Accelerating Innovation through Automated Design Optimization Erik D. Goodman Professor, ECE, ME

1. Accelerating Innovation through Automated Design Optimization Erik D. Goodman Professor, ECE, ME MSU VP Technology Red Cedar Technology, Inc.

2. Analysis versus Design • Analysis • Given: system properties and loading conditions • Find: responses of the system • Design • Given: loading conditions and targets for response • Find: system properties that satisfy those targets

3. Design Complexity Design Complexity Design Time and Cost

4. Specific Design Candidate Build Analysis Model(s) Modify Design (Intuition) Execute the Analyses Design Requirements Met? No Typical Design Process Initial Design Concept HEEDS Yes Final Design

5. Initial Design Concept Representative Design(s) Build Analysis Model(s) Modify Design (HEEDS) Design Model (HEEDS) Convergence Criterion Met? No Automated Design Process Execute the Analyses Yes Optimized Design(s)

6. Main Benefits • Automates search for design alternatives with improved performance and cost • more efficient and thorough search • Reduces design time from weeks to days • significant cost reduction • Accelerates product and process innovation • increased competitive advantage • Integrates and leverages existing investment in CAD/CAE tools and hardware better utilization of capital • Improves design robustness six sigma

7. Example Application Areas Automotive Civil Infrastructure Biomedical Aerospace

8. Examples of Benefits* Crash rails: 100% increase in energy absorbed 20% reduction in mass Composite wing: 80% increase in buckling load 15% increase in stiffness Bumper: 20% reduction in mass with equivalent performance Coronary stent: 50% reduction in strain * Percentages relative to best designs found by experienced engineers

9. Sizing Optimization Design variables are thickness or cross-sectional area of each member Domain is fixed Shape Optimization Design variables are boundary shape parameters Domain is the design variable Topology Optimization Design variables are geometric features such as number, location and shape of holes, or connectivity of the domain Sometimes called material layout or material distribution Some Common Types of Structural Optimization

10. Topology Optimization • Suggests material placement or layout based on load path efficiency • Maximizes stiffness • Conceptual design tool • Works with commercial FEA solvers

11. Parameter Optimization Minimize (or maximize): F(x1,x2,…,xn) such that: Gi(x1,x2,…,xn) < 0, i=1,2,…,p Hj(x1,x2,…,xn) = 0, j=1,2,…,q where: (x1,x2,…,xn) are the n design variables F(x1,x2,…,xn) is the objective (performance) function Gi(x1,x2,…,xn) are the p inequality constraints Hj(x1,x2,…,xn) are the q equality constraints

12. Parameter Optimization Objective: Search the performance design landscape to find the highest peak or lowest valley within the feasible range • Typically don’t know the nature of the surface before search begins • Local searches may yield only incremental improvement • Number of parameters may be large (1 – 1,000) • Evaluations may be expensive

13. Optimization Scenarios • Seek small improvements to an existing design • Local search, small variable range • Manual iterations reduce work needed by optimizer • Seek best design or concept within a large design space • Global search, large variable range • Very little initial effort used to set up analysis • Optimizer reduces need for manual iterations

14. Some Unique Features in Tool You Are Using • SHERPA – Simultaneous Hybrid Exploration that is Robust, Progressive and Adaptive • A hybrid, adaptive search method that works for nearly all problems • Makes product optimization accessible to non-experts • Increases robustness of most searches • CIA – Cooperative Independent Agents • Allows more effective search of challenging problems via decomposition • Speeds search by using inexpensive models to guide refined models • COMPOSE – COMPonent Optimization within a System Environment • Reduces design time by factor of 10 – 1,000 for certain problems • Allows search over large number of design variables • Makes intractable problems solvable

15. SHERPA – a Hybrid, Adaptive Method • Hybrid • Multiple methods used simultaneously, not sequentially • Takes advantage of best attributes of each method • Both global and local search techniques are used • Adaptive • Each method adapts itself to the design space • Master controller determines which methods get used and how much • Efficiently learns about design space and effectively searches even very complicated spaces

