Understanding variations in Robot Welding
Challenges and Pitfalls
TowardsProactiveControlofbothWeldQuality& Productivity
P.Hammersberg1,a,H.Olsson1,b1 ChalmersUniversityofTechnology,Dep.ofMaterialandManufacturingTech.,SE-41296Göteborg,Sweden2VolvoConstructionEquipmentAB,SE-67127Arvika,[email protected],[email protected]
usingLeanSixSigma
principle:Customerbasedoperationaldevelopment
Probleminanutshell:
Howtocontroltheweldingprocesssoproductdevelopmentdaretoreducemargins?
ExploringtheMAGweldingsystem
Remaining1. Mappingrelevantprocessfactors(p-diagram) à 24factors2. Qualitativefiltering(usingexperts) à 11
factors3. Quantitativescreeningexperiment(Saturated211-7=16runs) à 5factors
Surprisingscreeningresult:Allparametersrelatedtoweldingbeam(I,U,wirefeed,speed,etc.)definingtheWPS,wereoflessimportant fortheweldingdimensionsthantheweldinggeometry:steelplatesandweldinggunangles
ResearchQuestion:Ifthetraditionalbeamparameters(U,I,speed,threadfeed,etc.)
(normallyusedcontroleverything:productivity,metallurgyandoutgoinggeometryandquality)(withanormallyFIXEDweldinggeometryforplateandweldgunangles)
isoflessimportancethantheWELDINGANGLESforthefinalBEADSHAPEandQUALITY
Woulditthanbepossibleto:1. FirstsettheWPS-parameterstoreachnominalmetallurgyandproductivity
• WPS15*forthe5mmFilletWeldofinterest• includingadjustingcorrespondingU,I,travellingspeed,wirefeed,etc.
2. Shapetheweldbeadgeometryusingthesteelplateandweldgunangles?
ResearchAnswer:YES!
sofar….andthisprobablymeansalotofmoneyintheend
*15m/minwirefeed
Potentialresult
TodayssituationFactors
controlling Productivity Welddimension
Workorder
Weldbeam Tradeoff 2Weld
geometry Almost fixed:PBorPA 1
Tomorrowsdecoupled/uncoupled*situation:- towardsfunctionalindependence
Factorscontrolling Productivity Weld
dimension Workorder
Weldbeam
Factorset1 1
Weldgeometry
Factorset2 2
Axiomaticprocessdesign,HouseofQualitythinking(QFD)
*)Suh,NamPyo.(2001).“Axiomaticdesign,advancesandapplications”
WebToeRadius[mm]
Penetration[mm]
Beadthickness
WeldingGeometryControlFactors(x’s)
andBeadQualityResponses (y’s)
Gap[0mm,2mm]
Up(+)/Downslope(-)[-10°,20°]
GunangletoWebPlate[35°,55°]
Webplatetovertical[35°,55°]
Gunangleaboveunwelded seam[80°,120°]
Platethickness[10mm,40mm]
FlangeToeRadius[mm]
DeviationfromFlat[mm]
y =f(xi)
Process / Product
LCL
UCL
Target
USL
LSL
Ratherthanqualitybyinspectionsandpost-processing…Whencontrollimits(---)arewider
thanspecificationlimits(---)Increasedcostfromwaste&rework
andall-partinspection
Inspectionandpost-processingaddCOST
Process / Product
LCL
UCL
Target
USL
LSL
…qualityshouldbecontrolledonxfactorsupstream
Control factors
DrivingprincipleinQuality(robust)Engineering:Monitory,controlx!
Step1:IdentifyProductionSet-up
A. IdentifySet-up:• MAGwelding• Filletweld:
• 5mmthick(a)• 2mmpenetration(i)
• WPS15[wirefeedm/min]• Solidwire• Robotequipment• Etc.
