PULSE DISCRIMINATION FOR ELECTRICAL...

PULSE DISCRIMINATION FOR ELECTRICAL DISCHARGEMACHINING WITH ROTATING ELECTRODE

Kotler Ter Pey Tee, Reza Hosseinnezhad, Milan Brandt, and John MoSchool of Aerospace, Mechanical and Manufacturing, Royal Melbourne Institute ofTechnology (RMIT) University, Bundoora, Victoria, Australia

& This article presents a new method for discrimination of various types of pulses generated duringan electrical discharge machining process in presence of a rotating electrode. Existing pulse discrimi-nation methods do not perform efficiently in an electrical discharge machine with rotating electrode,as arcs rarely occur during the machining process. Our method involves simultaneous comparison ofthe gap voltage and current signals with various thresholds. The main advantage of our proposedmethod is its efficient computation and significantly better accuracy in discriminating betweenvarious pulse classes for electrical discharge machining devices with rotating electrode. Experimentalstudies demonstrate a superior performance of our method in distinguishing normal pulses fromharmful arcs, open circuit and short circuit pulses, compared with the state-of-art methods.

Keywords electrical discharge grinding (EDG), electrical discharge machining (EDM),pulse discrimination, spark erosion

INTRODUCTION

Electrical discharge machining (EDM) is a process involving theremoval of conductive materials by a series of rapidly recurring currentdischarges between electrode and workpiece in the presence of a dielectric.In this process, a spark discharge is produced by controlling DC voltagepulses between a workpiece and the tool, which are separated by a gapdistance of 0.01–0.05mm for achieving precision machining.

Since 1943, EDM technology has developed rapidly and despite itscomplexity and relatively low material removal rate when compared with tra-ditional mechanical machining or grinding computer numerical control(CNC) machines, it is employed in a wide range of manufacturing applica-tions such as die and mold making, and micro-machining. The relative

Address correspondence to Reza Hosseinnezhad, School of Aerospace, Mechanical and Manufac-turing, Royal Melbourne Institute of Technology (RMIT) University, Bundoora, Victoria, Australia,3085. E-mail: [email protected]

Machining Science and Technology, 17:292–311Copyright # 2013 Taylor & Francis Group, LLCISSN: 1091-0344 print=1532-2483 onlineDOI: 10.1080/10910344.2013.780559

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popularity of EDM technology is due to its various advantages over conven-tional machining or grinding CNC machines. EDM is capable of machiningextremely hard materials with high tensile strength, it can achieve excellentsurface roughness, and it can machine workpieces that are too fragile towithstand the shearing force produced by the traditional machining process.

Despite its common use, EDM process involves complex andtime-varying phenomena that are yet to be well understood. One of thechallenging yet crucial tasks for stable and efficient machining usingEDM technology is the monitoring and control of electro-discharge status.

Electrical discharge in EDM occurs over a very short period of time in avery narrow gap filled with liquid. Modern EDM machines are equippedwith gap control to control the inter-electrode distance for stable machin-ing. Figure 1 shows the core components of a typical EDM process ormachine. The electrical discharge process is created by a high voltageacross the gap, henceforward called the gap voltage. Upon the start ofthe discharge process, the discharge current (also called gap current)needs to be disrupted as soon as an arc or short circuit happens.

In the EDM process, the final surface finish quality of the workpiece isdetermined by the energy per spark that is applied to the gap. In conven-tional EDM process (with static electrode), arc pulses are caused by imper-fect flushing of debris and result in occurrence of continuous sparking onthe same location which could create a large and uneven crater size andsignificantly affect the surface quality. However, in an EDM with rotatingelectrode, due to the high rotational speed of electrode, the probabilityof consecutive discharges taking place at the same location is very small.Indeed, in such machines, accumulation of debris caught in the gap cancause sparks with short duration (shorter than a normal spark, but com-paratively longer than a short-circuit pulse). In the context of EDM withrotating electrodes, such pulses are called arc pulses. Pulses of this type

FIGURE 1 Typical components of an EDM process. (Figure available in color online.)

Pulse Discrimination for Electrical Discharge Machining 293

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may not necessarily cause discharge at the same location, and the surfacefinish damage is not as critical as conventional EDM with static electrodeand workpiece.

