iee572 doe red wine verses white shirt 5-3-2010b.doc

8/11/2019 IEE572 DOE Red Wine Verses White Shirt 5-3-2010b.doc

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IEE572Design and Analysis of Engineering

ExperimentsDr. Douglas C. Montgomery

Final Project

Eric F. Wong


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Its Friday night and youre stuck at the com any Christmas arty retendingto enjoy chea red !ine and the ominous music. "oure s orting your #est !hiteshirt in ho es o$ im ressing your #oss% and may#e that girl $rom the o&ce. 'hings

seem to #e going !ell% until disaster strikes( the red !ine that use to #e in your cu %is no! all o)er your !hite shirt.

'he moti)e here is to ensure your !hite shirt doesnt share the same tainted$ate as your re utation.

*sk any !asher machine or detergent roducer in the market and they !illmost likely guarantee to #e the ultimate solution $or stain remo)al. +o!e)er% surely

there must #e more o!er on the user end to ensure their stained !hite clothing isrestored to its original #rilliance.

'he o#jecti)e o$ this e, eriment is to -uanti$y the di erence in stain remo)ale ecti)eness% judged on a grayscale% #et!een ercei)ed detergent -uality%detergent -uantity% the use o$ a re/treatment% and !ater tem erature% in the stainremo)al rocess 0 and assess statistical signi1cance 2com are to 3456.

'he degree o$ strain remo)al !ill #e measured using a gray scale #ased onthe 7892,%,%,6 units. Where , is an integer #et!een : and ;44 inclusi)ere resenting the saturation o$ red% green% and #lue res ecti)ely. 'hese 789 )alue!ill #e the a)erage o$ 4 sam led oints $rom an image using Paint Sho Pro


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"esponse#ariable

$ormalOperating"ange

Meas%rementPre!ision /A!!%ra!y

"elations&ip toOb e!tive

8rayscale using789 Anits

:/;44B Where :!ould re resent no

color and !ouldsho! as #lack.>ike!ise% a ;44)alue !ouldre resent $ull colorand #e seen as!hite.

Scanner and imageso$t!are com#o

are assumed to #ehighly relia#le.7e eated sametest sho!consistent results.

'he higher the grayscale )alue% the

closer to !hitethe sam le is%!hich correlates to#eing less stained.n the other hand%a stained item!ould ha)e a lo!er)alue.

Control


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e, eriment.


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2C6Ase o$ Pre/ 'reatment

*ll cloth!ashers eitheruse% or do notuse re/treatmentchemicals.

nKa +igh( Ase o$Pre/treatment,i/Clean Ma,Force>aundry stainremo)er

>o!( No re/treatment

Asing the re/treatment !illincreasegrayscale.

2D6WasherMachine Water

'em erature

+otKMildKCold Digital Meat 'hermometerClaims a LK/J C accuracy

+igh(C KJJJ F

>o!(; .4 CKGO F

+ighertem eraturemay helremo)e stains%and may alsointeract !iththe cleaningagents.

+eld Constant Factors

)eld'onstant*a!tors


Meas%rementPre!ision /A!!%ra!y

Settings Predi!tedE(e!t

WasherMachine

Maytag / .:Cu. Ft. J=/Cycle Washer 0White Model(M


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Nuisance Factors

$%isan!e*a!tors


Meas%rement Pre!ision/ A!!%ra!y

Settings Predi!tedE(e!t

ClimateKWeather

nKa Climate canchange thetem erature o$ cold !aterused in the!ashermachines. 'omitigate thee ects o$!eather% allsam les !ereran in thea$ternoon inrandom order.

nKa Negligi#le

7un rder nKa Washermachine isassumed todeli)er thesame cleaningcycle e)erytime.

nKa Negligi#le


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* randomi ed ; /J $ractional $actorial design !ill #e used. 'here are $actorso$ interest !hich !ould ha)e needed JO runs total in a $ull $actorial. +o!e)er% JOruns on a !asher machine !as e,cessi)e and !ould ha)e s anned across se)eral

days ossi#ly ske!ing the data.

I R *9CD

All single fa!tors also aliased +it& , fa!tor intera!tions

Design


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'he $ollo!ing ta#le dis lays the ra! data collected in the e, eriment.

