modelling in manufacturing industry para

8/16/2019 Modelling in Manufacturing Industry Para

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Introduction

In this research the authors study more specifcarea in beltline moulding in automotivemanuacturing.

Beltline moulding is a process with many variationsin raw materials, machinery conditions and ambientconditions. It also has a temporal aspect where lineconditions change during operation, aecting theend product.

Typical process control procedures include statisticalanalysis o periodic batch samples, control charts osample mean or range, and trial and error.


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Signi"cance and Bene"ts of Pro#osedResearc

The application o quantitative technique in improving aproduct process thus ar is still a recent phenomenon.

There is an urgent need or more objectives, realisticand accurate model or uture planning and policyevaluation.

This is quite obvious as the automotive manuacturing

sector beltline part o car body! undergoes structuralchanges and is becoming more comple" due totechnological advances, manuacturing management,product demand and competition rom othermanuacturer.


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Te Ob$ecti%e of te Study

In view o the importance o having suchtools, the study aims to achieve theollowing objectives#

i. To select the best parameter settings romthe our actors.

ii. To apply $orrelation %odeling approach orparameter selection.


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Table &: Te list of te de#endent and

inde#endent %ariables

+o. ependent -ariables

'

nd /roduct

i. The new input parameter setting

ii. /rocess $apability Inde", $p&

iii. 0uality o product

Independent -ariables

'

%achinery $onditions 1actors

i. Temperature -ertical and 2ori3ontal "truder

ii. 2eater setting 4mpere!

iii. 5crew speed rpm!iv. The line speed m6min!

v. 7ooper 4mp!

vi. 8oller 4mp!

vii. /ulling 4mp!

-iii $utter 4mp!


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$ontinue 99.

+o. Independent -ariables

:

2uman 1actors

i. rit time during operation

ii. ;or&ing time duration

iii. ;or& shit

iv. ;or&er s&ill level

v. ;or&er e"periencevi.5alary, allowance, benefts etc

vii. ;or&ing environment

<

8aw %aterial $omposition 1actors

i. 5pecifc =ravity

ii. Tensile 5trength

iii. longation at Brea&

iv. 2ardness

> 4mbient $onditions 1actors

i. Temperature o$!


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'( )iterature Re%ie*

'(& Belt )ine Moulding Process'(&(& +trusion Process The very important part in roll orming process

is e"trusion process.

Basically many defnitions authors ound aboute"trusion which is (:) defned e"trusion isprocess by which polymer is propelled

continuously along a screw through regions ohigh temperature and pressure where it ismelted and compacted, and fnally orcedthrough a die slit! to orm a thin flm.


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$ontinue 999999.

%eanwhile, (


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'(&(-Roll .orming Process

The belt line mouldings that border theinterace between a car door panel and thebottom outside edge o the door windows, ithas become aesthetically ashionable toprovide a strip o sti decorative or ornamentalplastic material on the outer or inner side othe arch or channel shaped moulding in

combination with the coil loo& o an e"posedportion o the core material.


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/( Researc Metodology

The research purpose or apply parametersselection analysis using 8egression analysisand -ariance?$ovariance %atri" methods.

1actor analysis is used to uncover the latentstructure dimensions! o a set o variables.

It reduces attribute space rom a largernumber o variables to a smaller number oactors and as such is a Cnon?dependentCprocedure based on linear regression model.


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$ontinue 991actor analysis could be used or any othe ollowing purposes#

To reduce a large number o variables to asmaller number o actors or modelingpurposes.

To select a subset o variables rom a larger set.

To create a set o actors to be treated asuncorrelated variables as one approach tohandling multi co linearity in such procedures asmultiple regression.


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8esults 4nd iscussions

$orrelation analysis is a technique or

investigating the relationship between twoquantitative, continuous variables, /earson*scorrelation coeDcient, r is a measure o the

strength o the association between the twovariables.

In this study, we shall discuss the analysis o therelationship between two quantitative outcomesusing scatter plot. 4 scatter plot is simply a cloud

o points o the two variables under investigation.

;e use the correlation coeDcient, r to describethe degree o linear relationship between the twovariables.


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$ontinue 999.

Table : gives a guideline on the strength o thelinear relationship corresponding to the correlationcoeDcient value.

Table -# 5trength o 7inear 8elationship

$orrelation $oeDcientvalue

5trength o linearrelationship

4t least E.F -ery strong

E.A up to E.F %oderately strong

E.< to E.@ 1air

7ess than E.< /oor


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.igure -# 5catter plot or $ylinder

CY1

CY2

CY3

CY4

ADPT

DIE

.93 .45 .87 -.098 -.3

.503.839 .026 -.262

.625 .133 -.411

.024-.273

.051


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$ontinue 999.

