Intelligentized Methodology for Arc Welding Dynamical Processes

295 Pages·2009·18.4 MB·English

by  Shan-Ben Chen

Lecture Notes in Electrical Engineering
Volume 29
·
Shan-Ben Chen Jing Wu
Intelligentized Methodology
for Arc Welding
Dynamical Processes
Visual Information Acquiring, Knowledge
Modeling and Intelligent Control
123
Shan-BenChen JingWu
InstituteofWeldingEngineering InstituteofWeldingEngineering
ShanghaiJiaoTongUniversity ShanghaiJiaoTongUniversity
DongchuanRoad,800 DongchuanRoad,800
Shangahi,200240 Shangahi,200240
PRChina PRChina
[email protected] [email protected]
ISBN:978-3-540-85641-2 e-ISBN:978-3-540-85642-9
LibraryofCongressControlNumber:2008935359
(cid:2)c Springer-VerlagBerlinHeidelberg2009
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Preface
Welding handicraft is one of the most primordial and traditional technics, mainly
by manpower and human experiences. Weld quality and efficiency are, therefore,
straitly limited by the welder’s skill. In the modern manufacturing, automatic and
robotic welding is becoming an inevitable trend. However, it is difficult for auto-
maticandroboticweldingtoreachhighqualityduetothecomplexity,uncertainty
anddisturbanceduringweldingprocess,especiallyforarcweldingdynamics.The
informationacquirementandreal-timecontrolofarcweldpooldynamicalprocess
during automatic or robotic welding always are perplexing problems to both tech-
nologistinweldfieldandscientistsinautomation.
Thisbookpresentssomeapplicationresearchesonintelligentizedmethodology
in arc welding process, such as machine vision, image processing, fuzzy logical,
neuralnetworks,roughset,intelligentcontrolandotherartificialintelligencemeth-
odsforsensing,modelingandintelligentcontrolofarcweldingdynamicalprocess.
Thestudiesinthebookindicatethatthedesignedvisionsensingandcontrolsys-
temsareabletopartiallyemulateaskilledwelder’sintelligentbehaviors:observing,
estimating,decision-makingandoperating,andshowagreatpotentialandpromis-
ingprospectofartificialintelligenttechnologiesintheweldingmanufacturing.
The book is divided into six chapters: Chap. 1 gives an introduction on de-
velopment of welding handicraft and manufacturing technology; Chap. 2 mainly
addresses visual sensing systems for weld pool during pulsed Gas Tungsten Arc
Welding (GTAW); Chap. 3 mainly address information acquirement of arc weld-
ing process by image processing methods, including acquiring two dimensional
andthreedimensionalcharacteristicsfrommonocularimageofGTAWweldpool;
Chap.4mainlyaddressesmodelingmethodsofweldpooldynamicsduringpulsed
GTAW, including identification of linear models and nonlinear transfer function
modelsofweldpooldynamicalprocess;artificialneuralnetworkmodelsandknowl-
edge models for predicting and control of weld pool dynamical characteristics;
Chap.5mainlyaddressesintelligentcontrolstrategiesforarcweldingprocess,in-
cluding self-regulating PID, fuzzy, PSD controllers, neural network self-learning
controllers,model-freecontrollerandcompositedintelligentcontrollersfordynam-
ical weld pool during pulsed GTAW; Chap. 6 mainly addresses real-time control
of weld pool dynamics during robotic welding process, including intelligentized
v
vi Preface
weldingrobotsystemswithreal-timemonitoringandcontrolofweldpooldynam-
ics;andanapplicationofintelligentizedweldingrobotsystems.
Theordinalreadingofthisbookhastwooutlines:onereadinglineiscompiled
by current outline, i.e., sensing, modeling and control methodology for welding
process; the other by classifying of welding technics and conditions, or weld-
ing materials, e.g., bead-on-plate, welding with wire filler, gaps variation condi-
tions; steel and aluminium alloy welding workpiece. Bead-on plate welding is ad-
dressed in Sects. 3.1.2.2, 4.1, 5.3.2. Welding with wire filler is mainly addressed
inSects.2.2.2,3.1.2.3,3.2,4.3.3,5.6.4,6.3.Gapvariationconditionismainlyad-
dressedinSects.5.4.2,5.6.2,6.4.1.Aluminiumalloyweldingismainlyaddressed
in Sects. 2.3, 3.1.3, 3.2, 4.2.1, 4.4.2, 4.4.3, 5.1, 5.2.2, 5.6.3, 6.2.2, 6.3, 6.4. Steel
weldingismainlyaddressedinSects.2.2,3.1.2,3.2.3,4.2.2,4.3,5.2.2,5.3.2,5.6,
6.1.
