Virtual Manufacturing Empowers Digital Product Development Case - - PowerPoint PPT Presentation

Virtual Manufacturing Empowers Digital Product Development Case Study E-Coat Simulation

Klaus Wechsler

Abstract

Recent progress in simulation methods for the manufacturing industry has reduced the need for expensive test hardware which could be gratis used in manufacturing. Using manufacturing simulation tools starting at the design stage helps to optimize product development and corresponding manufacturing systems. Whenever there is a need for an early design input in order to ensure quality and manufacturing costs - virtual manufacturing methods will have a profitable chance. For the case study E-Coat simulation STAR-CCM provides an improved workflow from CAD-data and meshing to E-coat deposition as well as modeling of fill and drain behaviors in vehicle paint shops. Simulation results provide the design engineer with answers to questions such as ´is the E-Coat providing corrosion protection in all the cavities´? or ´is there a corrosion risk based on air bubbles or paint ponds´? The combination of an implemented fast algorithm with the chance of describing customer developed material properties by Field Functions allows best fit to complex chemical material behavior. Customized material development is kept inside

• customers. Based on process knowledge we are also providing multiple simulation

support. An overview of future manufacturing simulation topics will be given.

2

Virtual Manufacturing is a Consequence of Hardware 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 ensure quality and manufacturing costs: Virtual Manufacturing methods will have a profitable chance. 3

The E-Coat Deposition Process Provides Corrosion 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 on surfaces and cavities)

Dipping in:

(Air Bubbles, Pressure Distribution)

Dipping Out

(Puddles, Drainage - Time, Pressure Distr.)

Data Freeze of Digital Product Development

Suggestions for Design Optimization

Goal: 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 the resin fraction but sensitive to carryover

• f conductive pre treatment materials

from previous dipping steps ..

6

Electro Static Paint E-Coat

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

Anode

• Anode:

Anolyte Circulation with influence

• n pH and film re-dissolution.
• Dip Tank:

Mixture of old (aged) and new material as well as recirculation from Rinse Tank. Needs permanent agitation and precise temperature control.

• Pretreatment:

Carryovers affect conductivity.

7

Input Parameters of Standard Deposition Model Range of Values form calibration 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 Ω m

q0: Minimum Accumulated ´Activation Charge ´which

is 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+ Standard Electro-Deposition Model

 

P PL PL min n P P PL

r dt dh dt dR j j ρ c dt dh     

 

         dt n j q with q q if q q if n j j

min



       dt j q with q q if q q if j j

n n min

8

ℎ

Paint layer thickness in m Paint layer resistance in Ω m2 Specific electric current in A/m2

ℎ

Paint layer thickness in m Paint layer resistance in Ω m2 Specific electric current in A/m2

( )

Electric Potential at top of paint layer in V

Example of Enhanced (Customized) Electro-Deposition Model Using Field Functions for Detailed Calibration Measurements 9

 

P PL PL min n P P PL

r dt dh dt dR j j ρ c dt dh     



       dt j J with J J if J J if j j

2 n 2 2 2 2 2 n min

Input Parameters of Enhanced Deposition Model: - Variable Coulomb Efficiency Cp

• Deposition Starts if J2 > J0

2

Cp(t) = f( ( ), (t)) (fb, C2u, C1u, C0u = based on detailed calibration 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 Ω m

J0

2: Minimum Accumulated ´Activation Work ` (which is

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

2

A2s/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 parts

Calibration:

• Mesh real part and tank and adjust the

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

• Use conductivity and paint layer density

from direct measurement/supplier.

E-Coat Thickness (µm)

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

x = measurement x x x Inner: x = 18 µm Outside: x = 25 µm Hidden: x = 8 µm

10

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 added

to CAD model of part being simulated at a similar position.

• adjust the parameters of the deposition

model until ´best fit´ of tubes is reached.

Provides a pragmatic calibration with standardized test geometry

11

x = measurement

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

Box is closed

• n bottom

and side. Top is above electrolyte 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 16 Video

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 Poor Medium 20

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

Good Poor Medium 21

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

Geometrical Variation will be necessary

Holes have influence on E-Coat thickness

Bigger 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 should be without air bubbles. Simulation gives information for positioning 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) 30

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

Calculation of Drainage Time 31

Video

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 Ready Pilot Exploration Prospects Clients Industry Awareness

E-Coat Simulation to Improve Corrosion Protection E-Coat Simulation to Improve Corrosion Protection Flow Fronts in Fiber Reinforced Plastic Manufacturing(SMC) Flow Fronts in Fiber Reinforced Plastic Manufacturing(SMC) Additive Manufacturing (AM) Additive Manufacturing (AM) Corrosion Test Chamber Simulation (Multiphysics

without Chemical Reactions)

Corrosion Test Chamber Simulation (Multiphysics

without Chemical Reactions)