Siemens Digital Industries Software - Virtual Manufacturing...

Virtual Manufacturing EmpowersDigital Product Development

Case Study E-Coat SimulationFrank Pfluger Klaus Wechsler

Abstract

Recent progress in simulation methods for the manufacturing industry has reduced theneed for expensive test hardware which could be gratis used in manufacturing. Usingmanufacturing simulation tools starting at the design stage helps to optimize productdevelopment and corresponding manufacturing systems. Whenever there is a need foran early design input in order to ensure quality and manufacturing costs - virtualmanufacturing methods will have a profitable chance.

For the case study E-Coat simulation STAR-CCM provides an improved workflow fromCAD-data and meshing to E-coat deposition as well as modeling of fill and drainbehaviors in vehicle paint shops. Simulation results provide the design engineer withanswers to questions such as ´is the E-Coat providing corrosion protection in all thecavities´? or ´is there a corrosion risk based on air bubbles or paint ponds´?

The combination of an implemented fast algorithm with the chance of describingcustomer developed material properties by Field Functions allows best fit to complexchemical material behavior. Customized material development is kept insidecustomers. Based on process knowledge we are also providing multiple simulationsupport.An overview of future manufacturing simulation topics will be given.

2

Virtual Manufacturing is a Consequence ofHardware Reduced Digital Product Development

Doing it in a physical way takes too long.Whenever there is a need for an early design input in order to ensurequality and manufacturing costs:Virtual Manufacturing methods will have a profitable chance.

3

The E-Coat Deposition Process ProvidesCorrosion Protection in all Cavities

4

Overview of the E-Coat Simulation Steps

BIW-Meshing( for ex. Wrapper,Boolean Unite)

E-Coat Dip-Tank(Paint Thickness onsurfaces and cavities)

Dipping in:(Air Bubbles,Pressure Distribution)

Dipping Out(Puddles, Drainage -Time, Pressure Distr.)

Data Freeze ofDigital ProductDevelopment

Suggestions for Design OptimizationGoal: Corrosion protective E-Coat thickness, minimized

air bubbles and puddles in all parts and cavities….)

5

E-Coat Modeling: Deposition of charge carrying paint polymers.Increasing resistance gives a chance for deposition inside cavities

Electrolyte

BIW=Cathode

Paint material:..solids (pigments, resin/binder,.),solvent (de-ionized water) and co-solvents(glycol ether..)Conductivity is mainly based on theresin fraction but sensitive to carryoverof conductive pre treatment materialsfrom previous dipping steps ..

6

Electro Static Paint

E-Coat

E-Coat Modeling: ..a small Chemical Plant..

Anode

- Anode:Anolyte Circulation with influenceon pH and film re-dissolution.

- Dip Tank:Mixture of old (aged) and new materialas well as recirculation from Rinse Tank.Needs permanent agitation and precisetemperature control.

- Pretreatment:Carryovers affect conductivity.

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Input Parameters of StandardDeposition Model

Range of Values formcalibration measurements

cP: Coulomb efficiency 2-4·10-5 kg/As

ρP: Paint layer density 1200-1800 kg/m³

rP: Paint layer resistivity 2-5·106 Ωm

q0: Minimum Accumulated ´Activation Charge ´whichis necessary to start deposition in standard material)There is no deposition as long as q<q0

300-400 As/m²

σliquid: Bath (Electrolyte) conductivity 0.14-0.22 S/m

Equations of the STAR-CCM+ StandardElectro-Deposition Model

PPLPL

minnP

PPL rdt

dh

dt

dRjj

ρc

dt

dh

dtnjqwithqqif0

qqifnjj 00

0min

dtjqwithqqif0

qqifjj n

0

0nmin

8

ℎ Paint layer thickness in m

Paint layer resistance in Ωm2

Specific electric current in A/m2

ℎ Paint layer thickness in m

Paint layer resistance in Ωm2

Specific electric current in A/m2( ) Electric Potential at top of paint layerin V

Example of Enhanced (Customized) Electro-Deposition ModelUsing Field Functions for Detailed Calibration Measurements

9

PPLPL

minnP

PPL rdt

dh

dt

dRjj

ρc

dt

dh

dtjJwithJJif0

JJifjj 2

n2

20

2

20

2n

min

Input Parameters of Enhanced DepositionModel: - Variable Coulomb Efficiency Cp

- Deposition Starts if J2 > J02

Cp(t) = f( ( ), (t))(fb, C2u, C1u, C0u = based on detailedcalibration measurements)

cP: Coulomb efficiency (1-exp(- ))*(fb*exp(- /h0))+(-C2U*U²+C1U*U+C0U)

ρP: Paint layer density 1200-1800 kg/m³

rP: Paint layer resistivity 2-5·106 Ωm

J02: Minimum Accumulated ´Activation Work ` (which is

necessary to start deposition.There is no deposition as long as J2 < J0

2A2s/m4

σliquid: Bath (Electrolyte) conductivity 0.14-0.22 S/m

Calibration of E-Coat Simulation Parameters:(1) Using Existing Real Parts (if CAD Data are Available)

Where can these data be found:- Sometimes paint shop regularly opens

parts for quality assurance- Durability and other testing departments

might have opened partsCalibration:- Mesh real part and tank and adjust the

parameters of the deposition model until´best parameter fit´ is reached.

