The presentation give an overview on the research activities undertaken in the AMTG, CPT in the University of Huddersfield

Chen, Xun

English

University of Huddersfield Repository

University of Huddersfield RepositoryChen, XunFundamental Study of Corrective Abrasive Machining TechnologyOriginal CitationChen, Xun (2009) Fundamental Study of Corrective Abrasive Machining Technology. In: CIRP UK Meeting , 8th May 2009. (Unpublished)This version is available at http://eprints.hud.ac.uk/10621/The University Repository is a digital collection of the research output of theUniversity, available on Open Access. Copyright and Moral Rights for the itemson this site are retained by the individual author and/or other copyright owners.Users may access full items free of charge; copies of full text items generallycan be reproduced, displayed or performed and given to third parties in anyformat or medium for personal research or study, educational or not-for-profitpurposes without prior permission or charge, provided:• The authors, title and full bibliographic details is credited in any copy;• A hyperlink and/or URL is included for the original metadata page; and• The content is not changed in any way.For more information, including our policy and submission procedure, pleasecontact the Repository Team at: E.mailbox@hud.ac.uk.http://eprints.hud.ac.uk/26/05/20111Fundamental Study of Corrective Abrasive Machining TechnologyDr Xun ChenAdvanced Machining Technology GroupCentre for Precision TechnologiesSchool of Computing and EngineeringCIRP UK Meeting 8th May 2009Centre for Precision TechnologiesPrecision machining technologyCurrent Research Themes Precision machining Abrasive machining Diamond turning Finite element analysis and molecular dynamics Intelligent process monitoring and control  Contact detection using acoustic emission Abrasive machining monitoring using AI techniques Knowledge support systems Database / Knowledge warehouse Unique pioneer challenge in developing next generation machining technologyEducating tomorrow’s professionalsElectrical & Electronic EngineeringMechanical & Manufacturing EngineeringComputing & information Technology26/05/20112Centre for Precision TechnologiesBasic principle of precision corrective machiningSa = 344.8nm                Sa = 9.7nmPelvisAcetabulum  cupCup holderStemTribological analysisAE pream-plifier 1& 2Acc. couplerDynamo-amplifierThermo-amplifierData Acquisition cardSCB100Spindle powerPAC signal cardOn-line monitoring3Centre for Precision Technologies4Framework of a grinding process controlControl methods• adaptive control, • constraint control.Monitoring & control; Surface integrity; Coolant delivery; Wheel dressingEnvironmental factors:•Machine•Workpiece•CoolantWheel preparation:•Wheel type•Dressing conditions•dressing depth•dressing leadGrinding process:•Force•Power•Vibration•Temperature•Acoustic emission•Material removal•Displacement•Wheel wear•Grinding burnControl parameters:•Grinding conditions•grinding speed•workspeed•feedrate•Coolant delivery•Grinding cycleWheel conditioning:•Wheel balance•Wheel topographyControl strategiesQuality inspection:•Surface roughness•Surface wave•Surface integrity•crack•hardness change•residual stress•phase changes•Form error•Size accuracyProcess parameters Quality parametersOutputsThree major measures of grinding qualities• accuracy of size and form, • surface roughness,• surface integrity.Sensors:• Power and torque• Forces• Size gauging• Grinding force• Vibration monitoring• Temperature sensing• Eddy current sensing • Acoustic emission• Image sensing26/05/20113Centre for Precision TechnologiesFrom the macroworld to the nanoworld (The Hitchhiker’s Guide to Nanotechnology, 2006)Micro-machining is considered to cover the production of minute components and features from a wide range of materials, generally in the size range of 200 microns to a few nanometres and may also be known as micro drilling, micro cutting, micro milling, micro grinding and micro etching.  