"%nOrder Pattern

Detergent%ality

Detergent%antity

Pre01reatment 1emp " 3

J L L E, ensi)e J.4 "es ;4J; LL E, ensi)e J.4 No ; .4 ;@O@ LLLL Chea J.4 No ;;:

LL Chea J.4 "es ; .4 ;@=4 L L Chea :.4 No ; .4 ;:@O LL E, ensi)e :.4 No ; OG E, ensi)e :.4 "es ; .4 ; ;= L L Chea :.4 "es ;;3

'he *N


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S%mmary of *it7S-uare :.=@G;437S-uare *dj :.GJ4;:@7oot MeanS-uare Error =.;33:3O

Mean o$7es onse ;@@.J;4

#ser)ations2or Sum Wgts6 =

'he high 7S-uare term suggest that the model 1ts reasona#ly !ell. +o!e)er%looking at the lot o$ *ctual #y Predicted sho!s a )ery im ro#a#le attern !ithe)ery other oint #eing a#o)e the redicted )alue. Perha s an e ect added to theerror estimate should ha)e #een ke t as a model.

7e)ie!ing residual lots #y redicted and #y ro! sho! that the )ariance o$ errorremains reasona#ly constant. 'he model may #e slight o in the lo!er region o$ res onse )aria#le.

Parameter Estimates


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1erm Estimate Std Error t "atio Prob tInterce t ;@@.J;4 ;.3@ JG G3. 4 V.:::JDetergentuality E, ensi)eX J:.O;4 ;.3@ JG @.O; :.:;;@

Pre/'reatment "esX O.=G4 ;.3@ JG ;.@ :.:G3J

Detergentuality E, ensi)eXTPre/ 'reatment "esX

/ .J;4 ;.3@ JG /J. J :.;@;4

9ased on the arameter estimates% #oth 2*6Detergent uality and 2C6Pre/'reatment!ould hel stain remo)al% ho!e)er% together they seem to mitigate their a ects.

9elo! is the Prediction E, ression.

Prediction Expression


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It !as rather une, ected that only the 2*6Detergent uality had a signi1cante ect on the stain remo)al rocess. 'his su orts the old saying o$ you get !hatyou ay $or. 'his notation ho!e)er is not a arent !ith the o#ser)ed e ects o$ the

Pre/'reatment% !hos #ottle cost nearly t!ice as much as the #ottle o$ cheadetergent. 'he estimated im act o$ using the e, ensi)e detergent o)er the cheadetergent !as a#out ;J oints on the 789 grayscale. +o! does that look e,actlyY

ne could argue that on your $a)orite !hite shirt% e)ery oint is )alua#le.

Its use$ul to kno! that tem erature did not hel or hinder the stain remo)ala#ilities o$ the !ash. Since cold !ater 1lls the !asher machine much $aster than thehot !ater% and doesnt !arm u the !ash room as much% cold !ater !ill $or no! #eP 7 2 lan o$ record6.

Asing the hind/sight #ias% it makes sense that detergent -uantity had noo#ser)a#le e ect. +al$ the manu$actures recommended amount !as ro#a#ly morethan enough to saturate the !asher machine !ith only a small iece o$ $a#ric in it.

In conclusion% someone !ho !ants the !hitest clothing should !ithout-uestion% use the higher -uality detergent. +o!e)er% the manu$actures o$ thechea er detergent !ere sa))y in ricing their roduct to remain com etiti)e. 'herice er load di erence is nearly to JZ 9ased u on this in$ormation% I lan to #uythe chea er detergent in #ulk% #ut kee a small #ottle e, ensi)e detergent handy%in the e)ent that I 1nd my !ine on my shirt% and not in my #elly.

Future !ork !ould 1rst and $oremost conduct more runs to get a strongerestimate o$ error. It !ould also #e interesting to add the num#er o$ !ashes as a$actor% to see i$ a$ter se)eral !ashes% some o$ the insigni1cant e ects #ecomesigni1cant. Furthermore% it !ould #e interesting to turn the 2*6Detergent uality$actor into a continuous $actor #y mi,ing di erent ercentages $rom each -uality.

S%mmary / 'on!l%sion

iee572 doe red wine verses white shirt 5-3-2010b.doc

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