle Speed Variation~SSV!has the potential to suppress [email protected]., @87–89##. This tech-nique constantly changes the delay between the current andvious passes, thereby disrupting the regeneration effects. Olbet al. @90# investigated spindle speed variation for a face millioperation. An optimal spindle speed trajectory was determioff-line and an optimal feedback controller was designed to trthe trajectory. Lin et al.@91# extended this work by investigatindifferent spindle speed trajectories; namely, square, triangular,sinusoidal. They concluded that the sinusoidal was the best tratory since it was the easiest to track. Zhang et al.@92# investigatedthe effect of amplitude and frequency when varying the spinspeed in a sinusoidal manner and developed an analytical tnique for determining the optimal value of these two parametRadulescu et al.@93# numerically and experimentally investigatethe effect of SSV when face milling parts with complex structurYang et al.@94# utilized the concept of SSV by varying the rak

Fig. 13 First five lobes in a stability lobe diagram


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angle of the cutting tool in a turning operation. Nyquist analyswas used to analyze the system and experimental results shothat varying the rake angle in a triangle pattern was effectivesuppressing chatter. Soliman and Ismail@95# used an R value,similar Bailey et al.@36#, based on machining force to detect chater and then ramped the spindle speed to find a stable machiregion. This methodology will work well for spindles capable ohigh speeds; however, for low speed spindles, this algorithm wnot find a stable machining region for large depths-of-cut. Athough SSV is a promising technique, there is little theoryguide the designer as to the optimal variation and, in some sitions, SSV may create chatter that would not occur when usinconstant spindle speed. The SSV methodology is illustrated vsimulation of a turning operation in Fig. 14. Note that force flutuations occur even after chatter is suppressed. The SSV metology may also be implemented passively via the design of cutttools whose teeth are not evenly spaced@96#.

Burr and Chip Formation, Cutting Temperature, and ToolCondition Control. Burr formation is a very complex phenomenon that strongly depends on the process parameters, the tangle of approach, etc. It is often desired to eliminate the burrminimize the effort required to remove the burr. Burr formatiocontrol is typically accomplished by adjusting process paramefrom part to part~or feature to feature! since burr measures~e.g.,dimension, toughness! cannot be effectively measured during th

Fig. 14 „a… Chatter suppression via spindle speed variation.Ff-feed force. FC-cutting force. „b… Chatter suppression viaspindle speed variation. Z-tool displacement. Ns-spindlespeed.

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machining operation. Dornfeld and his colleagues@97–99# haveutilized experimental data for off-line process planning to contburr formation. Furness et al.@100# regulated the feed such thathe burr rating of a through-hole drilling was acceptable. The rerence feed was determined via off-line testing

The major issue in chip formation is to ensure discontinuochips are formed and clear the cutting tool-part interface zoChip control, typically accomplished via chip breakers on the cting tool, has received very little attention in terms of active feeback control. Ralston et al.@101# investigated chip length controvia simulation studies utilizing Proportional plus Integral~PI! con-trollers and a fuzzy logic controller. Jawahir and Luttervelt@102#provide a comprehensive review of off-line chip control

Cutting temperature in machining operations affects tool wrate, part surface integrity, and contributes to thermal deformaat the tool-part interface. While temperature generation hasceived substantial attention in terms of modeling, the literaturedirect automatic control of cutting temperature is lacking. Oinvestigation was performed by De´rrico et al. @103#. Using astatic, nonlinear equation relating cutting temperature with cuttvelocity, a self-tuning regulator was implemented to controlcutting temperature via the real-time adjustment of the cuttvelocity.

Tool condition control refers to the regulation of the amouand rate of tool wear and tool breakage/chippage. One issutool wear regulation is to automatically adjust the tool positioncompensate for the part geometric errors caused by the tool wChoudhury and Ramesh@104# utilized optical sensing techniqueto measure tool wear on-line and adjusted the tool positioncompensate for the wear. Warnecke and Kluge@105# used an ar-tificial neural network to predict the tool wear in a turning opetion and adjustments were made between parts to maintain dimsional accuracy. Fraticelli et al.@106# utilized SequentialTolerance Control~STC! techniques to compensate for tool weand random effects. Given measurements from the previouseration, STC was used to adjust the tool position to compenfor these effects. Another issue in tool wear regulation is to aumatically adjust the process parameters such that the tool lifmaximized in a job shop production environment or correspoto a scheduled tool change period in a mass production enviment. This research has received little attention in the literatWhile there has been a significant amount of research inbreakage detection, very little work has been conducted on amatically controlling tool breakage. Typically, when tool breakahas been detected, an emergency stop is initiated and the pscrapped. Techniques to determine how to automatically chathe process parameters if tool failure is predicted or detectedautomated procedures to handle tool breakage that avoid thvere consequences of an emergency stop are still needed.