B. Optimizeproductivity
Step2:Buildpredictionmodels:yi =f(xj)usingdesignofexperiments(DOE)
Controlfactors(x) Outputresponses(y)
Step2a:Selectexperimentaldesign
Run Thickness[mm]Up(+)/Down(-)slope[°]
Gunangelabove
unweldedseam[°]
WebPlateto
Horizontal[°]
GunangletoWebPlate[°] Gap[mm]
ToeradiusonFlange[mm]
ToeradiusonWeb[mm]
PenetrationdepthWeb
[mm]
PenetrationdepthFlange[mm] a-dim[mm]
Deviationfromflat[mm] Voltage[V] Current[A]
Wirefeed[m/min]
1 10 5 120 35 35 22 40 -10 120 55 35 13 40 -10 100 35 55 24 10 20 80 35 55 15 10 20 100 55 35 06 10 -10 120 45 55 07 40 20 80 45 35 28 10 -10 80 55 45 29 40 5 80 55 55 010 40 20 120 35 45 011 25 -10 80 35 35 012 25 20 120 55 55 213 25 5 100 45 45 1
DSDthickplate
InDSD:#runs=2x#factors+1**)PricewinningdesignofexperimentmethodologyfromBRADLEYJONESandCHRISTOPHERJ.NACHTSHEIMin2012
Forexample:a. Useaclassicalsequentialscreeningapproachor,b. DefinitiveScreeningDesign(DSD)estimatinguptosecond-ordereffects-
curvature – withoutcorrelations
Distributions
Input Control Factor DistributionsThickness [mm]
5
10
15
20
25
30
35
40
45
50
Up (+) / Down(-) slope [°]
-15
-10
-5
0
5
10
15
20
25
30
Gun angel above unwelded seam [°]
80
90
100
110
120
Web Plate to vertical [°]
35
40
45
50
55
Gun angle to Web Plate [°]
35
40
45
50
55
60
65
Gap [mm]
0
1
2
3
Ouput Respons DistributionsFlange Toe Radius [mm]
0
LSL
0,5
1
1,5
2
2,5
3
Web Toe Radius [mm]
0
LSL
1
2
3
4
5
Penetration [mm]
0
1
LSL
3
4
5
6
7
Bead Thickness [mm]
LSL
Target
5,5
6
6,5
Deviation from Flat [mm]
-2
-1
LSL
Target
USL
1
2
3
x’s:
Balanceddesign– alllevelsofthex’susedequallymanytimes
y’s:
Experimentalresult– rangeoftheresponsescovertheactualtargetrangeforallresponses.Potentialprocesswindowexist
Ouput Respons DistributionsFlange Toe Radius [mm]
0
LSL
0,5
1
1,5
2
2,5
3
Web Toe Radius [mm]
0
LSL
1
2
3
4
5
Penetration [mm]
0
1
LSL
3
4
5
6
7
Bead Thickness [mm]
LSL
Target
5,5
6
6,5
Deviation from Flat [mm]
-2
-1
LSL
Target
USL
1
2
3
Input Control Factor DistributionsThickness [mm]
5
10
15
20
25
30
35
40
45
50
Up (+) / Down(-) slope [°]
-15
-10
-5
0
5
10
15
20
25
30
Gun angel above unwelded seam [°]
80
90
100
110
120
Web Plate to vertical [°]
35
40
45
50
55
Gun angle to Web Plate [°]
35
40
45
50
55
60
65
Gap [mm]
0
1
2
3
Highersettingsonthesetwo
Darkgreenisconnected– largeradiusontheflangealsogenerallygavehighradiusontheweb.
x’s:
Balanceddesign– alllevelsofthex’susedequallymanytimes
y’s:
Experimentalresult– rangeoftheresponsescovertheactualtargetrangeforallresponses.Potentialprocesswindowexist
PredictiveModellingofResponseBehaviorone-by-oneExample:Penetration[mm](1of5outputs)
5,05533333333333
+ 0,879 * Gap [mm] - 1
+ -0,8098 *Gun angel above unwelded seam [°] - 100
20
+ -0,4364 *Up (+) / Down (-) slope [°] - 5
15
+ Gap [mm] - 1 * Gap [mm] - 1 * -1,1323333333333
+ Gap [mm] - 1 *Up (+) / Down (-) slope [°] - 5
15* 0,77825
MultipleLinearRegressionof‘Penetration[mm]’
MultipleLinearRegressionof‘Penetration[mm]’
Projectionoftheresponsesurface
MultipleLinearRegressionof‘Penetration[mm]’
LSL
Penetrationvs.twooftheimportantx’s
AlltogetherOptimalsettingswhenGapis….
LSL
11,5
22,5
3
Pred
For
mul
a Fl
ange
Toe
R
adiu
s [m
m]
0,959241
LSL12345
Pred
For
mul
a W
eb T
oe R
adiu
s [m
m]
2,116754
1LSL
3456
Pred
For
mul
a Pe
netra
tion
[mm
]
3,26743
4LSL
Target5,5
USL6,5
Pred
For
mul
a Be
ad T
hick
ness
[m
m]
5,455627
-1,5-1
LSLTarget
USL1
1,52
2,5
Pred
For
mul
a D
evia
tion
from
Fl
at [m
m]
-0,05867
00,
250,
751
Des
irabi
lity
0,439824
0
0,5 1
1,5 2
0Gap [mm]
35 40 45 50 55
56,777778Web Plate tovertical [°]
-10 -5 0 5 10 15 20
-7,759601Up (+) / Down(-) slope [°]
80 90 100
110
120
120Gun angel aboveunwelded seam [°]
35 40 45 50 55
55,444444Gun angle toWeb Plate [°]
0
0,25 0,
5
0,75 1
Desirability
WithaGapof0mmDesirabilitybelowshowthatUp(+)/Down(-)slopeisthemostcriticalfactortokeepright.Togetherwithastrongpushwelding.
