EDSA(2)
E.2. I SchwefelBEl&
E.2 jlX 7‑Bg&%@?r=J&eEaagi 163 Velocity [m/s]
Fig. E.3 Optimum values of each trial by using CA Velocity [m/s]
Fig. E.4 Optimum values of each trial by using MSSA
E.2 5:A LF;q&%#?f=JEE#[1=mi
164 (J‑JT..JtE 53uEI{l=1D‑L}7pSPuiLJirf1)I,J:!ITl‑;A(E7)Lj11)XlhO)J[1F.fJtE.a.iTJd A 8 Schwefel Bg&%jgu'T*ER%1i? T=. Eq.(E.3)L2Schwefel LAgkO)&f#ri?1b 8.
i‑1
f(x1,...
,Xn) ‑418.9829n‑E
n(E.3)
h$lR#Eg%0 5;Xj i
500(i
‑ 1 ‑n)
7:1%r]bt L,f=t%, Bi,]lh$8ix*‑[421,...‑,421]
tr38.
E.2.2 2
2&C=h'Bj.8&i51bgB!
2 'J5f&I(E=.TD‑(,}73Ii‑li&(ElS[!i‑i)!&/(i‑‑?r=. .i‑1*j=8ijB(̲TJ'T'.J7)Ljt7)Xbti Table 5.I i fEj]L:
bO)i, MSSA8iTableE.1 8=/jilbO%bi3U'r=. a =?1, TableE.1 0);&E8=?U,I
(i:, 9'tJ{}J 7J‑ 0)[.rulHii (ijih ,.‑‑..I;.(LlliJiufkl=‑> i i L'‑ Lj0)tiL1'‑‑W}L,))URLI L,fd:U,f=b[L').'J!
:I.r?1.'uJJqi:I)1i fJ:E), 3: f=)J1,W.Jrj:ill3LunJ.'II‑o)‑).‑'‑.i.&1Y5J!b73 J,Z,AEhSa5 8 r= a, 5'r)J7)V jt 1)XhLO)‑&JIE (wR, ba)%1i?TU)8. S[h7)Vjll) XhLO)‑dkL=?U,T8L 10tglt?)
Rrnax 0)#Bg%Srh8=18rcb8= wR ‑ 0.1 a L, 3:r=5FEfjb'6 30[91o]nlfO)J#6B%Srj]
*JL't18r=bba ‑ 0.3 i Lr=.
Table E. 1 Parameters for MSSA Number OI Variable 2 Number OI population 30
Radd 0･001
F12m 0･1
WR 0･1
ba 0･3
Fig. E.6 8ihSfW L,r=BSo)RfE4RR%'j%1. Fig. E.5 ttEF&18 i, Bi4g (421,230)tBE IS!(230,421)L=h‑U'TB?x5RB,h'i** L,TU'8 = tb,6SL&EE%b?TU,8 a tb'ibh,8.
Fig. E.7 8=Bii@h$7?1b 8AilS! (421,421)%#ji18 2?>L={#b'? f=HXBitglt% a: i a f= i, 0)}=y7Ji1. fd‑JDllll.‑)(fu̲1tiFx(i45 Iji‑[ti L,rCU'8. I.'),'fiFJT.0),1:fi:‑"i,3B[L‑,.n'J7)LjlT)i
i>7:.(i 7 JHH=f0);I.‑i‑Uj‑‑0)[fL‑:E=).J[‑irTInJ.'li(=J''l'L'77 TL ,8 I i h'ibh,8. uXni IH̲.ftO)LLT9t[Lb‑iET:l1‑i 8 i, iB[ZBg7)Vjh1) X7{Eli 24 tglt7t>? 7tO)L=}qL,I, MSSA?1i 14 taft?,uR#
b'iliATU'8 = tb'ibh' 9 ,
,SBB!,42[97o]0)#gJr$8Eo)i9*r6]tbS5a641r=. r3h‑, 5F
tgblB%8iiEIiiBq7)Vj1)
XhLb,i 27 taft MSSA bH5.2 tglWhb 9, 44[97.]0)ih*6]ttrd:8.
