Ops a la carte mechanical design for reliability (mdfr) seminar

&

We Provide You Confidence in Your Product ReliabilityTM
Ops A La Carte / (408) 654-0499 / askops@opsalacarte.com / www.opsalacarte.com

MECHANICAL DESIGN
for RELIABILITY
(MDfR)
SEMINAR
April 9, 2008

T. Kim Parnell, Ph.D.,P.E.
408-203-9443
kimp@OpsALaCarte.com
Ops A La Carte LLC
www.OpsALaCarte.com

© 2006 Ops A La Carte

The following training and presentation
materials are copyright protected by:
Ops A La Carte LLC
and
Parnell Engineering & Consulting (PEC)

April 9,9, 2008
April 2008 MDfR-Mechanical Design for Reliability 2

Presenter’s Biographical Sketch
Kim Parnell
◈ Dr. Kim Parnell is a Senior Consultant with Ops A La Carte and Principal/Founder of
PEC (www.parnell-eng.com), an engineering consulting firm that focuses on providing
support for high-tech and medical device companies. He specializes in the mechanical
engineering design and behavior of electronic and miniature components, MEMs,
wireless sensors, telecommunications devices, biomedical devices, and shape memory
metals. Dr. Parnell is an expert in the application of finite element analysis (FEA) to the
solution of engineering problems, and consults actively in these areas as well as failure
analysis and reliability. He served as a Visiting Associate Professor in the Mechanical
Engineering Department at Stanford University and is a coach and mentor for the
innovative Stanford Biodesign program. He is a member of the NanoBioConvergence
(www.NanoBioConvergence.org) Board, and the CSIX Connect (www.csix.org) Board
Dr. Parnell is a member of ASME, IEEE, ASM, and is currently Chair of the IEEE-CNSV
(Consultants’ Network of Silicon Valley) (www.CaliforniaConsultants.org).
◈ He has worked with companies including Ops A La Carte, MSC Software, Rubicor
Medical, Exponent Failure Analysis Associates, SST Systems, ATT Bell Laboratories,
◈
Stanford University, and General Motors.
◈ Dr. Parnell holds Ph.D. and MSME degrees from Stanford University in Mechanical
Engineering and a BES from Georgia Tech. He is a registered Professional Mechanical
◈
Engineer in the State of California.

April 9,9, 2008

Mechanical Design for Reliability-MDfR
T. Kim Parnell, PhD, PE

8:15 8:30 Setup
8:30 9:30 Introduction to Mechanical Reliability & Robust Design
9:30 11:00 Finite Element Analysis (FEA)
11:00 11:30 Random Vibration & Shock

11:30 12:00 Tolerance & Worst Case Analysis

12:00 1:00 Lunch
1:00 1:30 Thermal Analysis & Electronic Cooling
1:30 2:45 Probabilistic Design System (PDS) & Stochastics
2:45 3:00 Forensic Methodology
3:00 4:00 Failure Analysis & Prevention; Fatigue & Fracture
4:00 4:30 Conclusions & Discussion

April 9,9, 2008

Introduction to
Mechanical Reliability &
Robust Design

April 9,9, 2008

Six Sigma in Practice
What does it mean?
99% Performance 6 Sigma Performance
99.999663% Meet Spec
202 Billion Pieces of Mail Delivered by USPS per year
2.02 Billion pieces Lost per Year 680740 pieces lost per year

24/7 Power Delivery to Your Home
87 Hours without Power every Year Less than two minutes without power
per year
510 Million Prescriptions Worldwide per year
5.1 Million Wrong Prescriptions per Year 1719 Wrong Prescriptions per Year

27 Billion Credit Card Transactions per year
0.27 Billion wrong transactions per year 90990 wrong transactions per Year

Ref: Scott Burr, 2005, “Design for Six Sigma” Hubenthal-Burr Associates
April 9,9, 2008

Two Types of Quality
Two Types of Quality

• Type 1: Customer Quality - The features that customers want.
• Type 2: Engineered Quality - The problems customers do not want.

