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J Intell ManufDOI 10.1007/s10845-011-0550-4Basic computational tools and mechanical hardwarefor torque-based diagnostic of machining operationsI. N. Tansel M. Demetgul K. Bickraj B. Kaya B. OzcelikReceived: 28 March 2011 / Accepted: 9 June 2011 Springer Science+Business Media, LLC 2011AbstractIn the industry, only rotary dynamometers canbe used for monitoring when multiple spindles are used inmachining operations. The current commercial rotary dyna-mometers are bulky and expensive for most machining cen-ters. The basic hardware and computational tools proposedare for a smaller, more cost effective Torque-based Machin-ing Monitor (TbMM). The objective of the TbMM conceptis to estimate the remaining tool life, detect chatter fromthe torque signal inside the proposed device, and commu-nicate with the central computer only when problems arise.Theremainingtoollifeestimationandchatterdetectionalgo-rithmsoftheTbMMweredevelopedbyanalyzingtheexper-imental data collected by a commercial rotary dynamome-ter. The mechanical hardware of the TbMM was designedto generate voltage proportional to the cutting torque usinga piezoelectric composite element. The remaining tool lifewas estimated from the standard deviation (or variance) ofthetorquesignal.Teager-Kaiseralgorithm(TKA)basedpro-cedure detected the chatter based on the frequency estima-tions only from four samples at a time. The accuracy andcharacteristics of the signal of the mechanical componentof the TbMM were found satisfactory in the estimation ofmachining problems such as wear and chatter. The TbMMI. N. Tansel K. BickrajDepartment of Mechanical and Materials Engineering,Florida International University, 10555 West Flagler St.,EC 3400, Miami, FL 33174, USAM. Demetgul (B)Department of Mechatronics Engineering, Technology Faculty,Marmara University, Goztepe, Istanbul, Turkeye-mail: mdemetgul.trB. Kaya B. OzcelikDepartment of Mechanical Engineering,Gebze Institute of Technology, Gebze, Kocaeli, Turkeyis a good choice particularly when multiple spindles worksimultaneously on the same workpiece.KeywordsTool wear estimation Chatter detection Dynamometer Torque-based machining monitor Milling Multi-spindle machiningIntroductionInternational competition forced many manufacturers to usemulti-spindle machine tools to increase productivity. State-of-the-art automation techniques are needed for monitoringthe automated manufacturing processes to maintain qual-ity. The research community continuously finds new waysto increase productivity and introduce diagnostic methods.However, the implementation of low cost sensors and reli-able diagnostic methods at the shop floor needs many manyears of work (Jeong and Cho 2002). In this paper, compu-tational tools and mechanical hardware of a Torque-basedMachining Monitor (TbMM) is introduced. All the compo-nents were designed to keep the cost and computational loadataminimum.Theperformanceoftheproposedcomponentsof TbMM was evaluated in separate experiments.Today, most manufacturers use advanced CNC millingmachines and lathes to maintain part quality and operationalflexibility. Multiple spindles CNC machine tools have beenwidely adapted to further boost manufacturing (Gale 2004;Murray 1998; Kamarthi 1994). These machines use multipleindependent live spindles to perform several operations suchastapping,milling,anddrillingsimultaneously(Korn2007).To monitor the tool condition and part quality, engi-neers have installed sensors to measure temperature, force,vibrations and currents of the motors (Dimla 2000; Rehornet al. 2005; Juo 2000; Stein and Wang 1990; Altintas 1992;123J Intell ManufLee et al. 1995; Kim and Kimt 1996). The sensory informa-tion has been processed using various algorithms to detectchatter, estimate cutter wear, identify broken tooth and eval-uate machined surface quality (Choi et al. 2004; Minis et al.1990;LeeandTarng1999;HuangandChen2000;Liangetal.2004;Heynes2007;Castroetal.2006).Dynamometersaccu-rately measure cutting forces and the torque in the desireddirections (Tangjitsitcharoen 2006). The collected data pro-videsvaluableinformationabouttheconditionofthemachin-ing operation, the integrity of the tool, and even the surfaceroughness. However, this approach has a substantial initialcost, requires constant maintenance and necessitates consid-eration of the dynamics of the dynamometer as part of themachining system (Ming et al. 2005; Benes 2006; Schmitzet al. 2006). Design of a dynamometer with desired transferfunction requires perfect tuning of two opposite concepts:maximum rigidity to maintain the dynamics of the overallsystem along with maximum elasticity for maximum accu-racy(Minisetal.1990;Tlustyetal.1987;Lapujoulade1997;Tounsi and Otho 2000a,b). Some other low cost alternativesfor monitoring machining operations include spindle-basedforce sensors (Jun et al. 2002), force rings (Scheffer andHeyns 2004), strain gauges (Yaldz and Unsacar 2006; Kimand Kim 1997; Schmitz et al. 2002), piezo-electric acceler-ometers (Yaldz and Unsacar 2006), piezo-film accelerom-eters (Kim and Kim 1997), bending beam type load cells(Schmitz et al. 2002), shaft displacement measurement tools(Albrecht et al. 2005) and voltage sensors for the electro-magnetic bearings (Auchet et al. 2004). The studies on themodeling of the dynamics of machining operations havehelped design better machine tools and improve diagnos-tic systems (Yaldz and Unsacar 2006; Kim and Kim 1997;Sekeretal.2002;Korkut2003;Yaldzetal.2007;RubioandTeti 2009; Jemielniak et al. 1998).A 3-component dynamometer including a load cell canbe installed between the workpiece and machine tool tableto measure the cutting