It is important to note that accumulation of debris can lead to the gapdistance being bridged, which may cause a short circuit. In this case, theshort circuit pulse is more harmful than an arc pulse. Indeed, shortcircuit pulses, resulted by accumulation of debris usually cause processinstability and reduce the machining efficiency because the gap controlsystem would be automatically retracting the workpiece from theelectrode, eroding the accumulated debris rather than the workpiecematerial. Upon detection of an arc pulse, the off-time needs to beextended to allow more time for flushing off the debris from the gapand preventing debris accumulation.

An intelligent routine is needed to detect an arc or short circuit andtemporarily switch off the circuitry. Such a routine is labelled as ‘‘pulsecontroller’’ in Figure 1. It sends switching commands to a ‘‘power module,’’which in turn creates a high voltage pulse for the ‘‘eroding process.’’ Anefficient pulse controller design would result in higher percentage of nor-mal pulses which would give higher machining rate as the accurate detec-tion of normal pulses will result in larger number of eroding pulses duringa single operation. More precisely, with accurate detection of normalpulses, ‘‘extension of off-time due to arcs and short-circuits’’ will occur lessfrequently, and more frequent pulses will be generated, i.e., larger quantityof pulses will exist, eroding more material in the same period of time.

This article focuses on the design of the pulse controller module inFigure 1, which is the most important component of any EDM system.In the heart of any pulse controller, there is a ‘‘pulse discrimination’’routine that detects a harmful pulse (e.g., an arc or short circuit) andswitches the gap current off accordingly. Most pulse discrimination tech-niques in the literature have been specifically designed for EDM systemswith non-rotating electrode where the machining takes place by the CNCcontrolling the movements of an electrode in close proximity to the work-piece in such a way that a narrow gap is maintained (Dauw et al., 1983;Hsue and Chung, 2010; Liao et al., 2009; Yu et al., 2001). Our focus ison devising an effective pulse discrimination method for EDM systemswith rotating electrode where the workpiece is actually fed in by theCNC to be machined.

The usual assumptions based on which various pulse types are detectedin the state-of-art methods, do not accurately apply to EDM designs withrotating electrode are shown here and this gap is filled by presenting a tech-nique that suits the pulse types involved in EDMs with rotating electrode inthis article. In addition to being well-suited for EDMs with rotatingelectrode, this method is computationally efficient and can be easily

294 K. Ter Pey Tee et al.

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implemented in a large variety of hardware platforms with limited memoryand processing power.

First, we present common pulse types and their criteria basis as classi-fied in the literature, followed by a brief review of common methods andthe state-of-art of pulse classification for EDM. Then our method is intro-duced and a computationally efficient pseudo-code for its implementationin various platforms is presented. Experimental setup and results are thendiscussed, as well as conclusions drawn.

EDM GAP STATE

Usually, EDM pulses are categorized into four possible classes based ongap voltage and current waveforms (Dauw et al., 1983). The first anddesirable class of pulseis called spark pulses or normal discharge pulses.With these pulses, the gap voltage includes an ignition delay time beforecurrent starts flowing through the gap. Ignition delay time is the timerequired for the formation of plasma channel before allowing the electronsto flow though the plasma channel. Thus, the ignition delay shows a reason-able size of gap width and normal discharge occurring instantaneously inthe process (Schumacher, 2004).

Short circuit pulses occur when the gap voltage drops to zero while gapcurrent remains at a typical short circuit value (Liao et al., 2009). Suchpulses may be the result of the electrode being touched by the workpiece.In addition, short circuit pulses can be caused due to large amount of deb-ris in the gap, which enables a relatively larger gap current to pass throughthe debris with low resistance.

Open circuit pulses occur when the gap between the electrode andworkpiece is not sufficiently small to form a plasma channel. The dischargeenergy is stored as no discharge happens, and consequently no machiningtakes place.

Arc pulses are formed when a small amount of debris is not flushed bythe dielectric. This promotes the subsequent discharge to occur at the samelocation, forming a relatively deep crater before moving to another region(Kunieda et al., 2005)

Figure 2 shows a snapshot of common waveforms corresponding toeach of the above-mentioned classes of pulses (Hsue and Chung, 2010).Many pulse discrimination methods have been developed for classifying dif-ferent pulse types. Some examples include methods based on dischargegap state detection with fuzzy inference machines (Byiringiro et al., 2009;Tarng and Jang, 1996; Tarng et al., 1997; Yan and Liao, 1996; Yang et al.,2010) and methods using wavelet transform analysis (Jiang et al., 2009;Yu et al., 2001).