5Centre for Precision TechnologiesChallenge of nanometre scale machiningGrain boundary indicates the limitation of the conventional turning operation limitation.What can be done?Aluminum (Al) Sa = 0.742 nm Nickel (Ni) Sa = 0.924 nm• Spindle RPM: 2000• Finish Feedrate: 7.5 mm/min• Finish Depth of Cut: 2 µm• Coolant: Odorless Mineral Spirits• Spindle RPM: 3000• Finish Feedrate: 5 mm/min• Finish Depth of Cut: 4 µm• Coolant: Odorless Mineral SpiritsSa < 1 nmForm error < 0.1 µmIt was claimed that grinding has no minimum depth of cut.626/05/20114Centre for Precision Technologies7grinding forces wheel weargrinding wheel topographysurfaceintegritysurfaceroughnessgrindingtemperaturegrindingvibrationsize errorOutputs of grinding processgrinding powergrindingwheel workpiecedressingtooldressingkinematicsgrindingkinematicsInputs of grinding processchip geometryGrinding processsingle grain loadenvironmentgrindingdeformationform errorBasic Relationships of a Grinding Process and SimulationInput : specifications of the wheel and the workpiece, dressing and grinding conditions.Generating the cutting surface of the grainSimulation based on a single grain in order k, j, i.Distributing the grain centresX (i = 1, 2, •••)Y (j = 1, 2, •••)Z (k = 1, 2, 3)Simulating the grinding action of the grainAll grains simulated?noOutput resultsyesAccumulating the actions of the grainsworkpiece surface before grinding workpieceZXSimulating the wear  of the grainCentre for Precision Technologies8Fundamental Mechanism of Grindingwheelvwvsworkpieceaachip formation ploughing slidinggrithml kvsThree stages of chip generationAE signals would have certain features that present three stages of chip generation.26/05/20115Centre for Precision Technologies9Rigs for single grit grinding tests Workpiece (2 workpieces 180° apart from each other) Single grit holder Force sensor AE Sensor and protective housing MaterialvStrokevGrit tipScratchStrokeCentre for Precision Technologies10AE signals of single grit scratch0 0.1 0.2 0.3 0.4-0.2-0.100.10.2Test 212 SG Scratches Channel  1Time (S)0 0.1 0.2 0.3 0.4-0.2-0.100.10.2Test 212 SG Scratches Channel  2Time (S)Amplitude (V)Amplitude (V)0 0.2 0.4 0.6 0.8 1 1.2x 10-3-0.2-0.15-0.1-0.0500.050.10.15Time (S)Amplitude (V)SG4 Experiment Test 212 Hit 2 (normalised)26/05/20116Centre for Precision TechnologiesSingle grit scratch testvScratchStroke towards pageGrit TipSteel plate0 5 10x 10501234 Cutting FFT Normalised Amplitude0 5 10x  10500.511.5 P loughing FFTFrequency  (Hz) 0 5 10x 10500.10.20.30.4 Rubbing FFT10 20 30-3-2-101Cutting P rofiledepth of cut (um)0 20-1-0.500.51 P loughing profilec ross  sec tion length (um )0 20 40-0.1-0.0500.050.1 Rubbinging profile401µm11Centre for Precision Technologies12Grinding mechanism classification using Neural Networks0 10 20 30 40 50 600.511.522.533.5NN Input Vectors Verification Test Set1: Cutting, 2: Ploughing and 3: rubbingNN Results for STFT SG4 AE Signalsx : classificationx : misclassificationx & x : Unseen DataNetwork classification: 92%Unseen Data classification: 83%0 10 20 30 40 50 600.511.522.533.5NN INput Vectors Test Set1: Cutting, 2: Ploughing and 3: rubbingNN Results for STFT SG4 AE Signalsx : classificationx : misclassificationx & x : Unseen DataNetwork classification: 93%Unseen Data classification: 87%Wheel rotational speed = 4000 rpm, Feedrate = 4 m/s. Workpiece materials: CMSX426/05/20117Centre for Precision Technologies13Feature extraction of AE in grindingWheel rotational speed = 4000 rpm,Feedrate = 4 m/s, Workpiece materials: CMSX4.0 10 20 30 40 50 6000.511.522.533.5NN Input Vectors (STFT of Hit 14)1: Cutting, 2: Ploughing and 3: RubbingHit 14 of 1um grinding cut with VIPER 60 Wheel0 10 20 30 40 50 60 7000.511.522.533.5NN INput Vectors (STFT of Hit 15)1: Cutting, 2: Ploughing and 3: RubbingHit 15 of 1um cut with VIPER 60 Wheel0 10 20 30 40 50 60 700.511.522.53NN Input Vectors (STFT of Hit 20)1: Cutting, 2: Ploughing, 3: RubbingHit 20 of 0.1mm grinding cut with VIPER 60 WheelC% P% R%1 µm cut 45 29 261 µm cut 45 47 80.1 mm cut 57 40 3Centre for Precision TechnologiesLaser irradiation imitating grinding thermal behaviourLaser machine Lumonics:JK704 Nd:YAGWave length 1.06 ηmPulse energy 1.36JMaximum peak power 2.5kWLaser irradiation time 0.06 msFocal length 120 mmLight beam diameter 12 mmOff-focal length 34~46 mm1426/05/20118Centre for Precision Technologies15Features of thermal