TheresearchresultsinthisbookweremainlyimplementedintheIntelligentized
Robotic Welding Technology Laboratory (IRWTL), Shanghai Jiao Tong Univer-
sity, P R China. The content in the book involves the following doctoral disserta-
tions: Dr. Yajun Lou, Dr. Dongbin Zhao, Dr. Guangjun Zhang, Dr. Jianjun Wang,
Dr.BingWang,Dr.LaipingLi,Dr.WenjieChen,Dr.QuanyingDu,Dr.Wemhang
Li, Dr. Xixia Huang, Dr. Hongyuan Shen, Dr. Chongjian Fan, Dr. Fenglin Lv and
Dr.HuabinChen’sworks,etc.Asasupervisoroftheirdoctoraldissertations,Pro-
fessorShan-BenChenwouldliketothanktheircontributionstothisbook.
We wish to give expression on acknowledgements for the researched works in
this book supported by the National Natural Science Foundation of China under
GrantNo.50575144andNo.60474036;andsupportedbytheKeyFoundationPro-
gramofShanghaiSciences&TechnologyCommitteeunderGrantNo.06JC14036
andNo.021111116.
WewouldliketothankProfessorTzyhJongTarnandProfessorLinWufortheir
directionsontheresearchworksincludedinthebook.Andlastbutnotleastthank
toDr.ThomasDitzngerforhisadviceandhelpduringtheproductionphasesofthe
book.
Shanghai,China Shan-BenChen
JingWu
Contents
1 Introduction................................................... 1
1.1 DevelopmentofWeldingandManufacturingTechnology ......... 1
1.2 SensingTechnologyforArcWeldingProcess ................... 3
1.3 VisualSensingTechnologyforArcWeldingProcess ............. 3
1.3.1 ActiveVisualSensing ................................ 4
1.3.2 PassiveDirectVisualSensing.......................... 6
1.3.3 ImageProcessingMethods ............................ 9
1.4 ModelingMethodsforArcWeldingProcess .................... 13
1.4.1 AnalyticalModel .................................... 13
1.4.2 Identification,FuzzyLogicandNeuralNetworkModels ... 14
1.4.3 RoughSetModel .................................... 18
1.5 IntelligentControlStrategiesforArcWeldingProcess............ 19
1.6 TheOrganizedFrameworkoftheBook ........................ 23
References..................................................... 23
2 VisualSensingSystemsforArcWeldingProcess................... 35
2.1 Description of the Real-Time Control Systems with Visual
SensingofWeldPoolforthePulsedGTAWProcess ............. 35
2.2 TheVisualSensingSystemandImagesofWeldPoolDuringLow
CarbonSteelPulsedGTAW.................................. 38
2.2.1 AnalysisoftheSensingConditionsforLowCarbonSteel .. 38
2.2.2 CapturingSimultaneousImagesofWeldPoolinaFrame
fromTwoDirections ................................. 38
2.2.3 CapturingSimultaneousImagesofWeldPoolinaFrame
fromThreeDirections ................................ 43
2.3 TheVisualSensingSystemandImagesofWeldPoolDuring
AluminiumAlloyPulsedGTAW.............................. 44
2.3.1 AnalysisoftheSensingConditionsforAluminiumAlloy... 44
2.3.2 CapturingSimultaneousImagesofWeldPoolinaFrame
fromTwoDirections ................................. 47
2.3.3 CapturingSimultaneousImagesofWeldPoolinaFrame
fromThreeDirections ................................ 51
vii
viii Contents
2.4 TheChapterConclusionRemarks............................. 54
References..................................................... 55
3 InformationAcquirementofArcWeldingProcess ................. 57
3.1 Acquiring Two Dimensional Characteristics from Weld Pool
ImageDuringPulsedGTAW ................................. 57
3.1.1 DefinitionofWeldPoolShapeParameters ............... 58
3.1.2 TheProcessingandCharacteristicComputingofLow
CarbonSteelWeldPoolImages ........................ 59
3.1.3 The Processing and Characteristic Computing of
AluminiumAlloyWeldPoolImage..................... 69
3.2 AcquiringThreeDimensionalCharacteristicsfromMonocular
ImageofWeldPoolDuringPulsedGTAW ..................... 78
3.2.1 DefinitionofTopsideWeldPoolHeight ................. 78
3.2.2 ExtractingSurfaceHeightoftheWeldPoolfromArc
ReflectionPosition................................... 79