T,[ja‑ZAiBk&7:1L&*R8E%Ef[d L,JtfB%% Fig. E.8 8=ii1. = 0) i i
Jj5
5;0.5 tr3?7tt%%uX*tgLLtt L,TSEB, tE*R%1i5. 3:f=
Ji5
5;0.5 i=fr]3iL,r3b,?T=tB%LiBi )(Ttb7a 45 ttJ̲‑[tr.rlTmitii‑(ti L,f=.E.2 j=X hBg&%6E?r=J&#EgFgi 165
0 I00 200
X̲axis
300 400 500
Fig. E.5 Location of the searching points using CA
501
ol 0
*i
I
I J̲
200
,i
=a
500 X‑axis
Fig. E.6 Location of the searching points using MSSA
:?'l. . .:‑. .i='./I.+I./.. /.:"I:!t...
./:.!/.I..//.:i.:....̲‑i..rJ ̲‑ ::..{.. .:,.:.
I‑:...ilhl
̲:... .:
I
;:l'...::.!lI..: I.:..:.:I.::.:..
..;:,/
;'l...
.!l.../!.:lL. :I.:.I. ..:.:...;:I:.I }L,j1) Xb7?h8n4 tg1‑e7:{1?f=0)E=#1 L,I, MSSA Th8j: 7 tg1{'EIL&*hS11kTL,,8 I i hbh'9, *B5R, 50[%] 0)#*r6Jthq3i6nr=. r3:A‑, iFfEgo)iB%L*iBliiBq7)VjnT) XI Llhli16.5 tglt MSSA hu7 tgltT>b 9, 59[97.i0)#*r5ttr3%.
I(.h :i :I:J :‑ :....'.:..::.:I‑/.::I...:.I̲..I;:.:::..I.1:lt/I.:̲:I. lJ. ./ ::I...ll Convergence to the local solution
=
･Bd 35
= O
g30
bJ) V
S25
U bL)
g20
E O
U l5
0 1 2 3 5 6 7 8 9 10
Number of trial
Fig. E.7 Convergence generation of the optimization using GA and MSSA
= O eG L O G U bJ) O O
= U bJ) L O
>
= O
U 35 30 25 20 15 10 5 0
0 1 2 3 4 5 6 7 8 9 10
Number of trial
Fig. E.8 Convergence generation with median of the optimization using CA and MSSA
E.2 jlX I.Ba&%J*‑3f=lJ$8Eaa4i 167 E.2.3 5
*#L=iH+8&i51E#5R
(I..̲.::̲J I:i.I.:
.I:.:i<7:.)..:‑/ll.. .I. I;/I!L:i.;I: ..̲/:I+... . /:I‑:ll..I.I//.:..:.J.
(421,421,421, 42l, 421)%5iBiTa i:?.B=%+h'?r=L&#tg:1t%Stii18. Fig. E.9 L=uX f%tglt%3: tbT=tO)i)‑J<1. fgh.Bij(tglt&B3: 250 tg:1tt L,TU,8, L&#tg:1Leo)FP 5klli7%gx8 i. ii31ZW.]7)i/j17) 7:1LhT.Ln43 tgltfEt? f=0)L=*4 L,I, MSSA 7=.Li 64 tg
I/:::./I::.‑..I...:.:;:‑..i:.:..i,'/.I:. I:I:il..IJ.L J‑:./‑:, I.:I. ..L..:..
'..
i ..i:.,‑.:.Jl.
r3h‑,
‑1!‑‑EJO)ti3&L3:iEiliil'T'J7)Lj1I) XtLhH71 tg:1‑iMSSA hV2.2 tgli:77,A9 , 48[%]
a)iL))*[[',IT..tri8.