• Customer quality leads to the size of the market segment and
includes items such as function, features, colors and designs. The
better the customer quality, the bigger the market size becomes. In
order to obtain the market size, the price must be reasonable.
• Engineered quality includes defects, failures, noise, vibration,
unwanted phenomena, lowering the cost of manufacture, and
minimizing manufacturing problems.

Ref: “Design for Six Sigma Roadmap”, Shree Phadnis

April 9,9, 2008

Results & Meaning
• Process provides snapshot of current system

• No one tool makes an entire reliability program

• Check step is critical before moving to
recommendation around improvement plan

April 9,9, 2008

Robust Design
• Engineers dream of designing a product or
process that exhibits the state of 'robustness.'
However, few can actually make the claim that
they know exactly what robustness means.
Formal definition by quality guru Dr. Taguchi:
• Robustness is the state where the technology,
product, or process performance is minimally
sensitive to factors causing variability (either in
the manufacturing or user environment), and
aging at the lowest manufacturing cost.
Ref: “Design for Six Sigma Roadmap”, Shree Phadnis

April 9,9, 2008

How to Achieve Robust Design?
• “Brute Force” techniques
– Intuitively select design parameters and
tolerances and make system design trade-
offs
– Added design margins
– Tighter tolerances
– Does not work in this hyper competitive
economy

April 9,9, 2008

Cost to Repair Design Problems

• As a rule, design engineering has lagged behind the shop
floor in awareness of product and process quality.
• Very real costs are associated with inattention to design
quality. If errors or omissions in design data are not
addressed early, more costly changes are required later in
the product development process.

Ref: “Building Quality into Design Engineering”, Gavin Finn
April 9,9, 2008

Robust Design Outline
• 21st century challenges
• Tools & techniques for Design For Six Sigma
(DFSS)
• Definition of robustness
– What, how, significance
• Business impact of robust design solutions
• Systematic approach to robust design

• Ref: Ansys, 2004

April 9,9, 2008

21st Century Challenges
• Product life cycles are expected to last for
just a few months
• More customized products
– 1700 car models as opposed to 900 ten years ago
– More focus on “built-in-quality” or “built-in-reliability”
• To meet these demanding requirements
– Products need to be developed in the shortest amount of time
and
– They have to be safe, reliable, and competitive
– Robust and Affordable !
“Product quality requires managerial,
technological and statistical concepts
throughout all the major functions of the
organization …” Joseph M. Juran
April 9,9, 2008

Tools & Techniques
for Design for Six Sigma
Planning Concept Optimization
Design Tolerance
VOC Development Verification
Development Variation Analysis Manufacturing
QFD Screening

• Quality Function Deployment (QFD)
– Customer requirements to technical specs
• Failure Modes and Effects Analysis (FMEA)
– To study failures, their impact and frequency
• Other statistical tools for tolerance analysis,
etc.
• Tools for concept screening, design,
optimization, verification
• Probabilistic simulations for robust design
– Design of Experiment (DOE), response surface
April 9,9, 2008

What is Robust Design?
• Works as intended regardless of variation in:
– Design
– Manufacturing process
– Material
– Resulting from Deterioration
– Operation (misuse)
• For Achieving robust design
– Understand potential sources of variations
– Quantify the effect of variations on product behavior and
performance
– Take steps to desensitize the design to these variations
– Adjust the output to hit the target performance
April 9,9, 2008

What is Robust Design?
• De-sensitizing the design to variations

April 9,9, 2008

How to Achieve Robust Design?
• “Intelligent Design” by understanding
– Product & Process design parameters that are
critical to achieving a performance
characteristic
– What are the optimum values to both
• Achieving the desired performance
• Minimize the effect of naturally occurring and difficult to
control variations (noise)
– Requires a systematic approach of deploying
engineering simulations as part of the design
process
April 9,9, 2008

Significance of Robust Design
• Robust design optimization helps
– Identify parameter values that maximize
performance and minimize the effect of “noise”
• More robust and affordable design
– Identify parameters that have no significant
effect on performance
• Relaxed tolerance, reduces product cost
– Identify parameter values that reduce cost
without affecting performance or variation
– Cost-effective quality inspection
• Do not have to inspect for parameters that are not critical to
performance