force(s) of a machine tool with a sin-gle spindle and detect problems. The same approach cannotbe used to monitor two or more ongoing cutting by a multi-spindle machine tool since the cutting forces of each cuttingoperation cannot be separated. An individual rotary dyna-mometer should be installed at each spindle to monitor thecutting operation when they work at the same time. Currentcommercial dynamometers are very bulky and need addi-tional support components such as an inductive pick up, acharge amplifier, and a computer. It is challenging to fit mul-tiple dynamometers within the working space of a machinetool and to dedicate space for the support components. Cur-rently, rotary dynamometers cost over $50 K including thesupport components. The proposed TbMM design was pre-paredtokeepthemarketpricebelow$1Kwhilemaintainingtorque measurement accuracy capable of detecting machin-ing problems such as wear and chatter in milling operations.TheTbMMcanbeinstalledbetweenthespindleandthetool,requiresasignificantlysmallerworkspacethanconventionaldynamometersanddoesnotrequiresupportcomponents.TheTbMMisdesignedtoanalyzethetorquesignalinternallyandto communicate with the central computer wirelessly onlywhen problems are detected. Only the computational toolsand mechanical component of theTbMMispresented inthispaper.TheelectronicsfortheTbMMshouldbedesignedsep-aratelybyconsideringtheavailablecommercialcomponentsand legally available communication platforms.In the following sections, the theoretical background,the computation tools, the mechanical design, the proposedoperation of the integrated system, the experimental set-up,resultsand conclusion of the proposed TbMM are presented.Theoretical backgroundIn this section, standard deviation, variance, s-transforma-tion, Teager-Kaiser algorithm (TKA), piezoelectric charac-teristics of piezoelectric materials and force measurementwill be briefly discussed. For this study the machining anal-ysis package (MAP) was developed using MATLAB for theanalysis of the experimental data.The variance of data is calculated with the followingequation: =?1n 1?n?i=1(xi x)2(1)where, is the standard deviation. xiis the data with n sam-ples. x is the average of the samples.The short time Fourier transformation (STFT) of an x(t)timeseriesiscalculatedwiththefollowingequationtovisual-izethevariationofthetime-frequencydomaincharacteristicsof the signal:STFT(,f) =?x(t)w( t)exp(i2ft)dt(2)where,tandfarethetimeandfrequency.w indicatesthattheFourier transformation (FT) of the different sections of thedata is calculated. This is called the translating window. Theposition of this window is (time). The time and frequencyresolution of the STFT contradicts. Stockwell (Stockwellet al. 1996) used a special window. The height and widthof the window changed with frequency to obtain the bestpossible frequency and time domain resolutions at the sametime:S(,f)=?x(t)|f|2exp?f2(t)22?exp(i2ft)dt(3)123J Intell ManufThe inverse of the Eq. (3) gives the original time series x(t),x(t) =?S(,f)dexp(i2ft)df(4)S-transformation gives excellent information about the char-acteristicsofasignal;however,itrequiresmanycalculations.A simple microcontroller or DSP cannot perform the calcu-lations and analysis within a short timeframe.When chatter develops, the amplitude of the oscillationsatoneofthestructuralmodegrows.Teager-Kaiseralgorithm(TKA) Frank Pai (2010), Maragos et al. (1993), Vakman(1996) calculates the frequency and amplitude of a pure sinewave by using the following equations:c1= (x (t) x (t 1)(x (t + 1) x (t)x (t)(x (t + 1) 2x (t) + x (t 1)(5)c2= (x (t) x (t 1)2(x (t 1) x (t 2)(x (t + 1) x (t)(6)2f =1?tcos1(1 c22c1)(7)a =?c11 ?cos2(w?t)?(8)where ?t is the sampling interval. Where, f and a are thefrequency and the amplitude of the signal respectively.Piezoelectric materials generate charges when their origi-nal shape is changed by applying tension, shear or compres-sion (APC International Ltd. 2002). The generated chargeis proportional to the strain. Load cells are built to applypart of the external load to their piezoelectric component(Figliola and Beasley 2005; Yan et al. 2004). A matchingcharge amplifier converts the charge into measurable volt-age according to selected gain, linearizes the system char-acteristics and offers the user several time constant choicesto be able to perform the static or dynamic measurementsmore accurately. Piezoelectric patches have been used forthe measurement of strain without a charge amplifier (Sirohiand Chopra 2000; Yenilmez et al. 2007). In this case, insteadof only creating a charge, the piezoelectric element is usedlike an energy harvester. The cost, complexity and weightof the measurement system is reduced; however, the sen-sorresponsecharacteristicsarecompromised.ThePZTfibercompositesgeneratesignificantamountsofchargewhentheyaresubjectedtoforces(Yapicietal.2007).Theircharacteris-tics may be adjusted by selecting the fiber density and direc-tiontogenerateelectricitymoreefficientlythanconventionalpiezoelectric materials. For these characteristics, piezoelec-tric composites are an excellent choice for energy harvestingapplications and strain sensing (Yenilmez et al. 2007).Forthisstudy,MAPwasdevelopedusingMATLABscriptfor reading data at different lengths, composing smaller rep-resentative data sets, performing s-transformation, calculat-ing spectral characteristics, estimating amplitude and fre-quency from four samples by using TKA, and simulating theoutputsoftheproposedcontroller.Inaddition,thebasicfunc-tionsandsignalprocessingtoolboxoftheMATLABpackagewereusedforestimationofthepowerspectrum(FastFourierTransformation (FFT), squared coherence, and frequencyresponse estimation (Signal Processing 2008).The proposed remaining tool life estimation procedureThecuttingforcesincreasewithflankwear(Longetal.2010;Bao and Tansel 2000). Our experimental results affirm that(please see the “The variation of the characteristics of thetorque with wear” for details) the increments of the standarddeviation of the torque increase with the time or growth ofthe flank wear. In this study, the relationship was representedby two linear analytical expressions to prevent the use of anyexponential orothercomplex functionwhichwouldincreasethe computational load of the processor and program size.One expression represented the relationship from beginningto the transition point, while the other expression was usedfor the rest of the tool life. The transition point was aroundthe 2/3 of the tool life in our tests. For the programming ofthe proposed controller, only the torque values at the begin-ning, the transition point and the end of the tool life wereneeded. The relationship between the standard deviation ofthetorqueandthetoollifewasrepresentedwiththefollowingequations:(T) = a1+ m1tif t ttp(9)(T) = a2+ m2tif ttp(10)where,T isthetorque.Thestandarddeviationofthetorqueisrepresentedby(T).Thetimeist.ttpcorrespondstothecut-ting time where the slope of the standard deviation changes.This point may be selected approximately at 2/3 of the toollife. In our case, we selected ttp10,560 s when the tool lifewas 14,400 s. The remaining tool life (tremaininglife) at anytime (t) is estimated by the following equations:tremaininglife= tfulllife? (T) a1m1?if(T) (T(ttp)(11)tremaininglife= tfulllife?(T) a2m2? ttpif(T) (T(ttp)(12)where the tool life is the tfulllife(T(ttp) is the standarddeviation of the torque value when the tool life reaches thetransition point.To obtain the coefficients in Eqs. (9) and (10), the usershould collect experimental data for the selected cutting123J Intell Manufconditions until the tool completely wears out. Once the toolwears out, the user will determine the typical tool life. Thettpshould be selected around the 2/3rd of the tool life. Thestandard deviations of the torque should be calculated fromexperimental torque measurements of three cases. These arewhen the tool is new, when the tool reaches to ttpand whenthetooliscompletelywornout.Thea1andm1willbecalcu-lated from the standard deviation values determined for thet = 0 and t = ttp. Similarly, the a2and m2will be calcu-lated from the standard deviation values determined for thet = ttpandt = tfulllife.Afterthecoefficientsarecalculated,the Eqs. (11) and (12) could be used to estimate the remain-ing tool life at the selected cutting conditions for any given(T) value.The proposed chatter detection procedureThe proposed chatter detection algorithm filters the torquesignal with a high pass filter to eliminate the forced vibra-tions created by the machining activity of the cutting edges.Thechatterfrequencyisabove300Hzinmanymillingoper-ations. The cut-off frequency of the 4th order Butterworthfilter was selected at 250 Hz in this study. The frequency andamplitude of the signal was estimated as if it is a pure sinewave by using the TKA.Thefilteredsignalconsistsofnoiseandsomeactivityatthenatural frequencies and harmonics of the structural compo-nents. The TKA cannot estimate any meaningful frequencyand amplitude values for this signal. The variation of the fre-quencyestimationsisveryhigh.Whenthechatterstarted,thetorque signal became a pure sine wave as expected (pleasesee Sect. “Detection of chatter” for the experimental verifi-cation). The noise and contributions of the other frequencieswas minimal. The frequency estimations of the TKA algo-rithm started to become consistent and their standard devia-tion drastically decreased.TheTKAcalculatesthefrequencyandamplitudeforeachsampled point of the torque signal using the recent four sam-ples. Their mean and standard deviation are calculated afterevery 100 estimations. If the standard deviation of the fre-quency is found below a critical value, it indicates that thesignal is a pure sine wave. If the average of the amplitudeestimations is high and the average of the frequency estima-tions are in a predefined critical range, the chatter indicatoris raised from 0 to 1.The proposed mechanical hardware of the torque basedmachining monitor (TbMM)First, the mechanical hardware of the TbMM will be intro-duced for monitoring the torque signals during milling oper-ations. Secondly, the proposed procedure will be outlinedfor creation of end milling like cutting forces experimentallywhile the tool is fixed on the table of the machining center.The mechanical hardware of the torque based machiningmonitor (TbMM)ThedesignoftheproposedTbMMispresentedinFig.1.TheTbMM is comprised of cylindrical upper and lower halves.Theupper halfisdesignedtoattachtothespindle.Thelowerhalf holds the tool. They are connected to each other by thecylindrical extension of the upper half. A roller bearing isattached to this extension. The upper half and the roller bear-ingisinstalledinsidethecylindricalcavityonthetopsurfaceof the lower half. The upper and lower halves of the TbMMmay turn around their joint axes about 90 degrees. A piezo-electriccompositesensorisheldbetweentwoextensionssep-arately attached to the lower and upper halves. A spring (likeapaper clip)forcestwoextensions tostaytogether and holdsthe piezoelectric composite material between them (Yapiciet al. 2007). The wires are connected to the two flat surfacesof the piezoelectric composite. During the machining oper-ation, the applied torque to the tool varies. The design ofthe proposed TbMM allows the piezoelectric composite tobe subjected to pressure fluctuations while