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Chapter 4 Robust Design 9.0

Chapter 4 Robust Design 9.0. Robust Design Overview. Background of Robust Design What is Robust Design, DFSS, …? Design for Quality Robust Design in Engineering Analysis Illustration Example Sources of Uncertainty Effects of Uncertainty Compare Deterministic and Probabilistic Approach

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Chapter 4 Robust Design 9.0

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  1. Chapter 4Robust Design9.0

  2. Robust DesignOverview • Background of Robust Design • What is Robust Design, DFSS, …? • Design for Quality • Robust Design in Engineering Analysis • Illustration Example • Sources of Uncertainty • Effects of Uncertainty • Compare Deterministic and Probabilistic Approach • Enabling Technologies • Demonstration • Overview of Application Example • Demo • Questions

  3. Background of Robust Design What is Robust Design, DFSS, etc.? • Uncertainty Analysis • Quantify the effect of uncertainties on the performance of a product (mean value, standard deviation, etc.) • Reliability Analysis • Quantify the reliability (failure probability, defects per million) • Robust Design or Design For Six Sigma (DFSS) • Optimize the design such that it is insensitive to unavoidable uncertainties (e.g. material,loads,…) • Reliability-based Optimization • Optimize the design such that reliability is maximized or failure probability (defects per million) is minimized Robust Design is often synonymous to “Design for Six Sigma” or “Reliability-based Optimization”

  4. Six Sigma Quality = Only 3.4 out of 1’000’000 parts fail LSL = Lower Specification Limit USL = Upper Specification Limit Six Sigma Quality is inherently a probabilistic statement P.S.: Gaussian distribution is not realistic, but does convey the idea correctly Background of Robust Design Design for Quality

  5. Six Sigma = Optimize manufacturing processes such that they automatically produce parts conforming with six sigma quality Design For Six Sigma = Optimize the design such that the parts conform with six sigma quality, i.e. quality and reliability are explicit optimization goals • Design for Six Sigma: • Achieve “Designed-In” quality as opposed to letting customers find out about quality problems • Make informed decision that are critical to quality early in the development process Background of Robust Design Design for Quality

  6. Background of Robust Design Design for Quality Robust Design is a Paradigm Shift … • FROM: • Reactive Quality Management • Extensive Design Rework • Assess Performance by “build-test-build-test-…” • Fix performance/quality problems after manufacturing • Quality is “Tested-In” • TO: • Predictive Quality Management • Controlled Design Parameters • Estimate likelihood/rate of performance problems in design & development • Address quality problems in design & development • Designed for robust performance and quality • Quality is “Designed-In”

  7. Background of Robust Design Robust Design in Engineering Analysis Purpose of Robust Design Input Output ANSYS • Material Properties • Geometry • Boundary Conditions • Deformation • Stresses / Strains • Fatigue, Creep,... It’s a reality that input parameters are subjected to scatter => automatically the output parameters are uncertain as well!! Uncertain ! Scatter !

  8. Background of Robust Design Robust Design in Engineering Analysis Purpose of Robust Design ANSYS DesignXplorer Uncertain ! Scatter ! • Questions answered with Robust Design: • How large is the scatter of the output parameters? • What is the probability that output parameters do not fulfill design criteria (failure probability – defects per million)? • How much does the scatter of the input parameters contribute to the scatter of the output (sensitivities – critical-to-quality)?

  9. Background of Robust Design Sources of Uncertainty Source: Klein, Schueller et.al. Probabilistic Approach to Structural Factors of Safety in Aerospace. Proc. CNES Spacecraft Structures and Mechanical Testing Conf., Paris 1994

  10. Background of Robust Design Effects of Uncertainty Materials, Bound.- Cond., Loads, ... ±5-100% Thermal Analysis CAD FEM CFD LCF ±??% Geometry FEM ± 0.1-10% Materials, Bound.- Cond., ... Structural Analysis Materials Materials, Bound.- Cond., Loads, ... ±30-60% ±5-50% ±5-100%

  11. Background of Robust Design Effects of Uncertainty Influence of Young’s Modulus and Thermal Expansion Coefficient on thermal stresses: thermal = E ·  ·T Deterministic Approach: Mean = EMean · Mean · T Mean = typically used results Probabilistic Approach: Probability that (thermal >= 105% Mean) (thermal >= 110% Mean) ‘E’ scatters ±5% 16% (~1 out of 6) 2.3% (~1 out of 40) ‘E’ and ‘‘ scatter ±5% 25% (~1 out of 4) 8% (~1 out of 12) ‘E’, ‘‘ & ‘T’ scatter ±5% 28% (~1 out of 4) 13% (~1 out of 8)