April 9,9, 2008

Business Impact
of Robust Design Solutions

Profitability = Market Size
x Market Share
x Margin on Sales

April 9,9, 2008

Market Size
Increased market size by entering related
markets. Some markets require product
designs to be robust:
– Defense, aerospace, jet engine, nuclear
power, biomedical, oil industry, other mission
critical solutions
– Auto industry – 1st tier suppliers capturing
more value

April 9,9, 2008

Market Share
• Increased market share (in slow growth
industry) through
– Better quality products
• Less maintenance
• Higher resale value
– Quick response to customer requirements
– Affordable pricing
– Attract business by implementing DFSS

April 9,9, 2008

Margin on Sales
• Product Cost Reduction Through
– Relaxed tolerances
– Material savings
– Cost-effective quality inspection
– Less rejects (lower scrap rate)
– Shorter development time
– Less warranty cost
– Customer loyalty
• Command Premium price
– Better quality
– Timely delivery
– Satisfied customers
April 9,9, 2008

Reliability Philosophies
Two fundamental methods to achieving
high product reliability:
– Build, Test, Fix

– Analytical Approach

April 9,9, 2008

Build, Test, Fix
• In any design there are a finite number of
flaws.
• If we find them, we can remove the flaw.

• Rapid prototyping
• HALT
• Large field trials or ‘beta’ testing
• Reliability growth modeling
April 9,9, 2008

Analytical Approach
• Develop goals
• Model expected failure mechanisms
• Conduct accelerated life tests
• Conduct reliability demonstration tests
• Routinely update system level model

• Balance of simulation/testing to increase
ability of reliability model to predict field
performance.
April 9,9, 2008

Issues with each approach
Build, Test, Fix Analytical
• Uncertain if design • Fix mostly known
is good enough flaws
• Limited prototypes • ALT’s take too long
means limited flaws • RDT’s take even
discovered longer
• Unable to plan for • Models have large
warranty or field uncertainty with new
service technology and
environments
April 9,9, 2008

Drop Tests: What is the physics?
• Is drop deterministic or stochastic?
• Is drop predictable?
• Is drop optimizable?
• Does it make sense to speak of precision in drop
simulations?
• Do we need to increase the number of elements
in our drop models? What is the reasonable
limit?
• What is the future of computer-based drop
analysis?

• Is drop a chaotic phenomenon?

April 9,9, 2008

Electronic Component Drops

April 9,9, 2008


April 9,9, 2008


April 9,9, 2008

Example of Measured
Acceleration Signal
• A series of tests for chaos are performed with this signal.

April 9,9, 2008

Log-linear Power Law
• Systems that exhibit a log-linear Power Spectrum are
potentially chaotic.

April 9,9, 2008

Typical Tests for Chaos
• Hausdorff (Capacity dimension). Signal has fractal
dimension (1.8).
• Log-linear Power Spectrum (yes).
• Correlation dimension (5).
• Lyapunov Characteristic Exponents (+0.4).
• Poincare’ sections or Return Maps (check for structure).

• According to these tests, the measured drop/impact
signal possesses a clear chaotic flavour. This explains
why each drop is a unique event and cannot be
optimised.
April 9,9, 2008

Drop = Chaos
• Chaos can be described by closed-form deterministic
equations. Chaos does NOT mean random.
• Chaos is characterized by extreme sensitivity to initial
conditions.
• “Memory” of initial conditions is quickly lost in chaotic
phenomena (“butterfly effect”).
• Examples of chaotic phenomena:
– Tornados (weather in general)
– Stock market evolution, economy
– Crash, drops, impacts, etc.
– Earthquakes
– Avalanches
– Combustion/turbulence
– EEG (alpha-waves in brain)
– Duffing, Van der Pol, Lorenz oscillators, etc.