Supervisory Systems. Research in the area of machining prcess control has largely focused on regulating a single prophenomenon~e.g., force, chatter! using a single process variab~e.g., feed, spindle speed!. Recently, there has been work focusinon integrating multiple controllers leading to research in supesory control systems. Teltz and Elbestawi@107# proposed a hier-archical control system consisting of a supervisory level anprocess level~force and chatter controller!. The supervisory levelmonitored signal and alarm events and utilized an inferencegine that searched a knowledge base to relate these evenrecovery actions. Ramamurthi and Hough@108# used a MachiningInfluence Diagram~MID ! for supervision and applied it to a drilling operation. A knowledge base is tuned during a training phand the MID is used to identify failures. The supervisory contof a through hole drilling operation was investigated in Furnet al.@100#. The process controllers were supervised using anline optimization technique where the controller configuration wa function of part location. At entry, speed and feed control wutilized to minimize hole location error. Torque control waimplemented while the drill was fully engaged in the part to p

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vent drill breakage. At exit, speed and feed control was utilizedreduce burr formation. A state-based, on-line supervisory contler was developed in Landers and Ulsoy@109# that integrated thecontinuous process controllers with the machine tool logic funtions. The logic of the supervisory machining controller is dscribed via Grafcet in Fig. 15 and experimental results for a familling operation are shown in Fig. 16. Katz and van Nieke@110# developed a supervisory machining system implementedan open PC-based controller. Neurofuzzy sensing was useindirectly monitor surface finish, tool wear, etc. When perfomance parameters exceeded predetermined limits, a knowlebase was utilized to adjust the process parameters. In contracoordinating individual machining controllers, work in hierarchcal control by Dasgupta et al.@111# uses aggregation relationshipbetween the different levels to form a single controller designthe servomechanism level.

Fig. 15 Grafcet diagram of a supervisory machining controllerfor a face milling operation †109‡

Fig. 16 Experimental implementation of a supervisory ma-chining controller for a face milling operation †109‡


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Trends and Impact of Machining Process Control. Re-search in machining process control has overwhelming contrated on force control due to the importance machining forhave on the entire operation. Many control techniques have bapplied and issues such as inherent process nonlinearities annificant parameter variations have been systematically takenaccount. There has also been a substantial amount of work inarea of chatter suppression. Topics for further research in thisare in designing systematic techniques for spindle speed variaexploring the effects of spindle speed variation on tool wear, sface finish, etc., and investigating the use of feed to suppchatter. Direct automatic feedback control of tool wear and breage, cutting temperature, and chip and burr formation is still laing. Therefore, further research will be required before these tenologies can be reliably utilized in industrial applications. Anotharea of research will be the integration and coordination of pcess controllers, which has already begun with supervisory ctrol. As mentioned above, a majority of the research in machinprocess control has concentrated on regulating a single prophenomenon using a single process variable. Complete procontrol systems will regulate the entire process via the simuneous utilization of all process variables. Given the complexthat will be encountered, the successful integration of processtrol technologies will require systematic design procedures.

There are also innovations in machine tool designs and sware structures that are affecting machining process controlParallel machine tools~i.e., machine tools where multiple toolwork on a part simultaneously, but independently! have existed intransfer lines for decades. However, parallel machining cenhave recently been widely developed in industry. Most of thmachine tools have two lathing tools that process one part simtaneously. Related to parallel machine tools, mill-turns havedexable turrets that contain live tooling and, thus, are capablparallel milling and drilling in addition to parallel turning. Appling process control to these systems will require the use of Mtiple Input-Multiple Output~MIMO ! control techniques. A studyof output feedback force control of parallel machining systemay be found in Sudhakara and Landers@112#. Another innova-tion in machine tools is reconfigurable machine tools@113#. Thesemachine tools are designed such that they may be cost-effectreconfigured, in both hardware and software, such that they cacustomized to changing operation requirements that are the rof changing product requirements in the marketplace. Proccontrol will provide manufacturers with a reconfiguration optito increase operation productivity or part quality for new opetion requirements. This will require that process controllers doadversely interact with the existing control software. To facilitathis type of extendable software, there has been a tremenamount of research in open architecture control, which isviewed below.

Ulsoy and Koren@114# reviewed the research contributionsmachining process control in the 1980s and the early 1990s. Tconcluded that, despite the significant amount of research thabeen conducted and validated in laboratory settings, machiprocess control had little impact in industry. Unfortunately, tsame conclusion can be drawn today. While machining proccontrol has been utilized in industry, the implementations are vlimited, the applications are typically in industrial research labratories@100,115#, and they are implemented as an add-on systhat requires specialized training to operate and maintain.reasons for the lack of transfer to industry are presented beThe next major hurdle for machining process control will be tsystematic integration of this technology in machine tools atdesign stage.