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Such methods are not efficient in terms of memory and computationalrequirements and their complexity does not always allow implementationusing common types of programmable integrated circuits that are widelyused in the industry for various control applications, such as ComplexProgrammable Logic Devices (CPLDs) or Field-Programmable Gate Arrays(FPGAs). Consequently, they have not been widely embraced by theindustry.

More straightforward techniques with computational simplicity andsmall memory requirements have been widely used in nowadays EDM tech-nology. An alternative approach is based on detecting the falling slope ofgap voltage (Hebbar, 1992; Janardhan and Samuel, 2010). The rationalebehind this method is that in a spark discharge, the dielectric is fully deio-nised before the next discharge takes place. Therefore, in this type of dis-charge, the voltage variations are slower than arc pulses. In other words, therate of voltage drop in an arc pulse is lower than that in the spark pulse andthe gap voltage signal may be characterized by its slope (Hebbar, 1992; Kaoand Shih, 2006). However, in EDM machines with rotating electrode, thedifferences in the gradients of voltage pulses are not large enough, basedon which a measuring device can detect the changes of the slope inreal time.

A recent yet popular method used for pulse discrimination in EDMmachines nowadays is based on an ignition delay timing algorithmpresented in (Chang, 2007; Hsue and Chung, 2010; Liao and Woo,1997; Yan and Chien, 2007) and henceforward called delay time-basedpulse discrimination. In this method, a number of pulse trains, each

FIGURE 2 Common waveforms corresponding to various pulse types, considering pulses with shortignition delay time as arc pulses.


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corresponding to a voltage threshold, are fed into a CPLD for pulseclassification.

The results of comparator trigger outputs are used by counters tomeasure the period of time during which the voltage is larger than eachthreshold. One of these time periods is the delay time. The pulse is deter-mined to be a normal pulse only if the delay time is non zero and less thana maximum given on-time. If it is more than the given on-time, then thepulse is classified as open circuit, and if it is zero, the pulse will be classifiedas an arc (provided the voltage is above a middle threshold) or short circuit(if it is below a small threshold).

Algorithm 1 shows the pseudo-code of the delay time-based pulsediscrimination method. It uses four constant parameters as inputs: themaximum on-time Ton and three voltage thresholds Vm>Vh>Vi. Thereare three counters used as timers that keep track of the period of timesduring which the gap voltage exceeds each of the thresholds. The output


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of the first timer, tm is considered to be the delay time and if it is more thanthe maximum on-time Ton an open circuit pulse is detected, otherwise aslong as tm is non-zero, the pulse is considered to be a normal dischargepulse.

In the case of zero delay time (tm¼ 0), the pulse is classified as anarc if the voltage level is still greater than the second threshold Vh, i.e.,th> 0. In case the voltage is very small (under the smallest threshold Vi)the pulse is classified as a short circuit. The outputs of the algorithmcomprise four binary pulse trains to represent the occurrence of a nor-mal, arc, short circuit or open circuit pulse, and denoted by SN(k),SA(k), SSC(k) and SOC(k), respectively. It is important to note that in thisalgorithm, pulse discrimination only occurs when the gap current is on.At each time k, only one of the pulse trains can be ‘1’ and the restare ‘0.’

PULSE TYPES IN EDMS WITH ROTATING ELECTRODE

Similar to the delay time-based pulse discrimination method, mostmethods in the literature are designed based on categorizing pulses withvery short or no ignition delay time as arc pulses (see Figure 2 andAlgorithm 1). This assumption is not always valid for EDM machineswith rotating electrode. In such machines, debris is efficiently flushedfrom the gap and arcs rarely occur. On the other hand, in EDMmachines with rotating electrode, the gap can be so narrow that aninfinitesimal delay time might be sufficient for preparing the plasmachannel for discharge. Hence, many normal discharge pulses can haveno detectable delay time. For instance, the pulse labelled as an ‘‘arcingdischarge’’ in Figure 2 could be a normal pulse in EDM with rotatingelectrode. With current methods, such pulses would be incorrectlyclassified as arcs.