April 9,9, 2008

The Logistic Map
• X(n+1) = k X(n)(1-X(n))
Shows astonishingly complex
behaviour:
– 0 < k < 1, Extinction regime
– 1 < k < 3, Convergence regime
– 3 < k < 3.57, Bifurcation regime
– 3.57 < k < 4, Chaotic regime
– 4 < k, Second chaotic regime

For more details see:
Weisstein, Eric W. "Logistic Map." From MathWorld--A Wolfram
Web Resource. http://mathworld.wolfram.com/LogisticMap.html

April 9,9, 2008

Chaos & Predictability
• Phenomena that are chaotic, are unpredictable
(nonrepeatable). The main reason is extreme sensitivity
to initial conditions.
• Phenomena that are unpredictable, cannot be optimized.
They must be treated statistically.
• All that can be done with chaotic phenomena is increase
our understanding of their nature, properties, patterns,
structure, main features, quantify the associated risks.
• Models for Risk Analysis must be realistic to be of any
use.

April 9,9, 2008

Understanding Risk
• Essentially, risk is associated with the
existence of outliers Outlier:
- warranty
- recall
- lawsuit }
Most likely
response
(highest density)

Note: DOE and Response Surface techniques
cannot capture outliers
April 9,9, 2008

What is Risk and Uncertainty
Management?
• Understand and remove outliers
• Shift entire distribution

is safe fails

Improved design
Initial design Outliers:
unfortunate
combinations
of operating
Outlier conditions and
design variables
that lead to
unexpected
behaviour.

April 9,9, 2008

Example of Robust Design: MIR
Space Station
• Robustness = survivability in the face of unexpected
changes in environment (exo) or within the system (endo)

April 9,9, 2008

Example of Optimal Design
• M. Alboreto dies (Le Mans, April 2001) due to slight loss
of pressure in left rear tire. The system was extremely
sensitive to boundary conditions (was optimal, and
therefore very very fragile!).

April 9,9, 2008

Optimization: a Dangerous Game

Second order RS

First order RS

Optimum?

Different theories can be shown to fit the same set of observed
data. The more complex a theory, the more credible it appears!
April 9,9, 2008

Some Lessons

• Boundary conditions are most important
• Small effects can have macroscopic consequences
(watch out for chaos, even in small doses!)
• Precision is not everything!
• Optimal components don’t give an optimal whole
• Optimality = fragility
• Robust is the opposite to optimal

April 9,9, 2008

Conclusions – Robustness, Chaos,
and Predictability
• Phenomena that possess a chaotic component
cannot be optimized, but can be improved in
statistical sense.
• With such systems, it is possible to address:
– Risk Analysis
– Design for robustness
– Increase understanding
• Realistic models necessitate:
– continuous 3D random fields (geometry)
– discrete random field (spotwelds, joints)
– randomization of ALL material properties
– randomization of ALL thicknesses
– variations of boundary/initial conditions
April 9,9, 2008

Finite Element Analysis (FEA)

April 9,9, 2008

Finite Element Analysis (FEA)
• FEA is generally applicable for analyses such as
stress, thermal, vibration, and dynamic cases
1) Create a geometric model; subdivide into
elements to create a finite element mesh
2) Specify material properties for all components
3) Apply loads and boundary conditions: thermal,
pressure, deadweight, wave loads, etc.
• Linear analysis is typically used for design;
nonlinear analysis is frequently required for
failure investigation

April 9,9, 2008

FEA & Testing

• Finite element analysis (FEA) and physical
testing are complementary
• A comprehensive program needs to include
both components
• With judicious experimental validation, FEA
can be used to reduce the amount of physical
testing that is needed and shorten the design
cycle

April 9,9, 2008

FEA Simulation Tools
FEA simulation can
• Be a cost effective way to evaluate design choices.
• Provide insight into how varying parameters affects the
design outcome.
• Simulate expensive processes where downtime is
unacceptable.
• Help establish the critical relationship between design
parameters and process parameters. THIS IS BIG IN
SIX SIGMA!!!! And can translate into an enormous and
unfair Competitive Advantage.
Ref: Scott Burr, 2005, “Design for Six Sigma”, Hubenthal-Burr Associates

