Virtual Manufacturing Empowers Digital Product Development Case Study E-Coat Simulation Contact Partner: Klaus Wechsler (klaus.wechsler@cd-adapco.com)
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. Page 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. Page 3
The E-Coat Deposition Process Provides Corrosion Protection in all Cavities Page 4
Overview of the E-Coat Simulation Steps Data Freeze of BIW-Meshing Dipping in: E-Coat Dip-Tank Dipping Out Digital Product ( for ex. Wrapper, ( Air Bubbles , ( Puddles, Drainage - ( Paint Thickness on Development Boolean Unite) Pressure Distribution) surfaces and cavities) Time, Pressure Distr.) Suggestions for Design Optimization Goal: Corrosion protective E-Coat thickness in all cavities, no air bubbles and paint ponds after dipping. Multifunctional use of holes for drainage, E-coat current flow, waxing etc. Page 5
E-Coat Modeling: Deposition of charge carrying paint polymers. Increasing resistance gives a chance for deposition inside cavities E-Coat Paint material : ..solids (pigments, resin/binder,.), solvent (de-ionized water) and co-solvents Electrolyte (glycol ether..) Conductivity is mainly based on the BIW=Cathode resin fraction but sensitive to carryover of conductive pre treatment materials from previous dipping steps .. Electro Static Paint Page 6
E-Coat Modeling: ..a small Chemical Plant.. Anode - Anode : Anolyte Circulation with influence on 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. Page 7
Equations of the STAR-CCM+ Standard Electro-Deposition Model ℎ dh c dR dh Paint layer thickness in m PL P PL PL j j r n min P dt dt dt ρ Paint layer resistance in Ω m 2 P Specific electric current in A/m 2 j if q q j n if q q 0 j with q j n dt n 0 j with q j dt min 0 0 if q q min n 0 0 if q q 0 Input Parameters of Standard Range of Values form Deposition Model calibration measurements 2-4· 10 -5 kg/As c P : Coulomb efficiency ρ P : Paint layer density 1200-1800 kg/m³ 2-5· 10 6 Ω m r P : Paint layer resistivity q 0 : Minimum Accumulated ´Activation Charge ´which 300-400 As/m² is necessary to start deposition in standard material) There is no deposition as long as q<q 0 σ liquid : Bath (Electrolyte) conductivity 0.14-0.22 S/m Page 8
Example of Enhanced (Customized) Electro-Deposition Model Using Field Functions for Detailed Calibration Measurements ℎ dh c dR dh Paint layer thickness in m PL P PL PL j j r n min P dt dt dt ρ Paint layer resistance in Ω m 2 P Specific electric current in A/m 2 2 2 j if J J 2 2 n 0 j with J j dt Electric Potential at top of paint layer min n ( ) 2 2 0 if J J in V 0 Cp(t) = f( ( ) , Input Parameters of Enhanced Deposition (t)) Model: - Variable Coulomb Efficiency C p (f b , C2 u , C1 u , C0 u = based on detailed - Deposition Starts if J 2 > J 0 2 calibration measurements) c P : Coulomb efficiency (1-exp(- ))*(f b *exp(- /h 0 ))+(-C2 U * U²+C1 U * U+C0 U ) ρ P : Paint layer density 1200-1800 kg/m³ 2-5· 10 6 Ω r P : Paint layer resistivity m 2 : Minimum Accumulated ´Activation Work ` (which is J 0 A 2 s/m 4 necessary to start deposition. There is no deposition as long as J 2 < J 0 2 σ liquid : Bath (Electrolyte) conductivity 0.14-0.22 S/m 9
Calibration of E-Coat Simulation Parameters: (1) Using Existing Real Parts ( if CAD Data are Available) Outside: Inner: x = 25 µm x = 18 µm Hidden: x = 8 µm Where can these data be found: E-Coat Thickness (µm) x - Sometimes paint shop regularly opens x = measurement parts for quality assurance x - Durability and other testing departments might have opened parts x Calibration: - Mesh real part and tank and adjust the parameters of the deposition model until ´best parameter fit´ is reached. Provides a fast pragmatic calibration with - Use conductivity and paint layer density focus to final (corrosion relevant) thickness from direct measurement/supplier. Page 10
Calibration of E-Coat Simulation Parameters: (2) Using Calibration Tubes Fixed to an Existing Part x = measurement 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 Provides a pragmatic calibration with model until ´best fit´ of tubes is reached. standardized test geometry Page 11
Calibration of E-Coat Simulation Parameters: (3) Lab Measurements with Test Box and Plain sheets Box is closed on bottom and side. Top is above electrolyte level
Calibration of E-Coat Simulation Parameters: (3) Lab Measurements with Test Box (Medium Throw Power) 100V 200V Measurement Measurement
Calibration of E-Coat Simulation Parameters (3) Lab Measurements with Test Box (Good Throw Power) 100V 200V Measurement Measurement
Application of STAR-CCM+ E-Coat Simulation: Thickness Building over Time
Application of STAR-CCM+ E-Coat Simulation: Visualization of Thickness in Cavities Page 16
Application of STAR-CCM+ E-coat Simulation: Example of Good Throw Power 17
Application of STAR-CCM+ E-coat Simulation: Example of Medium Throw Power
Application of STAR-CCM+ E-coat Simulation: Example of Poor Throw Power
Application of STAR-CCM+ E-coat Simulation: Comparison of different Throw Power Capabilities Good Poor Medium
Application of STAR-CCM+ E-coat Simulation: Comparison of different Throw Power Capabilities Geometrical Variation Good will be necessary Poor Medium
Application of STAR-CCM+ E-coat Simulation: Evaluation of Gemetrical Variation (for better Corrosion Protection) Holes have influence on E-Coat thickness Bigger Diameter or more holes improve corrosion protection Page 22
Simulation of Dipping in: (1sec=1h on 32 CPU, 8 Cores/CPU) Visualization of Trapped Air (Slow Dipping, no Rotation) 50 L 10 L Video Page 23
Simulation of Dipping in (1sec=1h on 32 CPU, 8 Cores/CPU) Remaining Air Bubbles Avoid E-Coat Film Building (no rotation) Page 24
Simulation of Dipping in: (1sec=1h on 32 CPU, 8 Cores/CPU) Visualization of Trapped Air (Fast Rotation Dipping) (Red = Trapped Air Bubbles) Video Page 25
Simulation of Dipping in: (1sec=1h on 32 CPU, 8 Cores/CPU) Visualization of Trapped Air (Slow Rotation Dipping) (Red = Trapped Air Bubbles) Video Page 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 final bleeding holes. Page 27
Simulation of Dipping out: (1sec=1h on 32 CPU, 8 Cores/CPU) Visualization of Trapped Liquid (Fast Rotation) Page 28
Simulation of Dipping out: (1sec=1h on 32 CPU, 8 Cores/CPU) Visualization of Trapped Liquid (Slow Rotation) Page 29
Simulation of Dipping out: (1sec=1h on 32 CPU, 8 Cores/CPU) Visualization of Trapped Liquid (Slow, no Rotation) Page 30
Simulation of Dipping out (no rotation): Quick optimization check by adding holes and continuing simulation: (Blue = Residual ponds) Page 31
Simulation of Dipping out (no rotation): Quick optimization check by adding holes and continuing simulation: (Blue = Residual ponds) Holes added at t = 20s Page 32
Details of Dipping out Simulation: Remaining Ponds Contaminate next Dipping Process Step Page 33
Forces on Hood during Dipping: Optimized Design: Les damage to Bonded Hood parts Page 34
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