Jaguar Land Rover - Robust Design Optimization of a Knee Bolster

Signpost the Future: Simultaneous Robust and
Design Optimization of a Knee Bolster

Tayeb Zeguer
Jaguar Land Rover
W/1/012, Engineering Centre, Abbey Road, Coventry, Warwickshire, CV3 4LF
tzeguer@Jaguar.com

Stuart Bates
Altair ProductDesign
Imperial House, Holly Walk, Royal Leamington Spa, CV32 4JG
Andy.burke@uk.altair.com

www.altairproductdesign.com
copyright Altair Engineering, Inc. 2011


Abstract
The future of engineering design optimization is robust design optimization whereby a design
is optimized for real world conditions and not just for one particular set of ideal conditions (i.e.
nominal). There is no practical point trying to get to the peak of a mountain to get the best
view when a slight gust of wind can blow you off, what is practical is to find the highest plateau
where the view is unaffected. The same is true for engineering design, there is no point in
coming up with a design which is optimized for a set of ideal conditions when in reality there
exists uncertainty in the materials, manufacturing and operating conditions.

This paper introduces a practical process to simultaneously optimize the robustness of a
design and its performance i.e. finds the plateau rather than the peak. The process is applied
to two examples, firstly to a composite cantilever beam and then to the design of an
automotive knee bolster system whereby the design is optimized to account for different sized
occupants, impact locations, material variation and manufacturing variation.

Keywords: Optimization, HyperStudy, Stochastic, Uncertainty, LS-DYNA

1.0 Introduction
The competitive nature of the automotive industry demands continual innovation to enable
significant reductions in the design cycle time while satisfying ever increasing design
functionality requirements (e.g. minimising mass, maximising stiffness etc). The challenges
for computer-aided engineering (CAE) to overcome are:

Development cycle must be reduced.
Failure modes have to be found and resolved earlier.

The enablers for CAE are: Faster model creation, CPU, Automation, Material property
Identification, Robustness Optimisation and Validation. The aim of this work is to show that
Altair HyperStudy [1] can be used as powerful CAE enabler to facilitate robust design.

Over the last decade industry has been indoctrinated into the philosophy of manufacturing
quality to six sigma. This paper presents increasing applications of designing systems to
sigma levels of quality. Thus ensuring that designs or numerical models perform within
specified limits of statistical variation.

Copyright Altair Engineering, Inc., 2011 2


DEFINE
CHARACTERIZE

OPTIMIZE

Robust VERIFY
Optimized
Design

Figure 1: Design for Six-Sigma Process

An overview of each stage of the Design for Six Sigma (DFSS) process is given below.

1.1 Define

The first step is to carry out brainstorming to define the system inputs, outputs, controllable
and uncontrollable factors. The Parameter Diagram or p-diagram (Figure 2) is a useful tool for
such a purpose.

DEFINE

CHARACTERIZE
Uncontrollable
OPTIMIZE
Factors
VERIFY

INPUT OUTPUT
Performance DESIGN Performance
Targets

Controllable
Factors

Figure 2: Define – P-Diagram

1.2 Characterize

The characterization phase involves the following :-



Key parameter identification: identifies the parameters which have the most
significant effect on the performance (output) of the design. This is done typically
through the use of design of experiments (DoE) and statistics (e.g. analysis of variance
ANOVA).

Surrogate Model generation: Typically, in CAE the analysis of a non-linear design
will require simulation times ranging from one hour to a day, making the use of full
analyses for iterative design optimisation computationally expensive and a robustness
assessment requiring hundreds or thousands of Monte Carlo simulations impractical.
To overcome these problems a response surface approximation or surrogate model is
required. This is done using the information generated by the DoE together with
advanced surface-fitting algorithms. The surrogate model gives the value of a key
output variable in the design space, e.g. peak deceleration, as a function of the design
variables. Thousands of simulations of the surrogate model can be run in a few
minutes.