April 9,9, 2008

FEA Concepts
• Linear Analysis
– Small Deflection & Small Strain
– Elastic material
• Nonlinear Analysis
– Large Deflection &/or Large Strain
– Nonlinear material
• Elastic/plastic
• Rubber & Polymers
• Temperature dependent properties
– Contact
– Shock & impact
• Multi-physics: thermal, fluid, electromagnetic, etc.
April 9,9, 2008

Finite Element Software
• MSC.Marc
• MSC.Nastran, MSC.Adams
• MD-Nastran
• LS-Dyna
• Ansys
• Abaqus
• Comsol
• Cosmos
• Pro-Mechanica
• Algor
April 9,9, 2008

The Real World is Nonlinear
• Simplified approximations
compromise accuracy & F Linear

safety Load
Behavior

– Cannot reliably predict geometric, F Nonlinear
Behavior
material, & boundary nonlinearities
u u
Displacement

• Nonlinear analysis is Linear
essential for investigating: Stress Behavior

C
PLASTIC
Nonlinear
– Large-strain effects, plasticity, friction, TI
AS Behavior
dynamic loads, large deflections & motion F F EL
Yield Pt.
– Metallic & non-metallic materials Strain
exhibiting nonlinear behavior (e.g., shape
memory alloys, plastics, elastomers,
composites, etc.)
– Effects of physical coupling with
thermal, electrical, fluid, magnetic,
acoustic, etc.

April 9,9, 2008
April 2008
MSC.Marc MDfR-Mechanical Design for Reliability 50

Simulate Reality With Nonlinear FEA
Capture fit, form, function, &
manufacturability through simulation
Examples:

Snap-Fit Automobile Machining of
Buckle Exhaust Aircraft
Pipe Fuselage
Hanger Component

- Make better design decisions earlier in your product’s life cycle
- Explore & optimize product performance more quickly through
less-expensive virtual testing
April 9,9, 2008
MSC.Marc

Introducing MSC.Marc®
Nonlinear analysis for a wide range of
applications:

MSC.Marc provides virtual insight into the behavior of components
experiencing geometric, material, and/or boundary nonlinearities
April 9,9, 2008

What is MSC.Marc?
Virtual Product Development (VPD) solution that helps
engineers simulate & optimize component performance
& manufacturing processes
– Long, proven history of innovation—established in
1971 as the world’s first commercially available
nonlinear FEA software
– Provides advanced, general-purpose, implicit
nonlinear
FEA technology with high-performance, linearly-
scalable
parallel processing capability
– Solves static & dynamic 2-D & 3-D structural &
non-structural coupled multiphysics problems with
highest accuracy & robustness
– Simulates all kinds of structural deformation &
material nonlinearities:
• Multibody contact & frictional effects
• Buckling, cracking, bending, cutting, shaping, forming,
welding, etc.
• Highly-nonlinear materials, such as elastomers,
composites, plastics, concrete, etc.
April 9,9, 2008

Elastic/Plastic Tensile Response
True Stress E’
Ultimate D Eng. Stress
stress
C E
Yield B
stress
A
Proportional
limit

0
Linear Plastic Strain Significant
hardening necking
Ref: Anderson, Biomaterials notes

Typical stress/strain curve for steels. Strains become localized when
necking occurs. Standard elongation highly dependent on gage
length. Area reduction gives local strain.
April 9,9, 2008

Tensile Test for Steel
Pipeline Material

April 9,9, 2008

Global Remeshing/Rezoning Capability
MSC.Marc has a unique automated capability overcomes
mesh distortions arising from extreme material deformation
– Remeshing: New, better quality global mesh is automatically
generated
– Rezoning: Solution is transferred from the old mesh to the new
mesh
– Contact boundary conditions are also automatically reapplied
– Triangular or quadrilateral elements for 2-D, tetrahedral or
hexahedral for 3-D
Compression

3-D Example Showing
Rubber Seal with With Global
Animation of Close-Up View of Mesh
Distorted Mesh Remeshing/Rezoning
Rubber Seal Refinements

Avoid simulation failure & loss in accuracy for materials
April 9, 2008
April 9, 2008
experiencing extreme deformation
MDfR-Mechanical Design for Reliability 56