The pulse controller block in Figure 1 usually includes short circuitand arc protection modules to interrupt the pulses when they are detectedto be harmful. If due to the extremely short delay time of a pulse andrelatively slow reaction time of the processor (such as a CPLD), the pulseis incorrectly classified as an arc, it will be suppressed by the pulse control-ler. As it was discussed earlier, for an EDM machine with rotatingelectrode, many normal discharge pulses include very short and undetect-able delay times. Hence, common delay time-based pulse discriminationmethods would naturally detect numerous arcing discharge pulses, andlead to frequent disruptions and long off-times average. Consequently,the machining efficiency, in terms of material removal rate, would bedrastically reduced.


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PROPOSED METHOD

The proposed pulse discrimination method is designed based on thefollowing principle: arc or short circuit events usually happen whenthere is a considerable amount of debris in the gap that are not flushedyet, and in presence of debris, the gap impedance is smaller. Directmeasurement and analysis of the gap impedance would be too expensivein terms of computational time and memory requirements for many hard-ware platforms.

In our method, the gap impedance is indirectly taken into account bycomparing the gap voltage and current signals with several thresholds inreal-time. In principle, when there is current flowing through the gap,the pulse is either a normal discharge, or an arc or a short circuit pulse.To discriminate between these types of pulses, it is noted that the currentwould be almost the same in all cases (indeed, the gap current jumps upas soon as a short circuit, arc or normal pulse starts, and the current is lim-ited by a current limiter at a fixed value).

Therefore, a large machining voltage means larger gap impedance, andby comparing the voltage with a threshold, the gap impedance is indirectlytaken into account and make sure it is sufficiently large before identifyingthis pulse as a normal pulse. To discriminate between an arc and short cir-cuit, two other thresholds are used (which are lower than the threshold fornormal pulses). For a short circuit the voltage is very close to zero, and foran arc, it is larger, although still smaller than a normal pulse. In principle, alarge number of thresholds can be used. However, to keep the methodcomputationally viable for real-time operation, four thresholds are set forgap voltage and one for analyzing the gap current as shown in Figure 3.

FIGURE 3 Gap voltage and current thresholds. (Figure available in color online.)


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Here, an algorithm for pulse classification, which is not based on thedelay time, is presented. Although this method is suitable for all types ofEDM systems, for EDMs with rotating electrode, its false alarm rate (therate of incorrect pulse classification) is significantly smaller and itsefficiency is substantially better than existing methods. Figure 4 showshow different pulse types are defined in our method. As it is demon-strated in the figure, both gap voltage and gap current values are beinganalyzed.

Detection of an open circuit pulse in our method is to some extentsimilar to the delay time-based methods: an open circuit pulse isdetected when the gap voltage remains above the open circuit thresholdVToc

for longer than a pre-set on-time Ton, while gap current is lower thanthe threshold IT. The rest of our algorithm, however, is different. If thegap current is high, we reset the delay timer and do not use it to dis-criminate between normal discharge, arc discharge and short circuitpulses.

A normal discharge pulse is detected if the gap voltage is above asecond threshold VTN (which is less than the open circuit thresholdVToc

) and gap current is above the current threshold, IT. A discharge willbe classified as an arc if the machining voltage is between a small shortcircuit threshold VTsc

and the normal pulse threshold, VTN. Similarly,

short circuit pulses should be switched off if gap voltage drops below

FIGURE 4 Different pulse types in EDM with rotating electrode.


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the short circuit threshold, VTsc, while the gap current is beyond the cur-

rent threshold, IT.The optimum values of voltage and current thresholds depend on the

workpiece material and preset maximum current and voltage values. Inpractice, for each category of workpiece material (e.g., 100% metal, PCDwith 10% cobalt-90% diamond, etc.), the optimum thresholds need to befound by trial and error through multiple experiments. This articleproposes the following routine of experiments to choose the optimumthreshold values.

VToc : In the application, the open circuit voltage is already known, and thisthreshold is set at 90% of the open-circuit voltage. This is justified bynoting that only for an open-circuit pulse, the gap voltage can exceed90% of the open-circuit voltage.

