optimization based chassis design

Optimization based Chassis Design

2015 Altair Technology Conference

5th – 7th May 2015

Adrian Chapple

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Introduction


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Gestamp Global Locations


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UNITED STATES

7 Production Plants

MEXICO

3 Production Plants

BRAZIL

6 Production Plants

ARGENTINA

4 Production Plants

1 - Alabama

2 - Lapeer

1 - Mason

1 - South Carolina

1 – Chatanooga

1 – West Virginia

1 - Aguascalientes

1 - Puebla

1 - Toluca

1 - Gravataí

1 - Pananá

1 - Santa Isabel

1 - Sorocaba

1 - Taubaté

1 - Córdoba

3 - Buenos Aires

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3

6

4


Gestamp in America

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Gestamp Chassis Products


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Vehicle Dynamics

Ride and Handling

Comfort

Noise and Vibration

Press and Customer

perception

Crash Performance

Euro/ US NCAP

IIHS Rating

Safety

Marketing

One off abuse

Chain of failure

Controlled failure

Abuse Durability

Vehicle is Durable

No Warranty

Robust Design

• The objective is also clear, low mass and low cost.

• The challenge, does the customer really know what they want, from the suppliers component?

• Each product has clearly defined performance and manufacturing constraints.

Gestamp Chassis Products – Requirements


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Challenge 1

Optimised Chassis Design


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Optimisation is now every day practice for most CAE enabled businesses.

After understanding the need to identify the optimum solution, the challenge for most chassis

component suppliers is to develop a product suitable for high volume manufacture.

Optimum sheet metal structures – Challenge 1

Package space

Skeleton solution

Tube copy

Sheet copy

The key was to define the performance of the perfect design and measure the efficiency of

the copy against this perfect design.

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Gestamp have developed a process to transform the

perfect design, the skeleton inside the package space,

into a sheet metal equivalent.

The process can give 25% mass reduction compared

to conventional approach.

Developed over several years and projects with

improved understanding on design for manufacture.

Optimum sheet metal structures – edict process

Package space Skeleton Sheet metal structureAnalysis Package space Skeleton Sheet metal structureAnalysis

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Optimization based Chassis Design - edict

Gestamp developed an algorithm to analyse the optimisation output density field and replace with

solid volume results with an equivalent sheet metal structure that best represented the solid

geometric properties.

The second stage of the algorith is to grow the sheet solution until connecting surfaces are

formed.

Optimum sheet metal structures – The translation tool

This tool is key to Gestamps comeptitive advantage in the design of lightweight steel chassis

frames.

optimisation analysis Sheet metal structureVolume modelGenerated sheet

metal structureAnalysisOptimisation

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Optimization based Chassis Design - edict

Optimum sheet metal structures – Results

Gestamp have developed a process to transform the perfect design, the skeleton inside the

package space, into a sheet metal equivalent.

The process can give 25% mass reduction compared to conventional approach.

Developed over several years and projects with improved understanding on design for

manufacture.

However, the vehicles programs developed with this total optimsaiton approach are now coming to

an end and need re-designing and the customer wants the same weight saving again.

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Challenge 2

Same weight reduction again please!


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Further Weight savings – Challenge 2 where next?

Only a small amount of weight saving will come from better application of the current tools (or

more resource), a more intelligent approach will yield greater results.

Gestamp have focussed recent efforts to reduce component mass by using optimisation to

challenge targets, and consider the real question that needs optimising.

Challenge targetsSystem level

optimisation

Multi-domain

optimisation

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Further Weight Saving – Challenge Component Targets


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• In order to predict the performance of the component based on the different design variables

and equation was required for each design response.

• Hyperstudy software was used to set up a Design Of Experiments study and extract

interpolations for each variable.


FCA_LHS_y = 0.25928 + (-5.08571e-05 * arb_brkt) + (-0.000155424 * diff_brkts) + (-0.00357842 * flca_brkts) + (-0.00300373 *

front_upper) + (-0.0305444 * lower) + (-0.00265223 * rear_closer) + (-0.00389033 * rear_upper) + (-0.00138019 * rlca_brkts) + (-

0.00926752 * siderail) + (-0.0041613 * tower_closer) + (-4.99368e-05 * arb_brkt * arb_brkt) + (-9.91163e-05 * arb_brkt * diff_brkts) +

(4.15957e-05 * arb_brkt * flca_brkts) + (8.13897e-05 * arb_brkt * front_upper) + (3.71936e-05 * arb_brkt * lower) + (3.14648e-06 *

arb_brkt * rear_closer) + (4.00817e-05 * arb_brkt * rear_upper) + (0.000108841 * arb_brkt * rlca_brkts) + (-0.000197566 * arb_brkt *

siderail) + (8.18722e-05 * arb_brkt * tower_closer) + (4.04482e-05 * diff_brkts * diff_brkts) + (-5.07071e-05 * diff_brkts * flca_brkts) + (-

