red cedar TECHNOLOGY Accelerating Innovation through Automated Design

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

Analysis versus Design red cedar TECHNOLOGY • 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 2

Design Complexity red cedar TECHNOLOGY xity le p om C sign De Desi gn T ime a nd C ost 3

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

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

Main Benefits red cedar TECHNOLOGY • 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 6

Example Application Areas red cedar TECHNOLOGY Automotive Civil Infrastructure Biomedical Aerospace 7

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

Some Common Types of Structural Optimization red cedar TECHNOLOGY • 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 9

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

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

Parameter Optimization red cedar TECHNOLOGY 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 12

Optimization Scenarios red cedar TECHNOLOGY v Seek small improvements to an existing design • Local search, small variable range • Manual iterations reduce work needed by optimizer v 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 13

Some Unique Features in Tool You Are Using red cedar TECHNOLOGY 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 14

SHERPA – a Hybrid, Adaptive Method red cedar TECHNOLOGY • 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 15

SHERPA Benchmark Example red cedar TECHNOLOGY Find the cross-sectional shape of a cantilevered I-beam with a tip load (4 design vars) Design variables: H, h 1, b 2 Objective: Minimize mass Constraints: Stress, Deflection 16

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

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

Example: Hydroformed Lower Rail Crush zone red cedar TECHNOLOGY Crush zone 19

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

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

HEEDS Optimized Design red cedar TECHNOLOGY 22

HEEDS Optimized Design red cedar TECHNOLOGY 23

Validation red cedar TECHNOLOGY 24

Lower Rail Benefits red cedar TECHNOLOGY 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 25

Hydroforming Process Optimization red cedar TECHNOLOGY 26

Hydroforming Model red cedar TECHNOLOGY 27

Formability Optimization red cedar TECHNOLOGY 28

Manual Optimization red cedar TECHNOLOGY 29

HEEDS Optimization red cedar TECHNOLOGY 30

Formability Results red cedar TECHNOLOGY Manual Optimization HEEDS Optimization (55% improvement) 31

Rubber Bushing red cedar TECHNOLOGY Parametric model: 6 parameters D 2 D 1 Fixed D 3 D 1 θ D 4 D 5 32

Rubber Bushing Target Response red cedar TECHNOLOGY 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 33

Rubber Bushing Final Design red cedar TECHNOLOGY Final design: 34

Rubber Bushing Response red cedar TECHNOLOGY 35

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

Sensor – Magnetic Flux Linearity red cedar TECHNOLOGY Displacement S N Rack 6. 0 mm Hall-effect Device Holder S N Cover Magnetic Circuit Magnets 37

Sensor – Magnetic Flux Linearity red cedar TECHNOLOGY 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 38

Piston Design for a Diesel Engine red cedar TECHNOLOGY • 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. 39

Piston Design for a Diesel Engine red cedar TECHNOLOGY • 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. 40

Front Suspension red cedar TECHNOLOGY Picture taken from MSC/ADAMS Manual 41

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

Results red cedar TECHNOLOGY 43

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

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

HEEDS COMPOSE red cedar TECHNOLOGY • 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) 46

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 red cedar TECHNOLOGY New design proposal Updated boundary conditions 47

Vehicle Rail – Shape Optimization red cedar TECHNOLOGY Objective : Maximize Energy Absorbed Constraint : Reaction Force 48

Subsystem Model red cedar TECHNOLOGY Boundary Conditions from System Model 49

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

Rail Optimization Results red cedar TECHNOLOGY Rail Energy Absorbed System Energy Absorbed (30% increase) (5. 5% increase) (Optimization over 140 variables using only 6 system evaluations. ) 51

CIA: Cooperative Independent Agents red cedar TECHNOLOGY DIFFERENT search agents at the same time, working with – DIFFERENT TOOLS – DIFFERENT views of the problem Optimal Design Team: Intellectual Diversity 52

Approaches to Heterogeneous Agents red cedar TECHNOLOGY Agents might differ according to their: • • • Physical/spatial domain Temporal extent of simulation Number of design variables Resolution of design variables Stochasticity of variables Performance measures Loading cases Constraint enforcement Analysis models Search methods … 53

Hydroformed Lower Rail Crush zone red cedar TECHNOLOGY Crush zone 54

Shape Design Variables red cedar TECHNOLOGY 67 design variables: 66 control points and one gage thickness z rigid wall y x lumped mass arrows indicate directions of offset crush zone cross-section 55

Optimization Statement red cedar TECHNOLOGY • Maximize energy absorbed in crush zone • Identify the rail shape and thickness • Subject to constraints on: • Peak force • Mass • Manufacturability 56

Simple, Three-Agent Topology red cedar TECHNOLOGY Treat DIFFERENTLY: • crush time simulated ( reduces CPU time ) • discretization of design variables ( reduces design space ) Crush Time Design Variable Discretization t=6 ms F Agent Topology 1 Coarse 2 Medium 3 Refined t=10 ms t=14 ms t 57

Energy Absorbed red cedar TECHNOLOGY 58

HEEDS CIA – Example Agent Topology red cedar TECHNOLOGY Lower Compartment Rail Example – 19 Agents/19 CPU’s Axial Load Case (Deterministic) Crush Time 3. 8 ms Crush Time 8. 4 ms Stochastic Load Cases (and Stochastic Design Variables) Offset Load Case (Deterministic) 5 4 3 Low Resolution 2 1 0 6 8 7 Medium Resolution Crush Time 12. 6 ms 9 10 11 12 13 14 15 16 17 18 High Resolution 59

red cedar TECHNOLOGY Red Cedar Technology East Lansing, MI USA 60

red cedar TECHNOLOGY Extra Slides 61

Design of a Composite Wing red cedar TECHNOLOGY • Design variables: – Number of plies – Orientation of plies – Skin, spars, tip • Objectives: – Minimize mass – Buckling, stiffness, failure constraints 62

Design of a Composite Wing • • red cedar TECHNOLOGY Buckling Load increased by 80% Failure index decreased by 30% Bending stiffness increased by 15% Mass increased by 6% 63

Stent Shape Optimization LOADCASE 1 red cedar TECHNOLOGY LOADCASE 2 Expand the stent in the radial direction by 8. 23226 mm. Crimp the annealed stent by 2. 0 mm. ANNEAL 64

Stent – Subsystem Design Model red cedar TECHNOLOGY 65

Stent – Baseline and Final Designs BASELINE DESIGN TECHNOLOGY FINAL DESIGN (Provided) red cedar (Found by HEEDS) Max. Strain = 3. 3% Max. Strain = 0. 99% 66