This article was downloaded by: [14.139.190.20] On: 21 February 2013, At: 03:19 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Human and Ecological Risk Assessment: An International Journal Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/bher20 Effect of Process Parameters on the Breathing Zone Concentration of Gaseous Hydrocarbons—A Study of an Electrical Discharge Machining Process S. P. Sivapirakasam a , Jose Mathew a & M. Surianarayanan b a Department of Mechanical Engineering, National Institute of Technology, Tiruchirappalli, India b Cell for Industrial Safety and Risk Analysis, Central Leather Research Institute, Adyar, Chennai, India Version of record first published: 16 Nov 2011. To cite this article: S. P. Sivapirakasam , Jose Mathew & M. Surianarayanan (2011): Effect of Process Parameters on the Breathing Zone Concentration of Gaseous Hydrocarbons—A Study of an Electrical Discharge Machining Process, Human and Ecological Risk Assessment: An International Journal, 17:6, 1247-1262 To link to this article: http://dx.doi.org/10.1080/10807039.2011.618390 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
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This article was downloaded by: [14.139.190.20]On: 21 February 2013, At: 03:19Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Human and Ecological Risk Assessment:An International JournalPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/bher20
Effect of Process Parameters on theBreathing Zone Concentration of GaseousHydrocarbons—A Study of an ElectricalDischarge Machining ProcessS. P. Sivapirakasam a , Jose Mathew a & M. Surianarayanan ba Department of Mechanical Engineering, National Institute ofTechnology, Tiruchirappalli, Indiab Cell for Industrial Safety and Risk Analysis, Central LeatherResearch Institute, Adyar, Chennai, IndiaVersion of record first published: 16 Nov 2011.
To cite this article: S. P. Sivapirakasam , Jose Mathew & M. Surianarayanan (2011): Effect of ProcessParameters on the Breathing Zone Concentration of Gaseous Hydrocarbons—A Study of an ElectricalDischarge Machining Process, Human and Ecological Risk Assessment: An International Journal, 17:6,1247-1262
To link to this article: http://dx.doi.org/10.1080/10807039.2011.618390
PLEASE SCROLL DOWN FOR ARTICLE
Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions
This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representationthat the contents will be complete or accurate or up to date. The accuracy of anyinstructions, formulae, and drug doses should be independently verified with primarysources. The publisher shall not be liable for any loss, actions, claims, proceedings,demand, or costs or damages whatsoever or howsoever caused arising directly orindirectly in connection with or arising out of the use of this material.
Effect of Process Parameters on the Breathing ZoneConcentration of Gaseous Hydrocarbons—A Studyof an Electrical Discharge Machining Process
S. P. Sivapirakasam,1 Jose Mathew,1 and M. Surianarayanan2
1Department of Mechanical Engineering, National Institute of Technology,Tiruchirappalli, India; 2Cell for Industrial Safety and Risk Analysis, Central LeatherResearch Institute, Adyar, Chennai, India
ABSTRACTThe purpose here was to study the effect of process parameters on breathing
zone concentrations of gaseous hydrocarbons generated from an Electrical Dis-charge Machining process. Peak current, pulse duration, dielectric level above thespark location, and flushing pressure were the process parameters considered. Gaschromatography coupled with mass spectrometry (GC/MS) was used to analyzethe hydrocarbon components of gaseous emission. Peak current and pulse durationappeared the most significant. A significant fraction of emission was of reaction prod-ucts of dielectric fluid that included high molecular weight hydrocarbons, branchedchain hydrocarbons and other reaction products. Possible measures to control andminimize risk of exposure were outlined as well.
Electrical Discharge Machining (EDM) is one of the most widely used nonconven-tional manufacturing processes in die and mold manufacturing, and the electronic,automotive, aerospace, and surgical components industries. Material removal in theEDM process occurs by the heat energy of an electric spark between two conductiveelectrodes (work piece and tool) in the presence of a dielectric medium (Ho andNewman 2003). Hydrocarbon-based oils and deionized water can be used as dielec-tric fluids. The selection usually recommended by the manufacturer is based on themachining performance, viscosity, dielectric strength, boiling point, and flash point.Because of better performance and good dielectric properties, hydrocarbon-based
Received 26 April 2010; revised manuscript accepted 9 December 2010.Address correspondence to S. P. Sivapirakasam, Department of Mechanical Engineering, Na-tional Institute of Technology, Tiruchirappall-620 015, Tamil Nadu, India. E-mail: [email protected]; [email protected]
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dielectric fluids such as kerosene and synthetic EDM oils are widely used in thedie-sinking EDM process.