E=
O cd
L 4) E]
O bD U O F:
O tLO
〜 O
>
= 0 U
0 1 2 3 4 5
Number of trial
Fig. E.9 Convergence generation of the optimization using GA and MSSA (5 variables) T,IW7Ji&jgyE.L&*%aE%3Ji4W Lr=j%%% Fig. E,10 8=j<1. = 0) i i 〜? 5 0.5 i r3?
r=t%%L&5EtglLet LT5̲tB, tt*%7i5.
llll..;I.
I.I..
..;/Ill/I.:.i "..:"I:̲i‑ . I.... LIL..‑l!.+.::..:lJ::..:‑..:.:!.I. ... ..i.:
‑ /:Ill"i.I.‑.. .::I..;.:..iI.. ‑. I.J; 'l.ll. I.:.../I.i.:.'tll..:....'...:.;'/:.
..l'!...:.r.I
)I/j' T)Xhi>Tlj: 48 tglt7:{h?T=q)E=}4 L,I, MSSA 7;13:29 li#1Le7TtL&#hSliA TL,,8 = tblbb' 9,
,%Ji‑nL40[%] 0)Ja*6]̲thSfa6rLf=. f3h,, 5FiEgo)iBt%L3:i81ii&g7)Vj1]) XhLlhq 62.4 tglt MSSA bS28.6 tglt?tb 9, 54[%] 0)ih*6]J=tr38.
l'iX :.
I.
[ .‑. ;̲..̲;.i:..ll.;::I:/:I.LIL;'j..el
..lil;./.I.:
:̲rlJ /hlt .:.:I
.'l:'..
s=
0 cdh V
= U bJ)
O U E=
O eLD L 4)
>
E O
U
0 1 2 3 4 5
Number of trial
Fig. E.10 Convergence generation with median of the optimization using GA and MSSA (5 variables)
E.3 2tb
**7?1 B3:i&LiiW7)i,j>T) XhL i $4@fE*gB%i*lE7)I/ j1) XtL (MSSA) 0)*aE%11?
I̲.
..I/I...̲./:.:I.I:‑/ l<i.::.I.I.:/'+I.: . I/I..3l̲.̲:..:...I..",//:.I::.../:..:ll::./. I...:.
#Eo)tE& i , [email protected]*4fFBO)‑??,A 8 Schwefel Bg&%jn'TuX5kJEEaEo)tE:*%1j:?
:‑. ‑:.
.:../I.I.̲.‑.'::"J I./.::;lJ.:.I.(I.(A:h.:.5̲!lI(I./:I.:̲.I
;: /..I.:.::i./../:.̲:..:.:I:.
i I./.:‑.:li.
,'.i.Y̲::
.:..:::;I/.I./̲;.I.1.I. :I I..:::
I,I.i. r.‑.:.A.Tl..‑I:lI/I.
･::..."i::I.rl/..i.‑.I..i‑!tll /̲I.:I :I:I.I::i.. .::i/:.::I..I..i.I;::.I..:.i :.I."1Y.:. I.lI .."
I
.:̲i:I.. : i:....:I:I... ::/I:I: I: ::I..:.
‑̲
ll 'l....I;.:;...i.1liLJ1..I.!L:i.i:̲Ll.i1/.ll.:I:̲.'I/.i.I:Ill
::.I I...I.:.: I:: :II I:.:.I. :I !.I:. ..‑:: h.htu.lL.1 1li'::I‑I:!.i.JILL.:ll:i.;.:i:ll:i../.;:I:/::.I...!l'
I
...Jl.:.. /.''../;..: ll..:I::.I
I.
‑.::.