VTsc: In a separate experiment, electrode rotating speed is minimised toreduce the rate at which melted material are removed from the gap.Then, the workpiece is manually fed to the electrode, while the gapcontrol system (that tries to maintain the optimal gap) is disabled.The gap distance between the workpiece and electrode is graduallyreduced to zero, and only short circuit pulses will be generated. Theaverage machining voltage gives an indication of expected values forgap voltages in case a short circuit is happening. The VTSC thresholdis chosen 5% under this value to make sure all short circuit pulsesare detected.

VTA : After the above-mentioned experiment, the workpiece is manuallyretracted from the electrode to synthetically create arcing pulses. Simi-lar to the previous procedure, the average machining voltage is calcu-lated and the threshold is set at 95% of this value to ensure all arcingpulses are detected.

VTN : The above experiment is continued by slowly retracting the workpiecefurther. It is expected that during this movement, the machining volt-age increases. The retraction continues until the gap current drops sig-nificantly and the machining voltage jumps to open-circuit voltagevalues. Taking into account the machining voltages from the start ofthe retraction right before the occurrence of open-circuit, the averagemachining voltage is an indication of what burning voltage is expectedduring normal pulses. The VTN

threshold is chosen slightly lower than(95% of) the calculated average.

IT: This threshold is mainly used to detect if current is flowing through thegap, and can be chosen as a small portion (10%) of the average normalpulse currents recorded during the last experiment.


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The pseudo-code of our method is shown in Algorithm 2. The inputsare the voltage and current thresholds and the maximum delay time usedto detect open circuit pulses. The outputs are four binary pulse trainsdenoting the detected pulse types. In each sampling time k, at most oneof the pulse trains is 1 and the others are 0. When the pulse is off and dur-ing the delay time of a pulse (possibly an open circuit or a normal dis-charge pulse), all pulse train values are 0. These pulse trains can bedirectly applied to suppress harmful pulses by switching them off in thepower module shown in Figure 1.

EXPERIMENTAL SETUP FOR SIMULATION PURPOSES

The performance of this method and the delay time-based method of(Hsue and Chung, 2010) are evaluated in several experiments conducted


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using a CNC rotating electrode EDM machine. This machine consists ofa transistor-switching power generator with a pulse control system thatmanages the on-and off-times, and voltage and current levels based onthe input parameters entered by an operator. Short circuit protection wasswitched on to prevent damages to the power generator and the workpiece.Several tungsten carbide drills and a copper wheel were used as workpiecesand electrode, respectively, for the eroding process.

Table 1 shows the machining condition used in these experiments.Figure 5 shows eroding process with rotating electrode. This experimentalsetup includes an EDMmachine, a power generator (comprising the powermodule and pulse controller blocks in Figure 1), and a data acquisitionhardware that comprises a current probe and a data logger. For datalogging, Tektronix MSO3034 high frequency digital oscilloscope withsampling rate of 2.5 GS=s is used for real time acquisition of the gap voltageand current samples during the eroding process. The Instrument ControlToolbox of Matlab was used as a development environment to configureand acquire data from an oscilloscope via the standard virtual instrumentsoftware architecture (VISA), which is provided by the oscilloscope

TABLE 1 Input Parameters for Experiments Testing

Parameters Values

Pulse on-time (Ton) 20 usPulse off-time (Toff) 20 usPulse Current Value 15AOpen Circuit Voltage (Vo) 160VWorkpiece Material Tungsten CarbideDielectrics Type OilElectrode Polarity Positive

FIGURE 5 Eroding process with rotating electrode EDM. (Figure available in color online.)


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manufacturer. Using this method, we could directly save the data samplesin Matlab data files for further analysis.

The highest sampling rate was utilized to observe the details of therapidly changing voltage and current when short on-and off-times wereselected in the range of 1�10ms. In each experiment, a pulse train of 10ms length (containing 5 million samples of voltage and current values)in every 5min is recorded throughout the eroding process to study thechange of different pulse types. At least 5 sets of 10 ms data were recordedfor each complete cycle. The typical gap voltage and current waveformsthat are acquired from oscilloscope are shown in Figure 6.