the torque varies.The piezoelectric material creates voltage variations. In thisstudy,onlythecharacteristicsofthesevoltagevariationswillbe investigated and compared with a reference signal froma drilling dynamometer. When the TbMM is used in manu-facturing, it will be fixed between the spindle and the tool.The TbMM will rotate with the spindle and tool. The elec-troniccomponentsoftheTbMMwillalsorotate.Thevoltagevariation should be measured by a microcontroller and pro-cessed while they rotate. The result of the diagnostic will bereported wirelessly to a station on the shop floor periodicallyor if problems are detected.Collection of end-milling like torque signals with fixed tooland rotating workpieceTheattachmentoftheTbMMtoarotarydynamometer,whilefixing them to the spindle and performing the machiningoperationbyrotatingthemistoocomplicatedandsubstantialrun off errors are expected. Additionally, passing the voltageoutput of the piezoelectric composite to an external mea-suring device while the TbMM is rotating requires complexinstrumentation and has many obstacles. The objective ofthe TbMM is to process data on the rotating device and onlyto communicate with the fixed stations when problems aredetected. To investigate the characteristics of the signal ofthe TbMM directly, a new procedure was created to collectend milling like signals with a fixed tool and rotating work-piece. The TbMM was attached to a 4 component drilling123J Intell ManufPiezoelectric composite Roller bearing Fig. 1 The mechanical design of the torque based machining monitor(TbMM). The upper and lower halves of the design (left), Close upshowing the piezoelectric composite disc between the extensions of thehalves (right)dynamometer. The tool was fixed to the other end of theTbMM. The drilling dynamometer which holds the TbMMwas bolted to the table of the milling machine. To generatetorque signal similar to the end milling operation, a tube likeworkpiece was prepared and cut down at the middle. Thisprocedure reminds the similar approach used for the drillingexperiments with a fixed tool (Rincon 1993); however, theworkpiece geometry is modified for simulation of the inter-ruptingcuttingofendmillingoperation.ThegeometryoftheworkpieceispresentedinFig.2.Theworkpiecewasattachedto the spindle. During the machining, the workpiece rotatedand moved down similar to a drill (Fig. 3). Since the work-piece has a tube like shape at the bottom, the cutting edgesof the end mill removed the chips similar to the end millingoperationwithtworelativelyinsignificantdifferencesforourapplication. The first difference is the constant thickness ofour chips (Fig. 4). The second difference is the missing hori-zontal relative motion of the cutter in the end milling. In ourcase, the tip of the cutting edges of the tool was continuouslyrising relative to the workpiece. However, the general char-acteristicsofthechipswereverysimilarandtheengagementof cutting edges was interrupted similar to the end millingoperations.Operation of the proposed integrated TbMMThe diagram of the operation of the proposed TbMM is pre-sentedinFig.5.SincetheelectronicsoftheTbMMisoutsideFig. 2 The half tube shaped workpiece designed to create interruptingcutting forces of end milling while the workpiece rotates and movesdown as a drill. The dimensions are in mmTool TbMM 4 component drilling dynamometer Spindle Fig. 3 Cutting a half tube like workpiece with fixed tool to simulatethe end milling operationTool positions at two subsequent rotationsThe drill like motion of the workpieceFig. 4 Comparison of the chip of end milling (simplified) (left) withours. The chip of the half tube shaped workpiece has constant thicknessand creates a 3-D spiral like relative motion (right)123J Intell ManufMulti-spindle machine tool Distributed transreceiver at the facility TbMMLocal infrared signal transmitter Fig. 5 Monitoring the machining operation using the TbMMthe focus of the journal, only the computational tools andmechanical hardware have been discussed in this paper. Theelectronics should be separately developed using a commer-cial wireless communication protocol such as WiFi or Blue-tooth. The addition of an infrared LED trigger would reducewireless communications and interferences.In mass production, identical shapes are cut from theworkpiece. The CNC controller would send an infrared sig-nal to the TbMM to sample the torque signal at the selectedlocations of the program. The remaining tool life would beestimatedusingtheproposedprocedureintheSect.“Thepro-posedremainingtoollifeestimationprocedure”.TheTbMMmaycontinuouslymonitorchatterusingthediscussedproce-dureintheSect.“Theproposedchatterdetectionprocedure”.If the remaining tool life is below a predetermined time orchatter develops, the TbMM reports the problem to the cen-tral computer using a wireless transreceiver.Experimental setupIn this study, three groups of experiments were performed.The tool wear related data was collected with 32 separatetests for the first group of experiments. In the second exper-imental work, development of chatter was monitored. Thethird group of experiments investigated the performance ofthe proposed low cost mechanical component to monitor thetorque signal.AISI-1040steelworkpiecewascutusinga4fluteSandvikendmillwith10mmdiametertoobservethecuttingforceandtorquevariationwithwear.Thespindlespeedwas4,775rpm.The depth of cut and the step over (radial depth) were 1 mmand3mmrespectively.Thefeedratewas1,050mm/min.Theexperimentwasrepeated32timesuntilthetoolwaswornout.The Kistler 9123C1111 rotational dynamometer was con-nected to the same manufacturers 5,223 signal conditionerandchargeamplifier.Fordatacollection,theNationalInstru-ment