1.3 Optimize

Figure 3 shows a typical design space (response surface) for two design variables. If you
assume there is no variation in the operating and manufacturing conditions then point A is the
optimum solution. However, in reality there are variations in the manufacturing and operating
conditions such that it is very easy to fall off this optimum point (A). A “better” or robust
optimum is point B since the design space is flatter in that region i.e. the performance of the
design is less sensitive to real life variations.

The aim of this optimization phase is to identify the most robust solution in the design space.

• SIMPLE OPTIMUM POINT
• Absolute highest peak ignored due to
sharp gradients surrounding it,
reflecting the non-robust nature of the
solution
• A small change in input (X or Y) will
result in a rapid change in output (Z)

• ROBUST OPTIMUM CLOUD
• Peak B has value lower than Peak A
• The flatter landscape in the region of
the peak results in more robust
solutions in that area
• The output (Z) will not be highly
sensitive to small changes X or Y

Figure 3: Robust Optimum Identification

The process developed here is shown in Figure 4 and consists of the following three stages:



Stage 1: Assessment & Optimization of the baseline design performance under ideal
conditions (i.e. deterministic optimization). This enables a rapid judgment as to
whether an improved/feasible design exists within the bounds of the design i.e. for
the initial structural layout within the allowable thickness ranges.

Stage 2: Robustness assessment: assessment of the mean and variation in the performance
of a design when subjected to real conditions.

Stage 3: Optimization under real conditions (robustness optimization) – simultaneously
optimize the mean and variation of performance when subjected to real life variations.

Previous studies have performed deterministic optimization followed by robustness
assessments [2]. However, this study presents the first HyperStudy applications of
simultaneous robust optimization.

Baseline
Stage 1
Design Design Assessment &
Optimization – Under Ideal Suitable
Conditions Design ?

Yes
No

Stage 2
Design Assessment – Under
Real Conditions

Stage 3
Robustness Optimization:
Robust
Design Optimization – Under
Optimized
Real Conditions
Design

Figure 4: Simultaneous Robust and Design Optimization Process

1.4 Verify

The staged optimization process (section 1.3) provides invaluable sensitivity data in order to
understand which variables are driving the robustness or optimization of the system. This
inevitably will produce better design. In addition, since a consistent virtual environment is
used for all three stages of this optimization process, a high degree of self checking is
automatically performed.

However, the true verification of the process is the production of the physical design which
exhibits a robust performance in any experimental testing programme and ultimately reduced
warranty claims from the field.

The methodology for generating optimal robust designs that has been developed in this work
is primarily focused on the “optimize” phase of the DFSS loop (Figure 1). It is described
through use of two examples described in Sections 2 & 3. The first is a composite cantilever



beam, on which the methodology was developed and the second is an industrial example: the
design of a knee bolster system.

2.0 Composite Beam Design
This example is concerned with the minimization of the weight of a cantilevered composite
beam (Figure 5) subjected to a parabolic distributed load (q) with uncontrollable and
controllable factors such as manufacturing or material variation. The DFSS process has been
applied to the problem and is described below.

Figure 5: Composite Beam Subjected to a Parabolic Distributed Load
2.1 Define

Figure 6 shows the p-diagram for the composite beam.

The performance targets for the beam are as follows:
Deflection at the free end of the beam < 1 (normalized).
Maximum bending stress in the beam < 1 (normalized).
Height < 10 times the width (to avoid torsional lateral buckling).

DEFINE
NOISE
CHARACTERIZE •fiber volume fraction ± 0.03
•Young’s modulus of the fiber ± 2%
OPTIMIZE •Young’s modulus of the resin ± 2%
•Density of the fiber ± 2%
VERIFY •Density of the resin ± 2%
•Width ± 0.3mm
•Height ± 0.3mm

INPUT OUTPUT
Deflection & Stress COMPOSITE Max Deflection, Max
Targets BEAM Bending Stress,
Height to width ratio

PARAMETERS
•Beam Height
•Beam Width
•Fibre Volume Fraction

Figure 6: P-Diagram for the Composite Beam



2.2 Characterize

The key parameters for the beam and their variations are as given in the p-diagram in Figure
6. The analysis of the beam is via an analytical expression, as such there is not a requirement
to replace the analysis with a surrogate model as is the case for the knee bolster analysis in
Section 3.