  12. Turbine What-If Analysis Series Background of Robust Design Compare Deterministic/Probabilistic

  13. Background of Robust Design Enabling Technology: Parameterization • Robust Design for all parameters including CAD

  14. Background of Robust Design Enabling Technology: Parameterization • Robust Design Supports APDL Parameters

  15. Background of Robust Design Enabling Technology: Parameterization • Robust Design Supports ParaMesh Parameters • ParaMesh for Geometric Type of Parameterization • For Legacy Models or • Models with Signifiant FEA Abstraction such that CAD update is problematic • Example Ansys Input File: ! File inp_parabatch.inp value=0 /syp,parabatch.exe,'testpb.rsx','testpb.cdb','location',%value%,'testpb_mod.cdb' /inp,testpb_mod,cdb ! Input the modified geometry /solu Solve ! Solve it fini /post1 set,first *get,ndepright,node,232,u,z ! Get results *get,ndepleft,node,2,u,z depdiff=ndepleft-ndepright fini Parameter value Paramesh db Initial mesh Output mesh Parameter name Import Output mesh

  16. Background of Robust Design Enabling Technology: DesignXplorer DesignXplorer manages the parameters and the uncertainties

  17. Robust Design Demonstration Overview of Application Example FEM Boundary Conditions CAD Geometry FEM Mesh

  18. Robust Design Demonstration Overview of Application Example Results for Maximum Principal Stress Design Variables and Uncertainties Pressure Side Suction Side Axial Leaning Tang. Leaning Material Density (Gaussian) Peak Value ss Peak Value sp Fillet Radius (Lognormal) Dove Tail Width Mass Imbalance: (sp – ss)2 Avg.Stress: 0.5(sp + ss)

  19. Robust Design Demonstration Demonstration Robust Design Demonstration

  20. DesignXplorer Family9.0

  21. DX Family 9.0 New Features • DesignXplorer Family (DesignXplorer and DesignXplorer VT) • GUI Structure • Robust Design - Parameterize DFSS Results • New Robust Design View • Optimize DFSS Results • New Trade-Off Study • Genetic Algorithm for Sample Generation • Variational Technology (DesignXplorer VT) • Support of Discrete Variables in Workbench • RSX File Viewing • Additional Contact Support • 2D Analysis

  22. DX Family 9.0 New FeaturesGUI Structure DFSS Charts Page Measures of robustness CDF Plot Y-Axis can be scaled as Gaussian, Log-Normal, Weibull, Exponential

  23. Separate DFSS Table Page Statistics Available Sigma-Level in Tables Customize Tables (add, delete) DX Family 9.0 New FeaturesGUI Structure

  24. New “Robust Design” View New DFSS Parameters showing up in Parameter View DX Family 9.0 New FeaturesParameterize DFSS Results

  25. DX Family 9.0 New FeaturesOptimize DFSS results 1) 2) 1) Random Variables: Uncontrollable – used to obtain DFSS results 2) Design Variables: Useable for Robust Design Optimization

  26. DX Family 9.0 New FeaturesOptimize DFSS results Same optimization functionality as for GDS

  27. DX Family 9.0 New FeaturesTrade-Off Studies Both 2D and 3D Tradeoff Plots are available Pareto Front for Conflicting Goals, Tradeoffs Occur Here Mouse-Over Results

  28. Feasible Infeasible Mouse-Over Results Pareto Front for Conflicting Goals Infeasible Points in Pareto Front DX Family 9.0 New FeaturesTrade-Off Studies

  29. Choice of Basic (Screening), which is pseudo-random sampling method typically done first, or Advanced (Genetic) Algorithm which is typically done second DX Family 9.0 New FeaturesGenetic Algorithm Sample Generation

  30. Advanced sample options DX Family 9.0 New FeaturesGenetic Algorithm Sample Generation

  31. DX Family 9.0 New FeaturesGenetic Algorithm Sample Generation Advanced Sample Screening Sample

  32. DesignXplorer VT 9.0Support of Discrete Variables in Workbench • Variational Technology is much faster than DOE for discrete parameters

  33. DesignXplorer VT 9.0Support of Discrete Variables in Workbench • Efficient multi-objective Boolean optimizer based on Bayesian sampling faster optimization of discrete parameters • Boolean scatter chart representing the solution points of all the parameter combinations of the selected parameters.

  34. DesignXplorer VT 9.0RSX File Viewing • Allows user to view Variational Technology results from a variety of sources including • DesignXplorer VT (of course) • ANSYS using VT • ParaMesh • 3rd Party Variational Technology Applications Hoover model solved with VT with the ANSYS Environment

  35. DesignXplorer VT 9.0Support for Face-to-Edge Contact

  36. DesignXplorer VT 9.0Support for Edge-to-Edge Contact

  37. DesignXplorer VT 9.02D Analysis • Allows Variational Technology analysis of 2D Simulation studies to include: • Axisymmetric • Plane Strain • Plane Stress

  38. DesignXplorer and DesignXplorer VT • Thank you! • Questions? • Additional Questions: Ray Browell (724) 514-3070 ray.browell@ansys.com • Additional Information: http://www.ansys.com/

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