Open Architecture SystemsSuccessful development and implementation of process m

toring and control demands high flexibility of the machine tocontroller. This flexibility, in software and in hardware, wou


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accommodate the use of various programming languages, oping systems, control strategies, system dynamic models, andsor signal processing algorithms. Open architecture controlconcept precipitated from this demand of flexibility. A PC-bassolution with a homogenous and standardized environment pvides flexibility and allows the incorporation of new hardware asoftware upgrades. Figure 17 summarizes the tendency of oarchitecture systems and its components@116#. The subject ofopen control is being addressed by large consortiums internatally: OMAC ~United States!, OSACA ~Europe!, and JOP~Japan!.The definitions of interoperability, portability, scalability, and interchangeability for an open architecture system were definethe Specification for an Open System Architecture~SOSAS! de-scribed by Anderson et al.@117#. Examples of such a concepwere a machining center based on sun/VME bus/Real-tiUnix/C architecture@118# and a grinding center based on PCdSPACE/Control Desk@44#. A variety of work @119–123# hasconstructed open-architecture machining platforms containreal-time process and servomechanism level functionalities.platform developed in Yellowley and Pottier@124# also consideredthe use of optimization. Other work@36,125# integrated processplanning with real-time process and servomechanism functionities on open-architecture platforms. In Pritschow@126#, a seven-layer system extending from the lowest levels such as physmachining functions up to the level of the market/consumcustomer is discussed. Commercially available open architecsystems include Delta Tau PMAC-NC, IBH PA 8000, Galil DM1000, Creonics MCC VME, Adept Series A, Aerotech Unidex 3CIMplus, and Typ3 osa. Although these controllers are excellsystems for research and development, the issues of liabilitystandardization have remained unresolved and their widespapplication in industry hampered.

DiscussionThe research and development effort in the automation of m

chining operations at the process level has been on going for othirty years. To date, various transducers, signal processschemes, control strategies, and actuators have been proposeextensively investigated. In the area of machining process sensresearch has focused primarily on the monitoring of tool conditand chatter and, to a lesser degree, part quality. In the aremachine tool process control, ACC systems with real-time feback has received the majority of attention; meanwhile, progrin ACO and GAC systems has also been realized.

Most of these systems have claimed reasonable success inratory and field testing. However, realization and commerc

Fig. 17 PC-based, software-oriented control system †126‡

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availability are fairly limited at the moment and there is little sigof accelerated acceptance in sight. This is attributed to:

• The lack of robust sensor hardware and monitoring alrithms. Currently achievable levels of sensor robustness cameet the requirements of real life applications. The harshcomplicated machining environment, combined with the preseof chips, fluids, noise, and vibration, makes rapid, precise,direct measurement difficult, if not impossible, in many situatioAdditionally, indirect measurements resorting to heuristic or alytical models often fail to grasp the disturbances, nonlinearand stochastic nature of cutting processes, while software-bsignal processing and analysis has improved sensor robusonly to a limited degree. Thirty-plus years of sensor researchnot broken through this bottleneck and, as a result, the usefulof sensor-based controls has not yet realized its potential.

• The lack of concerted effort in the research community. Ssor and control research has only been pursued in a very fmented way. There are a large number of individuals and reseorganizations around the world conducting investigations in mchining monitoring and control, but while they have significaoverlap, they have little coordination. This division of financresources has prohibited researchers from pursuing highlong-term studies that have the potential for ground-breakingsults with greater impact in the field.

• The lack of standardization in automation. The lack of stadards in sensor packages, signal processing algorithms, andtrol systems has been the victim of flexibility. Presently, thereno commonly adopted sensor codes and controller protocolsopen architecture systems similar to standard CNC systems. Msensors have to be configured and calibrated at the customsites to achieve specified functionality; and adaptive processtrollers have to be programmed and tested by the users to desatisfactory performance. As a result, the learning curve for sfloor technology users is steep and system integrators are reluto shoulder the liability.

• The lack of consistent success. Many attempts to integprocess monitoring and control systems into machine tools wattempted in the 1970s. However, these attempts, by and lawere premature and, thus, unsuccessful. These failures havemany manufacturers skeptical of the benefits of process moning and control. To achieve success, future systems will have trobust and integrated with the manufacturing system frominitial design phase.

In the next few years, machining process automation techogy is likely to move toward the following path:

• Embedded sensors and actuators. There is a strong nedevelop sensors, controllers, and actuators that are integratedthe machine tool structure. An example of this technology isezoelectric films embedded in the work holder for precise motcontrol, or eddy current probes mounted in the tool holdermonitor flank wear. These systems tend to minimize the cosvirtue of pre-packaging, decrease the user learning curve bycalibration, and offer greater robustness due to their direct cpling to the machine.

• Miniaturization of system components. With the adventMicro Electro Mechanical Systems~MEMS! technology, sensorand control systems are in an excellent position to downsize tomicro- or nano-scales. The advantage of miniaturized systemtheir increased precision. For example, a MEMS electromagnsensor may be positioned right next to the cutter tip to eliminpropagation barriers, and a MEMS linear motor can be usecontrol thermal deformation errors with the level of precisionnanometers.

