Virtual modeling of process variation for deep drawing simulations

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1 Virtual modeling of process variation for deep drawing simulations Florian Quetting 1, Karl Roll 1 Materials and Process Engineering, Daimler AG, Sindelfingen

Modeling Example Description Results Summary 2 Virtual modeling of

2 Agenda Introduction Product und process validation Deep drawing process Product Development Chain Definition Requirements Virtual Methods Virtual Modeling Example Description Results Summary 2 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

3 History of human invention: Trial and Error?! 3 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

4 Trial and Error Reference: Tyrol Museum of Archaeology 4 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

5 Trial and Error in the automotive industry Karl-Heinz Baumann, Daimler AG - Fahrzeugsicherheit der Zukunft, Stuttgart 9. Mai Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

6 Process variation and control The results of deep drawing processes underlie variations, even if process control is being kept constant. The sensitivity to input variations strongly depends on process design. This phenomenon can only be explained by material and process scatter. Process control can help keeping the process in a state of statistical control. 6 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

7 Deep Drawing Process Deep drawing is one of the most important method for car body parts production. Die Blank / Part Blank Holder Punch 7 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

8 Deep Drawing Process Deep drawing is one of the most important method for car body parts production. Before After Blank Die Blank Holder Punch 8 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

9 Quality / Failure criteria Cracks 1) Wrinkles 1) 1 st type 2 nd type Dimensional Accuracy / Spring Back 2) Surface Defects Scratches Stretcher strains 1) Doege, Behrens: Handbuch Umformtechnik, Springer Verlag, Heidelberg, 2007; 2) Voestalpine 9 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

10 Non robustness costs can exceed initial investment expenditures 60% control expenditure downtime scrap 37% 3% potential "non robustness costs" 7% initial tool investment 10 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST Reference: Grossenbacher, K.: Virtuelle Planung der Prozessrobustheit in der Blechumformung, 2008

11 Product Development Chain Development Design Process planning - Feasibility study Production - Cracking - Wrinkling - Surface quality - Spring back Tool design and construction - Process control - Spring back - Process robustness - Tool optimization and compensation - Try out 11 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

production costs increase. - Costs to change manufacturing processes raise rapidly.

=> Process robustness should be taken into account in every product development step.

12 Cost influence vs. cost estimation With increasing product development cycle: - The possibilities to calculate production costs increase. - Costs to change manufacturing processes raise rapidly. - The potential to influence production costs drops. => Process robustness should be taken into account in every product development step. Cost estimation Cost of change Cost influencee Development Tool design and constructionn Try out Series production 12 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

Development cycle time - More products have to be developed in a shorter time. - Classical methods are too expensive and too time consuming. => Virtual methods have to be used.

13 Development cycle time - More products have to be developed in a shorter time. - Classical methods are too expensive and too time consuming. => Virtual methods have to be used. Number of products Mercedes-Benz Cars (without Smart) Development Time-to-Market period Until 2002: 81 months # products Until 2006: 65.5 months Until 2010: 60 months Since 2011: 55 months year Product development time 13 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

Complex Rigid Shells No tool or machine elasticity Elastic tools using volume

forces Force transmission and elasticity of machinery using elastic planes

14 Introduction P.-Dev. Chain Virt. Modeling Example Summary Virtual Models: Degree of abstraction Medium Simple Complex Rigid Shells No tool or machine elasticity Elastic tools using volume elements Elastic tools using volume elements Direct transmission of machine forces Force transmission and elasticity of machinery using elastic planes Uniform transmission of machine force Δ 14 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST FF FF

15 Virtual Models Hardening Isotropic v. Mises Isotropic-kinematic Prager Chaboche Backhaus Yoshida Distorsion ICT theory Ludwik Swift Voce Hockett-Sherby Gosh Mixed Flow Curve simple enhanced complex Yield Locus v. Mises Tresca Hill 48 Hill 90 Barlat 89 Barlat 2000 Banabic 2005 Reference: Roll, K.: State of the Art of Numerical Modeling in Sheet Metal Forming, Erlangen, Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

Yield locus description Model σ 0 σ 45 σ 90 R 0 r 45 r 90 σ b r b Parameters Free Hill 48 X - - X X X - - 4 0 Hill 90 X -

Mises H90, B2000, BL90, BBC2005 Reference: Roll, K.