PCMCI 6034E daq card was used. The sampling ratewas 10 KHz in the experiments. Cutting forces and torqueweremeasured.Thedatawastakenduring12slongmachin-Fig. 6 The experimental setup for collection of the data during chatterdevelopmenting cycles at each wear level. The experiments were per-formed on a five axis Deckel Maho DMU 60 P high speedCNC milling machine.To study chatter development, an aluminum workpiece138.2 mm in length and 122.25mm in width was cut. Thethickness of the part was 5 mm. When the part was attachedto the vice on the table, 82.25 mm long section (average)remained available for machining. The top of the plate wasmachined with a slight slope to start the cutting operationwith 0.5 mm depth of cut and to end the stroke with 7 mmdepthofcutwhiletheendmillmovesonthehorizontalplane.The stepover (radial depth) was 3.5 mm. The spindle speedwas 1,592 rpm. The feed rate of the table was 657 mm/min.The cutting forces and torque were sampled at 5 KHz. Thetool was an HSS end mill with 10 mm diameter. Chatter con-ditions(noiseandseverevibrations)wereobservedwhenthedepth of cut was around 56 mm. The vibrations and noisesubdued before the tool left the workpiece at 7 mm depth ofcut. The experimental setup is presented in Fig. 6. The samemachine tool, dynamometer, amplifier and data acquisitionsystem listed in the previous paragraph were used during theexperiments.The last sets of experiments were performed to find thefrequency response of the mechanical component of theTbMM relative to a reference dynamometer. First, a leverwas attached to the lower half of the proposed device. A10 MHz BK Precision 4017 function generator was con-nected to a P-282 High Voltage Amplifier. A piezoelectricactuator was excited with the high voltage of the amplifier.The voltage of the piezoelectric composite and the Kistler8408 four-component dynamometer (after conditioned with123J Intell ManufDigital OscilloscopeTableXY ZVoltage AmplifierCharge AmplifierComposite Piezoelectric SensorAmplifierTorque Analysis DeviceMicrocomputer for Signal Processing 4 Component Dynamometer6-pin ConverterHolding block w/ setscrewTorque Arm extends outward in the z directionFunction GeneratorPiezoelectricactuatorFig. 7 The diagram of the experimental setup to obtain the frequency response of TbMMTorque Arm Torque- based Machining Monitor (TbMM) Piezoelectric Composite Sensor 4-component Kistler dynamometer Holding block Fig. 8 TheTbMMwasfastenedtothe4-componentKistlerdynamom-eter for dynamic tests and excited by a piezoelectric actuatorcharge amplifier) were digitized by using a Nicollet Model10 Integra four-channel digital oscilloscope and stored. Theexperiments were repeated between the 180 and 680 Hzrange.Thediagramandthepictureoftheexperimental setupare presented in Figs. 7 and 8, respectively.The machining experiments were performed on a Fadal 3CNC milling machine to test the proposed TbMM concept.The spindle speed was 2,800 rpm. The spindle moved downFig. 9 The machining tests with the TbMM and a drilling dynamom-eterwith the workpiece at 5?per minute (12.7 mm/min) feedrate. The cutting forces and the torque were measured usinga Kistler 9272 type drilling dynamometer. Nicolet 310 andIntegra model 10 digital oscilloscopes were used to monitorand save the data. The workpiece was cut from a cylindri-cal aluminum 6061 bar with 1.18?(30 mm) diameter. Theinternal diameter of the workpiece was 0.0787?(2 mm). Thediameter of the end mill was 1/8?(3.175 mm). The pictureof the experimental setup is presented in Fig. 9.Results and discussionThis section will start with the discussion of the variationof the torque characteristics with wear and chatter. The123J Intell ManufFrequency (Hz)S-Transform - Zoomed below 1000 Hz00100200300400500600700800900Time (sec)Fig. 10 The s-transformation of the torque signal at 10 different wearlevels. The time axis is compressed. The signal was obtained after, 0,3,000, 5,340, 7,620, 9,600, 10,560, 11,100, 11,520, 12,300, 13,320 s ofcutting. The intervals between the tests were ignoredperformances of the simulated controller which use the pro-posed procedures in the paper and the suggested mechanicaldesign will also be separately evaluated.The variation of the characteristics of the torque with wearTo simplify the analysis, a representative data set was pre-pared by taking 1,000 samples of the torque signal of thewear related experiments from the 1st, 4rd, 7th, and othertestswiththesamesequenceuptothe31stone.Thesesignalswerecollectedaftertheworkpiecewascutfor0,3,000,5,340,7,620, 9,600, 10,560, 11,100, 11,520, 12,300, 13,320 and14,160 s. The analysis was performed on this edited 11,000pointlongdata.Thes-transformationofthesignalofthefirst10testsispresentedinFig.10.Thetime-frequencyspectrumindicated that the amplitude of the torque signal increasedwith wear. The standard deviation of the 100 data point longsegments of the same signal is presented in Fig. 11. Thestandard deviation also increased with wear. The proposedlinear approximation lines from Eqs. (9) and (10) estimatedthe standard deviation of the torque signal with reasonableaccuracy. Instead of calculating spectral characteristics, thestandard deviation of the torque signal was satisfactory forestimation of the wear and remaining tool life.Detection of chatterThe variation of the cutting force and torque during the chat-ter development are presented in Fig. 12. Chatter could bedetected from the suddenly increasing slope of the envelopeof the cutting force signal; however, the same region is notas well defined at the torque signal. The s-transformation ofthe torque is presented in Fig. 13. The time-frequency plot05000100001500000.05Variation of moment with wear and linear Time (sec)Standard deviatin of torque (Nm)ExperimentalEstimatedestimation on edited dataFig. 