169
1q S3F 91tJ{hR L7oi>./‑?ti,0)&i5#
T{3E^h t= EelT i iEhni5*
I 4i EETtill{jlL,T=ll‑i;a(E7)L jt T)7:LhO)W)‑Lilt:.i:yf‑J‑‑?T=. j;,I.5!/RLi(ffikAJ,[IJ.Itn:(Low Speed),t5igAltHAJT (HighSpeed),iEliiBg7)Vj1) XhL>?hR5f? r=AS(GA), *1SiEi#
iRg7)Vj1) XhL7?A‑R5:? f=# (EDSA), Sf@fE*7)Vj1) Rb?R3f? 7tRS (MSSA), S*fBq]7)Vj1) XhL8=fFB#*7)Vj1) XhL%i&jg LTR3:? T=# (MSSA+SSA) q)
['lL.;‑I‑6 ^kJ'T‑‑FLYi T'L ,Tit‑7 f=. I 0) i f[[h)H L,r=8)i9 }‑9 t4J..LfJ.iJ'‑J7[T‑i‑[LLJ&1YTable
F 8=/j%1, 9;,S*#BB!% Fig. F.1 ‑ Fig. F.6 L=/jiL,, %41EHA4#8=h‑Lj‑87h1) x9‑a5*
i&q)5ii&i5G58=?UIT3: taJtbD% Fig. F.7 8=/j%1. Fig. F.7 A,6, %B3i@lE7)Vj1 7)XL tilR3gh‑ a:Uhf53gaJtH#J# (Low Speed & High Speed)8=tE<TRf&f5G5bS,j>r3 <
fJ? TU '73 I i JJ1'1bh'7iD.:);L!jL')freti,LliI(j'‑fkf[nJ‑.'I!i1{'(I:):64t?3 I i 7t7Jn ngi L,7T=iliiuE7)Ljt T) X'L{LTtb ,r5i‑;ui(L1'LLiC[.):;'r3uiJ*U)L jl T)XIJEDSA) i
,.$1VuilJilII7 )Vjt T)X'LJ=JJ:iJ.rurlE.,<;
7)Vj1 1)XhL%iRb3AjtJE7)VjU XIL> (MSSA+SSA) 8=ck8#aB%?h8L j>Iin8=AW fiLqO;ii‑‑n7'}ir./(LJ:TL'7ED I i lf't4)i)'73.?U.7?.llkui[rf.1thuI]7‑?tb‑)f=$7*1Jir fT7)Ujt T) XtL
(MSSA)?,tiiEliiBq7)I/jll)XhL> (CA) i Ri;R8=faWr3,SBBi%,ji L,TU,8. 3:r=S*fE
*7)Vjll) XhL (MSSA) 82LIB3Ei#]2#7)Vjll) XhL (EDSA) 8=tE,i 150 ‑ 300 FE2 Z!h
# 9 ig L,a+SF&0)fE96uS5i3A3:n8 a i b 8=, MSSA 8ii6j% (llSW) i=T Schwefel ri!:‑J#'(I.i,‑(:I‑I;1‑JulYIiJLTJ;JigI i h'i77ti T=. L f= h',I7 I $6ruiLJiLI‑17)V j1) XtL l=JJ:WurnSJ*U )Lj1) XIL%5AbiAjL,7Z7)Lj1) RhL> (MSSA+SSA) %B]u,8 a i?1, 18ih5P < A,?fa /(J'‑rd:[nJ.'II}iT:frJ:/3I i i/,'T.i 7J.
TablelaUle F 1 Plungerr･1 rlunger Velocity Parametereloc't OI eaCn Input.
Xl [m] X2 [m] Vl [rnIS] V2 [nJS] V2 [mIS] tfill[S]
LOW SPe2d 0･05 0･1 0･21 0･21 0･21 2･86
High SPeed 0･05 0･1 0･50 0･50 0･50 l･20
GA 0･182 0･252 0･25 0･4 0･23 l･54
EDSA 0･015 0･105 0･27 0･24 OJ6 lJ2
MSSA 0･261 0･255 0･42 0･23 0･48 l･52
MSSA+=SA 0･214 0･253 OJl 0･24 OJ6 l･58
l(Ll ; I.I. I/.:‑:j
I".‑
I.: ...Ll..ll.ill:.:..I:::‑I;:.:..::i.I.:.H:
Fig. F. 1 Experimental results of low speed injection
Fig. F.2 Experimental results of highspeed injection
171
Fig. F.3 Experimental results of the optimum injectionderived by GA
Fig. F.4 Experimental results of the optimum injectionderived by EDSA
I::..I I. I.I. lt Ll:.I.;.":. .I. ...:..lil.J.I..̲::I̲"I.'..:.:.::....:,i..
