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    (机械加工学报)运用表面粗糙度测量改善磨削热分区的估计.pdf

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    (机械加工学报)运用表面粗糙度测量改善磨削热分区的估计.pdf

    Journal of Materials Processing Technology 211 2011 566–572Contents lists available at ScienceDirectJournal of Materials Processing Technologyjournal homepage www.elsevier.com/locate/jmatprotecUse of surface roughness measurements toheat partition in grindingAL-Mokhtar O. Mohamed∗, Andrew Warkentin, RobertDepartmentarticleArticleReceivedReceived15AcceptedAvailableKeywordsGrindingDressingTemperatureInfraredFiniteSurfaceisheatto improvewhichdetermineproposed5.1. IntroductionThe grinding operation requires a significant input of energy –most of which is converted into heat at the grinding zone betweenthe grinding wheel and the workpiece. A portion of the gener-atedWhendamageperformedaddedgrindingoperations.tomethodssured,estimatedelementmineisaspectsniversity,Tel.andrew.warkentindal.cawheel surface topography which is affected by wheel dressing andwear. In this paper, direct measurements of the workpiece surfaceroughness were used to indirectly account for the wheel surfacetopography. The resulting modified heat partition model was thenvalidated for different dressing conditions and techniques.0924-0136/doiheat enters the workpiece raising the workpiece temperature.the grinding temperatures exceed a critical limit, thermalsuch as workpiece burn can occur. Since grinding is usuallyas a finishing operation and is one of the last value-operations performed on a part, workpiece damage duringis proportionally more costly than damage during earlierForthisreason,significantresearchhasbeenconducteddevelopmethodstopredictgrindingtemperatures.Mostofthesehavethefollowingcharacteristicsthetotalpowerismea-the proportion of total power entering the workpiece isusing a heat partition model, and analytical or finitemodels based on a moving heat source are used to deter-the grinding temperatures. The weakest link in this processarguably determining the heat partition. One of the challengingofmodellingtheheatpartitionistoaccountforthegrinding∗Corresponding author at Department of Mechanical Engineering, DalhousieU-1360 Barrington Street, Room C360, Halifax, Nova Scotia, Canada, B3J 1Z1.1 902 494 6194; fax 1 902 423 6711.E-mail addresses mokhtar.o.mdal.ca A.-M.O. Mohamed,A. Warkentin, robert.bauerdal.ca R. Bauer.2. BackgroundWorkpiece-wheel heat partition models are used to determinethe proportion of heat generated in the grinding operation thatenters the workpiece. The model used in this work was proposedby Rowe et al. 1996. In this model, as shown in Fig. 1, the totalheat flux qtgenerated in the grinding zone is transferred to theworkpiece qw, the wheel qs, the chips qc, and the cutting fluidqf. Mathematically the heat flux to the various heat sinks can beexpressed byqt qw qs qch qf1The total heat flux can be calculated byqtPAcPbw lc2where P is the grinding power, Acis the grinding contact area, bwis the width of the grinding contact zone, and lcis the length of thecontact zone between the wheel and workpiece.– see front matter 2010 Elsevier B.V. All rights reserved.10.1016/j.jmatprotec.2010.11.008of Mechanical Engineering, Dalhousie University, 1360 Barrington Street, Halifax,infohistory8 October 2010in revised formNovember 201016 November 2010online 24 November 2010temperature measurementselementroughnessabstractIn grinding, the heat partitionmakes it possible to estimatesurfaceroughnessandtheThis correlation was usedto estimate the grain radius,accountforthegrindingwheeltice. To validate the proposedsimulations were used toFor the experimental conditionsone can use surface roughnesstition predicted using themaximum grinding temperaturesmeasurements to withinimprove the estimation of theBauerNova Scotia, Canada, B3J 1Z1used to estimate the percentage of energy entering the workpiece whichgrinding temperatures. In this work, a correlation was observed betweenpartitionwhengrinding1018steelwithan80gritaluminumoxidewheel.the usability of a popular heat partition model by making it easieris an important parameter in this model. The grain radius is used tosurfacetopographyandisvirtuallyimpossibletodirectlymeasureinprac-model, infrared temperature measurements along with 3D finite elementthe heat partition for different dressing conditions and techniques.used in this research, the measurements and simulations confirmed thatto estimate the grain radius. The average difference between the heat par-model and the conventional approach was 7.5, while the resultingpredicted using the proposed model agreed with infrared temperature 2010 Elsevier B.V. All rights reserved.A.-M.O. Mohamed et al. / Journal of Materials Processing Technology 211 2011 566–572 567in grinding.respectively,workpiece,titionin1developedTwhereferPecletgrinding,described1962Rwhereradicalbigbythepaper,radiustheareasgrainsratio.ageandpaper,developtheofpieceFig. 