Use of hydrocarbon-based fluids and synthetic oils in the EDM process is fraughtwith potential hazards like emission of gases and aerosols, fire, explosion, degreasingeffect, and electrical hazard (Tonshoff et al . 1996). The high temperature andpressure generated in the discharge channel cause decomposition and evaporationof a portion of the dielectric surrounding it, resulting in reaction products. While apart of these toxic substances will be dissolved and/or condensed into the dielectric,many will be emitted in the form of aerosols and gases. Jose et al . (2010) have analyzedthe effect of process parameters on aerosol emissions from die-sinking EDM. Theconstituents of gas phase emission depend on the composition of the dielectricfluid in use. When hydrocarbon dielectric fluids are used, the emitted gas is foundto contain vapors of the dielectric and their reaction products including aliphaticand aromatic hydrocarbons (Bommeli 1983).
The quantity of gaseous emissions from EDM workstations depends on the pro-cess parameters such as peak current, voltage, pulse duration, flushing pressure,and dielectric level above the spark location. In order to assess the occupationalhazard potential of these discharges, a systematic investigation of EDM process andparameters that lead to emissions in the breathing zone was necessary. Investigationson the characterization of gaseous emissions from EDM processes are few (Bom-meli 1983; Evertz et al . 2006). Earlier approaches were limited to one parameter ata time. Since the discharge of toxic substances from EDM processes depended onseveral interlinked variables (peak current, pulse duration, dielectric level, flush-ing pressure, etc.), a multivariate relationship study would help profile the releasemechanism and on process parameters. A well known statistical technique, Designof Experiments (DoE), was applied. Full factorial design, response surface design,and Taguchi methods are the commonly used DoE techniques. Among these tech-niques, the Taguchi methodology helps determine the effect of process parametersby conducting the fewest number of experiments.
The present investigation was conducted in a laboratory environment of a die-sinking EDM machine using kerosene as the dielectric fluid. The objectives wereto analyze the gaseous hydrocarbons generated from the EDM process, to comparethe constituents of hydrocarbons with that of the dielectric, and to analyze the effectthat the process parameters had by way of using Taguchi methodology on breathingzone concentration of such emissions. Also an attempt was made to suggest controlmeasures to reduce the risk of exposure.
MATERIALS AND METHODS
Die-Sinking EDM
The experiments were conducted on a conventional die-sinking electrical dis-charge machine manufactured by Victory Electromech, Pune, India. A high carbonhigh chromium tool steel plate of size 4 cm × 4 cm × 1.5 cm was used as theworkpiece material and a copper rod of dia 25 mm was used as the tool. Com-mercially available kerosene was used as the dielectric fluid and side flushing wasopted. Kerosene, a blend of hydrocarbons, widely used with EDM work was chosenbecause of its high flash point, good dielectric strength, transparent characteristics,
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low viscosity, and low specific gravity (Bhattacharyya et al . 2007). The gap voltagewas kept constant at 100 V. During machining, the electrode moved up to a heightof 1.75 mm from the work piece at a frequency of 0.042 Hz in order to clean thegap between the workpiece and the electrode. The duty factor was kept at 0.5.
Gas Sampling at the EDM Process Workstation
Gas samples at the EDM workstation were collected using a Universal Air Sampler(SKC model No. 224-PCXR8). An activated coconut shell charcoal tube (100 mg/50 mg) was used as the sampling medium. The velocity of the sampler was set to0.2 liters/min and the sampling was done for 120 min. The sampler was calibratedbefore and after sampling using a soap bubble meter, with the sampling mediumin line. The samples were collected at a distance of 200 mm vertically and 200 mmhorizontally from the dielectric surface. This location roughly corresponded withthe breathing zone of an EDM operator.
Analysis of Hydrocarbons
Samples were analyzed using gas chromatography with mass spectrometry(GC/MS) using a Hewlett–Packard model GCD 1800 equipped with a 30 m0.25 mm i.d. HP-1 capillary column (0.25 µm film thickness) operated in the elec-tron impact mode (70 eV). The gases sampled using charcoal tubes were extractedwith acetone and stored at a temperature less than 4◦C. The samples were analyzedwithin one week. The chromatographic conditions were as follows: injector temper-ature, 280◦C; ion source temperature, 180◦C; temperature program: 100–180◦C at10◦C/min, 180◦C (3 min) 180–280◦C at 10◦C/min, 280◦C (3 min). The carrier gaswas helium at a constant flow rate of 1.5 ml/min. The sample of 0.2 µl was injectedwith splitless mode. The detector was turned off for the first 3 minutes of the analysisto prevent overloading from the solvent peak.