2.3 Optimize

2.3.1 Stage 1: Design Assessment & Optimization – Under Ideal Conditions

Typically, during an engineering design process once a baseline design has been generated
(e.g. from a topology optimization) it is assessed to determine whether or not it meets the
performance criteria.

The baseline design performance is given in Table 2, it can be seen that the design meets the
targets and has a weight of 4.8N. The next stage is to determine the minimum weight design
which meets the targets.

In order to reduce complexity, ideal conditions are assumed at this stage and optimization is
carried out on perturbations of the initial structural layout and thicknesses. This stage rapidly
provides information as to whether or not an improved/feasible design exists within these
design bounds. The engineer can then make a judgment as to whether or not the design is
suitable for further development and can be taken forward to stage 3 or if a modified baseline
design is required.

The optimization of the beam is set up is as follows:

Objective:
o Minimize Weight

Constraints:
o Maximum deflection at the free end of the beam (normalized) < 1
o Maximum bending stress in the beam (normalized) < 1
o Height to 10 x Width ratio (normalized) < 1 (to avoid torsional lateral buckling)

Design Variables:
o 4mm ≤ Beam Width ≤ 20mm
o 20mm ≤ Beam Height ≤ 50mm
o 0.4 ≤ Fibre Volume Fraction ≤ 0.91

Altair HyperStudy is used for the optimization and the results are shown in Table 1. It can be
seen that, the optimum design (for ideal conditions) meets the targets and represents a 39%
weight reduction over the baseline design.



Optimum
Baseline Under Ideal
Design variables Min Max design Conditions
width [mm] 4 20 10.0 4.5
height [mm] 20 50 30.0 44.8
Fibre volume fraction 0.4 0.91 0.79 0.52
Objective (min): weight [N] - - 4.82 2.95
Constraints
Normalized stress constraint <=1 - - 1 1
Normalized displacement constraint <=1 - - 1 1
Normalized Height to 10 x width ratio <=1 - - 0.3 1

Table 1: Performance of the Baseline and Optimum Designs Under Ideal Conditions

2.3.2 Stage 2: Design Assessment - Under Real Conditions

At this stage, the design is subjected to variations in the uncontrollable/controllable factors
present in a real system. The mean and variation of the performance is assessed via a
“stochastic study” in HyperStudy. For the beam example the variations imposed on the design
are material and manufacturing tolerances. Note, the variations are assumed to be normally
distributed and ±3σ covers the interval of the tolerance where σ is the standard deviation of
the distribution. Table 2 identifies the tolerances and their assumed variations.

Material related tolerances Variation
fiber volume fraction ± 0.03
Young’s modulus of the fiber ± 2%
Young’s modulus of the resin ± 2%
Density of the fiber ± 2%
Density of the resin ± 2%
Geometric related tolerances
Width ± 0.3mm
Height ± 0.3mm

Table 2: Variations on Manufacturing and Material Tolerances

The mean and variation (σ) in the performance of a design is determined by executing a
10,000 Monte Carlo (MC) simulation run using a random Latin Hypercube DoE (RLH) (Figure
7(a)) on the design with the imposed variations listed in Table 1. Note, a 500 MC simulation
run (Figure 7(b)) was also carried out and the resulting statistics were similar to the 10,000
MC simulation as can be seen in Table 3, therefore for a more computationally expensive



analysis it can be reasonably assumed that the resulting statistics (using a RLH) will be
practically the same with a reduced number of runs.