1. Heat transferThe corresponding partition ratios Rw, Rs, Rch, and Rfare,the proportion of the heat fluxes conducted by thewheel, and chip, and convected by the fluid. These par-ratios can be determined by dividing Eq. 1 by qt, resulting Rw Rs Rch Rf3The maximum temperature can be calculated from the modelby Rowe et al. 1996 as followsmaxqt−bracketleftbigSUBw cw Tmp ae vw/lcbracketrightbigbracketleftbigˇw/Rws CbracketrightbigradicalbigETBw/lc 2/3hf4Tmpis the workpiece melting temperature, hfis heat trans-coefficient to the coolant, C is a constant that depends on thenumber and contact angle having a value C≈1.0 for shallowand Rwsis the workpiece-abrasive heat partition ratioin Rowe’s work 2001 which is based on Hahn’s model. This heat partition ratio is given bywsparenleftbigg1 0.974kgˇw√ro vsparenrightbigg−15kgis the thermal conductivity of the abrasive grains, ˇwkw SUBw cwis a thermal property of the workpiece determinedits thermal conductivity kw, density SUBw, and specific heat cw.The wheel surface topography is captured in this model usingequivalent contact radius rowhich will be referred to, in thisas the grain radius. As shown in Fig. 2, this grain radius is theof contact between an idealized truncated conical grain andworkpiece. Large values of grain radius represent large contactbetween the grain and the workpiece caused by dull or flaton the wheel surface, and correspond to a high heat partitionIt should be noted that the grain radius parameter is an aver-value that is representative of all grains on the wheel surfaceis virtually impossible to measure directly in practice. In thisthe present authors use surface roughness measurements toamodeltoestimatethegrainradius,whichhelpstoreduceamount of trial-and-error inherent in the conventional methoddetermining the heat partition.Once the heat entering the workpiece is determined, the work-temperature can be predicted using analytical or numericalapproaches. In this work, a thermal finite element numericalmethod was used to help develop the proposed grain radius modelbecause it has the advantage of being capable of calculating theentire temperature field of the workpiece during grinding whichcan then be compared to infrared images. Mahdi and Zhang 1995developed the first 2D finite element model of heat transfer ingrinding to predict the phase transformation and the marten-site depths for alloy steel EN23 workpieces. Following them,many researchers have used different finite element packages anddeveloped numerical models. For example, Mamalis et al. 2003developed a 2D finite element model using a uniform heat flux andthey examined, both numerically and experimentally, the resultsof the heat generated when using different workpiece materi-als. Anderson et al. 2008 implemented 2D and 3D numericalmodelsandcomparedthesenumericalresultswiththeexperimen-tal results for different material removal rates. A more completereview of finite element modeling in grinding can be found in thework of Doman et al. 2009.The present authors developed a 3D numerical thermal modelusing LS-DYNA finite element software. The energy entering theworkpiece was represented by a moving triangular heat flux dis-tribution. The boundary conditions were applied to all of theworkpiece surfaces. The initial condition of the workpiece tem-perature was defined by specifying all nodes at measured room2orConical GrainFig. 2. Grain radius Marinescu et al., 2004.568 A.-M.O. Mohamed et al. / Journal of Materials Processing Technology 211 2011 566–572temperature.directallroomenttopboundaryhowever,tioncoefficientconvectionet3.inginfraredera’spixel.ingAtocapturedgrindingpowercollectedandacquisitionNationalsavedRremovingusingrateePlanomat 408 creep-feed grinding machine utilizing an aluminumoxide Al2O3 wheel Radiac Abrasives RPA801 G800 VOS and anAISI 1018 steel workpiece 75mm6.23mm30mm.The corre-sponding workpiece and grinding wheel material properties arelisted in Table 1. The depth of cut, feed rate, and wheel speed grind-ing parameters were held constant for all experimental samplesand set to 0.089mm, 38.1mm/s, and 23m/s, respectively.In this study, a variety of dressing techniques and conditionswere tested to help develop and validate the proposed grain radiusFig. 3. ExperimentalSince the bottom surface of the workpiece was incontact with the workpiece fixture on the grinding machine,nodes at the bottom surface were specified to have a constanttemperature. A convection boundary condition of the ambi-air was applied on the four side faces of the workpiece. Thesurface of the workpiece was defined to have a convectioncondition at regions where no heat flux was applied;as the heat flux moved, the convection boundary condi-ofthetopsurfaceoftheworkpiecewasupdated.Aheattransferof 80W/m2K was used in the numerical model for theboundary conditions based on the work of Andersonal. 