3-D Cyclic Symmetry with Remeshing
• Examples: Screw Twisting

MSC.Marc
April 9,9, 2008

Advanced Friction Modeling Capability
MSC.Marc has simple and robust friction
capability. Simply turn it on & supply a
Insertion &
coefficient of friction—or, manually Extraction
provide necessary parameters Force on an
Elastomeric
Seal

Belt-Driven Pulley

Simulate realistic contact conditions by including effects
of friction
April 9, 2008
April 9, 2008 MDfR-Mechanical Design for Reliability 58

Enabling You to Simulate Reality
Use MSC.Marc’s
innovative contact
modeling capabilities
to satisfy your virtual
testing requirements
Stresses in Lug,
Pin, & Clevis

Gear Lever with Rubber Boot

Staking of Intersecting Friction & Heat
Hot Wires into a Plastic Block Generation in Disc Brake Pin Insertion

Validate product performance under a wide range of
operating conditions & constraints
April 9, 2008
April 9, 2008 MDfR-Mechanical Design for Reliability 59

MSC.Marc Tube Bending with
Contact & Remeshing

April 9,9, 2008

Coupled Thermal-Mechanical

April 9,9, 2008

MSC.Marc - Machining
• Pocket Cutting
– Definition of the Two Cutting Processes
Y Cutter and its axis definition 1
Surface for the first cut stage

Z

Y

X
Surface for the second cut stage

Cutter and its axis definition 2
April 9,9, 2008

MSC.Marc Machining - Verification
• Experimental verification
of machining behavior for
a 1.5 inch thick stock
aluminum beam that is
bent to a prescribed
radius.
• 2.5 inch slot is cut through
75% of its thickness,
spring back is plotted
along the ten gage points.

April 9,9, 2008

Predicting Distortion from Machining

• Machining
operations
cause distortions
• MSC.Marc can
be used to
predict the
distortion

April 9,9, 2008

Coupled Electrostatic-Mechanical
• Capacitor plates close
by increasing charge
due to Coulomb’s Law
• Voltage peaks at 1/3
of original gap opening
• Popular for MEMS
applications:
– Optical-network
components
– Print Heads
– Projectors
– BioMEMS
April 9,9, 2008

Electrostatic-Mechanical MEMs

April 9,9, 2008

Joule Heating-Mechanical MEMs

•Electrical current
flow causes heating
due to material
resistivity
•Mechanical
deformation due to
thermal expansion
of hot leg

April 9,9, 2008

Magnetostatic Improvements
• New tetrahedral
elements for
magnetostatics (181
and 182)
• New Line (wire) B

element for
magnetostatics (183)

A

A B
April 9,9, 2008

Coupled Electromagnetic-Thermal
• Induction heating used for various industrial
manufacturing processes, such as heat
treatment for metals, soldering guns, and
preheating for forging

April 9,9, 2008

Example: RF Attenuator
Simulation
RF/microwave energy is attenuated through resistive losses in a
Nichrome film attached to the microstripline waveguide. The
energy is lost in the form of heat which is conducted both through
the devices ceramic substrate and top insulating surface film.
Typical Packaged Device:

Image from KDI data sheet.

April 9,9, 2008

Multi-field Solver- RF Attenuator
• Analysis results:
E-field

H-field

Resultant
temperature

April 9,9, 2008

Random Vibration & Shock

April 9,9, 2008

Random Vibration Capabilities
• MSC.Patran and MSC.Nastran contain
very easy to use random vibration
analysis
• The overview will have some specific
references to the capabilities in these
codes

April 9,9, 2008

What is random analysis?
• Statistically based frequency-dependent
dynamic loading

Time Frequency

– Don’t know exactly what the load is at any
instant in time
– Do know the loading envelope

April 9,9, 2008

What calls for random analyses?
• Random/white noise events
– Acoustic pressure loading
– Rocket/jet engine exhaust
– Rocket/jet engine thrust vibration
– Road-loads/transportation
– Earthquakes
• Base vibration or Acoustic excitation

Loading defined in Frequency Domain
April 9,9, 2008

Frequency Response Analysis
• Frequency Domain
– Steady-state harmonic response
– Load/Response varies w/ Frequency
– Magnitude and phase are important

Tip
Base
1.