(a) 10,000 runs (b) 500 runs

Figure 7: Comparison of Monte Carlo Simulation Run Plots for the Baseline Design
Stochastic Assessment

Mean Standard Deviation
500 runs 10000 runs 500 runs 10000 runs
weight [N] 4.8240 4.8240 0.07300 0.07430
Normalized stress 1.0000 1.0000 0.01200 0.01200
Normalized displacement 1.0000 1.0000 0.04070 0.04060
Normalized height to width ratio 0.3000 0.3000 0.00316 0.00317

Table 3: Comparison of Statistics for the Monte Carlo Simulations on the Baseline
Design

The results of the stochastic studies carried out on the baseline and deterministic optimum
designs are given in Figure 8 and Table 4. Each point on the plots represents a run in the MC
simulation and the resulting “cloud” of points gives the resulting mean and variation in
performance of a particular design. The green circle (Figure 8) represents the boundary of
3σ i.e. 3-sigma design. Hence, if an engineer is aiming for a 3-sigma performance (99.73 %
reliability) then this circle must lie in the feasible region.

It can be seen, that the clouds for both the baseline and deterministic optimum designs are
centred on the point where the stress and displacement = 1 i.e. the mean performance is the
target value of 1, however it can also be seen that approximately 75% of the runs for both
designs are infeasible since their values >1 i.e. the 3σ boundary lies in the infeasible zone.
Note also, that the cloud for the deterministic optimum has a greater scatter than the baseline
i.e. it is less robust since it’s variation in performance is greater. As a result neither design can
be considered as “robust”.



2.3.3 Stage 3: Simultaneous Robustness Optimization Under Real Conditions

In order for a design to be simultaneously robust and optimized the centre of the performance
cloud (i.e. mean performance) must be as close to the constraint boundaries as possible
whilst ensuring that, for 3-sigma performance, the 3-sigma boundary remains in the feasible
region i.e. 99.73% of the points in the cloud are in the feasible region. Similarly, for 6-sigma
designs the 6-sigma boundary remains in the feasible region.

The robustness optimization of the beam for 3-sigma performance is set up is as follows (note
the mean and σ are calculated as in Stage 2) and carried out using HyperStudy.

Objective:
o Minimize Mean Weight

Constraints:
o σweight ≤ 3σ (assume σweight = 0.1)
o Mean Normalized Stress + 3σ ≤1 (assume σstress= 0.1)
o Mean Normalized Displacement + 3σ ≤ 1 (assume σdisp= 0.1)
o Mean Normalized height to width ratio + 3σ ≤ 1 (assume σh2w= 0.1)

Design Variables:
o 4mm ≤ Beam Width ≤ 20mm
o 20mm ≤ Beam Height ≤ 50mm
o 0.4 ≤ Fibre Volume Fraction ≤ 0.91
where σ is the standard deviation.

The results of the robustness optimization are given in Figure 8 and Table 4. The robust
optimum represents a 29% weight reduction over the baseline design. It can be seen, that the
cloud for the robust optimum design is centred within the feasible stress-displacement region
and the 3σ boundary lies in the feasible zone.

Baseline Deterministic Robust
Optimum Optimum

Indicates 3 sigma
boundary

Figure 8: Results of the Stochastic Studies



Baseline Deterministic Robust
Design variables Min Max design Optimum Optimum
Mean width [mm] 4.0 20.0 10.0 4.5 4.8
Mean height [mm] 20.0 50.0 30.0 44.8 44.8
Mean Fiber volume fraction v f 0.40 0.91 0.79 0.52 0.57
Objective (min): Mean Weight [N] - - 4.8246 2.9535 3.4474
Constraints
Mean Normalized stress constraint + 3 sigma <=1 - - 1.0361 1.0736 0.9965
Mean Normalized displacement constraint + 3 sigma <=1 - - 1.1234 1.1885 0.9959
Mean Normalized Height to 10 x width ratio + 3 sigma <=1 - - 0.3095 1.0674 0.9952

meets targets
fails targets
Table 4: Performance of Baseline, Deterministic Optimum and Robust Optimum

2.4 Verify

Since all of the performance calculations are carried out using the full analysis of the beam i.e.
an analytical equation, the verification phase is completed at the optimization stage.

3.0 Knee Bolster Study

3.1 Introduction

The aim of this study was to apply the same process as in Section 2 to determine a robust
and optimized design of a knee bolster.