SIMULATION RESULTS

To evaluate the classification accuracy and robustness of this methodto variations in the pulse waveforms, several eroding experiments areconducted, each with different voltage and current levels and on-and

TABLE 2 Comparison of our Simulation Results Based on our Proposed Method andthe Delay Time-Based Method of (Hsue and Chung 2010)

Ground Truth Our Method Delay Time-Based Method

Total No. of Pulses 304 311 326No. of Normal Discharges 157 173 86No. of Arc Discharges 21 25 110No. of Short Circuit Pulses 31 23 32No. of Open Circuit Pulses 95 90 98Total No. of False Alarms 40 186False Alarm Rate 13.2 61.2

FIGURE 6 Typical gap voltage and current waveform acquired during the EDM process. (Figureavailable in color online.)


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off-times. Our experiments showed rather similar waveforms for gap voltageand current signals (as shown in Figure 6). Due to space limits, only the resultsof one experiment will be discussed here, as the other experiments led to simi-lar results and conclusions. Our method and the recent delay time-basedmethod of (Hsue and Chung, 2010) based on the pseudo-code given in Algo-rithms 1 and 2 are implemented, in Matlab Simulink environment. The Simu-link diagram of proposed pulse discrimination algorithm is shown in Figure 7.Explanation of the major blocks in the diagram follows here.

Comparators

Four voltage thresholds and one current threshold are set and used byvarious comparators. The analogue signals that are acquired from oscillo-scope are imported from Matlab workspace into the various comparators.Whenever the voltage and current cross the respective threshold levels, atrue signal is generated for every sample, forming a binary pulse train data.

Pulse Train Processing (Pulse Discrimination)

The discrimination of discharge pulses (the pseudo-code given in Algor-ithm 2 is mainly implemented in this block as follows. A normal dischargepulse is detected if the I1 signal is triggered (i.e., I> I1 is correct), V2 is highand V1 is low. The gap state is determined as arcing discharge if I1 is trig-gered, V3 is high and V1 and V2 are low. The discharge pulse will be classi-fied as short circuit pulse, if I1 is triggered, V4 is high but V1, V2 and V3 arelow. Finally, if I1 is not triggered but V1 is high and an internal free runcounter J in the V>V1 loop (it’s hidden inside the block) is greater thanthe preset on-time, then this pulse will be classified as an open circuit pulse.

FIGURE 7 Simulink Block Diagram of our method.


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Counters

The number of discriminated pulses from previous block is accumu-lated in a preset period of time. This statistical information will be usefulfor future gap control. The ‘‘Edge Trigger’’ block merely prepares inputpulses for the counter module. As it was previously mentioned, we havecompared the performance of our method with the delay time-basedmethod of (Hsue and Chung, 2010) presented in Algorithm 1. Similar toour method, this algorithm is implemented in Simulink. Figure 9 shows

FIGURE 8 Pulse discrimination results with our method. Waveforms from top to bottom: ‘V’ is gapvoltage waveform, ‘I’ is gap current, the next four are binary pulse train outputs of the method, ‘OC’denotes open circuit, ‘NC’ denotes Normal. (Figure available in color online.)

FIGURE 9 Pulse discrimination results with delay time-based method of (Hsue and Chung 2010) forthe same gap voltage and current waveforms(best viewed in color – see the electronic version). (Figureavailable in color online.)


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the results of pulse classification using the delay time-based method for thesame gap voltage and current waveforms of Figure 8.

From Figures 8 and 9 it is evident that firstly there are not many shortcircuit pulses as the short circuit protection module suppresses them dur-ing the EDM process. Secondly, significant differences are observed inthe arc detection rates. The delay time-based method has classified manynormal discharge pulses as arc discharge pulses due to their short ignitiondelay times.

In practice, if this method is used and arc protection is enabled, manyof these wrongly classified pulses will trigger the CPLD to extend the off theoff period, thus eroding efficiency will be substantially reduced. To quanti-tatively compare the performance of our method with the delay time-basedmethod, all pulses and their ground-truth classes, are manually countedand recorded the pulse discrimination counts returned by each method,for around 5 sets of �10 ms data that are acquired from oscilloscope fora complete eroding cycle.

Table 2 summarizes the results. It is important to note that the differ-ence in the total number of detected pulses and the ground truth is dueto wrong detection of an off pulse as one of the four major classes of pulsesor the reverse. Each wrong classification is considered as a false alarm. Thenumber of false alarms for each ground truth class is the absolute differ-ence between the ground truth and number of pulses detected by themethod to belong to that class. This absolute difference for the first rowof the table means the false alarms for pulses wrongly detected duringthe off-times.

