Embed Size (px)
Citation preview
Optimization of Plate Forming Process Parameters
ByMohamed Jolgaf
Prof. Dr. S.B. Sulaiman, Dr. M. K. A. AriffinDr. A. A. Faieza and Dr. B. T. H. T. Baharudin
Institute of Advanced TechnologyUniversiti Putra Malaysia
Manufacturing Process
Independent variables
•Starting material•Tool Geometry•Workpiece geometry •Amount of deformation
Dependent variables
•Nature of metal flow•Stress, strain, deflection •Defects: Wrinkles, laps ...•Material Prop. of product
The Engineer has direct control on
Independent Variables (DV)
The Engineer has no direct control on dependent Variables (SV)
ExperienceExperiments
Modelling
The Material is Al-MMC1. Metal matrix composites consist of a metal or alloy as the matrix
and the reinforcement (particulate, whiskers and long fibers).
2. Particulate MMC can be conventionally processed (casting), then secondary processing such as forging,
extrusion and rolling can be used
3. MMCs are very attractive to aerospace and automotive applications (↓density, ↑strength, dim. stable at ↑Temp.
4. Formed products normally have better mechanical properties than their casted or machined counter parts.
For example, the Boeing 747 has about 18,600 forgings (Acharjee, 2006)
5. Research on metal matrix composites is still very limited (Sapuan and Mujtaba, 2010).
Particulate
Whiskers
Long Fibers
FE Analysis and Optimization Finite element modeling can greatly reduce testing and time during product design
FEA steps1. Creating model Geometry.2. Define Material Properties.3. Generate Mesh.4. Apply Loads.5. Obtain Solution.6. Present the Results.
What if we want to check another dimensions (DV) to reduce the strain or stress (SV)?
Do we repeat the FEA steps every time we want to check (DV)
Here we need to use Optimization in conjunction with FEA to really save time
Optimization Terms1- Design Variables. usually geometric parameters such as thickness, angle, radius etc that will be varied during the optimization process.
min < thickness (t)< max
2- State Variables. usually represent some design response and offer a means of limiting the design such as (stress, strain, laps, necking)
Strain (ε) < % of fractural strain
3- Objective Function.A state variable to be minimized. (for maximization 1/x)
State variable that represents the (1/tanθ) has been found and then minimized
Design Variable can not be defined as Objective function
To do conduct optimization on ANSYS we need to use APDL
APDL is a scripting language which can be used to automate common tasks or even build a model in terms of parameters
(variables).
windows APDL editor (http://www.apdl.de/cats/downloads/syntaxeditor_v1_7.zip)
Building the model parametrically will allow ANSYS optimizer vary theses parameters during the optimization process.
Copy and past the following lines in ANSYS command prompt. !*create, Analysis File Name/TITLE,sheet metal forming/PREP7*AFUN,DEGFRICTION=.1THETA=80 !Change to 40 and note the change x1=.06x3=.03offs=.04R1=offs/sin(THETA)x2=R1*cos(THETA)t=.006radius=.005K,1,0,0,0, ! Key Pointsk,2,x1,0,0,k,3,x1+x2,offs,0,k,4,x1+x2+x3,offs,0,k,5,x1+x2+x3,(2*offs)+t+.00001,0,k,6,x1+x2-(t*tan(THETA/2)),(2*offs)+t+.00001,0,k,7,x1-(t*tan(THETA/2)),offs+t+.00001,0,k,8,0,offs+t+.00001,0,k,9,0,offs+.000005,0,k,10,x1+x2+(X3),offs+.000005,0k,11,x1+x2+(X3),(t+offs+.000005),0k,12,0,t+offs+.000005,0,K,13,x1+(.4*x2)+.001,offs+.000005,0K,14,x1+(.6*x2)+.004,offs+.000005,0K,15,x1+(.4*x2)+.001,(t+offs+.000005),0K,16,x1+(.6*x2)+.004,(t+offs+.000005),0LSTR, 2, 1 ! LinesLSTR, 3, 2LSTR, 4, 3LSTR, 6, 5LSTR, 7, 6LSTR, 8, 7LSTR, 9, 13LSTR, 13, 14LSTR, 14, 10LSTR, 10, 11LSTR, 11, 16LSTR, 16, 15LSTR, 15, 12LSTR, 12, 09LSTR, 16, 14LSTR, 13, 15LFILLT,2,1,radius+(t/2), , ! Fillets RadiiLFILLT,3,2,radius-(t/2), ,LFILLT,5,4,radius+(t/2), ,LFILLT,6,5,radius-(t/2), ,
ANSYS Parametric Design Language
ANSYS Parametric Design Language
APDL enables the user to read many results after processing (/POST1) and defined these results as (SVs) to limit the design
space.
