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

©2012 GESTAMP 3
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
7
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 structureAnalysisPackage 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 model
Generated sheet
metal structure
AnalysisOptimisation

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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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Point
Target 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_lh
19.72 0.16 0.12 -0.16 -0.12 -0.45 -0.44 0.12 -0.12 -0
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 = 1.0
Mass = 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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