Fig, F.5 Experimental results of the optimum injectionderived by MSSA
Fig. F.6 Experimental results of the optimum injectionderived by MSSA with SSA
Ne 1.5
Qh
3
1.4A
=
･E I.3
Ld Cg
L= I.2
c<
q>
iH.I
Low speed
High speed
GA EDSA MSSA
MSSA + SSA
Fig. F7 Area of air bubbles of each condition
175
B] E] 3R
1.1 Comparison of the fluid behavior between experimental result and simulation result... ...
1.2 AconceptiondiagramofdiecastingandCFDsimulation...
1.3 Multimodalsearchspace...
1.4 Costfunctionofdiecastingprocess ...
2.I Pictureof6DOFmanipulator ...
2.2 Detailsofthespoonshape...
2.3 Meshsettingofthespoon ...
2.4 Referenceandexperimentalvelocityofthespoon...
2.5 Experimentalresultofthespoontransfer ...
2.6 Simulationresultofthespoontransfer...
2.7 Setupcoordinate...
2.8 Experimentalresultofangle. ...
2.9 Experimentalresultofangle...
2.10Experimentalresultofposition ...
2.ll Experimentalresultoforientation ...
2.12 Experimental result of position 2.13 Identifylstmotorofstepinput ...
2.14 Block diagram of feedback for motor control 2.l5 Identify lstmotorofsinewaveinput ...
2.l6 Identify2ndmotorofstepinput...
2.17 Identify 2ndmotorofsinewaveinput...
2.18 Identify3rdmotorofstepinput ...
2.19 Identify3rdmotorofsine wave input ...
2.20 Identify4thmotorofstepinput ...
2.21 Identify4thmotorofsine wave input ...
2.22 Identify5thmotorofstepinput ...
2.23 Identify5thmotorofsine wave input ...
2.24 Identify6thmotorofstepinput ...
2.25 Identify 6thmotorofsinewave input ...
2.26 Direction of the spoontransfer...
2.27 Measurementplaneofthespoon...
2 4 8 8 12 13 14 15 16 16 17 21 21 22 22 23 25 25 26 27 28 28 29 30 31 31 32 33 34 35 36
176 BI B )A
2.28 Planning of velocity curve to derive the transfer path with spilling avoidance 2.29 ConceptualdiagramofGeneticAlgorithm...
2.30Exampleofone‑pointcrossover ...
2.31 0ptimizedresultofvelocityandaccelerationcurves...
2.32 Angularvelocityof6DOFmanipulator ...
2.33 Fluidanalysissimulationresultofliquidtransfer ...
2.34 Experimental results of each anglervelocity of 6DOF manipulator by the proportionalcontro1 ...
2.35 Experimental results of each angler velocity of 6DOF manipulator by the hybridshapeapproach...
2.36 Experimental results of each angler velocity of 6DOF 'manipulator by the proposed method.
2.37 Transfer path by the each controller ..
2.38 Experimentalresultbytheproportionalcontro1 ...
2.39 Experimentalresultbythehybridshapeapproach...
2.40 Experimental result by the proposed method.
3.1 Pictureofthecoldchamberdiecastingmachineandthemold...
3.2 Resultsofabristertest (ve‑ 0.5[m/s],vh‑ 1.0,2.0[m/s])....
3.3 Entrainmentmode1. . . . . . 3.4 Meshsettingfor CFD simulation .
3.5 Result of air entrainment searched all velocity.
3.6 Simulation result ofve ‑ 0.50[m/s]
3.7 Simulation result ofve ‑ 0.21[m/s]
3.8 Resultofabristertest(ve‑0.21[m],vh‑2.0[nJs]) ...
3.9 Simulation result ofve ‑ 0.26[m/s]
3.10 Resultofabristertest(ve‑0.26[m],vh‑2.0[nJs]) ...
3.ll Plangervelocitiesofeachresult ...
