CASE S T U D Y
Dimensional Variation
Reduction for Automotive
Bodv Assemblv
Dariusz Ceglarek and
Jianjun Shi
S
l~orteningIauncli time, tlie period from no production to full ~roduction,nrl~ilesimultaS. 1 . I 1 1 u 1 c r i 1Ilcscar.cli
neously satisfying cluality requirements, is a
Cclrlel; Del)n~nre~r.!
of~llccl~n~ricnl
Elrgilrceri~rgn1rd /111/1licd
priority of the inost advanced autolnobile inanufac~llcclronics,Tfic Unir.crsi/y
tnrers. Sl~orte~li~lg
lanncli tiine goes parallel with imofdliclrign~r,/11zlr A ~ C OJII~
48109-212.5. c-nznil:
~ r o i i n gquality management, i.e., reducing the
daalalek@e~rgi~z.rrcnriclr.c~lr~
opl)oiStunityfor work to be da~nagecla i d sliorte~ling
THIS
PRESENTS THE
the time between defect occurreilce and defect detecMETHODOLOGY A N D F I N D I N G S O F
tion [I]. Some studies done by Ayres [2] indicate that
A S T U D Y O N T H E ROOTCAUSES
0F D I M E ~ S ~ OVARIATION
~ A ~
IN
a sui-prisingly large Action of prodi~ctioncost is diA U T O M O T I V E BODY ASSEMBLY.
rectly attributable to the prei7ention of avoidable deSOLVINGDIMENSIONAL FAILU R E ~DURING THE IB-MONTH
fects (e.g., quality control), their detection (e.g.,
S T U D Y L E D TO T H E R E D U C T I O N
inspection), or their eliiniilation after the fact (repair,
O F DIMENSIONAL VARIATION OF
THE AUTOMOTIVE
BODY FROM AN
I N I T I A L L E V E L O F 8.5 M M TO A
BEST-IN-CLASS L E V E L O F 2 M M
(6-SIGMA).THE S T U D Y F I N D I N G S
I M P L Y T H A T V A R I A T I O N REDUCT I O N A C T I V I T I E S S H O U L D B E ESTABLISHED EARLY IN THE
PRODUCT DEVELOPMENT PROC E S S S O T H A T PROBLEMSCAN
BE IDENTIFIED AND
DuRING
PRE-PRODUCT1ON
PHASES.
rework). Informal esti~natesfroill 17arious sources suggest that lapses in quality control, i.e., design, inspection, ren~ork,repair, and warranty, may account for
40°/o or inore of total cost [2].
One aspect of wllicle quality is tlie dimn~siol~al
integrity ol tlie ai~tolnotivebody (body-in-nrliite),
nliicli llas great elfects on tlie quality and fimctionality 01 tlie veliicle. In automotive body assembly, geoinetrical accuracy is o i ~ eol the most important
qnality factors. Variation in geometrical accuracy can
0 1995 AMERICAN SOCIETY OF MECHANICAL ENGINEERS
I
stclri fro111both tllc dcsign niitl tllc nssc~r~bly
of nli nutoclnssificntion of tlic root cnilscs of tlic diliicr~siolinlfailurcs tllnt occ~~rrctl
tl~rillgthe last 18 ~ ~ i o ~ iof
t l ins ric\v
~iiotivcbody. In fact, tliinc~isiolinlvnrintio~lis iutroprotl~lctdcvclop~iic~it
cyclc, \vl~icllwas cq~livnlc~it
to tlic
d~iccdinto virtunlly every tlcsig~~
\ ~ I I C I tlic
I dcsig~iis
period ~iccdcdto rcncl~t l ~ ctlcsig~l'si~ilicrc~it
lcvcl of
n~nnt~fncturctl
[3]. Bccnl~scsoinc mnnc~fncturilignl~tl
protll~cttli~~~c~isionnl
vnrintio~i.Tllc pnpcr provides n
tlcsig~ii~lducctlvnrintioii is incvitnllc, it is iniportnlit to
l~cttcrundcrstnndirig of tlic nrca of nssc~~ibly
~norc
11nvca rr~ctllotlologyfor idclitifyilig root cnuscs of tli~nc~isiolinl
\~nriatioli,as \vcll as tl~oroilgllly1111dcrstnnt1- prolic to clinicnsionnl failures, ant1 providcs rccolnmcndntiolls for fi~rtlicriniprovcrnclit. Adtlitiolinlly, it aims
iilg t l ~ cSO~ICCCSof variation during tllc lnilncll of n ~ic\\.
to liclp tllc rcscnrcl~co~nniuliityfocus 011 tlic critical
product.
nsl>cctsof cliliicl~siorinlvariation by bcttcr ~itilizi~ig
CSCurrc~ltly,lliost COIIIIIIOI~ tccIiiiiq~~cs
I I S C ~for ~ I I I isti~ig
a~ialj-tical
~iictl~ods,
or
011
dcvclopi~ig
Iilorc
cffccproving product quality arc 1):iscd oil Statistical Proccss
tivc npl~roncllcs.Tlic rcsi~ltsof this pnpcr arc based on
C o ~ ~ t r (SI'C)
ol
tcclu~iql~cs.
SPC tccl~~~iqiics
nrc used
studies co~~ductcd
in an nutoliloti\.c asscnibly facility
during tlic ~ i i n ~ ~ ~ ~ f process
n c t ~ rand
i ~ garc bnscd on tlic
\vlicrc
sport
utility
vcl~iclcsarc riialiufact~ircd.Tllc
statistical a~ialysisof ~i~cnsurcment
dntn. SPC tccliannlysis
of
t
l
~
c
root
carlscs of dil~~clisiol~nl
faults ocII~~LIC
nttc~ript
S
to ~ ~ l i d c r s t ntllc
~ ~critical
d
mrinblcs ill
cirrrctl
d
~
~
r
i
t
~
l
~
i
c
g
prc-productiol~
pllnsc
(pilot
procncll scqircl~ccof a ri~n~iufacturirl,nri~g
proccss :uld csti~~iatc
g
m
~
n
)
In~lncl~,
,
nlid
full
p
r
o
d
~
~
c
t
i
011
o
l
~
olic
nntl
two
IIO\\. tllcsc vorinblcs i~itcrrclatct l ~ r o i ~statistical
gl~
snnlsllifts.
Follo\viiig
tliis
introd~~ctioli,
t
l
~
c
pnpcr
is
tli\.itlcd
pli~ign ~ l dcspcl.il~lcrltntio~~
[f]. I-Io\vc\.cl; as stated in
irito
tllrcc
scctio~~s.
Tlic
followirig
scctiol~
pro\.itlcs
bnck[f], SPC tlocs not csplain tllc actunl cnuscs of tlcfccts
grour~d011 tlic nutoniotivc assc~nbl!- ~)roccss,n ~ illus~ d
during tlic proccss.
trntcs tlic product dcvclopmcnt pllascs. Tllcl~,\vc
SOIIIC
soorccs of asscrillly dcfccts arc givcii 1))prcsc~ltour ~ ~ ~ c t l ~ o d ofor
l o grctlucing
y
tllc di~rlcl~sio~~nl
[5]. Oric sourcc of nsscii~blydcfccts is iiitcrfcrcncc
\~nriatiollof t l ~ cproduct. Follo\\.ing t l ~ crnetl~odology
I)ct\vcc~~
rnating parts. Assc~l~bly
tlcfccts can also be
scction, \vc rcport on n ficltl study, summarizing root
cnilscd by i ~ l s t a l l i ~n ~part
g in arl irlcorrcct positio~~
or
cnilscs of di~i~cxlsio~inl
vnrintio~~
n~idprcscnting oilc iloricntntio~~
[5]. Solnc st~itlicsIlnvc bccn co~ldtictctlto
lustmtivc cnsc sttitly. In tllc filial scctiol~,\vc discuss tllc
n~inlyzct l ~ crclntionsliip bctwccn nssclriljly proccss mid
i~riplicntiolisof t l ~ cwork nr~ds ~ ~ ~ n ~ r i ntlic
r i zconductcd
c
product dcfccts in nssc~i~bly
of clcctro~iicsdcviccs [GI. ,
stl~tlics.
Currc~ltly,in n~~ton~otivc
intliistrics quitc n fc\\. studics
rclntctl to d i ~ ~ ~ c n s ifaults
o ~ ~ nIlavc
l
bccli conductctl
[7-IG]. Solllc of tlic~nn~inlpzctlic gcllcrnl cngi~iccri~~g I~UTOAIOTI\'E130DI': DESIGN,
ASSEZIIR1,Y I'ROCESS, AND PRODUCT
nntl ccorior~iicaspects of dilnc~isionnlfaults [7,8].,0t11DE\'ELOPI\IEI\I' I'IIASES
crs n~inlyzcn spccific part of tlic product dcvclopmcnt
h s s c ~ ~ ~of
b ltllc
y nutoliiotivc l)ody, sl~o\vnin
cyclc, usunlly durii~gtlic full productio~~
pllnsc [9] or
Figurc 1, is dol~~il~ntctl
by gco~llctricnlrclntio~~s,
so that
~ ~ C S C aI Igclicrnl
~
npproncll for r c d ~ ~ c tof
i odi~licl~sio~~nl
~~
tlic tludity of tllc nssclrlbly proccss cnli be dctcnliincd
vnrintiol~[lo-lG]. To t l ~ cbcst of our k~~o\vlctlgc,
110
I)y di~llcilsiol~al
ilitcgrity of tlic product. This liicnlis
col~~l)rcl~c~lsivc
study clnssifyi~~g
tllc root causes of t l ~ c
tlint
tllc
lcvcl
of
product t l i ~ ~ i c ~ i s ivnrintiol~
o ~ ~ n l call bc
t l i ~ ~ l c ~ ~ s fniltircs
i o l ~ n l d ~ i r i-~tilg~ c~ v l ~ oni~to~~iotivc
lc
body
n
critical
iridcs
for
final
cvnluntio~~
of
tllc nssc~~il)lctl
dcvclol)~i~cl~t
cyclc I~tivcbcc~lcol~ductcd.Currc~~tly,
tllc
vclliclc.
kr~o\vlcdgcnbout rclntio~~s
hct\vcc~~
tllc d i l l ~ c ~ ~ ~ i o l i n l
vnrintio~~
of tllc vcliiclc and its f~u~ctio~inl
~)crforr~~:i~~cc,
ns \vcll ns n s s c ~ ~ ~linc
b l y fnilurcs during p r o d ~ ~ c tarc
i o ~ ~ Proclucl n ~ l d1'1-occss
i ~ o vc1-y
t
clcnrly u~~tlcrstood.
