Post on 13-May-2020
MODELLING, OPTIMIZATION AND CONTROL IN SHEET METAL FORMINGTON VAN DEN BOOGAARD
Modelling of forming processes Optimization Deterministic Robust
Control
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CONTENTS
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NUMISHEET BENCHMARKS
20021996
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INTEGRATION IN PROCESS DESIGN
2005
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MATHEMATICAL OPTIMISATION
Solving
Optimisationalgorithm
Objective Constraints
Design variables Modelling
Design of Experiments (DOE)
Run FEM
Fit and validate metamodels
RSM and Kriging
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APPROXIMATE OPTIMIZATION
Modelling
Evaluate optimum (FEM)
Optimise
SEQUENTIAL APPROXIMATE OPTIMIZATION
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DETERMINISTIC OPTIMIZATION
Optimized
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DETERMINISTIC OPTIMIZATION
μz
σz
INPUT
FEMDesign variables
Explicit constraints
Objective function
Implicit constraints
RESPONSEμf, σf
μg, σg
x f
g
Response
ROBUST OPTIMIZATION
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ROBUST/RELIABLE FORMING PROCESSES
Reliability
Robustness
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ANALYTICAL TEST FUNCTION
What if noise??? x1,x2 ~ N(μ,0.4)
02
46
810
0
2
4
6
8
100
50
100
x1x2
f
Deterministic constrained optimum
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DETERMINISTIC CONSTRAINED OPTIMUM
Scrap rate 50.3%
Response f Response g
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CONSTRAINED ROBUST OPTIMIZATION
Deterministic constrained optimum
Robust constrained optimum
Modelling
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MCA ROBUST OPTIMUM
Scrap rate 0.1%
Response f Response g
Punch
Final productDie Sheet material
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APPLICATIONV-BENDING PROCESS OF A SPRING PART
Forming process
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APPLICATIONOPTIMIZATION MODEL
Parametric FE model
Objective f:
4 Design Variables: D, …
Constraints g1:g2:
D
2 Noise Variables: t, σy
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APPLICATIONMETAMODEL RESULTS (60 LHD)
Objective: Main angle
t
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APPLICATIONMETAMODEL RESULTS (60 LHD)
Constraints: Transition angle
USL: 96°
LSL: 92°
USL: 96°
LSL: 92°
Feasiblet
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APPLICATION
Robust optimization
0.45 0.5 0.55 0.6 0.65
0.45 0.5 0.55 0.6 0.65
88
90
92
94
96
98
88
92
96
100
104
0.4
0.4
Current process setting
Optimized setting
Reference setting
Sequentially Optimized setting
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EXPERIMENTAL VALIDATIONNOMINAL PROCESS DEPTH SETTING
Production trial runs with different coils (min-mean-max thickness)
Trial run 1
Trial run 2
Trial run 3
Numerical model
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EXPERIMENTAL VALIDATIONOPTIMIZED PROCESS DEPTH SETTING
Trial run 4
Numerical model
21
MONTE CARLO ANALYSIS
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CORRELATION BETWEEN YIELD STRESS AND HARDENING
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Rp
n
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PRINCIPAL COMPONENT ANALYSIS (PCA)
PCA- Orthogonal- Uncorrelated- Normalize data set- Linear combination
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PROPAGATION OF VARIATION
?
mean
variance
skewness
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SKEW NORMAL DISTRIBUTION
Azzalini et al., 1985
Paulini et al., Jan 2017
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EFFECT OF SKEWNESS PARAMETER
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REPRESENTATION OF STATISTICAL DATA
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SKEW NORMAL DISTRIBUTION
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NO FEASIBLE SOLUTION
What if no robust optimum can be found?
PLC
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SMART FACTORY CONCEPT
Multi-stage manufacturing process
Step 1 Step 2 Step N
capture interactions in metamodels
in-depth process knowledge process control &
actuatorsmeasurement
systems
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DEMONSTRATOR PROCESS
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BENDING PROCESS OVERVIEW
Nothing734
Second bending733
Anglemeasurement
732
angle732
punch_stroke733
Strip movementFirst bending
735
bending_force735
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TEST RESULTS
0 1000 2000 300032.5
33
33.5
34
34.5
35
product number [-]
angl
e [ °]
0 1000 2000 300033.5
34
34.5
35
35.5
36
product number [-]
angl
e [ °]
No control Linear control
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FINAL BENDING ANGLES WITHOUT CONTROL
2800 2850 2900 2950
32.5
33.5
34.5
product number [-]
angl
e [ °]
2900 2920 294034
34.5
product number [-]
angl
e [ °]
Controlledstage
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PREDICTIVE MODEL CONTROL
Punch displacement
Punch force Final angle
Overbending Backbending Anglemeasurement
Control input
Main question:
How to use the punch force as input for control?
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PREDICTIVE MODEL CONTROLSELECT FORCE CURVE CHARACTERISTICS
-0.04 -0.02 00
0.5
1
Time [s]
Forc
e [k
N]
-0.04 -0.02 00
0.5
1
Time [s]
Forc
e [k
N]
-0.04 -0.02 00
0.5
1
Time [s]Fo
rce
[kN
]
0 0.05 0.1 0.15 0.20
50
100Nominal
allowed error [°]
scra
p ra
te [%
]
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VIRTUAL RESULTSSHEET THICKNESS CORRELATION
no controlfeedback1 force point8 force points17 force points
0 0.05 0.1 0.15 0.20
50
100sheet thickness ρ = 0.9
allowed error [°]
scra
p ra
te [%
]
0 0.05 0.1 0.15 0.20
50
100sheet thickness ρ = 0.8
allowed error [°]
scra
p ra
te [%
]
0 0.05 0.1 0.15 0.20
50
100sheet thickness ρ = 0.99
allowed error [°]
scra
p ra
te [%
]
Wednesday 11:00-16:00, Session 400Applicability of in-line Controls in Industrial Sheet Metal Forming Processes
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IDDRG 2017
Modeling of forming processes is mature Optimization requires consideration of robustness Product-to-product variation dominates the presented process Linear control only corrects long term variation Model-based (feed-forward) control has potential to handle product-to-
product variation
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CONCLUSIONS
Bert GeijselaersMartijn BonteJan Harmen WiebengaJos HavingaOmid Nejadseyfi
European Commission, Dutch funding agencies, Tata Steel Europe, Philips
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ACKNOWLEDGEMENT
12-13 October 2017Enschede, Netherlandswww.utwente.nl/ftf2017
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FORMING TECHNOLOGY FORUM 2017MODEL BASED CONTROL FOR SMART FORMING PROCESSES