Optimization of overlapping mesh calculations for simulation of paint shop manufacturing processes

Transcription

1 Optimization of overlapping mesh calculations for simulation of paint shop manufacturing processes STAR Global Conference, Vienna, March 17-19, 2014 Madhusudhan Devanathan

2 Agenda 1. Simulation of Rodip pretreatment/ coating process 2. Simulation of adhesive smearing on hang-on parts Source Daimler AG 3. Simulation of automotive top coat spray booth 4. Conclusion Source Daimler AG Source Daimler AG Source : Autoline.tv 2014 Duerr AG 2

3 Case 1: Rodip simulation Dipping of Body-In-White (BIW) in liquid tank Rodip used in pretreatment / coating systems Source: Parker Engineering Co.,Ltd. Simulation Objective : Locate residual air pockets / liquid ponds Pressure forces on hang-on parts Optimum computation time Process time around 62s BIW Meshing : Automated workflow in STAR-CCM+ Presented in Star Global Conference 2012 Numerical model : Volume of fluid (VOF) to model air and liquid phase Hydrostatic boundary conditions Overlapping grids and superposing motion model Translational velocity in X and Z, rotation about Y Time dependant volume sources 2014 Duerr AG Rodip motion in simulation 3

4 Case 1: Creation of time dependant volume sources Local refinement of background mesh VOF model : Refined air-liquid interface Overset Grid : Refined motion space Mesh section Precalculation to create time dependant volume sources Movement of overset boundary along path with RBM Export of overset boundary at certain frequency Wrapping exported surfaces within remesh time interval Generation of background mesh with respective volume sources Simulation time

5 Case 1 : Analysing dip-in simulation results Air Location of trapped air 2 1 Liquid Source: Daimler AG VOF field along midsection of car 1 2 Smooth flooding during dipping in 180 deg rotation ensures movement of air bubbles Air pockets detected in rear floor region Optimization of BIW construction 5

6 Case 2 : Adhesive smearing during dip-in process Filling behavior during dip-in process Source : Daimler AG Source: Daimler AG Smeared adhesive seen after dip-in process Background of smearing phenomena Adhesive is not cured before dipping Non-uniform filling of cavities of engine hood Unbalanced pressure forces act on components Relative motion between components smear adhesive between them F Air Liquid Source: Daimler AG 6

7 Case 2 : Calculating deformations from dipping forces Mapping of forces from CFD to Structural Mesh Cells vertices of midplane F 1 F 2 Map F 1, F2 CFD mesh FE mesh Additional conditions for mapping F FE = F1+ F 2 n N F1 + F 2 = F FE Calculation of deformation in Abaqus mg + F = K U FE n: Number of CFD cells, N : Number of FE elements K: Stiffness matrix, U : Deformation matrix * Source: Daimler AG F FE U Visualizing and during dipping process * Deformation are scaled from original values 7

8 Case 2: Optimizing design to maintain open contact Air 4mm P6 P4P1 P8 P2 P7 P5 P9 P3 Initial design P6 P4P1 P8 P2 P7 P5 P9 P3 Liquid Air Contact open in [mm] Contact open in [mm] Optimized design Liquid - Additional Holes 8

9 Case 3 : Simulation of Automotive top coat spray booth Simulation objective : Determination of paint thickness on BIW Evaluation of robot programs Optimization of brush settings Prediction of overspray Source Daimler AG Process data required Position of robots and their motion programs Timeline of robots Nozzle gun ON/OFF program Brush change program and settings Spray booth layout and program timeline 9

10 Case3 : Physics of paint nozzle air inlet rotating bell paint inlet Source : Eisenmann Nozzle / Brush Parameters Inlet air flow rate N1,N2 (L/min) Rotation rate (RPM) Paint volume flowrate PFR (ml/min) Source Daimler AG Primary atomisation Numerical model Compressible air Primary atomisation Lagrangian particles Secondary breakup model Particle under influence of Drag and pressure forces Gravitational forces Two way coupling Source Daimler AG 10

11 Case 3 : Calibration of paint nozzle Velocity (m/s) 75 Brush A N1=400, N2=200, RPM = 25000, PFR= 180 Brush B N1=400, N2=600, RPM = 25000, PFR=240 A Simulation Vs Experiment Brush A Velocity (m/s) 150 Section A Brush B 75 0 Paint film (µm) Painting direction 0 Measuring line 11

12 Case 3 : Modeling robotic motion using overset grids Robot motion : Describe movement of tool center point (TCP) Robot path : Description of [ X, Y, Z, α, β, γ ] over time STAR-CCM+ requires First derivative of position : X, Y, Z, α, β, γ Starting position : X, Y, Z, α, β, γ [ ] mm (4) Setting robotic motion models and coordinate systems ( ) ( ) ( ) Rotation sequence : R α R β R γ x y z (XYZ) ɺ ( X Yɺ Z ) ( X Y Zɺ ) ( X Y Z ) Overset mesh for nozzle α β γ (0,1,2,3) Source Daimler AG Swept volume of overset boundary 12

13 Case 3 : Programming nozzle ON/OFF and brush changes Nozzle flow ON/OFF : Multiply flow inlet conditions by heavy side unit step function Brush changes : One or all brush parameters change Java script monitors all robots after every time iteration On brush change detection, trigger brush event Brush change event: Change inlet velocity table Change particle distribution table Change wall rotation Change particle flow rate Source: Daimler AG Implementing dynamic brush change program 13

14 Case 3 : Result of spray booth simulation Source Daimler AG 14

15 Conclusion Complex multi-dof motion in painting systems can be simulated using overset grid methods Reduction of computational time was achieved by developing overset grids with time-dependant volume sources Java based macros used to implement complex time dependant boundary conditions in STAR- CCM+ Automotive top coat simulation methodolgy developed to simulate the complex physical processes in an automotive top coat spray booth 15

16 Acknowledgement Mr.Lothar Haigis Head of structural analysis and computational fluid dynamics MBtech Group GmbH & Co. KGaA, Germany Mr.Jochen Rathfelder Innovative Technische Berechnungen GmbH,Germany 16