Identification of Compounds
The mass spectrum corresponding to each significant peak was obtained andthe compounds were identified using NIST database. For quantitative analysis, acalibration curve for the GC/MS was prepared using known quantities of dielectricfluid (kerosene). The total integrated response (which includes the response ofall identified and unidentified compounds) was calculated and calibrated againstthe standard dielectric samples in order to get the total concentration of hydrocar-bons. The relative fraction of area under the peak of each identified compoundwas obtained and it was multiplied with the total concentration to calculate theconcentration of individual compounds.
One important parameter in the GC/MS analysis is the limit of detection (LOD).For any analytical procedure the LOD can either be determined statistically orempirically (Armbruster et al . 1994). Using statistical methods, the LOD for GC/MSanalysis is 3.3 times the noise-to-signal ratio of the equipment. The noise-to-signalratio(S) can be calculated using the following equation.
S = σ
b(1)
where σ is the standard deviation and b is the slope of the calibration curve.
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Table 1. EDM process parameters and their levels.
Parameters Unit Level 1 Level 2 Level 3
Peak current (I) A 2 4.5 7Pulse duration (tp) µs 2 261 520Dielectric level (h) mm 40 60 80Flushing pressure (fp) kg/cm2 0.3 0.5 0.7
Since the compounds present in the gas samples are the components and re-action products of the dielectric fluid used (kerosene), the LOD for kerosene wasdetermined statistically. For the present study design, the LOD was found equal to0.077 µg/m3.
Design of Experiment
This article uses Taguchi methodology, which is a powerful Design of Experiments(DoE) tool, and provides a simple, efficient, and systematic approach to determineoptimal process parameters. This method is based on orthogonal arrays to studythe effects of a large number of variables with a small number of experiments. Theobjective of using the Taguchi method was to identify the key factors that contributedmost to the variation in occupational exposure to gaseous hydrocarbons. The majorsteps followed were:
• Selection of process parameters that likely affect gas phase emission fromthe EDM process (i.e., peak current, pulse duration, flushing pressure, anddielectric level). Three levels within the operating range of machining of small-and medium-sized components were selected for each of the factors (Table 1).
• Selection of an appropriate orthogonal array for conducting the experiments.The selection of the orthogonal array is subject to the condition that thedegrees of freedom for the orthogonal array should be greater than or at leastequal to those for the process parameters. A L9 (34) orthogonal array (Peace1992) was considered in Table 2. This basic design makes use of four control
Breathing Zone Concentration of Gaseous Hydrocarbons
factors, with three levels each. Nine experimental runs were planned, usingthe combination of levels for each input factor (Table 2).
• Experiments based on the arrangement of the orthogonal array as per themethodology discussed in the previous sections.
• Estimation of contribution of each factor on the breathing zone concentrationof gas phase hydrocarbons. Since the experimental design was orthogonal, itwas possible to note the effect of each operating parameter at different levels.For example, the mean effects for the peak current at levels 1, 2, and 3 werecalculated by averaging the output for the experiments 1–3, 4–6, and 7–9,respectively. The mean effects at each level for other process parameters werecomputed the same way.
• Statistical analysis of the results obtained. Experimental data were transformedinto a statistical measure of performance called Signal to Noise (S/N) ratio,as per the Taguchi method (Ross 1988). This method provides the level ofvariation in each performance characteristic. After the statistical analysis of S/Nratio, an analysis of variance (ANOVA) was undertaken in order to estimateerror and determine the relative contribution of each machining parameteron the breathing zone concentration of emissions.
In the Taguchi methodology, the S/N ratio provides an estimate of the effect ofnoise factors on output response. Here, the term “signal” represents the desirablevalue (mean) and “noise” represents the undesirable value (standard deviation).This ratio measures the effect of process parameters and the effects of noise factorson output response so that it can be employed as an indicator for the consistency ofa given system.