This lack or cnitlcrstnntli~~g
An nuto~~~otivc
botl!. is I)uilt fr0111sllcct nlctnl
results ill \vastcd cffort cluriug dcsigl~,I n ~ l l ~ cnntl
l ~ , propacts tl~ntI~avctliffcrc~~t
sllnpcs, sizcs, nricl tliick~~csscs,
clirctio~~
of IIC\V vcl~iclc,I)ccn~~sc
t l ~ c\vork tlocs 1101
clcpc~~tlil~g
011 their f i ~ ~ ~ c l i o
Tllc
i ~ sparts
.
arc cntcgorizctl
tnrgct tlic ~llostcritical proccss/~)rotliictissllcs.
ns structuml or 1io11str~1cti1ral.
Strlicti~rnlparts sillq)ort
Tllis pq1cr trics to fill tllis gap by l)rcsc~~ti~ig
n
tlic n~rto~liotivc
body stri~ctiircns (1) 11lai11parts, S I I C ~ I
MANUFACTURING REVIEW
' VOL. 8, NO.
2
J U N E 1995
Fig. 2 011rl;tteof ~ I I CnsscnrDIJ.pmcess: cri~ictrlslotiorrs of /Ire
fig. 1. 7%e Docb. coonlirrnre sj.dern rcilh n~re.rnnrl,lc of serrsor
locnlions.
fnrtt~ir/glitre.
as rails nlld ~)lcri~iiii,
or as (2) rciiiforccliic~~t
parts, for
csal~iplc,door llil~gcrcil~forcclnc~~ts.
Tlic otlicr parts
arc callcd nonstr~ictirralparts, for csar~iplc,tloor outcr,
co\vlsidc, roof, nrltl so 011. Bascd 011 tlrc rcscarcll coliductcd by [13, 171, it call 1)c co~iclildcdthat str~lctural
parts, ~rinirrlyduc to tlicir grcatcr rigidity, Ila\-c 111ucl1
grcatcr ilripact 011 nutoriiotivc I)otly dil~icl~siolial
accumc!- than ~iol~str~rcturnl
parts.
Tllc ai1tolnotivc botly asscr~iblyproccss il~cli~dcs
tllrcc lllajor clc~l~cl~ts:
( I ) nsscliibly fisturcs, (2) \vcldillg
robots or \\-cltliilg guris, nlitl (3) part Iiar~dlil~g
11iccI1arlisliis. Tlicsc tllrcc clc~iic~its
co11t111cttlrc tllrcc major
fi~l~ctiorrs
of tllc nssclilbly proccss: part positioliil~g,part
joilril~g,arid ~~art/subassc~r~l~ly
tmlisfcr.
011n typical plant floor, tllcsc clcrr~c~rts
arc lillkcd
.irlto asscrrll)ly lilics tlo~iii~iatctl
by pnrt positiorlilrg ant1
\vcltlillg stntiorrs. Eacli nssclnbly linc protluccs a lrrajor
s~lbasscmblyor fil~alprotluct. Tllcrc arc usunll!- two
typcs of lincs: (1) C O I I I I ) O I ~ C I Islrbnssc~nbly
~
lincs, nrltl
(2) fralliirlg asscl~iblylilics. Tllc c o r i ~ p o ~ ~subassclnc~it
1)1!- lilics arc: lrlitlcrbotly lilic, and two al)crturc lillcsIcft- mid riglit-l~alitl.Tllc liiajor clc~~lcrit
of tllc frall~il~g
lint is all a~~torllotivc
hotly fr:i~iiil~gstatioli, \vlicrc u11tlcrbotly, al)crturcs, nlitl roof Lo\\-s arc joil~tcdtogctllcr.
Figure 2 sllo\vs nil outlillc of nil assclllbly lil~c\vitli dcscriptioils of tllc gcollictrical statior~sill tlic fralrrillg
lilic. Tlrc frillilii~gstatioll is 111nrkct1as S3. 111gc11cra1,all
stntiolis call l)c tlivitlctI illto gcoll~ctric;rl1u1t1rc-spot
statiolls. 'l'lic gcorrrctrical statiol~sIlii~itllcIIarts \\'it11
p0sitio11-scttiligIIICCII~II~C;IIjigs dcsig~icdf01' cncli t y c
of vclliclc. 'rllc part is sct ill ~)ositiori11y tlicsc tlctlicatctl
jigs, illitinlly \vcltlctl, t11c11tral~s~crrcd
to n rc-sl~otstntioil for fil~al\vcltli~~g.
Durillg tllc rc-sl~otopcratiol~s,
robots add \vcldil~gspots to tlic parts to iricrcasc tlicir
strcllgtli. Rc-spot statiolls usr~allydo ~ i onffcct
t
tllc positioil or oricrltatiol~of tlic part or s11bassc1iibly.Bnscd oil
tlic rcscarcli dolic by [13], tllc gcolrictricnl statiolis Ilavc
a I I I ~ I C ~biggc;
I
illlpact 011tllc tli~iiclisior~al
variatioii of
tllc protluct than rc-spot statioris.
Tllc total liurlrbcr of gcol~ictricalstntiolls varics
fro111~ ~ O C C StoS ~ ~ O C C S S0.11avcmgc, at lcast 25-35
gcoliictrical stntiolls alid no lcss tliari 30-40 rc-spot
stntior~sasscniblc all nl~tolnoti\'cbody iiintlc of 150250 slicct nictnl pnrts.
T l ~ cuiijor fulictiori of tlic nsscliibly statioli is to
corrcctly p ~ s i t i o pnrts
l ~ bcforc nclding opcrntiol~sstart.
Tllc usonl ~~ictlrotl
of positiol~iligstalnpcd parts is bascd
on tl1c3-2-1 Inyo~rtprilrciplc [7]. I.lo~vc\-cr,d11ri11g
stutlics of actual asscl~~bly
proccsscs, it was also l~otctl
that nn n-2-1 layout prilrciplc, \vllcrc 11 > 3, is oftcli
11scd. Tllc sclcctio~iof tlic part locatilig prirlciplc dc1)critls OII tlrc sizc nlitl rigidity of tlrc pnrt [18-19], and
tlilrctl!. affccts tlrc ilillcrcr~tIcvcl of protluct ~ ~ I I I C I I siolral variatiol~.
Part tmllsport to and fro111statiolrs is ~ C C O I I I plislictl I>yirlnliy tlilfcrcli~~ncclianis~ns,
s~lclias clcctrifictl ~l~or~or;lils,
tip-1111tlrivcs, mid so 011. Dctailcd
tlcscril~tiolisof otlicr a~rtoli~otivc
body asscliibly proccsscs cilu I)c fourltl ill [12].
Alcns~~rct~~e~~t
I\lotlcnr tli~~~c~lsioirnl
dingl~osisof tllc a11t0111otivc
I~otlyrcq~lircsacclrlatc tlntn to satisfy quality assllraircc
rcq11ircl11clits.Currc~itly,tllrcc rlicasilrcrllcllt gngcs arc
111ostoftcli irsctl for cllcckil~gpnrt/s~rl~assc~iil~ly
tlilncl~siolls: (1) Iinrtl p g c listurcs nlitl layout plntcs [8,20],
(2) Coordi~~;~tc
XIc;~silril~g
hlncllil~cs(C;\IXIs), air11 (3)
CEGLAREK AND S H I
.
D I M E N S I O N A L V A R I A T I O N REDUCTION
Optical Coordiantc Alcns~rrilighlncl~i~ics
(OCI\II\Is).T l ~ c
~)rcsc~~tctl
stlidy rnninly usccl tlntn fro111CXlXI n~itl
OCAIhI gages.
.
Procluct D c r c l o ~ ) ~ i i cI'linscs
~it
Onc of the clinrncteristic fentilrcs of t l ~ cnutoiiiotivc ilidustry is tlic fretl~~c~icy
of 111otlclc l ~ n ~ i and
g s tlic
vast nliiolllit of ti~iica ~ i t1nl)or
l
rctluiretl to ~iinkca
c l ~ n ~ ~ g o v'fllc
e r . a ~ ~ t o ~ ~ i oI)otly
t i v cnssc11il)lyis rcgcnrdctl
as tlic lcnst flcsil~lcprocess ill tlic ovcmll veliiclc nsscliiCoorcli~iatcAlcasl~ri~ig
Alacl~i~ics
1)ly process [23]. Duri~iga ~iioclelc l i a ~ i gtooli~ig
,
nii~st
Introducetl t111ri11gtlic '19605, CXlAls arc vc1-y ocI)c clin~igetlto ~iiatclitlic 11cn.lycstnblisl~ctlproccss a~itl
cllratc and flcsil)lc. Tl~cy'allo\vfor Iiicasllrenicl~tsat
product dcsig~i.Givc~itllc co~iiplcsityof tllis proccss,
sclcctcd points 011tlic part \\.it11nli ilccllmcy of 0.01
tlic alito~iiotivcbody dcvclol)~i~c~it
c!-clc rcquircs tlirce
11iIn. TI~eirflcsiLility is Lascd on tl~circapacity folto
follr
ycars
of
lead
ti~iic.