• Telecommunication-based and wireless process monitoand control. With the increasing bandwidth of digital electronand the greatly increasing application of Internet communicatthe co-location of manufacturing shop floors and procmonitoring/control systems is no longer a must. In future ma

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facturing enterprises, canned codes and set-up modules forsors and controllers can be downloaded from a server sitemachine tools located anywhere in the world, while process inmation data can be forwarded to a centralized agent for exanalysis and decision making. In this way, flexibility and stadardization achieve a compromise while the utility of machiniprocess automation is maximized. Wireless communicationalso dramatically decrease the cost of cabling and system ramtime.

References1Gomes de Oliveira, J. F., and Dornfeld, D. A., 2001, ‘‘Application of AE ContaSensing in Reliable Grinding Monitoring,’’ CIRP Ann.,50, pp. 217–220.

2Kiran, M., Ramamoorthy, B., and Radhakrishnan, V., 1998, ‘‘Evaluation of Sface Roughness by Vision System,’’ Int. J. Mach. Tools Manuf.,38, pp. 685–690.

3Shunsheruddin, A., and Kim, S. W., 1985, ‘‘Adaptive Control of the CylindricPlunge Grinding Process,’’Proceedings of Symposium on Grinding, ASME WinAnnual Meeting, pp. 373–387.

4Jang, D. Y., Choi, Y. G., Kim, H. G., and Hsiao, A., 1996, ‘‘Study of the Corrlation Between Surface Roughness and Cutting Vibrations to Develop an On-Roughness Measuring Technique in Hard Turning,’’ Int. J. Mach. Tools Man36, pp. 553–464.

5Nowicki, B., and Jarkiewicz, A., 1998, ‘‘The In-Process Surface Roughness Msurement Using Fringe Capacitive~FFC! Method,’’ Int. J. Mach. Tools Manuf.,38, pp. 725–732.

6Tsai, Y. H., Chen, J. C., and Lou, S. J., 1999, ‘‘An In-Process Surface RecognSystem Based on Neural Networks in End Milling Cutting Operation,’’ Int.Mach. Tools Manuf.,39, pp. 583–605.

7Azouzi, R., and Guillot, M., 1997, ‘‘On-Line Prediction of Surface Finish anDimensional Deviation in Turning Using Neural Network Based Sensor FusioInt. J. Mach. Tools Manuf.,37, pp. 1201–1217.

8Inasaki, I., Tonshoff, H. K., and Howes, T. D., 1993, ‘‘Abrasive Machining in tFuture,’’ CIRP Ann.,42, pp. 723–732.

9Tonshoff, H. K., Mandrysch, T., Friemuth, T., 1999, ‘‘Micromagnetic In-ProceSurface Integrity Analysis of Ground Workpieces,’’ SME Technical Paper, PaNo. MR99–254.

10Malkin, S., 1984, ‘‘In-Process Control of Thermal Damage During GrindingSME Technical Paper, Paper No. MR84–532.

11Longanbach, D. M., and Kurfess, T. R., 1998, ‘‘Real-Time Measurement forInternal Grinding System,’’ Transactions of NAMRI/SME,26, pp. 317–322.

12Longanbach, D. M., and Kurfess, T. R., 2000, ‘‘In-Process Gage Frequencysponse Measurement,’’Proceedings of the 7th Mechatronics Forum InternationConference, Atlanta, Georgia, Paper No. M2000-140.

13Jeon, J. U., and Kim, S. W., 1998, ‘‘Optical Flank Wear Monitoring of CuttinTools by Image Processing,’’ Wear,127, pp. 207–217.

14Pedersen, K. B., 1990, ‘‘Wear Measurement of Cutting Tools by Computersion,’’ Int. J. Mach. Tools and Manuf.,30, pp. 131–139.

15Giusti, F., Santochi, M., and Tantussi, G., 1987, ‘‘On Line Sensing of FlankCrater Wear of Cutting Tools,’’ CIRP Ann.,36, pp. 41–44.

16Du, R., Zhang, B., Hungerford, W., and Pryor, T., 1993, ‘‘Tool ConditioninMonitoring and Compensation in Finish Turning Using Optical Sensor,’’Proceed-ings of Symposium on Mechatronics, ASME Winter Annual Meeting, pp. 245–251.

17Karthik, A., Chandra, S., Ramamoorthy, B., and Das, S., 1997, ‘‘3D Tool WMeasurement and Visualisation Using Stereo Imaging,’’ Int. J. Mach. ToManuf., 37, pp. 1573–1581.

18Gould, L., 1998, ‘‘Sensing Tool and Drive Element Condition in Machine ToolsSensors, pp. 5–13.

19Lister, P. M., 1993, ‘‘On Line Measure of Tool Wear,’’ Ph.D. thesis, Manufactuing and Machine Tools Division, Department of Mechanical Engineering, UMISManchester, United Kingdom.

20Oraby, S. E., and Hayhurst, D. R., 1991, ‘‘Development of Models for Tool WForce Relationship in Metal Cutting,’’ Int. J. Mach. Tools Manuf.,33, pp. 125–138.