The equation for calculating the S/N ratio depends on the category of the per-formance parameter to be analyzed. The performance characteristics can be cate-gorized into three: “lower the better,” “higher the better,” and “nominal the better.”The average S/N ratio for each level of the process parameters is computed basedon the S/N ratio analysis. The S/N ratios are expressed on a decibel scale. Onewould use the “lower the better” S/N ratio if the performance is desirable whenthe output is as small as possible, “higher the better” S/N ratio if the performanceis desirable when the output is as large as possible, and “nominal the better” S/Nratio if the objective is to reduce variability around a specific target. Regardless ofthe category of the performance characteristic, the larger S/N ratio corresponds tothe better performance characteristic. Therefore, the optimal level of the processparameters is the level with the highest S/N ratio. Since the desired objective was tolower the output value (breathing zone concentration of gas phase hydrocarbons),the following equation for “lower the better” type of S/N ratio (Peace 1992) wasapplied.
η = −10 log
[1n
n∑i=1
y 2i
](2)
where yi was the measured output value for the ith repetition and n the number ofrepetitions in a trial.
The purpose of the ANOVA is to investigate which process parameters signifi-cantly affect the emission. This is accomplished by separating the total variability of
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the S/N ratios, which is measured by the sum of the squared deviations from thetotal mean S/N ratio, into contributions by each of the process parameters and theerror. First, the total sum of squared deviations (SS)T from the total mean S/N ratiocan be calculated as (Peace 1992):
(SS)T =m∑
i=1
η2i − mη2
m (3)
where m was the number of experiments in the orthogonal array, ηi the mean S/Nratio for the ith experiment, and ηm the total mean of S/N ratio.
The total sum of squared deviations (SS)T is decomposed into two sources: thesum of squared deviations (SS)P due to each process parameter and the sum ofsquared error. The sum of squared deviations due to each process parameter (SS)P
was calculated using the following equation.
(SS)P =t∑
j=1
(sη j )2
t− 1
m
[m∑
i=1
ηi
]2
(4)
where j is the level number of the process parameter p, t is the repetition of eachlevel of the parameter p and sηj is the sum of the S/N ratio involving this parameterp at level j. The variance of the process parameters (VP) was calculated by using.
VP = (SS)P
(df)P(5)
where (df )P was the degree of freedom of the process parameter = t – 1. Since it wasa saturated design where all columns were assigned with factors, the variations dueto error were estimated by pooling the estimates of the factors having least variance.The corrected sum of squares (S)P was calculated as:
(S)P = (SS)P − (df)p Ve (6)
The percentage contribution ρ was calculated as:
ρ = (S)P
(SS)T(7)
RESULTS AND DISCUSSION
A representative gas chromatogram of gas phase emissions is shown in Figure 1.It was clear that the hydrocarbons present in the emission were in a complex mix-ture state with many aliphatic and aromatic hydrocarbons. The breathing zoneconcentrations of different samples are given in Table 3.
Gas liberation profiles varied with process variables. There is a variation of com-pounds from sample to sample. A notable quantity of gas phase alkanes was observedin the samples collected. The major part was made up of an unresolved complexmixture of aliphatic, branched, and cyclic hydrocarbons not separable as individ-ual GC peaks. Among alkanes, concentrations of n-dodecane, n-tetradecane, andn-hexadecane were the highest.
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Table 3. List of hydrocarbons identified using GC/MS.
Total 17.3 4.63 13.17 28.2 12.9 22.78 34.67 15.57 29.63
n.d. Not detected. Detection limit for vapors of kerosene –0.077 µg/m3.
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Figure 1. Total ion chromatogram of gas sample (Peak current: 7A, Pulse duration:261 µs, dielectric level: 40 mm, Flushing pressure: 0.7 kg/cm2).
Comparison of the Compositions of Dielectric and Gas Phase Emission
Four samples of the dielectric used (kerosene) were analyzed using GC/MS.The gas chromatogram of a dielectric sample is shown in Figure 2. The composi-tion of dielectric fluid is in Table 4. The dielectric was comprised of 55.1% straightchain hydrocarbons and 26.8% branched chain hydrocarbons, whereas the emissioncomprised of 42.5% straight chain hydrocarbons and 39.7% branched-chain hydro-carbons and reaction products (Figure 3). In general the hydrocarbons presentin the dielectric had 10–17 carbon atoms. However a significant amount of highmolecular weight components was identified in the emission (tetratriacontane, hex-atriacontane, tritetracontane, tetratetracontane, etc., in Table 3). This difference
Figure 2. Total ion chromatogram of dielectric fluid.
in the relative percentage between dielectric and emission is due to the reactionamong hydrocarbons occurring at high temperature and pressure generated inthe process location. Even if there was not much difference in the percentageof aromatic hydrocarbons, the compound structure was altered in the emission(methyl ethyl benzene was identified in dielectric whereas dimethyl ethyl benzeneand dimethyl ethyl phenol were identified in the emission). Some heterocycliccompounds were also observed in the samples (bemegride, diazine, piperazine,etc.).
