MODELLING, OPTIMIZATION AND CONTROL IN SHEET METAL FORMING€¦ · MODELLING, OPTIMIZATION AND...

MODELLING, OPTIMIZATION AND CONTROL IN SHEET METAL FORMINGTON VAN DEN BOOGAARD

Modelling of forming processes Optimization Deterministic Robust

Control

3 July 2017IDDRG 2017, Munich 2

CONTENTS


NUMISHEET BENCHMARKS

20021996


INTEGRATION IN PROCESS DESIGN

2005


MATHEMATICAL OPTIMISATION

Solving

Optimisationalgorithm

Objective Constraints

Design variables Modelling

Design of Experiments (DOE)

Run FEM

Fit and validate metamodels

RSM and Kriging


APPROXIMATE OPTIMIZATION

Modelling

Evaluate optimum (FEM)

Optimise

SEQUENTIAL APPROXIMATE OPTIMIZATION


DETERMINISTIC OPTIMIZATION

Optimized


DETERMINISTIC OPTIMIZATION

μz

σz

INPUT

FEMDesign variables

Explicit constraints

Objective function

Implicit constraints

RESPONSEμf, σf

μg, σg

x f

g

Response

ROBUST OPTIMIZATION


ROBUST/RELIABLE FORMING PROCESSES

Reliability

Robustness


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


DETERMINISTIC CONSTRAINED OPTIMUM

Scrap rate 50.3%

Response f Response g


CONSTRAINED ROBUST OPTIMIZATION

Deterministic constrained optimum

Robust constrained optimum

Modelling


MCA ROBUST OPTIMUM

Scrap rate 0.1%

Response f Response g

Punch

Final productDie Sheet material


APPLICATIONV-BENDING PROCESS OF A SPRING PART

Forming process


APPLICATIONOPTIMIZATION MODEL

Parametric FE model

Objective f:

4 Design Variables: D, …

Constraints g1:g2:

D

2 Noise Variables: t, σy


APPLICATIONMETAMODEL RESULTS (60 LHD)

Objective: Main angle

t


APPLICATIONMETAMODEL RESULTS (60 LHD)

Constraints: Transition angle

USL: 96°

LSL: 92°

USL: 96°

LSL: 92°

Feasiblet


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


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


EXPERIMENTAL VALIDATIONOPTIMIZED PROCESS DEPTH SETTING

Trial run 4

Numerical model

21

MONTE CARLO ANALYSIS

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22

CORRELATION BETWEEN YIELD STRESS AND HARDENING

3 July 2017IDDRG 2017, Munich

Rp

n


PRINCIPAL COMPONENT ANALYSIS (PCA)

PCA- Orthogonal- Uncorrelated- Normalize data set- Linear combination

3 July 2017 24

PROPAGATION OF VARIATION

?

mean

variance

skewness

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3 July 2017 25

SKEW NORMAL DISTRIBUTION

Azzalini et al., 1985

Paulini et al., Jan 2017

IDDRG 2017, Munich

3 July 2017 26

EFFECT OF SKEWNESS PARAMETER

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3 July 2017 27

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


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


DEMONSTRATOR PROCESS


BENDING PROCESS OVERVIEW

Nothing734

Second bending733

Anglemeasurement

732

angle732

punch_stroke733

Strip movementFirst bending

735

bending_force735


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


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


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?


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 [%

]


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


allowed error [°]

scra

p ra

te [%

]

0 0.05 0.1 0.15 0.20

50


allowed error [°]

scra

p ra

te [%

]

Wednesday 11:00-16:00, Session 400Applicability of in-line Controls in Industrial Sheet Metal Forming Processes


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


CONCLUSIONS

Bert GeijselaersMartijn BonteJan Harmen WiebengaJos HavingaOmid Nejadseyfi

European Commission, Dutch funding agencies, Tata Steel Europe, Philips


ACKNOWLEDGEMENT

12-13 October 2017Enschede, Netherlandswww.utwente.nl/ftf2017


FORMING TECHNOLOGY FORUM 2017MODEL BASED CONTROL FOR SMART FORMING PROCESSES

MODELLING, OPTIMIZATION AND CONTROL IN SHEET METAL FORMING€¦ · MODELLING, OPTIMIZATION AND...

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