11 The standard deviation of the 100 data point long segmentsand the proposed analytical model with two linear approximations. Theexperiments were performed at different time intervals. Ten standarddeviation values were calculated at each wear levels. They appear onthe same vertical line since the sampling was much smaller comparedto the time interval between the experiments at different wear levels02468101214-400-2000200400Cutting force variation during chatter Time (sec)Force (N)02468101214-0.500.511.5Torque signal variation during chatter developmentTime (sec)Torque (Nm)development (feed direction)Fig. 12 The variation of the cutting force and torque signals duringthe development of chatterindicated strong forced vibrations around the 100 and 200Hz. When the chatter development started, the torque varia-tions with 600 Hz frequency developed. The influence of theforced vibrations of the overall torque signal may be min-imized by filtering the signal with a high pass filter with acut-off frequency of 250 Hz. The frequency and the ampli-tudeofthesignalwasestimatedusingtheTKA.Theinstanta-neousfrequencyestimationsforeachdatapointofthetorquesignal,theaverage,andthestandarddeviationofthe100fre-quency estimations are presented in Fig. 14. Before the chat-123J Intell ManufTime (sec)Frequency (Hz)S-Transform - Zoomed below 1000 Hz00.511.522.50100200300400500600700800900Fig. 13 Thes-transformationofthetorquesignalduringchatterdevel-opmentter started, the torque signal had noise and contributions ofseveral frequencies. Since the TKA was developed for esti-mation of the frequency of pure harmonic signals, the esti-mated frequencies were almost randomly distributed. Whenthe chatter started, the signal became roughly a pure sinewave with single dominant frequency. At that time, the TKAstarted to estimate the frequency around 600 Hz consistentlyand the standard deviations of the 100 estimation long seg-mentsdrasticallydropped.Thefrequencyestimationsstartedto fluctuate and their standard deviations increased when thechatter ended but the torque signal variations continued toincrease with the increasing depth of cut. The standard devi-ation of the TKA based frequency estimations was an excel-lent indicator of the chatter development.The simulated performance of the proposed controllerThe remaining tool life estimations were calculated follow-ing the procedure presented in “Theoretical background”.To evaluate the performance of the proposed remaining toollife estimation procedure, a new representative data set waspreparedbyaddingthe1,000pointsegmentsfromthedataofthe 2nd,5th,8th, and the other wear related tests with thesame sequence up to the 32nd one. The simulated controllerthen estimated the remaining tool life of this new set auto-Fig. 14 The instantaneousfrequency estimation with TKAalgorithm (top), the mean(middle) and the standarddeviation of the 100 data pointslong data segments0246810121405001000150020002500Instantaneous frequency estimationTime (sec)Frequency (Hz)02468101214400500600700800Mean of the ins. frequency estimationsTime (sec)Time (sec)Frequency (Hz)02468101214050010001500Standard deviation of the ins. frequency estimationsFrequency (Hz)123J Intell Manuf050001000015000-20000200040006000800010000120001400016000Model peformance on test casesTime (sec)Remaining time (sec)ActualEstimatedFig. 15 The remaining tool life estimation using the proposed analyt-ical model with two linear approximationsmatically from the previously determined two linear modelsfrom the first representative data set discussed in Sect. “Thevariation of the characteristics of the torque with wear”. Theremaining tool life values were estimated using Eqs. (11)and (12). The coefficients used for the curve fitting in Fig. 8were used in Eqs. (11) and (12) to estimate the remainingtool lives. The actual and estimated remaining tool lives arepresented in Fig. 15. The estimation accuracy was very rea-sonableconsideringthesimplicityofthealgorithm.Thispro-cedure is very practical for the implementation of a low costTbMM.The performance of the proposed chatter detection algo-rithm is presented in Fig. 16. The chatter indicator rose to 1when the standard deviation of the frequency estimations oftheTKAdecreasedbelowacriticalvalue.Theamplitudeandthe frequency were calculated by averaging 100 estimationsof the TKA.0246810121400.81Chatter indicator signal of the multi-conf. req. controllerTime (sec)Frequency (Hz)0246810121400.00.25Chatter amplitude estimation of the multi-conf. req. controllerTime (sec)Amplitude (Nm)024681012140200400600800Chatter frequency estimation of the multi-conf. req. controllerTime (sec)Frequency (Hz)Fig. 16 The performance of the chatter detection algorithm. The indicator (top), amplitude (middle) and frequency estimations123J Intell ManufThe performance of the mechanical componentof the TbMMThe TbMM was designed as a low cost diagnostic tool formachining operations. It is not expected to have the accu-racy of the research dynamometers which cost more than 30timesthetargetedpriceofthecommercialTbMMs.However,the signal of the TbMM should represent the characteristicsof the torque of the machining operations with acceptableaccuracy to allow the software to detect machining problemssuch as chatter and tool wear.The sensor and the torque signal of the dynamometerwere compared by exciting the TbMM with a piezoelec-tric actuator. As outlined in the Sect. “The proposed chat-ter detection procedure”, the piezoelectric actuator pusheda lever which was attached to the tool end of the TbMM.TheTbMMwasboltedtothedrillingdynamometer.Thesig-nals of the TbMM and the dynamometer were compared inFig. 