21Yao, Y., Fang, X. D., and Arndt, G., 1990, ‘‘Comprehensive Tool Wear Estimatin Finish-Machining Via Multivariate Time-Series Analysis of 3-D CuttinForces,’’ CIRP Ann.,39, pp. 57–60.

22Bayramoglu, M., and Dungel, U., 1998, ‘‘A Systematic Investigation on the Uof Forces Ratios in Tool Condition Monitoring for Turning Operations,’’ TranInst. Meas. Control~London!, 20, pp. 92–97.

23Blum, T., and Inasaki, I., 1990, ‘‘A Study of Acoustic Emission from OrthogonCutting Process,’’ ASME J. Eng. Ind.,112, pp. 203–211.

24Moriwaki, T., and Tobito, M., 1990, ‘‘A New Approach to Automatic Detection oLife of Coated Tool Based on Acoustic Emission Measurement,’’ ASME J. EInd., 112, pp. 212–218.

25Liang, S. Y., and Dornfeld, D. A., 1989, ‘‘Detection of Cutting Tool Wear UsinTime Series Modeling of Acoustic Emission Signals,’’ ASME J. Eng. Ind.,111,pp. 199–205.

26Kakade, S., Vijayaraghavan, L., and Krishnamurthy, R., 1994, ‘‘In Process TWear and Chiping Monitoring in Face Milling Operation Using Acoustic Emsion,’’ J. Mater. Process. Technol.,44, pp. 207–214.

27O’Donnell, G., Young, P., Kelly, K., and Byrne, G., 2001, ‘‘Towards the Improvment of Tool Condition Monitoring Systems in Manufacturing Environment,’’Mater. Process. Technol.,119, pp. 133–139.


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28Moshref, S. B., 1980, ‘‘Cutting Temperature as and Approach to On-Line Msurement of Tool Wear,’’ SME Technical Paper, Paper No. IQ80–304.

29Choudhury, S. K., and Bartarya, G., 2003, ‘‘Role of Temperature and SurFinish in Prediction Tool Wear Using Neural Network and Design of Expements,’’ Int. J. Mach. Tools Manuf.,43, pp. 747–753.

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33Delio, T., Tlusty, J., and Smith, S., 1992, ‘‘Use of Audio Signals for ChatDetection and Control,’’ ASME J. Eng. Ind.,114, pp. 146–157.

34Altintas, Y., and Chan, P. K., 1992, ‘‘In-Process Detection and SuppressioChatter in Milling,’’ Int. J. Mach. Tools Manuf.,32, pp. 329–347.

35Tarng, Y. S., and Li, T. C., 1994, ‘‘Detection and Suppression of Drilling ChatteASME J. Dyn. Syst., Meas., Control,116, pp. 729–734.

36Bailey, T., Ruget, Y., Spence, A., and Elbestawi, M. A., 1995, ‘‘Open-architectController for Die and Mold Machining,’’American Control Conference, Seattle,Washington, pp. 194–199.

37Landers, R. G., 1997, ‘‘Supervisory Machining Control: A Design Approach PForce Control and Chatter Analysis Components,’’ Ph.D. thesis, DepartmenMechanical Engineering and Applied Mechanics, University of Michigan, AArbor.

38Lezanky, P., and Rafalowicz, J., 1993, ‘‘An Intelligent Monitoring SystemCylindrical Grinding,’’ CIRP Ann.,42, pp. 393–396.

39Bahr, B., Motavalli, S., and Arfi, T., 1997, ‘‘Sensor Fusion for Monitoring Mchine Tool Conditions,’’ International Journal Computer Integrated Manufacing, 10, pp. 314–323.

40Lou, K-N., and Lin, C-J., 1996, ‘‘An Intelligent Sensor Fusion System for ToMonitoring on a Machining Center,’’Proceeding of the 1996 IEEE/SICE/RSInternational Conference on Multisensor Fusion and Integration for IntelligeSystems, pp. 208–214.

41Kim, J-D., and Choi, I-H., 1996, ‘‘Development of a Tool Failure Detection Sytem Using Multi-Sensors,’’ Int. J. Mach. Tools Manuf.,36, pp. 861–870.

42Ertekin, Y. M., Kown, Y., and Tseng, T-L., 2003, ‘‘Identification of CommoSensory Features for the Control of CNC Milling Operations under Varying Cting Conditions,’’ Int. J. Mach. Tools Manuf.,43, pp. 897–904.

43Lindsay, R. P., 1983, ‘‘The Effect of Wheel Wear Rate on the Grinding Permance of Three Wheel Grades,’’ CIRP Ann.,32~1!, pp. 247–249.

44Hecker, R. L., and Liang, S. Y., 2000, ‘‘Power Feedback Control in CylindriGrinding Process,’’ASME International Mechanical Engineering Congress aExhibition, Orlando, Florida, DSC 69~2!, pp. 713–718.