Eve11
tllc
filial slngc of the
quick r c p r o g r a ~ ~ i ~llieasllrc~iic~it
~~i~rg
cycles \vl~cncl~cckal~to~iiotivc
botly
d
c
v
c
l
o
p
~
~
i
cycle,
c
~
~
t
after vcl~iclca ~ ~ d
i l ~ gdiffcrcrit parts or points. T l ~ cdra\vl)ack of CXIXls is
lrard
tooli~~g
arc:
dcsigl~cd,
lasts
OIIC ycnr. This final
tlic ti~ilclag Lcforc rcccivi~~g
llieasllrc~llcliti~~foniiation.
stage i~lclr~tlcs
t l ~ cfollo\vi~igpl~ascs:(1)pilot pmgra111,
1111 nl~to~notivc
body 111l1stLC take11off-1i11cautl tmlls(2)
~
)
r
c
v
o
l
i
~productio~i,
~~ic
(3) Ialr~icli,and (4) 11111
fcrrcd to tllc ChII\l roo111 for ~~icasllrclliclit.
11f1111yopcrproductioli.
Tlicsc
pl~ascs
nrc
dcscriLccl ill thc follo\vi~ig
atiolial Chlhl call Ilicasllrc up to eight al~to~~iotivc
pamgmplls,
foci~sing
011 t l ~ c
di~iic~isio~ial
~SSIICS.
Lodics d l ~ r i r ~ciglrt
g 11o11rs.Tlic rcsl~ltsfro111tlic Chlhl
arc 11scf111for asscssi~igt l ~ cfiual cll~alityof an alltornoPilot Progl-a111
tivc body or si~basscnillics.
Tlic pilot progrnni is when prototype vcl~iclcsarc
processes aftcr 100% of
built to verify ~iin~ir~facti~rir~g
t l ~ cvcliiclc d i ~ ~ i c ~ ~ and
s i o ltolcm~iccs
~s
arc npprovctl.
Optical Coorclir~ateAIcnst~riilgAlechincs
In rcccnt years, thc i~iiplc~iie~ltntio~i
of tlic i ~ i - l i ~ ~ c Tlic pilot progralii is t l ~ cfirst pl~nsc,1)cgi111ii1ig011nvcragc Gvc lnol~tl~s
allcad of t l ~ cI ~ I I I ~ C I 11
I . ~ I I C ~ I I ~ C(1)
S:
Optical Coortli~intchicns~~ring
hlnclii~ic(OCXDI) in the
tlicdcsigncd
PLP
(I'ri~~cil)nl
Locnti~~g
I'oinls)
verifying
nuto~iiotivci n d i ~ s t qlins provided ncnvopport~~nitics
for
scl~c~iics
for 'tooling Gsturcs t111ri11gt l ~ c~ S S C I I I LproI~
nutoniotivc body nssc~iillydiag~~osis.
OChII\Is arc illccss,
mid
(2)
setting
t
l
~
c
proccss
cnpal~ility
of
t
l
~
c
clcstnllctl in-liric nt tlic c~iclof ~ilnjornsscmlly proccsscs,
sigricd tooling.
sl~clias framing, side frnmcs, untlcrl)otly, nl~tlso on.
(Fig. 2). Each OCXII\I cor~sistsof lnnlly laser sensors
Pre-Voluaic Productioo
nllowing for nolicontnct Incnsllrcliic~itof a I~otlyor s111)Prc-volu~ilcp r o d ~ ~ c t i starts
o ~ i OIIC to two ~ n o n t l ~ s
nsscmLly rclativc to tlcsig~~
~ ~ o ~ n i On
~ l nnvcmgc,
l.
tlic
allcad of ln111ic11,after tlic tooling is sct up nt tlic nsscmIncasurcmcnt cycle takes a lc\v scconds, \\.it11 accllracy
bly plant. The goal of prc-volunlc prod~lctio~i
is to valiaro1111d0.25 1n111, and stntic nlid dy~in~uic
rcpcatobility
dntc process cnpnlility, n ~ i~iitially
~ d
identify t11c sollrces
\\.itliin 6-0 cqual to 0.14 and 0.25 111111, rcspcctivcly
of v;~rintio~~.
[21]. The OCAIXI gage ~~uensi~rcs
fro111100 to 150
poiuts 011 cncli r~iajorassc~liblywit11 100% snnlplc mtc.
As a rcsolt, tlic OCXIXI provitlcs ;i t r c l ~ ~ c ~ i cnlllolllit
lo~~s
La~liicll
of d i ~ ~ l c l ~ s iio~~~~f aolr ~ ~ i \vl~icl~
n t i o ~call
~ , Lc used lor asAftcr ( l c t c r ~ ~ i itl~at
~ ~ i t~l ~ cgvcl~iclccall rcncl~ncsc11lLliproccss co~itrol.1\11 OCXlXIs ill tllc plnl~tuse t l ~ c ccptnllc c111nlityIcvcls, tlic lnluicl~pl~ascfollo\vs. 'l'ypisalilc coortli~~atc
s y s t c ~callctl
~ ~ , t l ~ cLody coordi~~atc
syscnlly, at I ~ I I I I C 1)11asc,
~I
;i 1iu111bcr
of prol)lciiis I I I I I S ~still
tc~ii,becansc it is msy to usc :uld co~iiparcstlntn fro111
I)c rcsolvccl bcforc a vcl~iclcof tlcsircd quality Icvcl cnli
diffcrc~~t
gages. Fig. 1 sI~o\\.st l ~ ccoordiiintc nscs a ~ ~ d I)c Luilt. Tlic 1~11gtli
of t l ~ c1a1111cll
I)liase tlcpc~~tls
oil
rcfcrc~lccpoiuts ill tlic body coordir~atcsystclli. T l ~ c
Iio\v fnst ill1 tliniensio~~al
~)rol)lel~is
call l ~ rcsolvccl
c
\vliilc
pri~~ciplcs
of t l ~ cllicnsllrclnclit sc~isorsare rlcscril)ctl Ly
si~ilulta~~col~sly
s p c c d i ~ ~upg the protlr~ctio~i
rate. Itlcliti[22], nntl tllc olltli~lcof t l ~ csc~isorsetup for nl~to~iiotivc ficiltio~iof d i ~ ~ ~ c ~ ln111ts
~ s i o I)eco~iics
~ ~ a l O I I ~of t l ~ cLottlcbody assc~i~bly
cnli I)c fo1111t1
in [lo].
~ ~ c c ill
k srcacl~i~rg
t l ~ cf11l1 ~)rotluctio~l
rate.
M A N U F A C T U R I N G REVIEW
VOL. 8, NO. 2
J U N E 1995
FnII I'mcl~~ctino
T l ~ cfrdl protlr~ctionpl~nscI~cgins,nftcr tltc proclr~ctio~i
mtc rcacl~cst l ~ nssigncd
c
Icvcl \vitl~ncccptnblc
qridity. Typically, at this pl~asc,nssc~nblylinc maintcllallcc issrlcs al~clprotlllct quality assrlralicc rcqr~irc~ncntsbccol~~c
clo~~ii~~a~it.
t l ~ ca ~ ~ t o ~ i ~ oI~ody.
t i v c Tl~cscpoi~itsnrc c11osc11for ~ncas ~ ~ r e i n cI~ccar~sc
~ ~ t s tl~cyarc t l ~ clilost critical points on
tllc I)ody, cnllccl KI'C (Kcy I'rotlrlct Clinractcristics), or
KCC (Kcy Control Cl~nmctcristics).
T l ~ cKPC is n protl~lctclrnractcristic for \vl~icl~
di~nclisio~inl
variatio~~
c o ~ ~ fsigliificantly
tl
affect n
protlr~ct'ssnfcty or coinplia~~cc,
as \vcIl'as cllstolilcr satisfactio~iwit11a product. Tlic KCC is n proccss pamlllc\'tlRMBILI'A1 COhTROL OF PRODUCT
tcr for \vlticll tlic variation ~ i i r ~bc
s t co~itrollcdto clisrlrc
CEOAIETRY 1)URIKC ASSEfiIBLY
t1i;it \.;rriatio~iill a KI'C is ~ i ~ a i ~ i t adi u~ r~ic~
g asscl~i- d~tllc
I'ROCESS-I\IETIIODOLOGY
bly proccss. For csa~i~l)lc,
Illcnsurc~iic~it
~)oillts4, 17,
Tlic tli~liclisionalquality of a product is directly
18, a~itl19, \vliicli tlefilic tlic frolit cloor opc~iing,arc
rclntctl to t l ~ variability
c
of tlic proccss that crcatcd it.
rccog~iizcdas KI'Cs. Tlicy colltrol tlic door-fitting proDi~i~ciisio~ial
~nriatioiicall IN usctl as nli i~itlcsto c \ . a l ~ ~ - ccss in tlic final vcliicIc. Tlic I I I C ~ S I I ~ C I ~ ~ CofI I II'L
S I' loatc tllc di~iiclisio~inl
quality of n prodrict. I h t a~ialysisof
cators nrc t~suallyrccogiizctl as KCCs.