17 to demonstrate the similarity. The ratio of the ampli-tudes of the sensor output of the TbMM and torque signalof the drilling dynamometer (after charge amplifier) is pre-sented in Fig. 18. It is seen that this ratio varied between 2and 10. If this ratio were constant, with simple scale adjust-ments, both signals would be identical. It is clear that iden-tical output cannot be expected from the TbMM and dyna-mometer during the machining operation. If the dynamome-ter signal is accepted as the reference, we can conclude thatthe output of the piezoelectric composite sensor providesmeaningful information but does not have the pinpoint accu-racy of a research dynamometer.Fig. 17 ComparisonofthesignalsofTbMMand4-componentKistlerdynamometerFig. 18 Sensor and dynamometer torque voltage output ratio in thefrequency range of 180680 HzyoaeocTorque (V)-0.30.400.PiD02ezn0 Smram0miete.04rTim0e (.06sec)0.081Fig. 19 Torque of the Dynamometer and the Piezo CompositeThe signals of the sensor of the TbMM and the drillingdynamometer were collected during the machining of a halftubeshapedworkpieceasoutlinedintheSect.“Theproposedchatter detection procedure”. Both signals are presented inFig. 19. The feed rate and spindle speed of the workpiecewere2,800rpmand5”perminute(12.7mm/min)duringtheexperiment respectively. Since the characteristics of the sen-soroftheTbMManddynamometerwerenotthesameatdif-ferent frequencies, the signals were not identical. We believethat the signal of the TbMM was representative enough inthe diagnostic of machining operation based on the compar-ison of two signals. The total cost of using the TbMM at theshop floor is expected to be similar to monitoring accelera-tion,displacementoracousticemission.Inthiscostcategory,the TbMM provides more meaningful information than thelisted sensors for diagnostic purposes. Most importantly, theTbMM would work reliably even when multiple spindlesperform machining operation simultaneously on the sameworkpiece. The FFT of the torque signal of the dynamome-ter and the output of the sensor of the TbMM are presentedin Fig. 20. The characteristics of the signals were very sim-123J Intell Manuf01002003004005000100200300400500600700800Frequency content of DynamometerFrequency (Hz)010020030040050000.010.020.030.040.050.060.07Frequency content of Piezo CeramicFrequency (Hz)Fig. 20 FFT of the torque signal of the dynamometer (top) and thesensor (bottom) of the TbMM100101102103-40-2002040Magnitude (db)100101102103-200-1000100200Frequency (Hz)Phase (degree)Fig. 21 Frequencyresponseoftheconventionaldynamometerandthepiezo composite material01Normalized Frequency ( rad/sample)Magnitude (dB)Coherence Estimate via WelchFig. 22 Coherence estimation between the torque signals of the con-ventional dynamometer and the TbMMilar in the frequency domain. We assumed the signal of thedynamometer as the input and the signal of the TbMM as theoutputofasystem.Themagnitudeandthephaseofthetrans-fer function of such a system was calculated and presentedin Fig. 21. The coherence of both signals (Fig. 22) indicatedthe similarity of the signals particularly at low frequencies.ConclusionThecomputationalandmechanicalcomponentsforaTorque-based Machining Monitor (TbMM) are proposed for mon-itoring milling operations. The computational tools in theestimationofremainingtoollifeanddetectionofchatterwasdeveloped based on experimental data and verified. To studythecharacteristicsofthesignalofthemechanicalcomponentoftheTbMM,anewexperimentalprocedurewasproposedtoobtain torque signals similar to milling without rotating thetool. The specially designed workpiece rotated and moveddown to create end milling like cutting forces while the toolwas fixed to the table. This approach allowed for the test-ing of rotary dynamometers designed for milling operationswithout turning them at the development stage. The sameprocedure could be used to study milling operations withcomplexinstrumentationthatcannotberotated.Someexam-ples are the measurement of heat distribution at the surfaceof end mill with an infrared camera or the measurement ofvibration variation with a laser vibrometer. In this study, theTbMM was installed on a drilling dynamometer which wasbolted to the machine tool table.The computational hardware of the TBMM should besmall and robust to work while it rotates at high rpms withminimal influence on the balance. If the TbMM is used inproduction lines, it would wear out quickly and need to be123J Intell Manufreplacedinaboutoneyear.Therefore,thecostofthecompletehardware should be a fraction of the cost of todays researchsystems. The proposed computational tools for estimatingthe remaining tool life and for detecting chatter were simpleenough for quick calculations using commercial DSPs.The experimental data demonstrates that the standarddeviation of the torque correlates with the remaining toollife and can be estimated using two linear expressions. TheTKA algorithm was used in the detection of harmonic signalcreated by chatter. The standard deviation of the frequencyestimations reduced drastically when the harmonic signalappeared with the development of chatter. This proceduredid not give false alarm with the increase of the variation ofthe torque signal when the forced vibrations increased withthe depth of cut. The TKA updated the dominant frequencyand amplitude estimations for each sample and only the lastfourreadingswereused.Thisapproachwasmuchfaster,hadbetter resolution, and required fewer computations than anyother method as long as the signal is a pure sine wave.ThesignalofthemechanicalcomponentoftheTbMMwascompared