45Hekman, K. A., Hecker, R. L., and Liang, S. Y., 2001, ‘‘Adaptive Power Contof Cylindrical Traverse Grinding,’’3rd International Conference on Metal Cuttingand High Speed Machining, Metz, France, II, pp. 262–264.

46Tomizuka, M., Oh, J. H., and Dornfeld, D. A., 1983, ‘‘Model Reference AdaptControl of the Milling Process,’’ASME Winter Annual Meeting, Boston, Massa-chusetts, pp. 55–63.

47Masory, O., and Koren, Y., 1983, ‘‘Variable Gain Adaptive Control SystemTurning,’’ Journal of Manufacturing Systems, ,2, pp. 165–173.

48Altintas, Y., 1994, ‘‘Direct Adaptive Control of End Milling Process,’’ Int. JMach. Tools Manuf.,34, pp. 461–472.

49Ardekani, R., and Yellowley, I., 1996, ‘‘The Control of Multiple ConstrainWithin an Open Architecture Machine Tool Controller,’’ ASME J. Manuf. ScEng.,118, pp. 388–393.

50Centner, R., 1964, ‘‘Final Report on Development of Adaptive Control Technifor Numerically Controlled Milling Machining,’’ USAF Technical DocumentarReport, ML-TDR-64-279.

51Amatay, G., Malkin, S., and Koren, Y., 1981, ‘‘Adaptive Control OptimizationGrinding,’’ ASME J. Eng. Ind.,103, pp. 103–108.

52Koren, Y., 1989, ‘‘The Optimal Locus Approach with Machining ApplicationsASME J. Dyn. Syst., Meas., Control,111, pp. 260–267.

53Ivester, R., Danai, K., and Malkin, S., 1997, ‘‘Cycle-Time Reduction in Maching by Recursive Constraint Bounding,’’ ASME J. Manuf. Sci. Eng.,119, pp.201–207.

54Hekman, K. A., and Liang, S. Y., 1999, ‘‘Feedrate Optimization and Depth ofControl for Productivity and Part Parallelism in Grinding,’’ International Journof Mechatronics,9, pp. 447–462.

55Li, Y., Rowe, W. B., Chen, X., and Mills, B., 1999, ‘‘Study and SelectionGrinding Conditions. Part 1: A Hybrid Intelligent System for Selection of Grining Conditions,’’ Proc. Inst. Mech. Eng.,213, pp. 131–142.

56Lundholm, T., 1991, ‘‘A Flexible Real-Time Adaptive Control System for Turing,’’ CIRP Ann., 41, pp. 441–444.

57Koren, Y., 1989, ‘‘Adaptive Control Systems for Machining,’’ Manuf. Rev.,2, pp.6–15.

58Coker, S. A., and Shin, Y. C., 1996, ‘‘In-Process Control of Surface Roughndue to Tool Wear Using a New Ultrasonic System,’’ Int. J. Mach. Tools Man36, pp. 411–422.

59Ulsoy, A. G., Koren, Y., and Rasmussen, F., 1983, ‘‘Principle Developments inAdaptive Control of Machine Tools,’’ ASME J. Dyn. Syst., Meas., Control,105,pp. 107–112.

60Lauderbaugh, L. K., and Ulsoy, A. G., 1989, ‘‘Model Reference Adaptive FoControl in Milling,’’ ASME J. Eng. Ind.,111, pp. 13–21.


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61Elbestawi, M. A., Mohamed, Y., and Liu, L., 1990, ‘‘Application of Some Parameter Adaptive Control Algorithms in Machining,’’ ASME J. Dyn. Syst., MeasControl,112, pp. 611–617.

62Rober, S. J., and Shin, Y. C., 1996, ‘‘Control of Cutting Force for End MillinProcesses Using and Extended Model Reference Adaptive Control ScheASME J. Manuf. Sci. Eng.,118, pp. 339–347.

63Liu, Y., Cheng, T., and Zuo, L., 2001, ‘‘Adaptive Control Constraint of MachininProcesses,’’ International Journal of Advanced Manufacturing Technology,17, pp.720–726.

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66Elbestawi, M. A., and Sagherian, R., 1987, ‘‘Parameter Adaptive Control inripheral Milling,’’ Int. J. Mach. Tools Manuf.,27, pp. 399–414.

67Elbestawi, M. A., Liu, L., and Sinha, N. K., 1991, ‘‘Some Advanced ContrStrategies for Modern Machine Tools,’’ Comput. Ind.,16, pp. 47–57.

68Landers, R. G., and Ulsoy, A. G., 2000, ‘‘Model-Based Machining Force Ctrol,’’ ASME J. Dyn. Syst., Meas., Control,122, pp. 521–527.

69Carrillo, F. J., Rotell, F., and Zadshakoyan, M., 1999, ‘‘Delta Approach RobController for Constant Turning Force Regulation,’’ Control Eng. Pract.,7, pp.1321–1331.

