Optimization in Auto Industry - ESTECO

Optimization And RobustnessResearch & Advanced Engineering

Optimization in Auto Industry

Ren-Jye YangSenior Technical Leader

Ford Research & Advanced Engineering TEL: (313) 549-6946

E-mail: [email protected]

2013 modeFRONTIER 2013 user's meeting

Optimization And RobustnessResearch & Advanced Engineering2

Outline� Background

� Topology Optimization

� Multidisciplinary Design Optimization (MDO)

� Restraint System Optimization

� Process Integration & Design Optimization (PIDO)

� Summary and Future Directions


Numerical Optimization

� What is Optimization– IS a general automated design tool which

systematically searches for the best design which satisfies specific criteria.

– NOT: DOE, parametric studies

� Benefits:Can be applied to a wide range of design tasks and improve the designs– Eliminates trial and error design processes– Reduces weight and lead time– Improves product quality

� Risks:– Local optimum possible– May miss important constraints

VR&D


Design -vs- Analysis

Initial Design

FEAnalysis

EvaluateResults

Initial Design

FE Analysis

Performance Measures

DesignSensitivity

OptimizationAlgorithm

Convergence ?optimalYesNo

� CAE Analysis � CAE Design

design


Optimization in Computational Mechanics

� Computation intensive functions and their sensitivities, e.g., CFD, impact, reliability constraints.

� Highly nonlinear, non-convex functions

� Numbers of constraints & design variables may be large

� Problem formulations essential

� Shape parameterization not trivial

Find design variable X that will

Minimize Fk(X)

Subject to gi(X) ≤ 0, hj(X)=0, Xl ≤ X ≤ ≤ ≤ ≤ Xu


Numerical Optimization

Analysis (& Gradients)

Approximation

Optimization

Outer Loop

Inner Loop

Optimization

Analysis









Topology Optimization

� Conventional optimization techniques modify the initial design restricted to the original topology of the components

� Topology optimization helps engineers design the topology of a structure (e.g. locations of holes & stiffeners, and layout of a grillage)

P P PP

PlateTrussTruss


Topology Optimization Methods

� Homogenization Method:Bendsoe et al., Kikuchi et al., Dias, Soto, Papalambros, Olhoff, Haber, Chirehdast, etc.

� Density Method (or SIMP: Solid Isotropic Material with Penalization): Bendsoe, Rozvany, Mlejnek, Yang, Wang, Gea, Lu, etc.

� Simultaneous Analysis & DesignHaftka, Shankar, etc,

� Other Approaches:Level set, Hyper Radial Basis Function Network, Rozvany, EMRC, etc.


Homogenization Method

� Assume structure is composed of microscopic holes

� Use homogenization to determine material properties

� Design variables parameterize size and orientation of holes

� Solve optimization problem with optimality criteria method

ba

E

G

0.5 1.0

E: Young’s Modulus

G: Shear Modulus

a, bplate with holes homogenized plateunit cell

θ


Density Method

� Approach– Design variables parameterize element

densities and moduli

– Intermediate densities are penalized

– Optimization problem solved with Mathematical Programming Algorithms

� Advantages– One design variable per element

– Interfaces well with commercial FE software such as MSC and CSA Nastran

– May include multiple objectives and constraints

0 10

1

ci

ρ/ρ0

E/E0

E = c2

Eo

ρ = c ρo


Truck Frame

Finite Element Model

Topology Optimization Result

CAD Interpretation


LiftgateKnuckle

Vehicle Body

Topology Optimization Applications

Rear LCA

Typical Problem Formulation:

Min Structural Compliances

Subject to Material Usage <= 25%


Lower Control Arm - Weld Pattern Design

� Objective: Minimize total number of welds during assembly process

� Topology optimization:

• Weld element as design variables

• 350 mm weld length elimination

� Results:

• Met target after reanalysis

• Significant tooling cost saving

• Manufacturing time saving

Rear LCA


Spot Weld/Adhesive Pattern Design

� Objective: Maximize joint stiffness for 2 loads

� Design Variables: Spot weld/Adhesive

� Constraints:

adhesive <= 30% of available design spacespot weld <= 40% of available design space


3.5L-3V Front Cover Concept

Front View Rear View

Structural

Aluminum

Section

Non-Structural

Plastic Sections


3.5L-3V Front Cover Topology Optimization

� Objective: Maximize mounting natural frequencies for the alternator and power steering pump

� Constraints: – Aluminum material < 20% of available design space

– Natural frequencies > 500 hz

� Outcome:– Topology optimization showed that a structural

section was only required on upper left hand side.