~ ~ O C C Svariability,
S
cspccially ill ii~tl~~strial
practice a11t1
Tlic KPC and KCC arc sclcctctl during t l ~ cpilot
rcscarcl~,can be a vcry co~iiplcsproblcni. 111protlucprogmm to dcscribc tlic d i ~ ~ i c ~ i s i ocl~arnctcristics
~ial
of
tioli, a ~ n c a s ~ ~\,ariati011
rcd
of product colisists of a sct of
tlic protluct and proccss, rcspcctivcly. r\ para1ilctcr dcsmallcr \.ariatio~iscauscd by ~ i i a ~diffcrclit
iy
factors
scribing tlin~c~~sioi~al
\.ariatioil of t l ~ a~~toniotivc
c
botly
cnllcd root causes. Once t l ~ cparticl~larvarintion of n
is tlcfi~icclns 6-sig~i~a
sta~idartldcviatio~icalc~~latcd
for
protluct is clctcr~ninctlIly mcas~lrc~ilcllt
data, it is inicncli liicnsllrcliicilt poilit \\-it11 n given sa~nplcsizc. Tlic
portaat to n~ialyzcit aiid llrcak it dowri to tlic i~idivid6-signin for cncl~Iiicnsurcnlclit poilit call Ilc prcsc~itcd
ual root causcs tllnt niny Iinvc occurrcd during tlic
ill tlic for111 of n pcrcc~~tilc
clinrt (Figurc 3), slio\ving tllc
proccss. Tlic proccd~vcof tlctcr~liiningan individ~~al
distribution of tlic cli~iic~isio~inl
vnrintio~it l i r o i ~ g l ~ tol~~~ct
root callsc of di~iiciisio~ial
varintio~iis prcscntcd in tliis
Incasrlrc1nclit:sclisors.Tlic f i ~ ~i~itlicator
al
of protluct
papcr as a casc stutly.
variatio~~
lcvcl can IIC prcsc~itctlas a C o ~ i t i ~ i 1111~~o~~s
Tlic ~nctl~otlology
applictl tluri~igtlic study call bc
pro\.cnidiit I~ltlicntor(CII). Tlic Cil is sclcctctl as all r~pdivided into two parts. Tlic first part is usctl to c v a l ~ ~ a t c per liniit of 95% of nll 6-sign~nscalcl~latctlfor all
or "tmck" t l ~ cIcvcl of cli~iic~isio~ial
\.ariati011 r ~ s i ~
n~g
~ l i c a s ~ ~ r c ~points
n c n t of tlic protlnct. For ccsnl~iplc,ill
dcfincd variation indicator. Tlic scco~~tl
part tlcscribcs
Fig. 3, tlic CII i~ltlicntorslio\vs vnrintiol~\ ~ i t l i i 4.5
~ i 111111
tlic casc study groccdurc riscd to identify nl~tllocalizc
(6-signin) for 95% of olcnsllrclncllt points.
root caliscs of dinicnsio~ial\.arintion bnscd on t l ~ cIncaTlic CII can bc ~isctlfor fnst tracking of product
sorcnicnt data. Dot11parts arc dcscribcd in tlic followi~~g
scctio~is.
Trncki~igthe D i ~ i i e ~ i s i o ~\'nriniior~
inl
Level
Tllc tracki~igof di~~ic~isiolial
varintio~~,
L I S C ~as n
q11nlityi ~ ~ t l c~~cccls
s,
to slio\v t l ~ cIcvcl of \.nrintion a ~ i d
the ti~iicof occ~~rrcIicc.
It is i111porta11~
to track varintio~l
Icvcl tliro~rgl~
tliffcrc~~t
~)liascsto k n o \ ~[lot only tllc currc~itqunlity Icvcl of tlic prodnct, or acl~icvcdi~iipro\.c111c1itin ti~iic,b l ~ also
t
to IISC it as a 1)~11cl111iilrki1~g
iridcs allo\viug for co~iq)nriso~i
of tliffcrcnt protlucts or
tllcir d c v c l o p r ~ ~cycles.
c~~t
Tlic di~i~c~isio~lal
variatio~iIcvcl is asscssctl basctl
011 t l ~ ~licasurctl
c
p o i ~ ~oil
t s t l ~ cprotl~rct.Fig. 1 slio\vs nil
c s a ~ ~ i ~of) l~c~ i c n s ~ ~ r pc o~ ii~i loci~tio~~s
c~ ~t ~ t 011 o~rcsitlc of
CEGLAREKA N D S H I
D I M E N S I O N A L VARIATION REDUCTION
variation Ic\-el. Tlic prcsclltctl st~~dics'
usctl tlic C1I indicator sllo~vriill tlic for111of a clinrt wit11 tlic liorizoiitnl
alitl vcrticnl ascs rcprcsclitil~gtlic coliscc~~tivc
\vccks of
prodllctio~~
n~itl6-sigrna vnriatio~~
for 95% of IllcasllrcI I I C I I ~points rcspcctivcly (scc llppcr part of Figure 4).
0
e
=
u
.s ;. 3 .8
B % y ' B
z .g v,
c
.s
&
2 .E
."
-5
I
U
=
a
Cnsc Stutly ~ \ p l ~ r o ; l ctol ~Diagnose Root Cn11scs of
I ) i ~ ~ ~ c ~ ~ s\'ariation
ioanl
TIic s t ~ ~ tof
l yd i ~ i i c ~ i s i ofaults
~ ~ a l rcquiccs tlic usc
of n ~~~ctliodolog!that will allo\\. for tlic itlc~itificatio~~
n ~ i dlocnlizntion of root cnrrscs of t l i n ~ ~ ~ svnrintior~.
io~~a~
=
-.$
.g .2n em Ez
G
-2 -2
sa
f
=
u
B g § $
v,
5
e
e
u
-$ r5 :
Fnture related faults
7Wo
40%
MANUFACTURING
REVIEW
VOL. 8, NO. 2
J U N E 1995
100%
c
.2 .$ g 2
8 = 5 a
70%
A nc\vl!- developed diagnostic ~nctliotlologywas preis basctl on a syssented in [13-161. Tlie ~netl~otlology
tenintic, data-driven case study approacl~that e~iablcs
qniek tletcction and locnlizatio~iof asscnil~l!- process
f a ~ ~ lbased
t s oil din~ciisionnl~neasllrell~c~~ts.
T l ~ case
c
stutlics group and prioritize the currcllt variation level
into tmccable antl solvable problc~nsin order to rcacli
t l ~ ei~~tcndctl
variation level. Therefore, n case study
failure cl~aracdescribes a body-ill-\vl~itcdinic~~sional
terized by a partict~lnrvariatio~~
pattern and caused by
one (or several) root cause(s).
Esaniplcs of dinlc~~sionnl
\.ariatio~~
root causes
are: asselilbly fixture-related (KC locator \\.car, inclusions OII the locating surface of locators, or cla~npsthat
do not propcrl!. force tlie part against tlie locator),
\vclding gun-rclatetl (niissi~~g
\veldi~igspot, n~isnligucd
\\.cldiiig gt111, \veltling tip \\-car), sta~llpcdpart-rclatccl
(iiiconsistcnt press tonlinge, press stroke), arid rnatcrinl
I~a~~dlir~g-related
(interferences I)ct\vce~ipart I~andling
Iocatow arid fixture locators).
T l ~ problcm-solviag
c
rii~diot10Iogy~ I I C I I I ~ C S
kno\\.lcdgc rcpresclltatio~iof protlact and nsscnl1)ly pro-
cess, ant1 fa111troot calrsc itlcntificatio~ia~itllocalizatio~i
npproaclies.
Kno\vlcdgc Rcprcscnlnlion of Protluct nrld Proccss
Tlie coniplesit!* of t l ~ ea i ~ ~ o ~ i ~ obody
t i v cprcvcnts
~licasl~rc~nent
data fro11111ei11gs~~fficient
to localize root
canses of dinici~sionalvariatioii. It is ncccssarp to inclr~tlekno\vlctlge a b o ~ the
~ t product a ~ ~t lt~l easscii~l)ly
proccss 1,cfore the root causes can be detcrn~incd.Our
kiio\vlcdgc rcprcsc~~tation
is I~ased011t l ~ efinictional
cliaracteristics of tlic protluct, tooling, process, and
nlcastlrellicllts, \vliicli are ill turn tlefined as collections
grotips [Id-151. The I~ierarcl~ical
groups
of I~ierarcl~icol
s l ~ o ~tlie
v esplicit rclationsliip bet\vee~~
the niost iniportant featu~.csof tlic antoniotivc body assen~bly,such
as: (1) product relations (part-to-part and part-tosubassembly; locators positions of ~)arts/sul)nssc~~il)lies),
(2) proccss relations (process layout; parts/s~~basse~nblies sequence; \velding spot locations n ~ scqucncc),
~ d
and (3) nieasure~iientrelations (location of i~ispectctl
poir~tsand type of mcnsnretl feature; allocation of encl~
mcasuretl point to ~ , a r t / s ~ ~ l ) a s s e ~ ~Figure
~ l ~ l y5
) . sl~o\vs
Aperture
LAYER 1
LAYER 2
LAYER 3
I
The hierarchical nrcuos of rneasurernenn
The h i e m h t a l groups of lccatrxs
CEGLAREK
AND SHl
.
I
D I M E N ~ I O N AVLA R I A T I O NR E D U C T I O N
n11 csn~nplcof tlic I~icmrcl~icnl
groups that i ~ i c l ~ ~ d c
k~~o\vlc~Igc
nbout product/proccss, Iiicnsurc~llcllts,nntl
locators. \i 111ajo1.ndva~itngcof this k~io~vlctlgc
rclmsc~i~atiori
is t1111t a grcnt tlc;ll of k~io~vIc(Igc
call IIC rcprcs c ~ i ~ c\vi~l~ili
tl
n 11nifictlfriinicn-orL, \vllicl~~ i r n ~ ~ l i f i c s
ncccss to n11t1spcccls tllc cliagl~osticr c n s o ~ ~to
i ~f~i ~gl t l
root cnrlscs of f n ~ ~ l tTliis
s . rcprcsc~itatio~~
is rlsctl t l ~ ~ r i ~ i g
fnr~ltlocalizntio~~.
Fi';lult Iclc~itificc~tio~~
As sllo\v~iill 1:igrlrc 6, fnrllt itlc~itificntio~i
is cclrlivnlclit to tllc itlc~~tilicatio~~
of sorlrccs of tli~~iollsiol~al
v:lriatio~~
ill tllc I~otly-ill-\vl~itc
by gror~l)iligIiicasrlrc111c11ts11si1igvnrintio~rIcvcl n11tlcorrclntiol~cocllicic~it.