Statistical Analysis
The S/N ratio calculated for the experimental results and the S/N ratios at threelevels of process parameters for the concentration of hydrocarbon are presented inTable 5.
Regardless of the type of performance characteristics desired (higher the better,lower the better, or nominal the better), a larger S/N ratio implied better perfor-mance characteristics. Therefore, the optimal level of the process parameters wasthe level with the highest S/N ratio. For all the performance characteristics, thelower value of current and medium values of dielectric level, pulse duration, andflushing pressure were favorable.
The ANOVA results (Table 6) give the contribution of each machining param-eter on output responses. Peak current and pulse duration were the most influ-ential parameters. The effects of other factors (flushing pressure and dielectriclevel) were comparatively low. Optimum value of peak current could lead to sub-stantial reduction in productivity. Therefore mere optimization of process param-eters was not a feasible solution for occupational exposure from an EDM pro-cess. Other control measures should be initiated in order to reduce the risk ofexposure.
Table 6. Results of ANOVA for concentration of hydrocarbons.
Degrees of Sum of Corrected sum %Source freedom squares Variance of squares Contribution
Peak current had a positive effect on the breathing zone concentration of hydro-carbons; that is, an increase in peak current caused an increase in the concentrationof hydrocarbons (Figure 4). Peak current was the main factor governing the processenergy of the EDM process. An increase in the peak current caused a rise in thedischarge channel temperature, which in turn led to the increase of vaporizationof dielectric near the spark location. Subsequently, emission increased. The rela-tive percentage of n–alkanes was low as compared to that of branched alkanes andother reaction products at high current value (7 A), perhaps due to the high dis-charge energy at high current that caused an increased rate of reaction among thecompounds.
Effect of pulse duration
The effect of pulse duration is depicted in Figure 5. Concentration of the hy-drocarbons decreased with an increased pulse duration at short pulse durations (2–261 µs). This was because at the beginning of short pulse durations, most of theenergy supplied was used up for vaporization of the dielectric, causing more emis-sion of reaction products. Gradually the energy was distributed to the work piece,reducing its supply to the dielectric and consequently lower emissions. However,this effect was less prominent at high values of pulse duration (261–520 µs). Thiscould be due to the fact that longer pulse durations caused an increase in total en-ergy supplied per spark, causing an increase in generation of gases. The fraction ofreaction products was high (branched hydrocarbons) at low pulse duration becauseof the concentration of high temperatures in the process location.
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Figure 5. Effect of pulse duration.
Effect of dielectric level
The effect of dielectric level is shown in Figure 6. Theoretically it was ex-pected that with the increase in dielectric level more substances were dissolved,precipitated, and/or condensed in the dielectric, resulting in a lower concentrationof hydrocarbons. But this was not so at lower-range dielectric levels (40–60 mm). Theconcentration of hydrocarbons was found to increase with an increase in dielectriclevel to a medium value of dielectric level (60 mm). At a higher range (60–80 mm),the concentration of hydrocarbons decreased with an increase in dielectric level.
Figure 6. Effect of dielectric level.
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This behavior could not be explained satisfactorily. Yet it was shown that the dielec-tric level was not having a significant effect on the breathing zone concentration ofhydrocarbons. The relative concentration of reaction products was high at a higherdielectric level. This was because of the reaction taking place during the transportof gases in the dielectric.
Effect of flushing pressure
At lower values of flushing pressure the emission was increasing with an increasein flushing pressure. Insufficient flushing would not properly clear the debris in theworking gap but lead to an increase of energy supplied to the dielectric (Lonardo andBruzzone 1999). This energy caused the vaporization and reaction in the dielectric.The reaction products were more at low flushing pressure (Figure 7) indicating thatstrong reactions were due to high temperature. At higher values of flushing pressurethe material removal mechanism became more stable and the effect of turbulencemore prominent, resulting in a decrease in emission.