2008.Experimental apparatus and test conditionsThe experimental apparatus is illustrated in Fig. 3. The grind-temperatures were measured using an Indigo Systems MerlinIR camera having a 320240 pixel resolution. The cam-lens system resulted in a spatial resolution of 0.25mm perThe workpiece samples were painted black prior to perform-the grinding experiments in order to enhance their emissivity.calibration procedure was performed on the infrared cameraobtain the relationship between the radiation intensity valuesby the infrared camera and the temperature values. Thepower was measured using a Load Controls Inc. [PH-3A]transducer.Themeasuredpowerandtemperaturedatawerevia a National Instruments Connector Block [BNC 2120]then sent to a National Instruments [PCI-MIO-16XE-10] databoard. The temperature images were acquired with aInstruments [IMAQ PCI-1422] frame grabber and werein raw 12-bit format. The arithmetic mean surface roughnessawasmeasureddirectlyusingaMahrFedralInc.brandPocketSurf.The specific grinding energy ecthe energy consumed whena unit volume of workpiece material is calculated bythe measured grinding power P and the material removalQwas followscPQw6apparatus.Table 1AISI 1018 steel and Al2O3grinding wheel properties.Property 1018 Steel RPA801G800 VOS UnitsDensity 7870 3980 kg/m3Specific heat 486 765 J/kgKThermal conductivity 51.9 35 W/m2KElastic modulus 213 27 GN/m2Poisson’s ratio 0.29 0.22 –Dry surface grinding experiments were conducted on a Blohm-model. To develop the model, single-point diamond dressing wasimplemented using a dressing depth of 0.076mm and dressingfeeds of 1.24, 1.60, 3.20, 4.83 and 9.64mm/s. To validate the result-ing model, both single-point and rotary diamond dressing weretested. In the case of model validation using single-point dressing,a dressing depth of 0.165mm was used with dressing feeds of 1.65,2.21, 4.44, 6.675, and 13.36mm/s. In the case of model validationusing rotary dressing, a dressing ratio of 0.85 at a dressing depthof 0.127mm was used with dressing feeds of 254, 762, 1524, 2286,3048mm/rotation 10−06.4. ResultsIn order to determine the heat partition, the experimentalresults were compared to finite element temperature simulations.An initial estimate of the grain radius parameter was used tocalculate the heat partition. A finite element numerical simula-tion was then conducted to calculate the temperature field in theworkpiece. The resulting simulated temperature field was com-A.-M.O. Mohamed et al. / Journal of Materials Processing Technology 211 2011 566–572 569paredparametersimulatedresultsgrainthethegoodofweretheatnumericallyTableSingle-pointFig. 4. Grinding temperature field simulationFig. 5. Numerical and experimental subsurface temperatures.with the measured temperature field and the grain radiuswas adjusted until the error between the measured andtemperature field was less than 5. Fig. 4 shows theof this conventional trial-and-error approach to finding theradius, where a numerical simulation top is compared withimage obtained using an infrared camera bottom. Visually,simulated and measured temperatures fields appear to be inagreement. In order to carry out a quantitative comparisonthe simulated and experimental results, vertical cross sectionstaken represented by the dashed line in Fig. 4 through bothexperimentally determined and simulated temperature fieldsthe point of maximum temperature. Fig. 5 plots the resultingpredicted and experimentally measured temperatures2dressing results ad0.076mm.Dressing feeds [mm/s] Surface roughness [H9262m] Specific energy9.64 3.30 354.83 1.59 443.20 1.14 491.60 0.76 581.24 0.70 66top and experimental bottom.2.533.54500600700800oC]00.511.52010020030040014121086420Dressing Feed, [mm/sec]Max. Temperature [Surface Roughness [m]Tmax 0.076 mmTmax 0.165 mmdadaRa 0.076 mmRa 0.165 mmdadaFig. 6. Maximum temperature and surface roughness as a function of dressing feedfor single-point dressing.as a function of subsurface distance for the case of single-pointdressing with a dressing depth of 0.076mm. In this figure, theexperimental measurements are represented by symbols whilethe finite element method simulation results are represented bylines, showing excellent agreement between the experimental andnumerical results.Fig. 6 plots the maximum temperature and surface roughnessresults as a function of the dressing feed for the two single-pointdressingdepthstestedinthiswork.Thesolidlineandfilledsymbolsin this figure correspond to the maximum temperatures left-handvertical axis, while the dashed line and hollowed symbols rep-resent surface roughness right-hand vertical axis. Fig. 6 showsthat, for a given dressing depth, the surface roughness increased[J/mm3] Max. temperature [◦C] Grain radius [H9262m]332 0.0010468 0.0020556 0.0025659 0.0035760 0.0050570 A.-M.O. Mohamed et al. / Journal of Materials Processing Technology 211 2011 566–572Table 3Single-point dressing results ad0.165mm.Dressing feeds [mm/s] Surface roughness [H9262m] Specific energy [J/mm3] Max. temperature [◦C] Grain radius [H9262m]13.36 3.60 26 252 0.00096.675 2.11 33 339 0.00174.44 1.43 41 448 0.00222.21 0.99 54 614

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