Tlic procctlr~rcof liit~ltidc~ltilicatiolifor sr~stniiicddi~iic~isioiial
vnrintio~~
sclccts alitl clnssifics tllc i~iforllinti011p c r t n i ~ i i ~to~ ~iicnstrrc~llclits
g
capttrrcd dr~rilign
givc~ipcriod ol production ti111cand arc bnscd or1 two
1. Far~ltscvcrity cl.itcrio11-sclcctio~i ol iiicasurc111c1itpoilits wit11 vnriatio~icscccdilig vnriatioll
t11rcslloltl T,.
(Fig. 3). This critcrio~ic~isr~rcs
that
t l ~ cfault itlc~~tificntiol~
proccss will sclcct tlie Iiicnsr~rc~ncllt
p o i ~ ~ttls~ n tlcscril)~
t
tlic 111ostsevcrc di~ i i c ~ ~ s i ofarllts.
~ i n l \'arintiol~ tl~rcsl~old
T,. was sct
so t1111t 30% of all 1iic:lsllrcIilcllts \vcrc iriclrrtlctl
for furtllcr n~inlysis.
Fn~lltroot cnrlsc isolntioii critcrio~i-sclcctiol~ of
Illcnsllrclllclit points \\-it11corrclntio~icscccdil~g
corrclatio~ltl~rcsl~old
T,. Tliis criterion sclccts
~llcirsrlrclllclit~ ) o i ~tlcscril)i~~g
~ts
far~ltswit11 a sillglc root CIIIISC. It is I)il~cd011 t l ~ c;issr~~i~ptio~l
tllnt
tllc ~licasllrc~ilc~its
wit11 1:lrgc vnrintio~~
will LC
strollgly corrclntctl if n~itlo111yif tlicir vnriatio~is
arc carrscd by tlic snmc root carlsc. For csn~iiplc,
if lilcnsrlrclilc~itdntn include vnrintiol~cnr~scdby
listrlrc locator fail~rrc,tlic pattcr~idcscribcd by
t l ~ cdata will follo\v t l ~ cprc-dctcr~nincdpnttcr~lof
tllc fnl~ltctllocator. Tl~crcforc,1)asctl OII t l ~ ccharacteristics of botl!. structure n~itlt o o l i ~ ~locators,
g
S O I ~ I C oilit its will c L ~ i ~ togctlicr
~ v ~ ' l during tllc nsscmbly proccss. 111n statistical scnsc, "~no\.ing"
togctlicr call bc i~itclprctctlby corrclntio~ibct\\*cc~i
tllc ~ n c n s u r c ~ ~poil~ts.
~ c n t Enc11 group ol
fig. 6. Tlrejloei~
clircrl of tliri~rloslic
rcnsorrir~g:Jirrrll
tlelrcliorr.
Selection of 111en~easrcren~en~s
wi111
large wnblion forficrther anabsis;
Settircg priorities ill diagtrosis:
I
Group all MLPs :vih large variation
+
Classification of /lie c ~ ~ e a s c t r e n ~with
en~s
large varialiorc illto groccps lvirh high
correlalio~r:
Enclt M P L group co~lslitulesone case;
Ass~rrnptiorr:
Single root calcse of the far111ntani/ets
i~self~l~rncgl~
higlcl~comelaledda/a;
1
Group 1
Iftlre measc1re171ents
are ~e~~correlaled,
tlcey Ime DIFFERENTroot calcses.
... . . . . ..
M A N U F A C T U R I N G R E V I E W * VOL. 8. NO. 2
J U N E 1995
corrclntcd lllcnsllrclilcllt poilits cnllctl cn~itlitl:itc
Iiicnsllrclilciit poilits (CAILP) rcprcsciits n cnsc
stutly.
fcrciit types of root cnllscs. Follo~vil~g
tllnt scctio~~,
tllc
nlinlpsis of all cnsc stlltlics is prcsc~itcd.
Esa1111)lcof Cnsc Stucly: Co\\.lsiclc Rcililbrccli~c~it
Cyclic \'nrintion
This cnsc stlldy ill~~strntcs
n di~ric~isio~inl
fnillt pcrtni~liligto toolilig il~stnllntio~l,
\vliicli wns dctcctcd Lp nu
OCJIAl ~i~cnsurcmclit
gngc ill t l ~ cfrnliiillg lillc. Tlic fn~llt
occ~lrrctltll~rilig1:i1111clip11:isc. l'l~isf:iliIt 11:is direct illf111ciicc011 tllc tluiility of tlic fc~itlcrnlig~l~iicrlt.
Corrcctilig this fn~lltinvolvctl rcprogm~ii~iii~ig
tlic origi~inl
progrn~ilof n \vcldilig robot.
Fault 1,ocnlizntioli
Tllc fnlllt locnlizntio~iprocctllrre locnlizcs tllc failthat
ing part, as ~vcllas tltc nsscriibly stntioli cnl~si~ig
fnil~lrcbnsctl 011 tllc sclcctctl CAILI's (Figllrc 7)
[13-141. Tlic fnlllt is locnlizctl by idclitif!.i~ig tlic positi011of ChILPs OII tllc c o ~ i ~ l ) o ~sllbassc~iiblics
~c~lt
of tlic
Lod!.-ill-\vllitc usi~lgIiicmrcl~icolgrol~lxoofproduct/
process. 'l'llc c o ~ i i l ~ o ~ wit11
i c ~ i ttlic I~iggcst~lulllLcrof
CA1LPs rcl)rcsc~~ts
tlic lilost likcly failed co~iipo~ic~lt
nud
is cnllctl t l ~ ccnlitlidatc collipollellt. Additiolinllp, tlic
Iiicrnrcliiciil g r o ~ ~ p s otllc
f procl~~ct/proccss
silo\\. tllc
nssc~ilblpstntio~i\vlicrc tlic cnlididntc c o ~ i i ~ ) o ~is
i c:is~it
sc~liLlcd.'Tliis stntioli liiost likcly cnilscs tlic fnillt nlitl is
cnllcd tlic colldidntc station.
I;IEI,D STUDY: SPORT UTILITY \'EIIICI,E
To tlctcr~lli~ic
tllc root cnllscs of di~nc~isioiiol
fnillts
n ~ i dto clctcr~iii~ic
Iio~vtllcsc faults \vollltl nffcct tllc di~iic~isio~inl
clunlity, 1111 nnnlysis of solvctl cosc studics is
contluctctl. Tlic objcctivc of tliis nlinlysis is to s111111ilnrizc tllc k~io\vlctlgcnlitl cspcric~iccsgainctl froin \.ariati011rctluctioi~ncti\,itics. It focllscs prillinrily 011 drn\vi~ig
n guiclclinc for fr~trlrci~ii~)lc~iic~itntio~i
tlurilig tlcsigl~as
~vcllas dllrilig ln~~iicli
of n ~ie\vprotluct. Tlic foIlo\viilg
scctioll prcscllts nn csnliiplc of olic cnsc stutlp wit11 tlif-
I
1;:iuIt Iclc~~tification
r\lcos~u.c~iic~it
poil~ts3L and 5 L 011 tllc Lody-in\vliitc sl~o\vcdInrgc 6-sig~iio\,nriotioli in tllc l'axis
(I~il)onrtl/Outl)onrtl)cqunl to 3.91 a11t1 3.16 111111, rcspcctivclp. Ilotli Iiicnslwcliicnt poilits slio\vctl strong COTrclntioli (0.94). Figilrc 8 slio\vs tlic locntiol~of tlic
co\vlsitlc rci~iforcc~iic~it
1)nlicl alitl sclisors 2L nlid 5 L i l l
tlic botly-in-~vliitc.Tlic otlicr liicnsilrcllicllt poiilts tlid
not slio~vstrolig corrclotio~iwit11 poilits 2L illit1 5L.
Falrlt 1,ocnlizntion
Dccnllsc I)o~lisclcctcd Iiicns~~rc~lic~lt
points 2L and
panel,
5 L wcrc locntctl 011tlic co\vlsitlc rci~lforcc~licnt
tllc gcoliictricnl stntioli nsscliibliiig tliis pnlicl ~vossllggcs~ctlas n cn1itlitl:itc stntioll. 'l'lic root cnusc of tllc
1. Fault identification:
measurement points (4.6. 17)
CEGLAREK AND S H l
D I M E N S I O N A LV A R I A T I O NR E D U C T I O N
I
arrd rrrensrrrerrrerrl
Cowlside reinforcement
serrsors 2L IC. 51,.
2L
6-sigma = 2.9 1 m m
Sample Size - 100
f n ~ ~was
l t itlc~~tificd
I)y 11singa n S-l)ar clinrt ant1 a tlctnilctl study of tllc nsscml~lyprocess in t l ~ ccniitlitlntc
stntion. Tllc S-l)nr clinrts of points 2 L n~itl5 L ill Ynsis
slio~vrcgulnr spikes o c c ~ l r r i ~c v~ cg ~ y2 6 1llcnsrlrciiiclits
(Figure 9). Dctnilctl nlinlysis rcvcnlctl tlint ill tlic frniiii ~ i gli~ic,oiic ~vcltli~ig
robot lnissctl two ~vcldingspots
locntctl 011 t l ~ cco~vlsitlcpanel (Figure 10) cvcry 2 6 pailCIS nftcr n rcgulnr tip drcssi~igo p c m t i o ~ ~ .
'
6-sigma = 3.16 mm
Sample Size - 100
so on; and (4) t l ~ cinfor~iiations o ~ ~ r c ~c s~ s to
c ddctcct
tl~cscprol)lcms.