stress induced AE signalsCentre for Precision TechnologiesComparison of AE signals generated by laser and grinding0 1 2 3 4 5 6 7 8 9 10x 10500.10.20.30.40.5Frequency(Hz)Normalized EnergyTest No.:  Laser23H8Pulse Time:  0.6msPulse Energy: 3.4JOff-focal:   15mmTm=781°CThermocouple: K-type867KHz 835KHz 820KHz 508KHz 461KHz 336KHz 0 1 2 3 4 5 6 7 8 9 10x 10500.10.20.30.40.50.6Frequency(Hz)Normalized EnergyPulse Time:  0.6msPulse Energy: 3.5JTemperature: 108°COff-focal:   30mmThermocouple: K-typeTest No.:  Laser37F1=266KHzF2=258KHzF3=141KHzF4=391KHzF5=836KHz0 1 2 3 4 5 6 7 8 9 10x 10500.050.10.150.20.250.30.35Frequency(Hz)Normalized EnergyTest No:Fd2-4Wheel: XA60F13VRPMaterial:CMSX4vs=40 m/svw=0.5 m/minap=0.074Direction: downCoolant: NoTm=784°C258 141 391 766 0 1 2 3 4 5 6 7 8 9 10x 10500.050.10.150.20.250.30.350.4Frequency(Hz)Normalised energyTest No:F155010Wheel:XA60F13VRPMaterial:CMSX4vs=50 m/svw=1 m/minap=1.3 mmDirection: downCoolant:48barTm=611°C391 141 406 156 438 LaserGrindingLow temperature High temperature1626/05/20119Centre for Precision TechnologiesGrinding thermal behaviour monitoring by using thermal AE signaturesThe NN created using AE signatures from laser irradiationGrinding burn identification using the NN from thermal AE signatures17Centre for Precision Technologies18Pattern recognition for grinding defects A Real Grinding Process (input) Sensors (AE signal) Impedance match & Amplifier  Data digitized & logging Quantification, Normalization, & Denoise etc  Feature extraction: joint-time-frequency domain (wavelet packet) Feature optimization Non-burn  Burn Signal condition Signal processing Pattern recognition Fuzzy classifier Minimum distance  Fuzzy c-mean cluster Feature extraction by wavelet packetand Joint Time-Frequency Analysis-Feature  calibration-Transitive  closure  calculation-Feature equalization-Feature normalizationMore than512 featuresSimilaritymeasure &clusteringLess than10 features=mnmjm2m1iniji2i12n2j22n1j12x ..., x ..., x x                          x ...,  x  ...,  x x                          x ..., x  x xx  x...,  ,x xn x mX,,,,,,,...,,,...,,,)(21111MMMM=nnnjn2n1iniji2i12n2j22n1j12Kr ..., r ..., r r                            r ...,  r  ...,  r r                            r ..., r  r rr  r...,  ,r rR~,~,~,~~,~,~,~~,~...,,~,~~...,,~~,~21111MMMMSelection &OptimisationTraining &Classification01234560 5 10 15 20 25 30 35Burn status classificationFuzzy recognition procedure Fuzzy recognition block diagraph• Success rate 92.3%26/05/201110Centre for Precision TechnologiesSupport vector machine classificationMaximum marginOptimal HyperplaneClass IClass IISupport vectors1-5 measurements across workpiece2 4 6 8 10 12 14 16 18 20 22 240246Ra measurements for each cut (3 Trials separated by lines)Test number19Centre for Precision Technologies20SVM classificationSmall amount of training data required Small amount of classifying time Benefits of SVM:26/05/201111Centre for Precision TechnologiesSVM ClassificationMachine: Makino A55 CNC machine centre. Workpiece: Inconel 718.Wheel: VIPER wheel. Depth of cut: 1 mm; Grinding speed: 35 m/s; Workspeed: 1000 mm/min. Machine: Makino A55 CNC machine centre. Workpiece: Inconel 718.Wheel: VIPER wheel. Depth of cut: 1 mm; Grinding speed: 55 m/s; Workspeed: 1000 mm/min. 21Centre for Precision Technologies++*Y X0.707 1.5*+*Y Y0.121*XX0.707Y + X – 1.5 XY (Y+0.121X)++X 1.5**YXY+0.121X+ X + 1.5XY(0.707Y)*Y0.707+Y0.121*X5A7A6A1A5B7B9B8B6B1B2B3B 4B2A3A 4AX20X8X10X14X8X10X8X10X14X14X8X13 X20  mydivide  plus  mydivide  mydivide  plus  plus  plus X20  mydivide  plus  mydivide  plus  plus  mydivide-400 -350 -300 -250 -200 -150 -100 -50 0 50 100-100-50050100150200250300350400DistanceDistanceBurn data & cluster centreChatter data & cluster centreNormal grinding data & cluster centre-400 -350 -300 -250 -200 -150 -100 -50 0 50 100-150-100-50050100150DistanceDistanceBurn data & cluster centreChatter data & cluster centreNormal grinding data & cluster centreMulti-classification of normal grinding, grinding chatter and burn using the GP2226/05/201112Centre for Precision TechnologiesClassification of grinding anomalies using genetic programming No. GP fitness function Data set Function Nodes