with the torque signals of the research dynamom-eter.ThefirstseriesoftestswereperformedwhentheTbMMandthedynamometerwereexcitedwithharmonictorquesig-nal.Theamplitudeofthesignalofthemechanicalcomponentof the TbMM was 210 times bigger than the dynamometersignal at different frequencies. Although the dynamometerand the TbMM employ piezoelectric materials, their designprinciples are different. Conventional dynamometers use thepiezoelectricmaterialstogenerateachargewiththeload.Thesystem is designed to be rigid and the charge is conditionedwith the matching charge amplifier and voltage is generated.The mechanical component of the TbMM was designed tocreate the maximum electricity by converting the torque tothe compressive forces applied to the sensor. The piezoelec-tric composite material of the sensor was mainly used as anenergy harvester. We believe the fidelity of the signal of theTbMM was more than sufficient in spite ofthe compromiseddesign to minimize cost. We determined that the signal ofthe TbMM would not be identical to the torque voltage ofthe research grade rotary dynamometers; however, the accu-racy of it would be satisfactory for diagnostic purposes.Themachiningtestsusingtheproposedexperimentalmill-ing procedure agreed with the harmonic excitation tests. Thesignals of the sensor of the mechanical component of theTbMM and the torque signal of the drilling dynamometerwere not identical but their characteristics were similar. TheFast Fourier Transformation (FFT) of the signals demon-stratedthatbothsignalscapturedthedominatingfrequenciesof the torque variation during the machining operation.The proposed experimental procedure for creation of thesimulated milling signals was very convenient for testing therotating diagnostic devices without using expensive or com-plicated means for passing the signal to a fixed base. Theproposed mechanical component of the TbMM was found tobe satisfactory for the diagnostics of machining operations.The signals of the TbMM could be perfected to improve theaccuracyofthesignalfurtherwithdigitallyprocessing.How-ever, such efforts may defeat the purpose of the TbMM byincreasing the cost, complexity, and weight.We believe the presented TbMM concept would allowfor the development of low-cost tool monitors for modernmanufacturing facilities. The TbMM can monitor the torqueusing a few piezoelectric elements. Their outputs could becontinuously sampled with a microcontroller for detectionof chatter. Periodically, wear level can be evaluated with thesignalsofaninfraredtriggerwhiletheselectedlinesoftheG-code is executed. An analog filter could be used to improvethe accuracy of the data. In mass quantities, the projectedmanufacturing cost would be below $1,000 per TbMM. Theonly parts required are one central monitoring station forseveral machining centers, a few repeaters distributed to thefloor, and an infrared trigger attached to each machine tool.The signal intensity in the facility will be minimal since theTbMMscommunicatewiththemonitoringstationonlywhenproblemsaredetected.Evenunderthemostdemandingoper-ating conditions, the TbMMs could be manufactured to beoperational for about one year. We believe the concept isfeasible based on the reported results.AcknowledgementsThe mechanical structure of the TbMM wasmanufactured by Mr. Fernando Da Cunha as a courtesy. The authorsappreciate his contributions to the project. The wear and chatter relatedexperiments were performed at the Gebze Institute of Technology. Theperformance of the TbMM was tested at the Florida International Uni-versity.ReferencesAlbrecht, A., Park, S. S., Altintas, Y., & Pritschow, G. (2005). Highfrequency bandwidth cutting force measurement in millingusing capacitance displacement sensors. International Journalof Machine Tools and Manufacturing, 45, 9931008.Altintas, Y. (1992). Prediction of cutting forces and tool breakage inmilling from feed drive current measurements. ASME Journal ofEngineering for Industry, 114, 386392.APC International Ltd. (2002). Piezoelectricceramics: Principles andapplications. APC International Ltd.Auchet, S., Chevrier, P., Lacour, M., & Lipinski, P. (2004). A newmethod of cutting force measurement based on command volt-ages of active electro-magnetic bearings. International Journalof Machine Tools and Manufacturing, 44, 14411449.Bao, W. Y., & Tansel, I. N. (2000). Modeling micro-end-millingoperations: III. Influence of tool wear. International Journal ofMachine Tools and Manufacture, 40, 21932211.Benes, J. (2006). Precision care for high-speed spindles: Care indesign and application delivers high performance and produc-tivity. American Machinist.Castro, L. R., Vie?villeb, P., & Lipinski, P. (2006). Correction ofdynamic effects on force measurements made with piezoelec-tric dynamometers. International Journal of Machine Tools andManufacturing, 46, 17071715.123J Intell ManufChoi, Y., Narayanaswami, R., & Chandra, A. (2004). Tool wearmonitoring in ramp cuts in end milling using the wavelet trans-form. International Journal of Advanced Manufacturing Technol-ogy, 23(5-6), 419428.Dimla, E. (2000). Sensor signals for tool-wear monitoring in metalcutting operationsa review of methods. International Journalof Machine Tools and Manufacturing, 40(8), 10731098.Figliola, R. S., & Beasley, D. E. (2005). Theory and design formechanical measurements. New York: Wiley.Frank Pai, P. (2010). Online tracking of instantaneous frequency andamplitude of dynamical system response. Mechanical Systemsand Signal Processing, 24, 10071024.Gale, N. (2004). Twin-spindle design benefits production. Tooling &Production.Heynes, P. (2007). 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