Root Cnrrscs of (Ire D i r ~ i c ~ r s i o r \~' n lr i n l i o ~ ~
Fifty_t~vo
cnscs ~ v c r citlc~itifictlant1 solvctl nt tlic
nssclnl~lyfacility to rcncli t l ~ cIlcst-iri-class Icvcl of tli~ I I C I I S ~ O I Ivariation,
~I
~ v l ~ i isc lcqual
~
to 2 111111 (6-sig111n
stn~ltlirdtlcvintion). I ~ I I I O-I I~IICSC
~
C ~ S Cs t ~ ~ d i c118
s , root
cnuscs 11-crc fo1111d.All f01111tlroot cnllscs call LC divitlctl
Corrective Action
T l ~ cproblcni was c a ~ ~ s cby
t l tlic progrn~nmi~lg
of
illto f o ~ i~~ r~ n j cntcgorics:
or
product n ~ i dproccss design,
thc rol~otsccluclicc, nliicl~wns corrcctcd.
toolil~gi~~stnllntioii,
tooling i i ~ n i ~ ~ t c i ~ nrind
~ i csc~, ~ p p l i c d
~ i ~ n t c r ivnrintio~~.
nl
Figurc 11 slio~vsthe d i s t r i b ~ ~ t iof
o~i
Evnluntior~
root cnllscs alllong tlicsc four cntcgorics. 11ccordi1igto
11ftcr corrcctivc nctioii was co~~ll)lctcd,
vnriatio~~
this classificntio~~,
t l ~ c o ~ ~ t r i l , u t i oto~ ~t11c
s vnrintio~i
was rctlucctl fro1112.91 to 1.87 111111n~itlfro1113.16
Ivcrc: ~ ~ i n i ~ ~ t c ~procctlurcs
i n ~ i c c (37%), product n~itlproto 2.01 1~1111ill t l ~ cY asis for sclisors 2 L n~itl5L,
ccss tlcsig11 tliscrcpa~icics(27%), supplictl ~ ) n ~ ii11ncc11cl
rcspcctivcly.
rncics (23%),nlitl tooli~igi~istnllntio~i
r~iistakcs(13%).
This n~inlysisil~tlicntcstlint 111ost di~iic~isioiinl
variatio~i
Cnsc Strrdics Classification Bnscd or1 Proclucliorl
problc~rlsof tlic botly-ill-~vliitcarc cnuscd by iilcorrcct
Pllnscs i111d Di~licrlsio~lnl
\'ilrintiorl
niniiitc~in~icc
procctl~~rcs
n11tl dcsigli discrcl)a~~cics
T l ~ IcX C S C I I ~ C ~n~ialysiscsnl~lilies5 2 case stl~tlics
rntl~crtlia11s ~ ~ l q ~ lpn~icl
i c d i ~ r n c c ~ ~ ~ xnlid
c i c tooli~ig
s
illnccortli~~g
to tllc iintllrc of tlic p r o b l c ~ i ~and
s clnssifics
stnllntio~it~iscrcpniicics.I lo~vcvcr,this clnssificntio~i
tl1c11111si11gfour diffcrc~itcriteria: (1) t l ~ croot cnllsc of
tlocs not n~lnlyzct l ~ cscvcrity of tlic occl~rrctlprol)lcnis.
tllc ~ ~ r o l ) l ci.c.,
~ ~ idcsigl~,
,
i~lstnllntio~i,
~ ~ i n i ~ ~ t c ~or~ n ~ i cnnsctl
c , 011 cspcric~icc,tlic lilost scvcrc nntl ti~licco1is11111~lintcrinlvnrintio~~;
(2) tlic p r o t l ~ ~ c t iplinsc
o ~ i in ~vl~icli
ilig prol)lc~nsarc citlicr si~pplicr-rcln~ctl
o r tlcsig~i-rctlic root cnllsc occ~~rrctl,
i.c., prc-lnui~cl~,
Ini~nch,o r full
lntctl prol)lciiis. 111t l ~ cnssc~~il)ly
pln~it,tlic nvcrngc
volu~iicp r o t l ~ ~ c t i o(3)
i ~ ;t l ~ careas o r stngcs in 1)otly nsrcnctio~ltiulc for ~ i i n i ~ i t c ~ ~rclntctl
n ~ i c c prol)lc~iiswns n
s c ~ ~ i b ~lroccsscs,
ly
i.c., sol)nsscn~bly,I)otl!- framing, n11tl
f c ~ vIio~irs( < 8 I i o ~ ~ r sIlo~vcvcr,
).
corrccti~igtlcsig~i
M A N U F A C T U R I N G REVIEW
VOL. 8. NO. 2
J U N E 1995
tlg. % .T-bar chart
i~i-oble~l~s
or panels discrepancies took on avcragc ol
lrorn 1 to 4 months.
Prod~~c:tion
Phases during which the Koot Cause of
the Dimensional Variation Occurred
Tn siinilar ways, tllc dirncr~siorralfa11lr.swere classified as a fuilction ol lirrie. This c:lassiIication provicles
vduablc insigh1 illlo the strengths and weakuesses of
the procl~c:~.
and assembly process vers~isthe time of
thcir dclcctior~.
Figure 1 shows identifiecl fault root causcs ill cab11
w
\
~)rocl~~ction
phase. The vertical axis indicates the
6-sigma variation of thc aulomolive bocly, 1)ased on the
weekly average of daily balches of 100 hoclies with 100
measnremelirs takctl lor each automotive body. The
d ~ t werc
a
captiucd during a 60-week periocl. The dimensional problerrls for each production phase are disc ~ ~ s s cwith
d particular emphasis on those that allcci
vuialion level.
'1. Pre-productioll [~llasc.
'l'hc 6-signa variation of
the 11ody-in-whi~cwas reducecl from 0.5 to 3.5
mm clurillg this phase. Tooling design and tooling
iilstallalion as well as variat:ion of si~ppliecl
Welding spots
.........
Missed welding spots
a
L k ~ g related
n
problems
lnstallatbn related problems
jTm Maintenance related problems
Cowlside Reinforcement panel
a
C E G L A R E K A N D SH1
Supplier related problems
DIMENSIONAL VARIATION REDUCTION
s t n ~ l l p i ~parts
~ g ~vcrct l ~ c . ~ ~ ~root
n j o rcntlscs of
varintio~~.
Fig. 4 sl~o\vsthat 42% of t l ~ cfi1111tsat
this stngc wcrc tooIi~!gdcsigu-rclntctl, 24% of t l ~ c
f o ~ ~ lCOIIIC
t s fr0111tooli~~g
ilistnllntio~l,nntl 34% of
root cntlscs C ~ I I I Cfro111s t n ~ ~ ~parts.
p i ~ ~Arol111t1
g
40% of all f n ~ ~ lwcrc
t s rclntctl to tllc fnilurc of
nssc~~ihly
fist~lrcs.Duc to tile lo\\- volu~ucof proc l ~ ~ c t itooliug
o ~ ~ , ~ ~ i n i ~ ~ t c\vns
~ l nnot
~ ~nc111njor
c
issue c111ri1igprc-l)rod~~ctio~l.
2. I'roiluctio~~
la111ic11.111 t l ~ cln1111c1iplinsc, \.nrintio~i
\vns rccluccd fro1113.5 to 2.5 I I I ~ S. i ~ ~ ~ itol ot lr~ c
first ~)llnsc,dcsign o ~ l dtooli~igil~stnlli~tio~~
rclatcd
faults \vcrc still t l ~ c~ ~ ~ nroot
j o rcnuscs of v;lriatio~~,
co~~tributi~lg
nl)ol~t50% of t l ~ ctotal I I I I I I I ~ C I -of
root causes. Root ciu~scsd r ~ cto t l ~ cst~pplicd
s t n ~ ~ l p ipn~lcls
l ~ g clccrcnsctl to 29%. I-lo\vcvcr,
\,nrintio~lcauscd by tooli~~g
~linilltcrla;~cc-rclatcd
fnlllts 1)cca111ci l ~ i p o r t n as
~ ~ttl ~ cproductiol~volu111ci~icrcascd.111gcncml, tllc f n ~ ~ lrclntcd
ts
to
nssc111l)lyfisturcs mpidly i~~crcnsctl
to 70% of tlic
tocnl nun~bcrof faults.
Figurcs 12 nntl 13 S I I I I I I I I ~ ~ ~t Zl ~C cclctcctio~~
of t l ~ c
toolillg dcsigll tliscrc~)a~lcics,
i~~stilIIntio~i
~l~istnkcs,
as
\vcll ns supplicrl pa~~cls'
i~laccl~rncics
tluri~lgt l ~ cI;roductlucctl fro1112.5 to 2.2 111111. .Alni~ltc~ln~lcc
rcIncccI
ti0111Il1nscs.T o o l i ~ ~dcsign
g
nntl il~stnllntio~~
tliscrc~)n~~problc~lis\vcrc the lnnjor root cnllscs in this
cics bccml~cOIIC of tllc 111njorroot co~lscsof tllc
pl~nsc.Rcfcrrillg to Fig. 4, it call bc seen tllnt olprol)lcnis oftcr tllc vnrintion \\.as rcduccd to 2.2 111111.
~liostall root cnuscs \vcrc cluc to ~ ~ ~ n i l l t c ~is~ a n c c This nllo\vs 11s to cstill~n~c
tllot t l ~ c2.2-111111Icvcl \\.as
sucs. Ib s ~ ~ ~ l ~ ~ toolilig
~ ~ n r itlcsigll
z c , nl~cl
tllc toolirlg tlcsign cnpnbility of t l ~ cproccss. Fr~rtl~cr
rci~lstnllntio~~
tliscrcpn~icicsllntl b c c ~itlc~~tifictl
~
nntl
cluctio~~
of variation to rcncl~t l ~ 2-nail
c
I c ~ c rcquircd
l
solvcd t l u r i ~ ~tllc
g prc-protluctior~n~icl1n111lcl1
so111ctoolillg tlcsig~l~~lodificotio~~.
pl~nscsnntl, nftcr illcrcnsi~~g
t l ~ cprod~lctio~l
volulrlc, tooli~~g
I I I ~ ~ I I ~ C I I ~ proccdurcs
IICC
bccn~~lc
lllorc clc~~ln~ldi~lg
o~itlcritical for d i ~ ~ l c ~ ~ s i o ~ ~ o l
vnri;~~io~~.