1rom the correlation analysis we ound $G'and $G: have strong correlation coeDcientswith E.H: and between $G: and $G< withE.F


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$ontinue 999 ;e can illustrate to


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$ontinue 999

1actors score covariance matri" shown as Table >that although theoretically the actor scores shouldbe entirely uncorrelated the covariance is not 3ero,

which is a consequence o the scores beingestimated rather than calculated e"actly.

Table 1# 1actor 5core $ovariance %atri"

1actor $G: $G>

$G: '.:>A H.F'

$G> H.F' .H


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$ontinue 999

.igure 1# 5catter plot or 2eater 1actor

H1_C

H1_T

H2_C

H2_T

E<:

.:@E

?.E'@

.


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$ontinue 999.

8eer rom 1igure >, we ound not have anystrong correlation between parameterswhere heater no. ' current unit!, 2'K$L

heater no. ' temperature unit!, 2'KTLheater no. : current unit!, 2'K$L andheater no. : temperature unit!, 2'KT.

The relationship between the all variableswith /earson correlation coeDcientbetween ?E.E'@ to E.


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$ontinue 999

.igure '# 5catter plot or /ower /anel 1actor

LOOPER

ROLLER

PULLING

CUTTER

.H .E>> ?.'

F

?.EF< ?.E@@

?.:A


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$ontinue 999.

1rom the 1igure @, the scatter plot orpower panel with our parameters arelooper, roller, pulling and cutter, most o

the correlation coeDcient is to negativeone, the more the points will all along aline stretching rom the upper let to thelower right.

2owever, looper and roller have strongcorrelation with E.HE, with a :?sided 'M.


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Table '# $orrelation between 7ooper and 8oller$orrelations

)oo#er Roller

)oo#er/earson

$orrelation5ig. :?

tailed!+

'.EEE

?

><

.HENN

.EEE

><

Roller

/earson$orrelation5ig. :?

tailed!+

.HENN

.EEE

><

'.EEE?

>


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$ontinue 9999999.

1rom the coil actor reer to 1igure A, allthe parameters which is coil thic&ness,width and burr where no any correlation

to each others have. That shown verywea& correlation in this actor.

4 relationship between all parameters is

not apparent rom the plot, /earsoncorrelation coeDcient less than E.<pOE.E@!.


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$ontinue 999.

.igure 2# 5catter plot or 8aw %aterial $omposition1actor

'(#i)i G*+,i-.

T#"$i/# '-*#"0-h

E/"0+-i" &*#+!

HARDNE''

.:A@ .:HF .>E'

.FH ?.:>F

?.


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$ontinue 999.

The scatter plot o 1igure shows

some degree o associationbetween tensile strength andelongation brea& which the /earsoncorrelation coeDcient, r is aboutPE.FH pOE.E'!.


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Table /# $orrelation between Tensile 5trengthand

longation Brea& $orrelations

Tensile5trengt

h

longation Brea&

Tensile /earson $orrelation5trength 5ig. :?tailed!

+

'.EEE.

HF

.FHNN

.EEE

HF

longation /earson $orrelationBrea& 5ig. :?tailed!

+

.FHNN

.EEE

HF

'.EEE

.

HF


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$ontinue 999.

.igure 3# 5catter plot or 4mbient $onditions 1actor

Ai* 2#/1i-. 345$6

T#4(#*+-7*# 38C6

R#/+-i,# H74i9i-. 3:

Ai* E;h+"0# R+-# 3

.:@' .E>A '.E

?.


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$ontinue 999.

1igure F clearly shows a linear associationbetween the two variables air velocity and aire"change rate coeDcient o correlation which

r is P'.

ata lie on a perect straight line with apositive slope.

This indicates as the air velocity score gethigher, so will the air e"change rate in higher.


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4onclusion 55(

;e can conclude rom the $orrelationmodeling analysis or si" actors not allactors gave the strong correlation

between parameters.

;e ound that model or selectedparameters involved in beltline

moulding process actor as shown Table below as a conclusion.


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$ontinue 9999

Table # %odel or 5trong $orrelation or 5elected

/arameters

$G' $G> 4irvelocity

$G: E.H< E.F E.F '.E ?

4ir "change 8ate ? ? '.E


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8eerences

(') 2. Ga3ici, 'HHE, Implementation o 5/$ techniques in the/-$ pipe industry, ngineering %anagement Qournal, :.

(:) www.ampe.com6gloss.html

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modelling in manufacturing industry para

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