16 Yield locus description Model σ 0 σ 45 σ 90 R 0 r 45 r 90 σ b r b Parameters Free Hill 48 X - - X X X Hill 90 X - - X X X X Barlat 89 X - - X X X X Barlat 2000 X X X X X X X X 8 1 Banabic 2005 X X X X X X X X 8 1 Tresca v. Mises H90, B2000, BL90, BBC2005 Reference: Roll, K.: State of the Art of Numerical Modeling in Sheet Metal Forming, Erlangen, Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

17 Example: Frame Rail E class 17 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

18 Example: Frame Rail E class Blank Gravity Closing Drawing Trimming Trimming Post Forming 18 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

19 Blank Flow Curve Anisotropy Process Numeric Input parameter: Distribution assumptions AF linear correlation Mean σ Min. Max. Mean σ Min. Max. Mean σ Min. Max. Thickness [mm] X-Position [mm] Y-Position [mm] 21 0, Rotation [mm] R p0,2 [GPa] Log-Normal Rm Log-Normal R R R Friction Delivery spec. no correlation Measured Blank holder force [kn] Yield Curve Barlat 89 Barlat 2000 Barlat 2000 FC Formulation Gosh Gosh Gosh + Cowper-Symonds Element Belytschko-Lin-Tsay Fully integrated Fully integrated # IP Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

Example: Results - Thickness AF linear correlation Delivery spec. no correlation Measured Min: 1.0526 Max: 2.4681 Min: 1.1052 Max: 2.5543 Min: 1.1865 Max: 2.5609 2 1.8 Min: 0.014295 Max: 0.

20 Example: Results - Thickness AF linear correlation Delivery spec. no correlation Measured Min: Max: Min: Max: Min: Max: Min: Max: Min: Max: Min: Max: Std.-Dev. Mean Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

Example: Results - Thinning AF linear correlation Delivery spec. no correlation Measured Min: -36.

0044213 % Max:11.839 % Min: 0.004649 % Max: 10.877 % Min: 0.0033011 % Max: 8.0358 % 5 0-5 -10 4 3 2 Std.

21 Example: Results - Thinning AF linear correlation Delivery spec. no correlation Measured Min: % Max: % Min: % Max: % Min: % Max: % Min: % Max: % Min: % Max: % Min: % Max: % Std.-Dev. Mean Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

Example: Results Effective Plastic Strain AF linear correlation Delivery spec. no correlation Measured Min: 0.

49674 Min: 0.00022041 Max: 0.3675 Min: 0.00019085 Max: 0.18149 0.3 0.2 0.1 0 0.1 0.08 0.06 0.04 Std.-Dev.

22 Example: Results Effective Plastic Strain AF linear correlation Delivery spec. no correlation Measured Min: Max: Min: Max: Min: Max: Min: Max: Min: Max: Min: Max: Std.-Dev. Mean Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

Summary Product development is facing increasingly shorter time-to-market cycles. Simultaneously, the complexity of production processes steadily increase.

23 Summary Product development is facing increasingly shorter time-to-market cycles. Simultaneously, the complexity of production processes steadily increase. This can only be handled by usage of virtual methods. Current mechanical material parameters used in quality control are insufficient to precisely describe material scatter. Virtual methods must be improved to incorporate real process and material scatter. Knowledge of real input scatter and distribution is evident for robustness analysis of deep drawing processes. Further research is necessary to analyze real material scatter and to incorporate these variations into virtual models. 23 Virtual modeling of process variation for deep drawing simulations Florian Quetting WOST

24 Thank you for your attention!

Contents Metal Forming and Machining Processes Review of Stress, Linear Strain and Elastic Stress-Strain Relations 3 Classical Theory of Plasticity

Contents Metal Forming and Machining Processes Review of Stress, Linear Strain and Elastic Stress-Strain Relations 3 Classical Theory of Plasticity Contents 1 Metal Forming and Machining Processes... 1 1.1 Introduction.. 1 1.2 Metal Forming...... 2 1.2.1 Bulk Metal Forming.... 2 1.2.2 Sheet Metal Forming Processes... 17 1.3 Machining.. 23 1.3.1 Turning......