PushdownhillwhenGapissmall
ally’s
vs.allx’s
LSL
11,5
22,5
3
Pred
For
mul
a Fl
ange
Toe
R
adiu
s [m
m]
1,564007
LSL12345
Pred
For
mul
a W
eb T
oe R
adiu
s [m
m]
3,110296
1LSL
3456
Pred
For
mul
a Pe
netra
tion
[mm
]
4,391701
4LSL
Target5,5
USL6,5
Pred
For
mul
a Be
ad T
hick
ness
[m
m]
5,381489
-1,5-1
LSLTarget
USL1
1,52
2,5
Pred
For
mul
a D
evia
tion
from
Fl
at [m
m]
-0,03529
00,
250,
751
Des
irabi
lity
0,629058
0
0,5 1
1,5 2
1Gap [mm]
35 40 45 50 55
56,777778Web Plate tovertical [°]
-10 -5 0 5 10 15 20
-0,002457Up (+) / Down(-) slope [°]
80 90 100
110
120
119,98446Gun angel aboveunwelded seam [°]
35 40 45 50 55
55,444444Gun angle toWeb Plate [°]
0
0,25 0,
5
0,75 1
Desirability
WithaGapof1mmStillsamefactorbutnotsocriticaltogetherwithastrongpushwelding.
PushhorizontalwhenGapismedium
ally’s
vs.allx’s
LSL
11,5
22,5
3
Pred
For
mul
a Fl
ange
Toe
R
adiu
s [m
m]
2,42022
LSL12345
Pred
For
mul
a W
eb T
oe R
adiu
s [m
m]
3,053831
1LSL
3456
Pred
For
mul
a Pe
netra
tion
[mm
]
5,43873
4LSL
Target5,5
USL6,5
Pred
For
mul
a Be
ad T
hick
ness
[m
m]
4,913004
-1,5-1
LSLTarget
USL1
1,52
2,5
Pred
For
mul
a D
evia
tion
from
Fl
at [m
m]
0,038164
00,
250,
751
Des
irabi
lity
0,815613
0
0,5 1
1,5 2
2Gap [mm]
35 40 45 50 55
55Web Plate tovertical [°]
-10 -5 0 5 10 15 20
8,4084818Up (+) / Down(-) slope [°]
80 90 100
110
120
86,192858Gun angel aboveunwelded seam [°]
35 40 45 50 55
55Gun angle toWeb Plate [°]
0
0,25 0,
5
0,75 1
Desirability
WithaGapof2mmTheprocesswindowchangetopullinguphillinsteadinordertokeepifthebeadgeometryrequirementsontarget.
PulluphillwhenGapisbig!
ally’s
vs.allx’s
SimulationofprocesscapabilityusingMonteCarlo
Addingrandomnoisetothepredictors…
Outgoingcapabilitycanbepredicted
Modelverification
Modelsaregoodcomparedwithreferencesampleforpenetration,a-dimandDeviationfromflat(lowerthree)
AndslightlyofffortheToeradii,whichneedsomefurtheranalysis.
Conclusions
• ControlfactorsforProductivityandQualitycanpotentiallybeseparated:• Firstoptimizeprocessspeedandmetallurgy• ThenutilizethedegreesoffreedomintheRobotManipulatorsinordertosetweldinggeometryanglestoshapeBeadandcompensateforprocessvariations,thatis,varyingGAP,improvingrobustness
• Processwindowexiststomeetallrequirements• Atleastfora5mmFilletWeldweldedwithWPS15
• CostislimitedusingefficientDOEmethodologiesexploringtheexperimentalset-up
• 13+3samplesinordertoevaluate6controlfactors
• PITFALLS:Baddata!• Measurementnoise<30%oftolerances
• Ifthetoleranceofatoeradiusis0,3mmtheprecisionofthemeasuresneedstobe<0,1mm…• Fixedduringoff-lineset-upwithrepeatedmeasurements• On-line….hmmm
Futurework
• Improverobustnesstoincomingvariationwithaself-adjustingweldingsystemusing:
• in-linemonitoringofincomingvariationinx’s• trailingmonitoringofoutgoinggeometry(laserscanning)• defineprocesswindowsusingpredictivemodelsfromoff-lineexperiments• utilising Machinelearningalgorithms–orinlineadjustment• 6-axisrobots
• PotentialreductionofQualityassessmentneeds• Increasedknowledgeofthecorrelationbetweenouterandinnerweldgeometrywouldreducetheneedtomonitorhard-to-measureparameters(…penetration…)
• Whymonitorthingsthatcanbepredicted…let’sconcentrateonthedifficultones…
? & !
References
• Suh,NamPyo.(2001).Axiomaticdesign,advancesandapplications.OxfordUniversityPress,NewYork.ISBN0-19-513466-4
• JonesB.,Nachtsheim C.J.,AClassofThree-LevelDesignsforDefinitiveScreeninginthePresenceofSecond-OrderEffects,JournalofQualityTechnology,Vol.43,No.1,January2011