1.59178e-05 * diff_brkts * front_upper) + (0.000136322 * diff_brkts * lower) + (-8.48643e-05 * diff_brkts * rear_closer) + (8.87484e-05 *

diff_brkts * rear_upper) + (1.59915e-05 * diff_brkts * rlca_brkts) + (-0.000178697 * diff_brkts * siderail) + (5.45731e-05 * diff_brkts *

tower_closer) + (0.000271955 * flca_brkts * flca_brkts) + (-1.32405e-05 * flca_brkts * front_upper) + (0.000140077 * flca_brkts * lower) +

(2.14879e-05 * flca_brkts * rear_closer) + (-3.93529e-05 * flca_brkts * rear_upper) + (3.32677e-05 * flca_brkts * rlca_brkts) +

(0.000179994 * flca_brkts * siderail) + (-7.69277e-05 * flca_brkts * tower_closer) + (0.000191444 * front_upper * front_upper) + (8.512e-

05 * front_upper * lower) + (-1.7937e-05 * front_upper * rear_closer) + (0.000103053 * front_upper * rear_upper) + (-0.000152548 *

front_upper * rlca_brkts) + (8.32334e-05 * front_upper * siderail) + (0.000131147 * front_upper * tower_closer) + (0.0028065 * lower *

lower) + (-0.000177823 * lower * rear_closer) + (6.8397e-05 * lower * rear_upper) + (0.000117493 * lower * rlca_brkts) + (0.000384257

* lower * siderail) + (0.00013488 * lower * tower_closer) + (0.000274493 * rear_closer * rear_closer) + (2.68192e-06 * rear_closer *

rear_upper) + (6.69264e-05 * rear_closer * rlca_brkts) + (0.000257607 * rear_closer * siderail) + (5.2742e-05 * rear_closer *

tower_closer) + (0.000267322 * rear_upper * rear_upper) + (6.3803e-05 * rear_upper * rlca_brkts) + (-6.93601e-05 * rear_upper *

siderail) + (1.02142e-05 * rear_upper * tower_closer) + (5.85151e-05 * rlca_brkts * rlca_brkts) + (7.32327e-05 * rlca_brkts * siderail) + (-

0.000134385 * rlca_brkts * tower_closer) + (0.000659127 * siderail * siderail) + (0.000169322 * siderail * tower_closer) + (0.000269377

* tower_closer * tower_closer)

Response 1 (quadratic interpolation all variables )

• After checking that the model used enough power and data points to give acceptable error (<1%),

each of the 12 responses was then added into excel so that predictions for the component could

be made without using finite element analysis for each possible option.

• This allows the solver in excel to be used to optimise the performance.

Response 1 (2 variable only)


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PointTarget Stiffness

N/mm

Actual Value

N/mm

FCA lhs y 5770 6159

FCA rhs y 5800 6227

RCA lhs y 7960 8210

RCA rhs y 7940 8307

arb lhs 2200 2200

arb rhs 2200 2250

rack lhs y 7870 8560

rack rhs y 7860 8575

diff lhs 2500 2820

diff rh rear 2950 2950

diff rh front 2880 2925

pt3 parallel lhs 3130 3354

pt3 parallel rhs 3130 3373

pt3 opp lhs 36955 37792

pt3 opp rhs 36955 40464

FCA lhs x 16190 16190

FCA rhs x 16591 17922

RCA lhs x 13670 18544

RCA rhs x 13800 18121

rack lhs z 2600 2600

rack rhs z 2640 2688

Mass (kg) 19.72

Current Value Initial Lower Upper

dv1 arb_brkt 2.43 2.50 2.00 5.00

dv2 diff_brkts 1.80 2.00 1.80 4.00

dv4 flca_brkts 4.00 2.50 1.80 4.00

dv6 front_upper 2.22 2.50 1.80 4.00

dv7 lower 1.82 1.80 1.80 4.00

dv8 rear_closer 3.15 1.80 1.80 4.00

dv9 rear_upper 1.99 2.00 1.80 4.00

dv10 rlca_brkts 4.00 2.50 1.80 4.00

dv11 siderail 1.99 2.50 1.80 4.00

dv12 tower_closer 4.00 2.00 1.80 4.00

mass flca_lhs_y rca_lhs_y fca_rhs_y rca_rhs_y arb_lhs_z arb_rhs_z rack_lhs_y rack_rhs_y diff_lhs_z diff_rhr_z

19.72 0.16 0.12 -0.16 -0.12 -0.45 -0.44 0.12 -0.12 -0.35 -0.34

1

5

2

3

4

• 1. Objective to minimize mass of component.