3.2.3 ExtractingSurfaceHeightoftheWeldPoolbyShape
fromShading ....................................... 81
3.3 TheSoftwareofImageProcessingandCharacteristicExtracting
ofWeldPoolDuringPulsedGTAW ...........................101
3.3.1 TheFrameworkandFunctionoftheSoftwareSystem......101
3.3.2 TheDirectionsforUsingtheSoftwareSystem............102
3.4 TheChapterConclusionRemarks.............................110
References.....................................................111
4 ModelingMethodsofWeldPoolDynamicsDuringPulsedGTAW ...113
4.1 AnalysisonWeldingDynamics...............................113
4.1.1 TransientResponseswithPulseDutyRatioStepChanges ..115
4.1.2 TransientResponseswithWeldingVelocityStepChanges ..116
4.1.3 TransientResponseswithPeakCurrentStepChanges......116
4.1.4 Transient Responses with Wire Feeding Velocity
StepChanges .......................................117
4.2 IdentificationModelsofWeldPoolDynamics...................118
4.2.1 Linear Stochastic Models of Aluminium Alloy Weld
PoolDynamics ......................................118
4.2.2 NonlinearModelsofLowCarbonSteelWeld
PoolDynamics ......................................123
4.3 ArtificialNeuralNetworkModelsofWeldPoolDynamics ........126
4.3.1 BWHDNNM Model for Predicting Backside Width
andTopsideHeightDuringButtPulsedGTAW ...........127
4.3.2 BNNMModelforPredictingBacksideWidthDuringButt
PulsedGTAW .......................................130
4.3.3 BHDNNM Model for Predicting Backside Width
and Topside Height During Butt Pulsed GTAW
BasedonThree-DimensionalImageProcessing...........131
4.3.4 SSNNMModelDuringButtPulsedGTAW ..............133
Contents ix
4.4 KnowledgeModelsofWeldPoolDynamicalProcess ............137
4.4.1 Extraction of Fuzzy Rules Models of Weld Pool
DynamicalProcess...................................137
4.4.2 KnowledgeModelsBased-onRoughSetsforWeldPool
DynamicalProcessBasedonClassicTheory .............139
4.4.3 AVariablePrecisionRoughSetBasedModelingMethod
forPulsedGTAW ....................................150
4.5 TheChapterConclusionRemarks.............................161
References.....................................................161
5 IntelligentControlStrategiesforArcWeldingProcess..............163
5.1 Open-LoopExperiments.....................................163
5.2 PIDControllerforWeldPoolDynamicsDuringPulsedGTAW ....165
5.2.1 PIDControlAlgorithm ...............................165
5.2.2 WeldingExperimentswithPIDController ...............166
5.3 PSDControllerforWeldPoolDynamicsDuringPulsedGTAW....168
5.3.1 PSDControllerAlgorithms............................168
5.3.2 WeldingExperimentswithPSDController...............170
5.4 NNSelf-LearningControllerforDynamicalWeldPoolDuring
PulsedGTAW .............................................172
5.4.1 FNNCControlAlgorithm .............................173
5.4.2 ExperimentofFNNCControlScheme ..................178
5.5 Model-FreeAdaptiveControllerforArcWeldingDynamics.......182
5.5.1 PreliminaryofModel-FreeAdaptiveControl(MFC).......184
5.5.2 The Improved Model-Free Adaptive Control with G
FunctionFuzzyReasoningRegulation...................186
5.5.3 RealizationandSimulationofImprovedControlAlgorithm.188
5.5.4 ControlledExperimentsonPulsedGTAWProcess ........190
5.6 CompositeIntelligentControllerforWeldPoolDynamicsDuring
PulsedGTAW .............................................194
5.6.1 FNNC-ExpertSystemControllerforLowCarbonSteel
DuringButtWelding .................................195
5.6.2 FNNC-ForwardFeedControllerforLowCarbonSteel
DuringButtWeldingwithGapVariations................200
5.6.3 CompensatedAdaptive-FuzzyControllerforAluminium
AlloyDuringButtWelding............................205
5.6.4 Adaptive-FuzzyControllerBasedonNonlinearModel
forLowCarbonSteelDuringButtWeldingwithWireFiller 210
5.7 TheChapterConclusionRemarks.............................218
References.....................................................220