3.12 Distinctionoftheshuttingtheairinthesleeve...
3.13 Velocitycurveofaplunger ..
3.14 Optimization result.
3.15 Velocitycurveofoptimumvelocitywith5variables...
3.16 CFD simulation result using optimum velocitywith 5 variables...
3.17 Resultofabristertestusingoptimumvelocitywith 5 variables ...
3.18 Resultofabristertestusipgoptimumvelocitywith 2 variables ...
4.1 ConceptoftheEDSA .
4.2 Extremumplotexample .
4.3 Individualselection .
37 39 39 40 41 41
44
44
45 45 46 47 48 53 53 55 56 58 59 59 60 60 61 61 62 63 65 66 66 68 68 70 71 73
[txTH ;A 177
4.4 Simplexcrossover...
4.5 FlowchartoftheEDSA . . . . . . . . . . . . . . . . . . . .
. . .
4.6 ThecostfunctionoftheGAandEDSA . . . . . . . . . . . . . . .
4.7 0ptimumplungervelocityinputofGA ...
4.8 Optimum plungervelocity input ofEDSA..
4.9 0ptimumplungervelocityofGA ...
4.10 0ptimumplungervelocityofEDSA...
4.ll AnotherresultofcostfunctionofGAandEDSA . . .. . . .. . .
4.12 0ptimumplungervelocityinputofGA ...
4.13 0ptimumplungervelocityinputofEDSA. ...
4.14 0ptimumplungervelocityofCA .... ...
4.15 0ptimumplungervelocityofEDSA...
4.16 ResultofbristertestofCA. . . . . . . . .
4.17 ResultofbristertestofEDSA . . . . . . . . . .
4.18 AnotherresultofbristertestforGA . . . . . . . . .
4.19 AnotherresultofbristertestforEDSA . . . . . . . .
5.1 Flowchart of the Multi‑subcenters Solution Search Algorithm....
5.2 Generatedagentsbyusingrandom...
5.3 Basicconceptofdistributecontro1...
5.4 Setting the searching point of next generation..
5.5 Movingconceptofsearchingpoint ...
5.6 Attractiveforcealgorithm ...
5.7 Arranged agents by using distribute control after the random location 5.8 Example of relationship between two variables and air entrainment.
5.9 Convergence performance comparison between MSSA and GA.
5.10 0ptimumvelocitycurvecalculatedbyGA...
5.ll 0ptimumvelocitycurve calculatedbyMSSA....
5.l2 Simulation result by using optimum velocity curve calculated by GA 5.13 Simulation result by using optimum velocity calculated by MSSA . 5.14 Experimental result by using optimum velocity calculated by GA..
5.l5 Experimental result of optimization calculated by MSSA....
74 76 77 79 79 80 80 81 81 82 82 83 86 87 88 89 92 93 94 96 96 98 99 loo 101 102 102 103 103 104 105 6.1 Location of search points by using multi‑subcenters solution search algorithm 108 6.2 Multi‑subcenters Solution Search Algorithm adoption of Space Searching
Algorithm ... ...
6.3 Velocity curve byusingthe MSSAresultmodifiedbySSA ...
6.4 BehaviorofthefluidbyusingtheMSSAresultmodifiedbySSA...
6.5 ExperimentalresultbyusingtheMSSAresultmodifiedbySSA ...
110 111 111 113
178 E4 a >R
C.1 Overview of the automatic pouring machine.
C.2 Tiltingvelocityofpouringmachine ...
C.3 Resultofidentification. . .
C.4 Meshsettingofpouringmachine ...
C.5 Tiltinginputfortheparameteridentification...
C.6 Comparison offlow line between experiment and CFD simulation ...
C.7 Comparison of flux in sprue cup between experiment and CFD simulation C.8 Cost function. . . . . .
C.9 Tiltinginputofoptimizationresult...
C. 10 Comparison of surface height between optimal input and conventional input C.ll Simulationresultwithoptimumtiltingvelocity ...
C.12 Air entrainment at machining surface . C.13Experimentalresults...