70Hayes, R. D., Shin, Y. C., and Nwokah, O. D. I., 1993, ‘‘Robust Control Desfor Milling Processes,’’ASME Winter Annual Meeting, DSC 50/PED 63, NewOrleans, Louisiana, pp. 119–125.

71Punyko, A. J., and Bailey, F. N., 1994, ‘‘A Delta Transform Approach to LoGain-Phase Shaping Design of Robust Digital Control Systems,’’ Int. J. RobNonlinear Control,4, pp. 65–86.

72Nordgren, R. E., and Nwokah, O. D. I., 1994, ‘‘Parametric and UnstructuUncertainty Models in Discrete Time Systems,’’ASME Winter Annual Meeting,DSC 55~1!, Chicago, Illinois, pp. 11–19.

73Rober, S. J., Shin, Y. C., and Nwokah, O. D. I., 1997, ‘‘A Digital Robust Cotroller for Cutting Force Control in the End Milling Process,’’ ASME J. DynSyst., Meas., Control,119, pp. 146–152.

74Kim, S. I., Landers, R. G., and Ulsoy, A. G., 2003, ‘‘Robust Machining ForControl with Process Compensation,’’ ASME J. Manuf. Sci. Eng.,125, pp. 423–430.

75Lee, A-C., and Liu, C-S., 1991, ‘‘Analysis of Chatter Vibration in the End MillinProcess,’’ Int. J. Mach. Tools Manuf.,31, pp. 471–479.

76Minis, I., and Yanushevsky, R., 1993, ‘‘A New Theoretical Approach for tPrediction of Machine Tool Chatter in Milling,’’ASME J. Eng. Ind.,115, pp. 1–8.

77Tsai, M. D., Takata, S., Inui, M., Kimura, F., and Sata, T., 1990, ‘‘PredictionChatter Vibration by Means of a Model-Based Cutting Simulation System,’’ CIAnn., 39, pp. 447–450.

78Lee, A-C., and Liu, C-S., 1991, ‘‘Analysis of Chatter Vibration in a CutteWorkpiece System,’’ Int. J. Mach. Tools Manuf.,31, pp. 221–234.

79Smith, S., and Tlusty, J., 1993, ‘‘Efficient Simulation Programs for ChatterMilling,’’ CIRP Ann., 42, pp. 463–466.

80Weck, M., Altintas, Y., and Beer, C., 1994, ‘‘CAD Assisted Chatter-Free NC ToPath Generation in Milling,’’ Int. J. Mach. Tools Manuf.,34, pp. 879–891.

81Altintas, Y., and Budak, E., 1995, ‘‘Analytical Prediction of Stability LobesMilling,’’ CIRP Ann., 44, pp. 357–362.

82Budak, E., and Altintas, Y., 1998, ‘‘Analytical Prediction of Chatter StabilityMilling Part I: General Formulation,’’ ASME J. Dyn. Syst., Meas., Control,120,pp. 22–30.

83Budak, E., and Altintas, Y., 1998, ‘‘Analytical Prediction of Chatter StabilityMilling Part II: Application of the General Formulation to Common Milling Systems,’’ ASME J. Dyn. Syst., Meas., Control,120, pp. 31–36.

84Shiraishi, M., Kume, E., and Hoshi, T., 1988, ‘‘Suppression of Machine-TChatter by State Feedback Control,’’ CIRP Ann.,1, pp. 369–372.

85Shiraishi, M., Yamanaka, K., and Fujita, H., 1991, ‘‘Optimal Control of ChatterTurning,’’ Int. J. Mach. Tools Manuf.,31, pp. 31–43.

86Landers, R. G., and Ulsoy, A. G., 1996, ‘‘Chatter Analysis of Machining Systewith Nonlinear Force Processes,’’ASME International Mechanical EngineeringCongress and Exposition, Atlanta, Georgia, DSC 58 pp. 183–190.

87Takemura, T., Kitamura, T., and Hoshi, T., 1974, ‘‘Active Suppression of Chaby Programmed Variation of Spindle Speed,’’ CIRP Ann.,23, pp. 121–122.

88Sexton, J. S., and Stone, B., 1980, ‘‘An Investigation of the Transient EffeDuring Variable Speed Cutting,’’ J. Mech. Eng. Sci.,22, pp. 107–118.

89Jemielnaiak, K., and Widota, A., 1984, ‘‘Suppression of Self-Excited Vibrationthe Spindle Speed Variation Method,’’ Int. J. Mach. Tool Des. Res.,24, pp. 207–214.

90Olbrich, R. J., Fu, H. J., Bray, D., and DeVor, R. E., 1985, ‘‘Study of ContSystem with Varying Spindle Speed in Face Milling,’’ Transactions of NAMRSME, pp. 567–574.

91Lin, S. C., DeVor, R. E., and Kapoor, S. G., 1990, ‘‘The Effects of Variable SpeCutting on Vibration Control in Face Milling,’’ ASME J. Eng. Ind.,112, pp. 1–11.