Occupational Safety and Risk Management
The study showed that emissions from the EDM process were a complex mix-ture of many hydrocarbons of n-alkanes, branched alkanes, aromatic compounds,alicyclic compounds, and heterocyclic compounds. A significant amount of highmolecular weight reaction products was also identified in the emission. The exactcomposition of the mixture was not known due to the uncertainty in the reactiontaking place in the spark location at very high temperature of the order of 20,000 K(Eubank et al . 1993). Generally, carcinogenic hydrocarbons like benzene and poly-cyclic aromatic hydrocarbons (PAHs) are expected while using hydrocarbon-based
Figure 7. Effect of flushing pressure.
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dielectric fluids (Bommeli 1983). Even though the present methodology is capableof analyzing the PAHs, their traces were not identified in the samples analyzed.However, the presence of PAHs cannot be ruled out because of the complex natureof reactions taking place and very high process temperature. Benzene was not iden-tified due to the limitations of the methodology. Further research is needed for theanalysis of benzene. Also, health effects of most of the hydrocarbons identified werenot fully explored.
The exposure levels of airborne contaminants were compared with ThresholdLimit Values (TLVs) developed by the American Conference of Governmental In-dustrial Hygienists (ACGIH). The TLV of a chemical substance is the time-weightedaverage concentration for a conventional 8-hour workday and a 40-hour workweek,to which unprotected workers can be exposed, without adverse effect. These valuesare revised periodically by the ACGIH. Individual TLVs of most of the compoundsidentified in this study are not available. Since the hydrocarbons present in the gasphase emission are the components and reaction products of kerosene, the breath-ing zone concentrations were compared with TLVs for vapors of kerosene specifiedby the ACGIH (200 mg/m3). The maximum total concentration of hydrocarbonsidentified in this study was 34.67 mg/m3 (Experiment No. 7, Table 3), which is wellless than the TLV for the vapors of kerosene.
Because of the uncertain nature of composition of gases and insufficient infor-mation regarding the exact hazard potential of the hydrocarbons identified in thegas phase emissions, the current TLVs may not be sufficient in order to assess thereal health risk of exposure. Moreover, the increasing trend of employing high cur-rent machines enhanced the risk of occupational exposure. So, appropriate controlstrategies should be implemented in order to minimize possible environmental andoccupational hazards associated with the EDM process.
The present study indicated that the quantity of emission can be considerablyreduced by adjusting the process parameters. But because of the wide use of highcurrent machines and a general trend in the industry to improve productivity of theprocess, it was not practical to keep the peak current value in the lower range. Localexhaust ventilation and fume extraction systems were available measures to reducethe breathing zone concentration of hydrocarbons. Exposure to hydrocarbonsfrom small-sized EDM machines could be reduced by proper ventilation. Speciallydesigned fume extraction systems are needed for high current machines. Theselection of exhaust devices depends on the size of the machine and specificationsof operation.
CONCLUSIONS
The major conclusions from this study are as follows:
1. Main process parameters affecting the quantity of exposure were peak currentand pulse duration. It was shown that the variation with dielectric level andflushing pressure were relatively less significant.
2. Results of GC/MS analysis showed that the hydrocarbons present in the work at-mosphere were a complicated mixture including straight chain, branched chain,
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aromatic, alicyclic, and heterocyclic compounds. So, there is an uncertainty ofthe risk associated with exposure to gas phase emissions generated from the EDMprocess. Hence, appropriate control strategies are to be implemented in order tominimize possible environmental and occupational hazards associated with theEDM process.
3. Apart from the constituents of dielectric fluid a significant amount of reactionproducts including high molecular weight hydrocarbons and branched hydro-carbons was identified in the gaseous samples. This meant that complex reactionswere taking place inside the dielectric at high temperature and pressure.
ACKNOWLEDGMENTS
The authors are grateful to the director, National Institute of Technology (NIT),Tiruchirappalli, for providing the facilities to conduct the experimental work. Theauthors thank Dr. N. R. Rajagopal, Professor, Birla Institute of Technology & Science(BITS), Pilani, for his valuable help. Thanks are also due to the Sophisticated Analyt-ical Instrument Facility, Indian Institute of Technology (IIT), Mumbai, for providingthe facilities to perform the GC/MS analyses. Part of this work is supported by theMinistry of Environment and Forests, Government of India (F.No.19/102/2008-RE).
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