C.14Defectfractionofmachiningsurface...
D.1 Locationofallagentswithoutdistributeinpattern1 ...
D.2 Locationofallagentswithoutdistributeinpattern2 ...
D.3 Locationofallagentswithoutdistributeinpattern3...
D.4 Locationofallagentswithoutdistributeinpattern4...
D.5 Location ofall agents with distribute inpattern1..
D.6 Locationofallagentswithdistributeinpattern2...
D.7 Locationofallagentswithdistributeinpattern3...
D.8 Locationofallagentswithdistributeinpattern4...
138 139 140 141 142 143 144 145 146 147 148 149 149 150 153 153 154 154 155 157 157 158 D.9 Location of the searching points using MSSA without attractive force algorithm159 D.10 Location of the searching points using MSSA with attractive force algorithm 159 E.1
E.2 E.3 E.4 E.5 E.6 E.7 E.8 E.9
Convergencegenerationoftheoptimizationtrials...
Determinate unbaiased variance of the optimization trials....
OptimumvaluesofeachtrialbyusingCA...
OptimumvaluesofeachtrialbyusingMSSA...
LocationofthesearchingpointsusingGA...
LocationofthesearchingpointsusingMSSA...
Convergence generation of the optimization using CA and MSSA
162 162 163 163 165 165 166 Convergence generation with median of the optimization using GA and MSSA 1 66 Convergence generation of the optimization using GA and MSSA (5vari‑
ables)
E. 10 Convergence generation with median of the optimization using GA and MSSA
(5variables)..
167
168
[1‑.<TH :yt
F.1 Experimentalresultsoflowspeedinjection ...
F.2 Experimentalresultsofhighspeedinjection...
F.3 ExperimentalresultsoftheoptimuminjectionderivedbyGA...
F.4 Experimental results of the optimuminjectionderivedbyEDSA ...
F.5 Experimental results of the optimuminjectionderivedbyMSSA ...
F.6 Experimental results of the optimum injectionderived by MSSA with SSA F.7 Areaofairbubblesofeachcondition . . . .
170 170 171 171 172 172 173
ji E] 3R
2.1 Maximumangularvelocityofthemotors ...
2.2 Settingmeshparametersofthespoon ...
2.3 Fluidparametersofwater ..
2.4 Linkparameterof6DOFmanipulator ...
2.5 Parametarofeachmotionmotor. . . . . . . . . . . . . . . . . .
2.6 Parametersforgeneticalgorithm...
3.1 Physicalityvalue (FluidpropertiesADC12) ...
3.2 Physicalityvalue(MoldpropertiesSKD61)...
3.3 MeshParametersfordie‑casting...
3.4 Quantityofairentrainment ...
3.5 Weightofevaluationvalue...
3.6 ParametersforGeneticalgorithm ...
3.7 Optimum plungervelocity.
3.8 Optimum plunger velocity.
4.1 Optimization results of the GA and EDSA.
4.2 AnotheroptimizationresultsofGAandEDSA ...
4.3 Plungervelocityparameters ...
4.4 Plunger velocity parameters of the another optimization results..
5.1 ParametersforGA. . . . . . . . . . . . . . . . . . . . . . . ..
5.2 ParametersforMSSA . . . . . . . . . . . . . . .
5.3 Performancecomparisonofoptimizationresults...
5.4 Experimenta1firstresult of optimization calculatedby GA ....
5.5 Experimental result by using optimum velocity calculated by MSSA 6.1 Classification ofmultivariate statistical methods.
6.2 SimulationresultusingtheMSSAresultmodifiedbySSA ....
6.3 0ptimumvelocitymodifiedbySSA....
C.1 Settingofthetiltinginput ...
C.2 FluidparameretsofAC2B...
C.3 Meshparametersofpouringmachine ...
C.4 Inputsettingfortheparameteridentification...
l2 13 14 18 33 40 54 54 57 58 64 64 66 67 77 78 85 85 99 100 102 104 105 108 109 112 139 140 141 141