3. First sllift I1111 l)rooductio~l.Duriug t11c first full
~jroductio~i
shift, the vnrintion \vns furtl~crrc-
,
t11ot t l ~ c11u111bcr
of tlcsig~~,
i~~stilllatio~~,
and s111)plicd pa~lclsrclntctl prol)lc~nsi~~crcnsctl
ngni11.
This call bc cs~)lnincdI)!. rcalizi~~g
tllnt I V ~ I C I It l ~ c
rnriatiol~rcochcs its inl~crcntIcvcl, furtllcr vnrintion rcductioll rcquircs cl~n~lgcs
in t l ~ cproduct
n~idproccsscs.
'
4. Scco~~cl
shift lnllllcl~al~tlI1111I)iocl~~ctio~~.
Tl~c
\ * n r i ; ~ ~Icvcl
i o ~ sligl~tly
~
illcrcnsctl to 2.5 111111nt t l ~ c
bcgil~uillgof tllc pcriod tl~rcto 1:1111lcl~il1g
of tllc
s c c o ~ ~shift.
t l llftcr solvi~lgscvcml cast studics, t l ~ c
vnriotio~ifi~inllyncllicvcd 2-111111IcvcI ill t l ~ cfifth
I ~ ~ O I of
I ~f111l
~ I ~)rod~~c
011
t itwo
o ~ ~shifts. D11ri11g
tl~is~~criotl,
~~~ni~itc~~n~~cc-rclntctl
problcn~s\vcrc a
111njormot cnusc, nltl1011g11
tl~cy\vcrc rclnti\.cly
less ( I O I I ~ ~ I I t11a11
~ I I ~ c111ri11gt l ~ cprcviolls ~)l~nsc.
Tllc ;1ssc1111)lyf i s t ~ ~ r fcns ~ ~ lc:~usctl
ts
70% of t l ~ c
total I I I I I I I of
~ ~ fnil~~rcs.
~
It is i~~tcl'csti~lg
to 11otc
M A N U F A C T U R I N G REVIEW
VOL. 8, NO. 2
J U N E 1995
Locnlization of Dimensional Variation withi11
Assembly Process
'Thc. root cause based analysis
preserlreil
in the
.
prcvious sec~ion~illowsus to estirrlate the cnusrs of clinlerisiorlal variation. In addition to that, one of the most
in~portantiss~lesis the loc:ilixatio~~
c~fthe variation
within the assembly process: body-in-white suhasscmhlies (nnderhocly mid jide apertures), framing opcrations, ancl silppliecl panels. This classificalioll is
sunlmarizecl in Figures 14 ancl 15.
Based on Figs. 1 4 and 15, it coudtl be cor~c:h.idc:tl
that thc variation rcdirction in the s1d)assernhlvlines
has a major iirlpact or1 the variarion of ihe hocly-inwhile. 111general, prol)leirls in s~ihasse~nbly
are twice as
fre.q~ierltas those in the framing station. This inclicatcs
thal for this pnrticl~lardesign of the hoclp-in-white, ~ h c
s~lhnsseml~ly
is much more seilsitive to variation tllc~n
the final framing of subassemblies.
111 additioll, the coopcration and support fro111
stamping operations playcd n major role in r.ecluc:ing
dilne~lsionalvariation. Ahout one-fourth of the root
ca uses wrcrccai~scdby the part suppliers.
Information Sources for Fauli Delection
T l ~ eallalysis of the inforlnntion sources is focused
on the rneasi.lrr.ment and verificarion. of gages such as:
(1)Optical Coordinate Measuring Ylachule (OCMM),
(2) C~oordinateYleasuriilg )lachine (CYIY), (3) visi~al
obse-l~ation,and (4) Theodolite gagc.
Figwe 1 6 shows the clistribi~tionol the infonr~atioil sources usccl lor dclcclion and ar~alvsisof the
sol\:cd casc s~uclics.'l'hc rlleasurelileIlt gag: llsecl rnost
oltcll LO idcn~ifyvariatioi~prollelns was the 0C:;LIhI
(56%). C:Y14ls artcl Theoclolires wcrc cac11 usccl in 19%
of all mse stuciies. mostly d.tlrillg analpis allcl vrrific.aL ~ ~ofI faults.
I
Sumnlarizi~lp~11ccxpcricl~ccgained while solving
tlicsc casc stildics. \\:c C O I I C ~ ~ I Cthat
~ ~ Cinformation
~
sourccs arc i~scd~r~i-lirlly
for t h e e purposes:
1. Dctcc~iont u ~ diclcn tificatior~of thr clirr~erlsionnl
variation-OCM-\I. visud ol)servnrinn, mlrl Feedback horn Pirlal Assernhly.
2. .4nal!.sis of already icle11tifiet:l fa1~lts-OC\li'~,l mid
C3iNl.
3. Qiian~i~ativc
verification of the root sn~:~se
of diuiellsio~lalvariatioi~-(:bINI and theodolite.
Thc, classificatiorl basecl or1 irlfonrl~~tion
source has
been corrclatccl largely with proc11lc:tion phases.
Figire 17 allocates tlifferc:nt information solirces to different proctuctioi~pl~ases.This classification call he
surrllr~arizetl as ~ollons:
1. In-line ineasurement cloinina~csas all informatioil
sotlrce during detection and ~~nalysis
of h e problems causing din~cnsionalvariation.
2. Mosl lrcy ucnt applicatior~of (:MI gage (35%)
was during pre-productior~phase, *.hen the voltune of produced cars was small (small salllple
size).
3. Theodolite was used during lannch phasc (34%)
for velific,ation of toolirig installatior1 problcrils..
and second shifr phase (35%)for vcrificatiori of
tooling clesigii modifications ncccssaly ~oreacl~
2-rrm~variation lcvcl ancl not preclicted earlier hy
designers.
CONC1,USIONS AhD SUMMARY
El Panel dated problem
a
Subassembly related problems
Framing related problems
Find Asembly rclated pmblems
There is a move ro blend control of dirliansional
variario~~
into the antomotivc ioclus~ry.\Vii:hir~the ut~rolnotive industl?, it is viewed as assets able to c:reotr
high qualit! product. This papcr srnrirrl:~rizc\s:I devcloped ~nethoclologyfor rcdt~ci~lg
cli~nensionalvariation
:is well as n study or] ~ h root
c causes of variation. This
s~lrily.cottductecl in oue of t11t facilities of the leaclil~p
;it110 I I I I I I I I I ~ I L C\\-;IS
~ L It'o(:~~s(!d
~ I W . 011 oljtainirlp tllc 1jcs1ill-class cli~r~c:~~sio~lcll
i~~uro~~lorivc
bocly bldlcl basccl on
clti~~liiy
O I : I I C ~ I I I I ;~I ~s~ ~~ i ~ l ~asl i2s IlI iI I~~( ~6 -~S~~ ~ I IAI ~ I ) .
CEGLAREK
AND SH1
.
D I M E N S I O N A L VARIATION REDUCTION
F(q. 15. Ilnnt
Pre-Production
Launch
One shift
Two shifts
100
cc~rrsrr / i s s ~ f i r f ~ / i n n
Lnsed on 111e
90
loculizcr/inn of /he
80
clinrc~r~.siorrcr,,/r~~~I~s
70
u>i&lrin
lfrc
-'2
ns.nerr~b(~.prucrss
Q)
60
a n d ocrilrlmc.ta
Q)
2 so
$1
during procirrc/tr,rr
40
phar~r.
30
20
10
0
--
3 $E 2'z
E
a
M
;5 .z5
q
rt:
2
vl
0,
m
data-tirive~i,casc-based approach was used in solving
s.
dimensional l a ~ ~ l tAn
of one case st~lclyis presented in die paper. All of the case st~idiesIleccssary to
achieve hesT-in-class climensionnl ~ariaTi011were analyzed and classified according to the iintnre of the fa~ilts
using four. different critexia: (1) root causes of h e
laults, such as tooling design, u~stallation.mainterrance,
or ir~corningmaterial v;uiation; (2) root causes in the
c:liffercni productioii ph:ises, i.e., prc-lauu~ch,launch:
u ~ full
d vol~uneproduction; (3) areas ol body assenihly
process, LC.,subassembly, body fia~l~ing,
clc.; and (4)
the infor~rle~tion
sources used to detect din~cl~sio~lal
faults.
Wlost of the \:arialion problems di~rir~g
the firsi 1.5
A
5 a
L
E
M
.5
B
?&i
3m
E
2
3
M
.E
E E
P
z
?i
'2"
-2
I0
~iionthsol the conclucted test werc cat~scclby prohleins
in ~ ) r ~ d uand
c l process (tooling) design, tooling installntion. tooling nlaintenallcc, and inc:ornirig lrlaterial variation. Their relativc contributions :ire classificcl
accorrling I O the nt~rlherof problem> ill each calcgoy:
Tooling ~naintcnanccrelatecl prohlerns: 37%.
Product and process clcsign related prohlnris:
27%.
Sta111pingpalel related prohlerns: 23%.
Tooling instnllation relatcd problems: 13%.
For a11 ol the cases icleiitified, spumdic problelns
or special cause prohlenls (such as spikes) arc as frecyuent a s chro~licp~'ohlcmsor common cause problc~ns
(35% of total case s ~ ~ r d i c sIIowever:
).
the imderlying
root causes for the ~rlajorityof the sporadic: ~)roblerrls
arc chronic iii nature. 'l'hcsc problems are indllc:etl by
in~crferellce,either pi-~rr/parlinterference or tooling1
part i11~crferenc:e.