- Use conductivity and paint layer densityfrom direct measurement/supplier.

E-Coat Thickness (µm)

Provides a fast pragmatic calibration withfocus to final (corrosion relevant) thickness

x = measurementx

x

x

Inner:x = 18 µm

Outside:x = 25 µm

Hidden:x = 8 µm

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Calibration of E-Coat Simulation Parameters:(2) Using Calibration Tubes Fixed to an Existing Part

Preparation:- Tubes are fixed to a part being coated- Tubes are opened and measured inside

Calibration:- CAD model of tubes should be addedto CAD model of part being simulated ata similar position.

- adjust the parameters of the depositionmodel until ´best fit´ of tubes is reached.

Provides a pragmatic calibration withstandardized test geometry

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x = measurement

Calibration of E-Coat Simulation Parameters:(3) Lab Measurements with Test Box and Plain sheets

Box is closedon bottomand side.Top is aboveelectrolyte level

100V 200V

Calibration of E-Coat Simulation Parameters:(3) Lab Measurements with Test Box (Medium Throw Power)

Measurement

Measurement

100V 200V

Calibration of E-Coat Simulation Parameters(3) Lab Measurements with Test Box (Good Throw Power)

Measurement

Measurement

Application of STAR-CCM+ E-Coat Simulation:Thickness Building over Time

15

Page 16Video

Application of STAR-CCM+ E-Coat Simulation:Visualization of Thickness in Cavities

17

Application of STAR-CCM+ E-coat Simulation:Example of Good Throw Power

18

Application of STAR-CCM+ E-coat Simulation:Example of Medium Throw Power

19

Application of STAR-CCM+ E-coat Simulation:Example of Poor Throw Power

Good

PoorMedium

20

Application of STAR-CCM+ E-coat Simulation:Comparison of different Throw Power Capabilities

Good

PoorMedium

21

Application of STAR-CCM+ E-coat Simulation:Comparison of different Throw Power Capabilities

Geometrical Variationwill be necessary

Holes have influence on E-Coat thicknessBigger Diameter or more holes improve corrosion protection

Application of STAR-CCM+ E-coat Simulation:Evaluation of Gemetrical Variation (for better Corrosion Protection)

Simulation of Dipping in: (1sec=1h on 32 CPU, 8 Cores/CPU)

Remaining Air Bubbles Avoid E-Coat Film Building

(Red = Trapped Air Bubbles)

Video 23

Simulation of Dipping in (1sec=1h on 32 CPU, 8 Cores/CPU)

Remaining Air Bubbles Avoid E-Coat Film Building

24

25

Simulation of Dipping in: (1sec=1h on 32 CPU, 8 Cores/CPU)

Visualization of Trapped Air

Video

Simulation of Dipping in (1sec=1h on 32 CPU, 8 Cores/CPU)

Remaining Air Bubbles Avoid E-Coat Film Building

26

Simulation of Dipping in:Positioning of additional Bleeding Holes

Final Position in E-coating shouldbe without air bubbles.Simulation gives information forpositioning of bleeding holes.

27

Simulation of Dipping in:Quick optimization check by adding holes and continuing simulation

(Red = Trapped Air Bubbles)

28

Holes added at t = 25s

Simulation of Dipping out:(Remaining Ponds Contaminate Next Dipping Process Step)

Video

(Blue = Trapped Dipping Liquid)

29

Simulation of Dipping out:(1sec=1h on 32 CPU, 8 Cores/CPU)

(Remaining Ponds Contaminate Next Dipping Process Step)

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Simulation of Dipping out: (1sec=1h on 32 CPU, 8 Cores/CPU)

Calculation of Drainage Time

31Video

Simulation of Dipping out:Quick optimization check by adding holes and continuing simulation:

(Blue = Residual ponds)

32

Simulation of Dipping out:Quick optimization check by adding holes and continuing simulation:

(Blue = Residual ponds)

33

Holes added at t = 20s

Details of Dipping out Simulation:Remaining Ponds Contaminate next Dipping Process Step

34

Virtual Manufacturing

Market ReadyPilotExploration

Prospects

Clients

IndustryAwareness

E-Coat Simulationto Improve

Corrosion Protection

E-Coat Simulationto Improve

Corrosion Protection

Flow Fronts inFiber Reinforced Plastic

Manufacturing(SMC)

Flow Fronts inFiber Reinforced Plastic

Manufacturing(SMC)

AdditiveManufacturing (AM)

AdditiveManufacturing (AM)

Corrosion Test ChamberSimulation (Multiphysics

without Chemical Reactions)

Corrosion Test ChamberSimulation (Multiphysics

without Chemical Reactions)