This will help ANSYS optimizer refine the optimization search and omits the Infeasible results.
/POST1*GET, react, NODE, 1, rf,fy, ,FoForce=-reactPLNSOL, S, EQV, 0, 1,*GET, eqvsts, PLNSOL, 0, MAX, , ,PLNSOL, eptt, EQV, 0, 1,*GET, eqvstn, PLNSOL, 0, MAX, , ,PLNSOL,CONT,GAP,0,1.0*GET,gap1,PLNSOL,0,Min, , ,
congap=-gap1*get,k14y,kp,14,loc,y,,*get,k14x,kp,14,loc,x,,*get,k13y,kp,13,loc,y,,*get,k13x,kp,13,loc,x,,*get,n14y,Node,10,u,y,,*get,n14x,node,10,u,x,,*get,n13y,node,9,u,y,,*get,n13x,node,9,u,x,,slop=((k14x+n14x)-(k13x+n13x))/((k14y+n14y)-(k13y+n13y))
finish*end*use, Analysis File Name
/OPTopanl, Analysis File NameOPVAR,THETA,DV,45,80,,OPVAR,t,DV,.003,.006,,OPVAR,radius,DV,.005,.02,,OPVAR,EQVSTN,SV, ,.75, ,OPVAR,congap,SV, ,0.1*t, ,OPVAR,slop,OBJ,,,,OPTYPE,SUBPOPSUBP,30,7,OPEQN,0,0,0,0,0,OPEXEfinish
Such as:
A design set with high strain (higher than the fracture strain ε = 0.75 )
Or with large contact gap 0.05t (sever necking more than)
Results
The optimizer runs the analysis file 8 times (8 iterations). Four sets are infeasible and 3 sets are feasible. the optimal design set
is one of the feasible design sets with maximum angle which is set no 8.
0.1
0.3
0.5
0.7
0.9
1 2 3 4 5 6 7 8Optimization iterations
Eq
uiv
alen
t S
trai
n
40
50
60
70
80
ben
d A
ngl
e
Equivalent Strain
Bend Angle0
1
2
3
4
5
6
1 2 3 4 5 6 7 8
mm
Optimization iterations
Pla
te t
hic
kn
ess
Con
tact
Gap
0
2
4
6
8
10
12
14
mm
Ben
d R
adiu
s
Plate thicknessContact GapBend Radius
AngleThickness
mmRadius
mmEqu. Strain
Contact Gap mm
SET 1 Infeasible 80.00 6.00 5.00 0.82 2.36 0.176 0.394
SET 2 Infeasible 74.65 4.37 10.82 0.40 0.51 0.274 0.117
SET 3 Feasible 69.23 4.61 14.27 0.33 0.44 0.379 0.095
SET 4 Infeasible 56.21 5.38 13.73 0.33 0.70 0.669 0.131
SET 5 Feasible 78.51 3.11 10.91 0.38 0.21 0.203 0.067
SET 6 Infeasible 79.63 3.03 6.79 0.47 0.37 0.182 0.122
SET 7 Feasible 79.85 3.01 8.51 0.43 0.27 0.178 0.089
*SET 8* Optimal 79.91 3.01 8.66 0.43 0.26 0.177 0.086
% of contact
gap
Objective Function
Design Variables State Variables
Results
The optimal Set Infeasible Set
FEA and optimization techniques are used in metal forming simulation to reduce the need for testing and experiments and to
save time in order to achieve the optimal design