Frequency Frequency

April 9,9, 2008

How random analyses typically happen
• Typically based on a PSDF spectrum

( ~ )2/Hz

Frequency

– Almost looks like Frequency Response
– Loading is in terms of (something)2/Hz
April 9,9, 2008

So what do you do???
• Frequency response analysis
– Apply 1 unit of what is specified in
(~)2/Hz across all frequencies
• Define input spectrum
– table of (~)2/Hz vs. Frequency
• Do the math…
– This is where it all comes together
• Data recovery
– Know what you are looking for/at

April 9,9, 2008

Frequency Response Post-Processing
Frequency Response

1. 10.

Frequency Frequency
Input spectrum Output
scales like this… spectrum scales
similarly…
5.8
580.

Frequency Frequency

Random Response
April 9,9, 2008

Frequency Response Post-Processing
Frequency Response

1.

Frequency Frequency

Frequency Frequency

Random Response
April 9,9, 2008

Random Results: What to look
for
• RMS values
– Area under curve
• Number of positive crossings Frequency

– Apparent frequency for fatigue
• Cumulative RMS
– Which frequencies contribute the most

April 9,9, 2008

So what is MSC.Random?
• User-developed MSC.Patran application
– Customer-requested features and
functions
• Officially available since MSC.Patran v9
– Available under Utilities/Analysis menu
• Improved User Interface for:
– Frequency Response Analysis
– Interactive Random Analysis
– Post-processing
April 9,9, 2008

• Improved frequency response user
interface
– Sets up frequency response analysis
– Unit loading(s)
– Large mass, force or acoustic pressure
– Support for FREQi, I=1,5 entries
• .xdb access of frequency response
results
– Fast & efficient

April 9,9, 2008

• Interactive Random Analysis
– Calculations done “on the fly” (w/o having
to re-run MSC.Nastran)
– RMS fringe plots
– Specialized XY-Plotting
• Interactive v. batch environment
– Change your random analysis inputs w/out
re-running MSC.Nastran

April 9,9, 2008

MSC.Random Benefits
• Simplified analysis setup
– GUI interface
– Engineer-driven specialized forms
– No XYPLOT entries required
– Full on-line help
• Improved productivity
– .xdb access of results
– Re-analyze without restarting
• Quality results
– Specialized interactive plots
– Log-log integration
– Cumulative RMS

April 9,9, 2008

New Patran Old GUI
New GUI
Random
• Similar over all operation.
• New interface has more
options and controls.
• New interface has easier and
more flexible ways to input
data.
• More result types supported
• New GUI allows more room to
add additional features.
• Incorporates many
enhancements requests.

April 9,9, 2008

Patran Random Enhancements
XY plot Num. Pos.
Node Ids Crossings
(NEW)

User Custom Title Reflector Clearance

RMS Value (NEW)

RMS Scale (NEW)

April 9,9, 2008

Tolerance & Worst Case
Analysis

April 9,9, 2008

Worst-Case vs. Statistical Tolerancing
• When solving tolerancing problems, one must
choose between worst-case tolerancing and
statistical tolerancing. Both of these methods
have their pros and cons.
• If worst-case tolerancing is used, all tolerances
must be specified as worst-case tolerances. If
statistical tolerancing is used, all tolerances must
be specified as statistical tolerances.
• In reality, the behavior of certain inputs is best
described using worst-case tolerances while the
behavior of other inputs is best described by
statistical tolerances. Still other inputs are not
adequately represented by either type of
tolerance.
Ref: “Process Tolerancing: A Solution to the Dilemma of Worst-Case vs. Statistical Tolerancing”, Wayne A. Taylor

April 9,9, 2008

Tolerance & Worst Case Analysis
Traditional Design
• Tolerances specify design requirements.
• Tolerances control design parameters by
defining how much variation is acceptable.
• Tolerances give insight into the designer’s intent.