92Zhang, H., Ni, J., and Shi, H., 1994, ‘‘Machining Chatter Suppression by Meof Spindle Speed Variation-Part I: The Numerical Solution and Part II: Expmental Investigation,’’Proceedings of the S.M. Wu Symposium on ManufacturScience, I, Evanston, Illinois, pp. 161–175.

93Radulescu, R., Kapoor, S. G., and DeVor, R. E., 1997, ‘‘An InvestigationVariable Spindle Speed Face Milling for Tool-Work Structures with ComplDynamics, Part 1: Simulation Results and Part 2: Physical Explanation,’’ASMManuf. Sci. Eng.,119, pp. 266–280.

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94Yang, F., Zhang, B., and Yu, J., 1999, ‘‘Chatter Suppression via an OscillaCutter,’’ ASME J. Manuf. Sci. Eng.,121, pp. 54–60.

95Soliman, E., and Ismail, F., 1998, ‘‘A Control System for Chatter AvoidanceRamping the Spindle Speed,’’ ASME J. Manuf. Sci. Eng.,120, pp. 674–683.

96Altintas, Y., Engin, S., and Budak, E., 1998, ‘‘Analytical Stability Prediction aDesign of Variable Pitch Cutters,’’ASME International Mechanical EngineeringCongress and Exposition, Anaheim, California, MED 8 pp. 141–148.

97Stein, J. M., and Dornfeld, D. A., 1997, ‘‘Burr Formation in Drilling MiniaturHoles,’’ CIRP Ann.,46, pp. 63–66.

98Kim, J., and Dornfeld, D. A., 2001, ‘‘Cost Estimation of Drilling Operations byDrilling Burr Control Chart and Bayesian Statistics,’’ J. Manuf. Sys.,20, pp.89–97.

99Kim, J., Min, S., and Dornfeld, D. A., 2001, ‘‘Optimization and Control of Driling Burr Formation of AISI 304L and AISI 4118 Based on Drilling Burr ContrCharts,’’ Int. J. Mach. Tools Manuf.,41, pp. 923–936.

100Furness, R. J., Ulsoy, A. G., and Wu, C. L., 1996, ‘‘Supervisory Control of Dring,’’ ASME J. Eng. Ind.,118, pp. 10–19.

101Ralston, R. A. S., Stoll, K. E., and Ward, T. L., 1992, ‘‘Fuzzy Logic ControlChip Form During Turning,’’ Computers and Industrial Engineering,22, pp. 223–230.

102Jawahir, I. S., and van Luttervelt, C. A., 1993, ‘‘Recent Developments in CControl Research and Applications,’’ CIRP Ann.,42, pp. 659–693.

103Derrico, G. E., Calzavarini, R., and Settineri, L., 1994, ‘‘Experiments of SeTuning Regulation of Cutting Temperature in Turning Process,’’IEEE Conferenceon Control Applications, Glasgow, United Kingdom, 2 pp. 1165–1169.

104Choudhury, S. K., and Ramesh, S., 1995, ‘‘On-Line Tool Wear Sensing and Cpensation in Turning,’’ J. Mater. Process. Technol.,49, pp. 247–254.

105Warnecke, G., and Kluge, R., 1998, ‘‘Control of Tolerances in Turning by Pretive Control with Neural Networks,’’ Journal of Intelligent Manufacturing,9, pp.281–287.

106Fraticelli, B. M. P., Lehtihet, E. A., and Cavalier, T. M., 1999, ‘‘Tool-Wear EffeCompensation under Sequential Tolerance Control,’’ Int. J. Prod. Res.,37, pp.639–651.

107Teltz, R., and Elbestawi, M. A., 1993, ‘‘Hierarchical, Knowledge-Based Conin Turning,’’ ASME J. Dyn. Syst., Meas., Control,115, pp. 122–132.

108Ramamurthi, K., and Hough, C. L., 1993, ‘‘Intelligent Real-Time Predictive Dagnostics for Cutting Tools and Supervisory Control of Machining OperationASME J. Eng. Ind.,115, pp. 268–277.

109Landers, R. G., and Ulsoy, A. G., 1998, ‘‘Supervisory Machining Control: DesApproach and Experiments,’’ CIRP Ann.,47, pp. 301–306.

110Katz, Z., and van van Niekerk, T., 2003, ‘‘Implementation Aspects of IntelligMachining,’’ Proc. Inst. Mech. Eng.,217, pp. 601–613.

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112Sudhakara, R., and Landers, R. G., 2003, ‘‘Output Feedback Force ControlParallel Turning Operation,’’American Control Conference, Denver, Colorado,pp. 2596–2601.

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117Anderson, B. M., Cole, J. R., and Holland, R. G., 1993, ‘‘An Open StandardIndustrial Controllers,’’ Manuf. Rev.,6, pp. 180–191.

118Schofield, S., and Wright, P., 1998, ‘‘Open Architecture Controllers for MachTools, Part 1: Design Principles,’’ ASME J. Manuf. Sci. Eng.,120, pp. 417–424.

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