16. SHERPA Benchmark Example Find the cross-sectional shape of a cantilevered I-beam with a tip load (4 design vars) Design variables: H, h1, b1, b2 Objective: Minimize mass Constraints: Stress, Deflection

17. SHERPA Benchmark Example Find the cross-sectional shape of a cantilevered I-beam with a tip load (4 design vars) Effectiveness and Efficiency of Search (Goal = 1)

18. SHERPA Benchmark Example Find the cross-sectional shape of a cantilevered I-beam with a tip load (4 design vars) Robustness of Search (Goal = 0)

19. Crush zone Crush zone Example: Hydroformed Lower Rail

20. Shape Design Variables 67 design variables: 66 control points and one gage thickness z y rigid wall lumped mass x arrows indicate directions of offset crush zone cross-section

21. Optimization Statement • Maximize energy absorbed in crush zone • Identify the rail shape and thickness • Subject to constraints on: • Peak force • Mass • Manufacturability

22. HEEDS Optimized Design

23. HEEDS Optimized Design

24. Validation

25. Lower Rail Benefits Compared to 6-month manual design effort: • Peak force reduced by 30% • Energy absorption increased by 100% • Weight reduced by 20% • Overall crash response resulted in equivalent of FIVE STAR rating

26. Hydroforming Process Optimization

27. Hydroforming Model

28. Formability Optimization

29. Manual Optimization

30. HEEDS Optimization

31. Manual Optimization HEEDS Optimization (55% improvement) Formability Results

32. D1 D1 D2 θ D1 Fixed D4 D3 D5 Rubber Bushing Parametric model: 6 parameters

33. Rubber Bushing Target Response F o r c e (N) Displacement (mm) 10 mm Load deflection curve when the bushing is loaded to the left Load –deflection curve while the bushing is loaded to the right

34. Rubber Bushing Final Design Final design:

35. Rubber Bushing Response

36. Bushing Benefits • HEEDS found solution 100% compliant to requirements • Solution found was non-intuitive

37. Rack Hall-effect Device Holder Cover Magnets Sensor – Magnetic Flux Linearity Displacement S N 6.0 mm S N Magnetic Circuit

38. Sensor – Magnetic Flux Linearity Compared to previous best design found: • Linearity of response ~ 7 times better • Volume reduced by 50% • Setup & solution time was 4 days, instead of 2-3 weeks

39. Piston Design for a Diesel Engine • Piston pin location is optimized to reduce piston slap in a diesel engine at 1100, 1500, 2000, and 2700 RPM • Design Variables: • Piston Pin X location • Piston Pin Y location • Design Objectives: • Minimize maximum piston impact with the wall • Minimize total piston impact with the wall throughout the engine cycle.

40. Piston Design for a Diesel Engine • 110 designs were evaluated for each engine speed (440 runs of CASE) • Total computational time was approximately 0.5 days using a 2.4 GHz processor. • Optimized pin offset was essentially identical to what was found experimentally on the dynamometer.

41. Front Suspension Picture taken from MSC/ADAMS Manual

42. Problem Statement Determine the optimum location of the front suspension hard points to produce the desired bump steer and camber gain.

43. Results

44. Suspension Benefits • Compliance to targets found with in half a day by an engineer new to HEEDS

45. Strategies / Algorithms Search Strategies (e.g., CIA, COMPOSE) Search Algorithms (e.g., SHERPA)

46. HEEDS COMPOSE • COMPOSE – COMPonent Optimization within a System Environment • New method for enabling high fidelity design of subsystems in highly coupled complex systems (101 – 103 times speedup)

47. HEEDS COMPOSE • Based on decomposition • Most CPU effort to design subsystem (component) • Small number (3-8) of system level analyses • Full coupling maintained between system and subsystem • Large number of variables can be studied • CPU time reduced by factor of 10 – 1,000 New design proposal Updated boundary conditions

48. Vehicle Rail – Shape Optimization Objective : Maximize Energy Absorbed Constraint : Reaction Force

49. Subsystem Model Boundary Conditions from System Model

50. Subsystem Design Variables • Individually designed rails • 7 Cross-sections on each rail • 10 Design- Master Points on each cross-section • Total of 140 Shape Design variables ***