As rar as the i.oot causes or variation during rlilfereiit Ij~~oclac-tiori
p1ias1:s. finclings related to llie loiincl~
stage arc as I'ollows:
Prol)lrrns during p1.c-proclnctio~~
ancl p r o c l ~ ~ r ~
I t i ~ ~ ~:lr('
i ~~
~~
l il i ~ irclatecl
~ l l ! to cle.sigrl arlcl i~ls~allalion. :i11(1are 1)nnc-1-rclniecl.
MANUFACTURING REVlEW
VOL. 8, NO. 2
J U N E 1995
fig. 17. IZoof
Pre-Production
Launch
One shift
100
Two shifts
cnrrsc clns$i/;cntiorr
1
Lnsecl orr tlre
irforrrrntion sorrrce
rritcrior~nrrrl
orcrrrmrcc clrrrirrg
pmdrrrtiorr plrases.
D ~ ~ r i n gIICW vclliclc program, prc-l~rotluction
nritl protll~ctln~llicllarc tllc i~uportnntplinscs in
\\,llicll to corrcct all di~ncnsiolinlp r o l ~ l c ~nlid
~is
ncllicvc tllc proccss cnpnl~ility.
lnost significantly to tllc varintio~~
of tllc body-ill-\vllitc.
In tllc co~iductcdstudy, 56% of tllc problclrls arc subnsscmblY-riclntcd.
111c\-nluntilig tllc iliiportnncc of ~iicasurc~ncrit
systclils ili. fault idcl~tilicntiolio l d localizntion, tlic li~idiligs
nrc: :.
Findings rclntcd to tllc f11Il prodr~ctio~i
stngc nrc ns
follows:
To sllcccssf~~lly
i(lc11tifytli~iic~isio~inl
problems,
sllfficicl~tulcnsllrcllicrlt is rlcccssn1-y. For fast tlctcctioli of tllc t l i ~ ~ ~ c ~ ~prol)lclrls,
s i o ~ ~ n11sc
l of in-linc
lricnsllrclrlcllts is cssclltinl. For fault clingr~osis,
flcsiblc ~ ~ l c a s u r c ~ ~
systcriis
l c ~ i t nrc i~liportarlt.
Each vcliiclc Ibody Ilns, as n rcslllt of product nlid
~ ~ O C C Stlcsig~i,
S
an irilicrcrit Ic\.cl of tlinlc~lsionnl
\ynrintio~~.
Oricc tllc proccss rcncllcs its irlllcrcllt
Icvcl of vnrintio~l,hlrtllcr rcductioll of tllc tlilncllsionnl vnriatio~iOII tllc botly call LC ncllicvctl.
I.Io\vcvcr, tllis cn11 LC dollc olily tllr011gl1costly nlltl
tiliic co~lsr~~iii~ig
product n ~ i dproccss ~iiodificntioll.
I11 tllc cnsc of tlic nsscliibly plnlit \vllcrc study \vns
co~ldllctcd,tllc tli~i~c~isio~inl
cnpnlility is nt 2.2
To sl~cccssfullyvcrify tliiilc~isio~lnl
problc~nsill
tooling, tlicodolitcs, or portnblc CABls arc
ncccssnry.
111111.
h1:11iy pr0bIc111sill ~)rotI11ctioll
st:~gcarc ~linilltclinlicc rclntctl. For csnlllplc, drlrirlg O I ~ Csllift productio~l,nll of tllc cnsc stutlics itlclitifictl wcrc
~~ini~~tc~~n~icc-rclntctl.
During two sllift procluctio~l,
52% of tllc cosc st~ltlics\vcrc ~~~ni~itc~la~lcc-rcl:~tctl.
for root causes of \.nrintioll ill tliffcrcllt arcits of
tllc nsscllil)ly proccss, s~~l)nssc~i~l)ly
wrintiol~co11tri1)lltcs
[\s
F ~ l s i aof
l ~ nsscllll)ly process kllo\vlcdgc wit11 t1uti1tlrivcll nllnlysis is tlic kcy to frlst itlc~~tificotio~~
of
tllc root cnllscs.
CEGLAREK
AND S H 1
DIMENSIONAL VARIATION R E D U C T I O N
REFERENCES
1. Hopp, W.J., Spearman, M.L., Woodruff, D.L., "Practical
Strategies for Lead Time Reduction," Manufacturing
Review, 3(2): 78-84, 1990.
2. Ayres, R.U., "Complexity, Reliability, and Design:
Manufacturing Implications," Manufacturing Review, 1(1):
26-35, 1988
3. Parkinson, A., Sorensen, C., Pourhassan, N., "A General
Approach for Robust Optimal Design," Trans. of ASME,
Journal of Mechanical Design, 115(1): 74-80, 1993
4. Gupta, Y.P., Kumar, S., "Controlling the Production Process
Through Statistical Process Control," Manufacturing
Review, 4(1):18-32, 1991
5. Barkan, P., Hinckley, C.M., "The Benefits and Limitations of
Structured Design Methodologies," Manufacturing Review,
6(3): 211-220, 1993
6. Brannan, B., "Six Sigma Quality and DFA-DFMA Case
Study/Motorola Inc., Boothroyd & Dewhurst DFM Insight,
2:1-3, 1991.
7. Plonka, F.E., "A Methodology for Tolerancing, Process
Evaluation and Control of Automobile Body Subassembly
Designs," Ph.D. Dissertation, Univ. of Michigan, 1974
8. Baron, J., "Dimensional Analysis and Process Control of
Body-In-White Processes," Ph. D. Dissertation, University
of Michigan, Ann Arbor, 1992
9. Roan, C., Hu, S.J., Wu, S.M., "Computer Aided
Identification of Root Causes of Variation in Automotive
Body Assembly," ASME Winter Annual Meeting, 391-411,
1993
10. Hu, S.J., "Impact of 100% Measurement Data on Statistical
Process Control (SPC) in Automobile Body Assembly,"
Ph. D. Dissertation, University of Michigan, Ann Arbor,
1990
11. Hu, S., Wu, S.M., "Identifying Root Causes of Variation in
Automobile Body Assembly Using Principal Component
Analysis," Trans. of NAMRI, XX: 311 -316, 1992
12. Roan, C., "Identification, Monitoring, and Diagnosis for
Dimensional Control of Automobile Body Assembly," Ph.
D. Dissertation, University of Michigan, Ann Arbor, 1993
13. Ceglarek, D., "Knowledge-Based Diagnosis for Automotive
Body Assembly: Methodology and Implementation," Ph.D.
Dissertation, University of Michigan, Ann Arbor, 1994
14. Ceglarek, D., Shi, J., Wu, S.M., "A Knowledge-based
Diagnosis Approach for the Launch of the Auto-body
Assembly Process," Trans. of ASME, Journal of
Engineering for Industry, 116(4):491-499, 1994.
15. Shi, J., Hu, S.J., Ceglarek, D., "Process Navigator for the
Automobile Body Assembly Process," Proceedings of the
First S.M. Wu Symposium on Manufacturing Science,
Evanston Il: 325-332, 1994.
16. Ceglarek, D., Shi, J., "Fixture Failure Diagnosis for
Autobody Assembly Using Pattern Recognition," accepted
for publication in the Trans. of ASME, Journal of
Engineering for Industry simultaneously in ASME Winter
Annual Meeting, PED- 68: 263-275, Chicago, Ill.,
November 6-11, 1994.
17 Takezawa, N., "An Improved Method for Establishing the
Process-Wise Quality Standard" Rep. Stat. Appl. Res.,
JUSE, 27(3):63 -75, 1980.
18. Menassa, R.J., DeVries, W.R., "Locating Point Synthesis in
Fixture Design," Annals of CIRP, 38(1): 165 -169, 1989.
MANUFACTURING REVIEW VOL. 8. NO. 2 J U N E 1995
19. Rearick, M.R., Hu, S.J., Wu, S.M., "Optimal Fixture Design
for Deformable Sheet Metal Workpieces," Trans. of
NAMRI, XXI:407 -412, 1993.
20. Datamyte Handbook, "A Practical Guide to Computerized
Data Collection for Statistical Process Control," AllenBradley Co. Inc, 1993
21. Perceptron 1000, ,“System Manual,” Perceptron Inc, 1991.
22. Greer, D., "On-line Machine Vision Sensor Measurements
in a Coordinate System," SME Paper #IQ88-289, 1988.
23. Sekine, Y., Koyama, S., Imazu, H., "Nissan's New
Production System: Intelligent Body Assembly System,"
SAE Technical Paper Series, No. 910816, pp. 1-12, 1991.
Dariusz Ceglarek is a research investigator at Department of
Mechanical Engineering and Applied Mechanics at the
University of Michigan. His research interests include quality
control and assurance methodology with implementation;
intelligent manufacturing, knowledge-based diagnosis using online fault detection and isolation with emphasis on automobile
industry, application of AI in manufacturing.
His current
research is being sponsored by General Motors Corp., Chrysler
Corp., National Institute Standard and Technology - Advanced
Technology Program, and the National Science Foundation. He
is a associate member of Society of Manufacturing Engineers,
and American Society of Mechanical Engineers.
Jianjun Shi is the associate director of the S. M. Wu
Manufacturing Research Center and a faculty member at
Department of Mechanical Engineering and Applied Mechanics
at the University of Michigan. His teaching and research
interests include quality control and assurance methodology,
systems, and its implementation; dynamic system modeling and
control; intelligent manufacturing using on-line fault detection,
isolation, and diagnosis with emphasis on automobile industry.
His current research is being sponsored by General Motors
Corp., Chrysler Corp., Auto Body Consortium, National Institute
Standard and Technology - Advanced Technology Program,
and the National Science Foundation. He is a member of
Society of Manufacturing Engineers, and American Society of
Mechanical Engineers.