The study has been carried out on a sub-system model of the knee bolster (Figure 9a). The
dynamic finite element analysis code LS-DYNA [3] was used to compute the response of the
system to various design inputs. The objective of the study was to automatically vary various
design variables to optimize the energy absorbing characteristics of the system whilst
satisfying various force and displacement limiting constraints based on federal requirements:
FMVSS 208 [4]; final verification was carried out using full occupant / interior model
simulations using LS-DYNA (Figure 9b).



Knee
Bolster

(a) Sub-System Model (b) Full Model

Figure 9: LS-DYNA Analysis of the Knee Bolster Design

The Design for Six-Sigma (DFSS) process (Section 1) has been applied to the knee bolster
design as is described in this section.

3.2 Define

The knee bolster system is defined through the p-diagram shown in Figure 10.

DEFINE NOISE
CHARACTERIZE •Material Yield Stress
•Manufactured Thickness
OPTIMIZE •Manufactured Shape
•Impactor type (5th%, 50th%)
VERIFY •Impactor position variation

INPUT
OUTPUT
FMVSS208 Knee Force-displacement
USNCAP
Bolster Pulse
EURONCAP

PARAMETERS
•Thickness
•Shape
•Material Properties
•Impactor position

P-Diagram
Figure 10: P-Diagram for the Knee Bolster System

It can be seen, that the inputs are the legislative targets for the system which are based on the
force-displacement and energy absorption of the knee bolster. Hence the output is the force-
displacement pulse measured from the LS-DYNA simulation. The targets for the knee bolster



are that the normalized force and displacement values are less than 1. The normalization is
done according to FMVSS 208 [4]. A set of typical force-displacement pulses for the 5th
Left/Right & 50th Left/Right impactors is shown in Figure 11. It can be seen, that this solution
is feasible since the corresponding normalized force and displacement values are less than
one i.e. in the feasible region.

DEFINE

CHARACTERIZE

OPTIMIZE

VERIFY

Feasible
Region

Figure 11: Typical Force-Displacement Output

The thickness and shape parameters are identified in Figure 12. The thickness ranges are
assumed to vary between 1 and 10mm. The shape factor varies between -1 and 1. Figure 13
shows the assumed variation of ±25mm in the centre point of the 5th and 50th impactors.



DEFINE

CHARACTERIZE

OPTIMIZE
Thickness 1
VERIFY
Thickness 2

PARAMETERS
•Thickness Thickness 3
•Shape

Thickness 4

Shape Variable

Note: the thickness and shape variables are the same for each knee bolster

Figure 12: Thickness and Shape Parameters for the Knee Bolster

DEFINE

CHARACTERIZE

OPTIMIZE

VERIFY

PARAMETERS
•Thickness
•Shape

(a) 5th Percentile Impactors (b) 50th Percentile Impactors

Figure 13: Impactor Position Variation



3.3 Characterize

The next stage was to identify the key parameters which have the greatest effect on the knee
bolster performance. This was done using Altair HyperStudy using the following process:

1 Run a DoE with all the parameters
2 Create an approximation of the responses
3 Carry out a statistical analysis of the approximation using Analysis of Variance
(ANOVA)

Figure 14 shows the results of the ANOVA study for the displacement of the 5th left Impactor.
This is typical of the results for the other responses. It can be seen, that the position of the
impactors, the shape and thickness variables and the yield stress contribute the most to the
response. It is assumed that changes to these parameters are sufficient to characterize the
knee bolster system.

DEFINE

CHARACTERIZE
20 ANOVA plot
OPTIMIZE
% Contributions of the Parameters
Contributing %

Impactor position Horizontal

VERIFY to Displacement of 5th Left Impactor
Impactor position vertical

Thickness 1
Thickness 2
Thickness 3
Thickness 4
Yield Stress
Shape

Other less significant parameters

0
Contributing Source

Figure 14: Key Parameter Identification – Typical ANOVA Plot

Following on from this, a response surface of the LS-DYNA analysis was generated for use in
the optimization phase. There are a number of possibilities available in HyperStudy for doing
this. However, the recommended approach (used here) is to carry out a DoE study using the
optimal design filling algorithm – Optimal Latin Hypercube, and then use this data to create a
surrogate model via the moving least squares method. Figure 15 shows a typical response
surface generated for the force response in the 50th left impactor.