6 Real-TimeControlofWeldPoolDynamicsDuringRoboticGTAW ..221
6.1 Real-TimeControlofLowCarbonSteelWeldPoolDynamicsby
PIDControllerDuringRoboticPulsedGTAW ..................221
x Contents
6.1.1 Welding Robot Systems with Vision Sensing and
Real-TimeControlofArcWeldDynamics ...............223
6.1.2 WeldPoolImageProcessingDuringRoboticPulsedGTAW 225
6.1.3 ModelingofDynamicWeldingProcess..................231
6.1.4 Real-TimeControlofLowCarbonSteelWeldingPoolby
PIDRegulatorDuringRoboticPulsedGTAW ............234
6.2 Real-TimeControlofWeldPoolDynamicsandSeamFormingby
NeuralSelf-LearningControllerDuringRoboticPulsedGTAW....236
6.2.1 NeuronSelf-LearningPSDControllerforLowCarbon
SteelWeldPool .....................................236
6.2.2 AdaptiveNeuralPIDControllerforAluminiumAlloy
WeldingPool........................................239
6.3 Vision-Based Real-Time Control of Weld Seam Tracking
and Weld Pool Dynamics During Aluminium Alloy Robotic
PulsedGTAW .............................................244
6.3.1 WeldingRoboticSystem ..............................245
6.3.2 ImageProcessingDuringtheRobotSeamTracking .......250
6.3.3 SeamTrackingControlleroftheWeldingRobot ..........256
6.3.4 ExperimentResultsofSeamTrackingandMonitoring
DuringRoboticWelding ..............................258
6.4 CompoundIntelligentControlofWeldPoolDynamicswithVisual
MonitoringDuringRoboticAluminiumAlloyPulsedGTAW......261
6.4.1 TheRoboticWeldingSystemswithVisualMonitoring
DuringPulsedGTAW ................................261
6.4.2 ImageObtainingandProcessingforWeldPoolDuring
RoboticWelding.....................................262
6.4.3 ModelingandControlSchemeforWeldingRobotSystem..265
6.4.4 Penetration Control Procedure and Results
byRoboticWelding ..................................269
6.5 TheChapterConclusionRemarks.............................271
References.....................................................271
7 ConclusionRemarks ...........................................275
Index .............................................................277
List of Figures
1.1 Keytechnologiesinthecontrolsystemoftheweldingprocess...... 2
1.2 Weldpoolimagewiththestroboscopicvisionsensingsystem[48]
(a)Schematicdiagram(b)Schematicdiagram ................... 5
1.3 Schematicofsensingtheimageofweldpoolusingstructurallight
system[51]................................................. 5
1.4 ThepoolimagewithstructurallightsensingsysteminGTAW[51]
(a)Originalimage(b)Stripeskeletonandboundary .............. 6
1.5 Themethodofspectralcensoring[57](a)Intensitydistributionof
thespectrallines(b)Imageofweldpool ........................ 8
1.6 MethodofcoaxialweldpoolviewinginGTAW[60](a)Systemset
(b)Imageofweldpool....................................... 9
1.7 The neural network models of welding process [158] (a) The
forwardmodel(b)Thereversemodel........................... 17
1.8 Fuzzyneuralnetworkcontrolsystemtocontrolthepenetration
depth[198]................................................. 20
1.9 Close-loopcontrolsystemofneuralnetworkduringGTAW[162] ... 21
1.10 Closed-loopcontrolsystemofneuralnetworkduringGTAW[189] .. 21
1.11 Principlediagramforself-learningfuzzyneuralcontrolforGTAW
process[215] ............................................... 22
2.1 ThestructurediagramofexperimentalsystemforpulsedGTAW .... 36
2.2 Thephotographofexperimentalequipment...................... 36
2.3 Thesensingsystem(a)thephotographofsensingsystem(b)The
lightpath .................................................. 37
2.4 ArclightradiationofGTAWwithmildsteelanode.(a)Thespectral
distribution(b)arclightradiationflux .......................... 39
2.5 Thelightpathofsimultaneousdouble-sidevisualimagesensing
systemofweldpoolinaframe ................................ 39
2.6 AframecompleteweldpoolimageofpulsedGTAW.............. 41
2.7 Thevisualimagesoftheweldpoolindifferenttimeofapulsecycle. 41
xi