Test Score Accuracy %1 sum diff fitness ICA Chatter and burn +,' -, '/, '* 32/40 802 sum diff fitness *reduction: burn and no burn +,' -, '/, '* 36/40 903 sum diff fitness *reduction: burn and no burn =<, '=>, if 36/40 904 sum diff fitness *reduction: burn and chatter +,' -, '/, '* 36/40 905 sum diff fitness *reduction: burn and chatter =<, '=>, if 38/40 956 classes overlap ICA Burn and no burn +,' -, '/, '* 33/40 82.57 classes overlap ICA chatter and no chatter +,' -, '/, '* 32/40 808 classes overlap ICA chatter and no chatter +,' -, '/, '* 40/40 1009 sum diff fitness ICA chatter and no chatter +,' -, '/, '* 36/40 9010 classes overlap ICA Burn and no burn +,' -, '/, '* 40/40 10011 classes overlap *reduction: burn and no burn +,' -, '/, '* 40/40 10012 classes overlap *reduction: chatter & no chatter +,' -, '/, '* 40/40 100*reduction: is based on the statistical window n-dimensional reduction technique23Centre for Precision TechnologiesMicro and nano scale corrective abrasive machiningProcess modellingTwo-body and three-body errosionsGrinding Lapping PolishingModels Suitable conditions On machine probingGrolishingProcess monitoring2426/05/201113Centre for Precision TechnologiesGrolishing Process ToolsCentre for Precision TechnologiesModels of material removalrp pvCdtdz=Preston material removal rate modelArchard material removal volume modelHsFKV nw =vp HfpfVS )( +=X. Wu, Y. Kita, K. Ikoku (2007) 2626/05/201114Centre for Precision TechnologiesGrolishing material removalαHertzian contact theoryReal resultModelling of Zeeko grolishing toolsSimulation of Deformations and Stresses27Centre for Precision TechnologiesTaguchi test resultsLevelsRepresentation Control Factors 1 2A Head speed (rev/min) 1000 2000B Tool Angle (o) 0o 10oC Grit Size (µm) 7 25TestNo.Control Factors MeanSa(av)S/NratioA B C1 A1 B1 C1 0.350 9.122 A1 B2 C2 0.470 6.453 A2 B1 C2 0.350 9.124 A2 B2 C1 0.221 13.12The best result is A2, B2, C1Further improvement could be achieved  by using different pad.0.0000.0500.1000.1500.2000.2500.3000.3500.4000.4500.5000 1 2 3 4 5Surface Roughness µmNo. of Inconel 718 test workpieceMean Sa(av)Mean Ra(av)2826/05/201115Centre for Precision TechnologiesError compensation On-Machine Metrology enhancedMachine partwith DIFFSYS® 3DX,Y,Z programGather data  from X,Y,Z by point-to-point analysis or by scanning Output data file to DIFFSYS®Auto-correct to desired tool pathRe-machine 3D part to desired profileOutput data file to 3rd party software Quality inspection2D and 3D MetrologyUtilizing UltraComp’s™ LVDT, 2D and 3D data files of the part profile can be gathered including the machine axes DIFFSYS® MC3 performs data import, graphically displays and auto-corrects the 3-dimensional part profiles from the LVDT measurement data to the desired part profile.29Centre for Precision TechnologiesAE detection of contactVoltage(mV) vs Time(us) <2>0 2000000 4000000 6000000 8000000 10000000 12000000-180-160-140-120-100-80-60-40-20020406080100120140160180Before contact After contactContact AE signals3026/05/201116Centre for Precision TechnologiesManufacturing Knowledge Warehouse DevelopmentFramework of the warehouse• date Input module• database module• problem solving module• knowledge discovery module• knowledge warehouse• knowledge analysis module.DatabaseDecision support Questions & answers Statistical Analysis  Query/ReportingKnowledge AcquisitionKnowledge Analysis Cases CollectionKnowledge Warehouse(Storage)Learning KnowledgeDiscoveryExternal sourcesGrinding CasesCases collection interfaceKnowledge engineersCoPsKnowledge Acquisition InterfaceProblem-solving  Knowledge DiscoveryContents of communication in CoP• Questions and answers.• Passing documents or web links.• Calling for events or conference.• Sending news box.• Others.31Centre for Precision TechnologiesOpportunities of further development Ultra precision corrective machining at nanometre scale (micro material removal). Intelligent machining monitoring and optimisation. Precision machining knowledge support system.Thanks for listening32

Fundamental Study of Corrective Abrasive Machining Technology

Abstract

Similar works

Full text

Available Versions

University of Huddersfield Repository