DEFINE

CHARACTERIZE Typical Response Surface
OPTIMIZE

VERIFY

Force 50th Left

Im
pa l
cto ica
rP ert
os nV
it ion s itio
Ho r Po
rizo cto
nta pa
l Im
Figure 15: Typical Response Surface

With the knee bolster system define and characterized the next step is then to optimize the
design.

3.4 Optimize

As described earlier the optimize phase has 3 stages which are shown in Figure 4, these are
described in this section.

3.4.1 Stage 1: Design Assessment & Optimization– Under Ideal Conditions

The first stage is to assess and optimize the design under ideal conditions i.e. no noise is
imposed on the system. Therefore, the only parameters under consideration are the thickness
and shape variables (Figure 12). The response surface generated in the characterization
phase is used for the analysis. The setup is as follows:

Objective:
o Maximize Sum Normalized Energies

Constraints:
o Normalized Force: 1.0
o Normalized Displacement: 1.0

Design Variables (Figure 12):
o 1mm ≤ 4 Thicknesses ≤ 10mm
o -1 ≤ Shape variable Scale Factor ≤ 1



The optimization is carried out using the gradient-based optimizer in Altair HyperStudy. The
results are given in Table 5 and Figure 16, it can be seen that for ideal conditions the solution
meets the performance targets. The question arises at this point: how does this solution
behave in reality? This is addressed in the next section.

Design Optimized for IDEAL
Design variables Min. Max conditions
Shape Variable -1.0 1.0 0.2
Thickness 1 [mm] 1.0 10.0 3.4
Thickness 2 [mm] 1.0 10.0 4.5
Thickness 3 [mm] 1.0 10.0 5.0
Thickness 4 [mm] 1.0 10.0 5.7
Objective (max): Sum of Normalized Energy - - 0.983
Constraints 5th 50th
left right left right
Normalized displacement constraint <=1 - - 0.70 0.84 0.78 0.77
Normalized force <=1 - - 1.00 0.97 0.91 0.89

Table 5: Assessment of the Design Optimized for Ideal Conditions

DEFINE 5th Left 5th Right
CHARACTERIZE

OPTIMIZE

VERIFY

50th Left 50th Right

Figure 16: Assessment of the Design Optimized for Ideal Conditions



3.4.2 Stage 2: Design Assessment - Under Real Conditions

In order to assess real life performance a robustness assessment (stochastic study) of the
design “optimized for ideal conditions” is carried out. This is done with a Monte Carlo
simulation carried out on the response surface, here a 500 run random Latin Hypercube is
used. The parameters and assumed real life variations imposed on the system are identified
in Table 6. Note, the following assumptions have been made: the variations are normally
distributed and ±3σ covers the interval of the tolerance where σ is the standard deviation of
the distribution.
Material related tolerances Variation
Yield Stress ± 10%
Geometric related tolerances
Thickness ± 0.1mm
Shape Scale Factor ± 0.01
Impactor position variation
Position ± 25mm

Table 6: Knee Bolster Noise Parameters and Variations

The results of the robustness assessment performed on the design optimized under ideal
conditions are shown in Figure 17. It can be seen from the resulting “performance clouds” that
there are a large number of solutions which fail the force performance targets and the design
is considered non-robust.