– Dual material, aluminum/plastic, structural/non-structural, solid model concept was created based on these results

• 2.5 lb weight savings vs. complete structural AL cover

• $6 piece price savings vs. complete structural AL cover


Bead Pattern Optimization

Membrane Stiffness:

Kx > Ky

Bending Stiffness:

Kx < Ky


Bead Pattern Optimization(Soto et al. ’99)

� A technique to optimally locate and orient beads in vehicle panels to maximize stiffness and natural frequencies.

topology optimization shape optimization


Examples


Design of a Negative Poisson’s Ratio Material(O. Sigmund, Denmark Technical U.)

design domain discretized by 30x30 finite elements.

This material expands vertically when stretched horizontally

Periodic material composed of repeated base cells.


Design of Compliant Mechanism(O. Sigmund et al., Denmark Technical U.)

Design of a micro-displacement amplifier

Design domain Optimal amplifier topology for 1:-3.7 amplification

Realization of the micro-amplifier


Design of MEMS(O. Sigmund et al., Denmark Technical U.)

The actuation principle is electro-thermo-mechanical, i.e. an electric input heats the structure locally due to Joule’s heating. The heating results in material expansion and thereby actuation.

Design of 2 degree-of-freedom micro-scanning device

Other Industrial Applications

Motorcycle Frame Design

Bus Frame Design

Topology Optimization Geometry Extraction

ICAD Solid Geometry

Extraction

Size and Shape Optimization

Geometry Extraction

Topology OptimizationMaterial Layout

Size and Shape OptimizationBuckling and Stress

Topology Optimization Package Space Definition

Courtesy of Airbus

Airbus A380 Droop Nose Leading Edge

Zhao Zhou Bridge (赵州桥赵州桥赵州桥赵州桥)built in Sui dynasty (581–618 AD)

by Yun Kang SUI, 2005


Recent Developments

� Extend applications from automotive, aerospace to other industries:biomedical, consumer goods, electronics, energy, heavy industry, marine, ….

� More innovative applications:ESL, inertia relief for crashworthiness design, local strain energy for designing desired load paths, ….

� Extend to other disciplines: nonlinear, transient problems, CFD, heat transfer, multiphysics, acoustics, MEMS, optics, electromagnetism, …

� Consider manufacturing constraints:no-hole option for draw-direction, stamping/sheet metal, and minimum member spacing, ….

� Other developments: Hybrid Cellular Automata, Level Set Method, Hyper Radial Basis Function Network, …


Shotgun –> door beam

Rocker

Rail –> rail extension

Subframe –> Tunnel

Compliance

0.0E+00

1.0E+06

2.0E+06

3.0E+06

4.0E+06

5.0E+06

6.0E+06

7.0E+06

0 10 20 30 40 50 60

Iteration #

Co

mp

lia

nc

e

Full Frontal Impact: IRM









Multidisciplinary Design Optimization

(MDO) is a methodology for improving

design of engineering systems, e.g.,

automobile, aircraft, or spacecraft, in which

everything influences everything else.

- By Dr. J. Sobieski -NASA Langley

What is MDO


Typical Aerospace MDO Problem

� Effective Integration of Individual disciplines/subsystems to capture the Interactions

� Novel solution procedures to enable system level solutions

� Characteristics: large-scale, needs decomposition, computation intensive, multiple simulations

CFD Structures

Controls

Loads

Deformation

ControlSurfaceDeflns

StressPressureMoments Design space discipline 1

Design space discipline 2

Design Variables

Performance

MultidisciplinaryOptimal Design

Discipline 1 Optimum

FeasibleDesignSpace

SuboptimalDesign

Conventional Trades

MDO Search

Discipline 2 Optimum


Vehicle Attributes/Disciplines

• Vehicle Dynamics (V)

- Steering

- Handling

- Ride

- Braking

• Chassis Systems (S)

- General Vehicle

- Front Suspension

- Rear Suspension

- Steering

----------

• Aerodynamics CFD Analysis (V)

• Heat Management (V)

• Coolant Flow Simulations (S)

• Vehicle Level ClimateControl (V)

- Front End Air Flow- Front End Openings

• System Level ClimateControl (S)

- A/C Performance- Heater Performance

-----

• Chassis NVH

- Frame Principal Modes

- Frame Static Stiffness

- Static Stiff. at Frame Attach.