• 2. Change cells to alter design variable (sheet thickness of each panel)

• 3. Re-calculate predicted performance based on new sheet thickness values.

• 4. Constrain panel thickness to be within sensible upper and lower bounds

• 5. Make sure that predicted performance is above the required minimum level.

3b


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• Driving targets identified, actual component performance presented along with panel gauge and

total mass.


Baseline

Proposal 1

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In order to identify which of the stiffness / modal targets were driving mass into this recent rear

chassis frame gauge optimisation was used to highlight and optimise the component targets.


Baseline

1

2

3

4

• 1. By reducing the bending mode target by only 10Hz it is possible to reduce the mass by 4%,

beyond this 10Hz other targets are driving the mass into the component.

• 2. Line 2 shows the effect of reducing the minimum gauge of panels used on the component.

• 3. Line 3 shows by slightly reducing the ARBz target another 5% mass reduction is achieved.

• 4. Finally with the ARB, bending mode and 1.5mm panels used it is the front body mount stiffness

dictating the mass of the frame.

• Overall 2kg or 12% mass reduction can be achieved through small reduction in 3 key targets.

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The previous study crudely used only gauge to increase or reduce performance. This study

used the topology results to define a structure to consider the impact upon mass for a possible

frequency target.

Link attachment stiffness targets

ONLY ~ 14.6kg

Link attachment stiffness targets +

torsional modal requirement ~ 15.2kg

Further Weight Saving – Challenge Component Targets (Topology)

Sheet translation 2

(tube version)

15.1kg

Sheet translation 1

All pressed solution

16.0kg

Sheet translation 1


15.0kg

• The topology results clearly show different loadpath requirements if the torsional mode is

included and there is a mass penalty for this target. However, when the topology result is

translated into a sheet metal finished frame, it is the quality of the copy that determines the level

of mass reduction.

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• Challenging component targets always results in our customers verifying

the performance of a reduced level frame in a system simulation, including

all of the other chassis parts and measuring vehicle performance metrics.

• The logical next step is to remove the component targets and optimize

against these system requirements.

Further Weight Saving - System Level Optimisation

System level Vehicle Dynamics

targets

Sheet translation 2

(tube version)

15.1kg

Sheet translation of system topology results

13.7kg

Sheet translation 1


16.0kg

Component targets derived to

achieve system level performance.

• Clearly there is weight saving in just translating the topology results well 16.0kg vs 15.1kg but

another 10% reduction in mass is achieved by translating the results based on the system level

targets.

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Topology Volume Model

• Again on a series production frame system level optimization has

successfully been used to add a missing vehicle dynamics attribute.

• In this particular case the system level topology optimization achieved the vehicle attribute with a

360g brace. Previous to this study 2.0kg of additional up gauge and a far less efficient brace was

proposed.

Further Weight Saving - System Level Optimisation

Topology optimisation results Final Design Solution

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Camber stiffness

“x-20” kN/o

Camber stiffness

“x+40”kN/o

Further Weight Saving - System Level Optimisation +

Challenge Targets

• It is obviously possible to challenge the vehicle system targets as well.

• In the example below one of the system vehicle dynamics targets is

challenged

Graph showing topology mass vs camber stiffness target

• In this study the topology result mass was used to show the effect on the target.

• The results show that the target can be increased up to a value of “x” kN/degree with little impact

on mass, beyond that this target become mass driving.

“x”

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• Finally on this latest project it has been possible to include a further

system target the dB(A) noise in the cable from a noise transfer function

applied through the wheel.

• The full impact of this different approach can be seen by considering the results from two frames

on the same vehicle platform, one designed based on component targets, the second designed

to system level requirements.

• Although in fact the Wave1 vehicle has some additional functionality the MDO system level

optimization resulted in a 27% mass reduction.

Further Weight Saving - System Level Optimisation + MDO

Wave 1 vehicle designed to

component requirements

Wave 2 vehicle designed to system

level Vehicle Dynamics and NVH

performance

Mass = 1.0Mass = 0.73

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• It is possible to achieve significant weight saving over traditionally designed chassis

components using optimization based on efficiently translating topology results into sheet steel

solutions.

• Challenging mass driving component targets using to tools provided by Altair provides the

opportunity to save mass on existing fully optimized components.

• Smart optimization based on designing to “more valid” system level targets and including most

if not all performance requirements into the topology optimization problem provides the

opportunity to save mass further.

• In the most valid complete case study carried out comparing the performance of component

targets vs system level requirements 27% mass reduction was achieved against a fully

optimized chassis component.

Conclusions

optimization based chassis design

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