The total number of false alarms and the false alarm rates of the twomethods are summarized in the last two rows of Table 1. The false alarmrates are calculated by dividing the total number of false alarms by theground truth total number of pulses.

EXPERIMENTAL RESULTS

This proposed pulse discrimination algorithm is evaluated by conduct-ing several experiments involving the actual eroding process. Only themost challenging experiment results are presented in this article. Similarcomparative results were obtained in other experiments. The process para-meters used for this pulse discrimination method in the experiments werethe same as the simulations. It is important to note that unlike the experi-ments explained in the previous section, where real data logged duringerosion were processed off-line for pulse classification, here the pulseclassification are actually ran in real-time and the outcomes were appliedto control the erosion process. An embedded processor was programmed


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to implement the proposed pulse discrimination algorithm and the con-troller for pulse discrimination and pulse logic control. In all the experi-ments, the same power module and feed-rate controller were used.

Figure 10 shows a typical normal pulse without ignition delay time.With a rotating electrode, such pulses result in good flushing conditions.The yellow signal represents the actual feedback gap voltage; the purpleshows the actual gap feedback current and the green shows the gate signalgenerated from the embedded controller.

Without ignition delay time, the gap distance is smaller than withignition delay time. However, with excellent flushing, debris is not caughtin the gap regardless of gap distance. Furthermore, with higher ignitionvoltage, plasma channel can be easily formed with lower or zero ignitiondelay time as compared with lower ignition voltage.

As explained previously, rotating electrodes promote good flushing; there-fore arcing pulses are not easily generated. To evaluate the performance of thecontroller equipped with the proposed pulse discrimination method, arcingpulses are synthetically created by reducing the electrode angular speed byhalf. Figure 11 shows a normal pulse and an arcing pulse next to each other.

Our pulse discrimination method detects the arc by thresholding themachining voltage (post-delay time voltage). More precisely, the arc hap-pens when the burning voltage is less than the threshold shown in whitein Figure 11. When an arc is detected, the embedded controller increasesthe off-time so that more time is allowed for a complete flushing of debristhat has caused the arc.

FIGURE 10 Normal pulses without ignition delay time (best viewed in color – see the online version ofthe article). The green dashed horizontal line shows the VTN

threshold. (Figure available in coloronline.)


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FIGURE 11 Normal and arcingpulses with ignitiondelay time (best viewed in color – see the online versionofthe article). The green dashed horizontal line shows the VTN

, the red shows VTA. (Figure available in color

online.)

FIGURE 12 Pulse Type Statistics (best viewed in color – see the online version of the article). (Figureavailable in color online.)


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To evaluate the performance of a complete eroding process, the totalnumber of pulses belonging to each type is recorded. Figure 12 showsthe statistics of the detected pulse types. In most of the times, a large ratioof pulses belong to the category of normal pulses are observed. This is theresult of the real-time pulse classification plus immediate action of the con-troller in response to detection of undesirable pulses (e.g., extending theoff-time in the event of detecting an arc pulse). It is important to note thatin addition to the excellent performance of the proposed pulse classi-fication method, the relatively large ratio of normal pulses (which meansefficient material removal rate) is the outcome of adjusting the feedratecontroller parameters. Indeed, the operator can use the informationprovided by the statistics shown in Figure 12 to fine tune the controllerparameters.

CONCLUSIONS

This article presents a new method for classification of various types ofpulses generated during an electrical discharge machining process in thepresence of a rotating electrode. The proposed method is based on theobservations of the plasma discharge process and involves simultaneouscomparison of the gap voltage and current signals with various thresholds.The main advantages of this proposed method are twofold. First, it is com-putationally efficient and does not require more than a few comparisonand incremental operations, which can be easily implemented using com-mon embedded processing chips. The second advantage of this methodis its suitability for discrimination of the narrow width pulses that areusually generated with our targeted types of EDM machines (with rotatingelectrode). Experimental results demonstrate a superior performance ofthis method in distinguishing normal pulses from harmful arcs, opencircuit and short circuit pulses, compared to the state-of-art methods thatclassify EDM pulses based on their measurements of the delay times.

ACKNOWLEDGMENT

This research was supported by Advanced Manufacturing CorporateResearch Centre under the Australian Government’s Cooperative ResearchCentre’s Program as Project No. 2.2.1.

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