CHARACTERIZE

OPTIMIZE

VERIFY


Figure 17: Design Optimized for Ideal Conditions - Robustness Assessment

3.4.3 Stage 3: Robustness Optimization: Design Optimization – Under Real Conditions

At this stage the robustness assessment is incorporated in the optimization loop. The output
from the robustness assessment used in the optimization loop is the mean and standard
deviation of the responses. The optimization is set up as follows:

Objective:
o Maximize Mean of the Summed Normalized Energies

Constraints:
o Normalized Displacement: Mean + 3σ 1.0
o Normalized Force: Mean + 3σ 1.0

Design Variables (Figure 12):
o 1mm ≤ 4 Thicknesses ≤ 10mm
o -1 ≤ Shape variable Scale Factor ≤ 1

The results of the simultaneous robustness and design optimization are shown in Figure 18. It
can be seen the “performance clouds” have been shifted into the feasible region, Although
there are a small number of solutions which fail the performance targets, the design is
considered as robust as possible for the current knee bolster structural layout.



CHARACTERIZE

OPTIMIZE

VERIFY


Figure 18: Design Optimized for Real Conditions - Robustness Assessment

3.5 Verify

At this stage of the DFSS process significant information about the performance of the knee
bolster has been generated. The next step is then to “plug” the design back into the full
vehicle model which has been concurrently updated with other optimized components of the
car.

It is a design challenge to produce a virtual design that can achieve the constraint targets
within ±3σ due to the conservative nature of this numerical test environment (e.g. totally rigid
backing structure, conservative impact velocity etc.). This technology can be efficiently used
to determine the most efficient design for the specified design variations.

The design determined by this process is similar to a production component used on a recent
vehicle. However, this design was achieved in a fraction of the design time with an increased
understanding of the performance drivers.



4.0 Conclusions
The future of engineering design optimization is robust design optimization whereby a design
is optimized for real world conditions and not just for one particular set of ideal conditions.
There is no point in coming up with a design which is optimized for a set of ideal conditions
when in reality there exists uncertainty in the materials, manufacturing and operating
conditions.

Altair HyperStudy has been used to simultaneously optimize the robustness and performance
of a real world component (i.e. automotive knee bolster). The resulting design was similar to
an existing production component. However, this design was achieved in a fraction of the
design time with an increased understanding of the performance drivers. A unique process
has been developed which is computationally efficient for complex non-linear systems. This
process can be further enhanced and automated. The study has shown that Altair HyperStudy
can be used as a key CAE enabler.

Achieving robust design is inherent in the quality philosophy of many companies. It will
become an increasing requirement to demonstrate that digital designs achieve the required
quality levels. This will initially be achieved on a component level and gradually migrate to
complex systems. The initial requirement will be to understand the probabilistic variation of
various parameters. This will require an increasing amount of measurement and an increased
understanding of the physical drives of the component / system. Robustness can only be
achieved by understanding the variation of the various factors.

Adding noise factors during optimisation is the best way in obtaining a robust solution the use
of DFSS principle helps identify failure modes and eliminate them earlier in the design
process.

For certain parameters, suppliers are already instructed to deliver product within specific
sigma quality levels. This technology can identify parameters which drive the quality and help
develop guidelines to control the variation of these quantities. This control will be
accompanied by an associated cost penalty.

Increased availability of inexpensive powerful computing and improvements to software
integration and the predictive algorithms heralds the new development of producing digital
designs to sigma levels of quality.

5.0 References
[1] ‘Altair HyperStudy 8.0’ Altair Engineering Inc. (2006).

[2] ‘Design Optimization and Probabilistic Assessment of a Vented Airbag Landing
System for the ExoMars Space Mission’, Richard Slade and Andrew Kiley, 5th Altair
UK Technology Conf., April 2007.



[3] 'LS-DYNA Version 970’, Livermore Software Technologies Corporation, LSTC
Technical Support, 2006.

[4] ‘FMVSS 208 – Occupant Crash Protection’, Federal Motor Vehicle Safety Standards
and Regulations.


Jaguar Land Rover - Robust Design Optimization of a Knee Bolster

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a Jaguar Land Rover - Robust Design Optimization of a Knee Bolster

Semelhante a Jaguar Land Rover - Robust Design Optimization of a Knee Bolster (20)

Mais de Altair ProductDesign

Mais de Altair ProductDesign (9)

Último

Último (20)

Jaguar Land Rover - Robust Design Optimization of a Knee Bolster