- PM at Frame Attachments

- Suspension Modes

• Chassis Durability

- Front Suspension

- Rear Suspension

- Frame and Mounting System

------------

• Trimmed Body Principal Modes (V)

• Trimmed Body Static Stiffness (V)

• BIP Principal Modes (S)

• PM at Body Attach. Loc.(S)

• LP6 for Body Attachments (S)

• Static Stiffness for Body

• Attachment Locations (S)

• Body SDS/WCR/FMVSS (S/C)

• Hood (S)

• Decklid (S)

• Doors (S)

• Trailer Tow (C)

• Dash/Cowl fatigue (C)

--

• FRONT IMPACT (V)

- New FMVSS 208

- NCAP

- OOP

- IIHS Offset

• SIDE IMPACT (V)

- 33.5 mph FMVSS214

- LINCAP

• Rear Impact (V)

- 35 mph RMB

- 50 mph C/C Inline

- 50 mph C/C Side

- 50 mph C/C 50% Offset

• Roof Crash (S)

• Head Impact (S)

---

• Idle Tactile (V)

• Idle Acoustic (V)

• Driveline Unbalance Tactile (V)

• Driveline Unbalance Sound (V)

• Glen Eagle Tactile (V)• Rough Road Tactile (V)

• Brake Roughness Tactile

• Impact Harshness Tactile

• R1H / CP2 Tactile (V)

• Glen Eagle Acoustic (V)

• Rough Road Acoustic (V)

• Impact Harshness Acoustic (V)

• Brake Squeal

• Exhaust NVH

• Wind Noise

• Shift Quality---

Body Structure(NVH & Durability)

Vehicle DynamicsChassis & Full Vehicle Durability

TASE* & Climate Control

SafetyVehicle NVH

Vehicle

Performance

V: Vehicle Level

S: System Level

C: Component Level

*TASE: Thermal Aerodynamics System Engineering


Typical Automotive MDO Problem

� Little or no coupling/interactions in vehicle attributes

� Coupling through common design variables

� Multiple models, multiple software

� Large number of design variables: continuous, discrete

� Large number of constraints

� Computation intensive for high fidelity models

� Highly nonlinear for many responses, e.g., restraint system responses

� Some disciplines not as mature


• Torsion Stiffness

• Bending Stiffness

• Frequencies (floor, global)

• Dynamic Equivalent Stiffness

(Y and Z for 8 mounts)

NVH

Seatbelt Pull

(FMVSS207/210/225)• Front Row Seat

• Second Row Seat

40% IIHS Frontal Offset Impact• Intrusion

Tailor Rolled Blanks

Side Impact

• IIHS

• Oblique Pole

Truck Underbody Application(Collaborated with Mubea, C. H. Chuang, et al. ‘08)

FMVSS: Federal Motor Vehicle Safety Standards


MDO Formulation

Minimize: TRB parts weightSubject to

� 40% Frontal Offset Impact (1 discipline)� Side Impact (2 disciplines)

• IIHS Side Impact• Oblique Pole Side Impact

� Seatbelt Pull - FMVSS 207/210/225, (2 disciplines)• Front row seat• Second row seat

� NVH (4 disciplines)• Torsion and bending stiffness• Normal modes (front floor, rear floor, overall torsion, and overall

bending)• Dynamic equivalent stiffness (Y- and Z-directions for 8 mounts)

With respect to� Thickness of upper and lower bounds

47 Responses

FMVSS: Federal Motor Vehicle Safety Standards


Parameterization

9 segments on CM #1

9 segments on CM #2

9 segments on Rear Sill11 segments on CM #4

18 segments on Side Sills

9 segments on CM #3

Total Number of segments: 65


Design Variables Summary

Independent DV: 15

12

3

15

4

151

1

1

2

44

4

5

5

6

7

7

7

7

8

8

9

10

10

11

11

12

12 13

13

14

� Symmetry� Connection� Transition

� Gauge can vary with a 2:1 ratio� 100mm transition required for

1mm gauge change


CM#2

0.60

1.10

1.60

2.10

0 500 1000 1500

Vehicle Y Coordinate (mm)

Thic

kness (

mm

)

MDO

1 lbs (8.3%) weight saving

Side Sills (L/R)

0.600

1.100

1.600

2.100

2.600

3.100

3.600

0 500 1000 1500 2000

Vehicle Y Coordinate (mm)

Thic

kness (

mm

)

6 lbs (17.6%) weight saving









Technical Challenges

� Highly Nonlinear, Non-convex Functions

� Discrete/Continuous Variables

� Multi-modalities, Instabilities� Large Number of Constraints

Unbelted

In-Position

HIII 50s

Belted

In-Position

HIII 50s

OOPO

HIII 50s

OOPO

HIII 05s

Unbelted

In-Position

HIII 05

Belted

In-Position

HIII 05

Present &

Future Interplay

FMVSS 208 Frontal Impact Test Suite


Structure Target Setting & Restraint Selection

A1

A2

a

b

Design Variables

Crush Distance

Front End Length

Stiffness

Intrusion

Restraint Selection

NCAP Performance

Objectives:

Min Crush Distance

Min Cost

Optimize Performance

OutputInput Simulation

Responses


Restraint System Design(Zhou et al., intl J Vehicle Safety, ‘05)

� Objective: minimize vehicle crush distance

� Design variables (10): bi-level crash pulse, restraint variables, e.g., load limiter load level

� Constraints (60): 12 constraints for each crash mode, 12*5 models = 60

� Optimization Method: Genetic Algorithm

� 50th belted dummy at 35 mph



� 50th unbelted dummy at 25 mph

� 5th unbelted dummy at 25 mph


Design Variables

Design Variable

Symbol Description

X1PulA Vehicle Front End

Length

X2 PulA1 Pulse 1st level

X3 PulA2 Pulse 2nd level

X4ColLoaB Adaptive Belted

column load

X5ColLoaU Adaptive Unbelted

column load

X6 VenBel Belted Vent Size

X7 VenUBel Unbelted Vent Size

X8 PBucFla Pyro Buckle Flag

X9 PRetFla Pyro Retractor Flag

X10Emr Retractor Load

Limiter load level

A

B C

Acc. PulA1

PulA2

Crush

D E

F

Crash Acceleration versus Crush

Bi-Level Pulse


Constraints Constraints Symbol Description

Yi1 (i=1-5) Hic15 15 ms HIC

Yi2 (i=1-5) ChestRCum Peak chest G

Yi3 (i=1-5) CheDefMax Chest deflection

Yi4 (i=1-5) FemLfPk Left femur load

Yi5 (i=1-5) FemRtPk Right femur load

Yi6 (i=1-5) NecFxMin neck shear -

Yi7 (i=1-5) NecFxMax neck shear +

Yi8 (i=1-5) NecFzMin Neck compression

Yi9 (i=1-5) NecFzMax Neck tension

Yi10 (i=1-5) NecMyMin Neck extension

Yi11 (i=1-5) NecMyMax Neck flexion

Yi12 (i=1-5) NijIndMax Neck injury criteria

Note: i represents different MADYMO model.

i = 1 means 50th belted dummy at 35 mph speed



i = 4 means 50th unbelted dummy at 25 mph speed

i = 5 means 5th unbelted dummy at 25 mph speed


Top Two Designs


5th Belted Dummy at 30MPH Speed

0%

20%

40%

60%

80%

100%

120%

Hic

15C

hest

GC

hest

Df

FemL

FemR

Fx-

Fx+ Fz-

Fz+

My-

M

y+

Nij

Baseline Design

Design 1

Design 2









Step 4: Perform MADYMO Simulations and Extract Occupant Injury Numbers

Step 2: Define PAB Shape VariablesConvert MADYMO PAB mesh to a Hypermesh readable format (e.g. Nastran)Create shape variables in Hypermesh using global morphing technique, and save the file in hm formatCreate a Hypermesh command file to perform shape change in batch mode

System Integration and Automation of PAB Shape Design Process

Morphing

Step 3: Perform PAB Shape ChangesChange values of shape variablesRun Hypermesh to perform PAB shape morphing in batchUpdate PAB finite element mesh file with the new PAB shape

Step 1: Prepare and Correlate MADYMO Models for Multiple Crash ScenariosPAB model as an include filePAB FE mesh as an include file for the PAB model

Input File for PAB Shape Design Variables

Output Files for Occupant Response Numbers


Flowchart for Airbag Shape Design

Engineering Data Mining

Color: gray feasible, yellow unfeasible

bubble size: safety index

Color: vehicle type, bubble size: safety indexColor represents safety index

Color: dark gray feasible, yellow

unfeasible


Summary

� PIDO is essential for product development

� MDO is a key enabler for light weight structure design

� GA becomes more popular

� Need to change mindset from “Analysis” to “Design”

� Optimization technologies are well developed and can be applied to a wide range of design tasks

� Make optimization standard enterprise practice will have a competitive advantage

PIDO: Process Integration and Design Optimization


Future Directions

� Methodology:– User friendly system well developed but need more – Integration of PIDO and database management – How to handle computation intensive applications– Advanced optimization algorithms for direct MDO– Others: manufacturing process optimization, reliability-

based design optimization (RBDO), robust design, cluster analysis/engineering data mining, global optimization, etc.

� Hardware/Software:– Seamless high performance computation

throughout inhomogeneous platforms/computers– Web-based, enterprise collaborative and distributed

system– MPI implementation, GPU (Graphics Processing Unit),

Cloud computingPIDO: Process Integration and Design Optimization

MPI: Message Passing Interface


Thank you for your attention!

Documents

Optimization in Auto Industry - ESTECO