April 9,9, 2008

Variation
Sources of Variation
• Processes
– Variation occurs when a part is processed.
– Different processes often have different inherent capabilities.
• Raw Materials
– Inhomogeniety
– Property variation
• Environment
– Temperature and Pressure Variation
– Noise
– Vibration
• Product Degradation
– Due to Heat
– Component variation through time
– Stresses and failure mechanisms

Traditional Design is unconcerned with Process Variation as long as
you meet SPEC!!

April 9,9, 2008

Variation in Mechanical Assemblies
Three sources of variation occur in
mechanical assemblies
• Dimensional variations (lengths & angles)
• Geometric form and feature variations (flatness,
roundness, angularity, etc.)
• Kinematic variations (small adjustments between
mating parts)

Ref: “A Comprehensive System for Computer-Aided Tolerance Analysis of 2-D and 3-D
Mechanical Assemblies” , Kenneth Chase, et.al.

April 9,9, 2008

There is Good News
and Bad News
• Tolerancing has NO influence on Variation
• You CANNOT count on Inspection to assure your
design parameters and tolerances.
• Proper Design Choice is a highly effective
countermeasure against Variation
• FEA Simulation is a highly effective method for
evaluating Design Choices, Variation and linking
process parameters to design performance.


April 9,9, 2008

Lunch!!

April 9,9, 2008

Thermal Analysis
& Electronic Cooling
Wireless Packages

April 9,9, 2008

Thermal Airflow Comparison
Laminar vs. Turbulent flow regimes
• Laminar airflow
– Possible overheating.
– Insufficient temperature gradient

• Turbulent airflow
– Heat transfer greatly enhanced.
– Boundary layer dissipated.

April 9,9, 2008

Thermal Airflow Comparison

Laminar Flow Turbulent Flow
Ta Ta
Tb Tb

Ta Ta

Tb>Ta Ta=Tb
Heat
Transfer 104 BTU/Hr 106 BTU/Hr
April 9,9, 2008

Why does Turbulence Enhance
Heat Transfer?

• Turbulent boundary layer is thin compared to laminar
• Turbulence has unsteady (time varying) velocity & pressure
components
• Turbulent flows include eddies & vortices; “eddy transport” is a major
heat transfer factor
• Turbulent flow has shorter entry length for flow to become fully
developed
• These features enhance the heat and mass transfer in the turbulent
regime
Ref: “Component or PCB Oriented Thermal Design” http://www.coolingzone.com/library.php?read=530
April 9,9, 2008

How do you Achieve Turbulent Flow?
• Typically easy in an electronics enclosure
• “Roughness” associated with components
(leads, packages, capacitors, etc.) trips the
boundary layer
• Add ribs, helical ridging or similar
• Related to the characteristic Re (Reynolds
number)

April 9,9, 2008

PCB Heat Transfer
• Thermal vias in the package and board serve as
significant heat flow path from the top plane of the
package to the top plane of the board
• Entry flow effects are important
Ref: “So Many Chips, So Little Time, Device Temperature Prediction in Multi-Chip
Packages”, http://electronics-cooling.com/articles/2006/2006_aug_cc.php

April 9,9, 2008

Cell Phone PCB Thermal Analysis

April 9,9, 2008

Thermal Design
• Both heat and cold adversely affect circuits
• Low temperatures cause different
problems
• Thermal cycling can lead to fatigue
• Thermal shock severe

Ref: “Hot, Cold, and Broken: Thermal Design Techniques”, 2007, EDN,
http://www.edn.com/article/CA6426879.html

April 9,9, 2008

System Thermal Design
• Airflow (forced convection) can reduce
thermal resistance up to 5X
• Fans add noise; need to be sized
based on volume flow in the system
• Ambient temperature effect
• Turbulence and natural convection
Ref: “Turbulent Heat Transfer in
Buoyancy-Driven Natural Convection
in Vertical Enclosures”

Ref:
www.ceere.org/beep/docs/FY2002/Turbulent_Flow_in_Enclosure.pdf

April 9,9, 2008

Thermal / Structural / Drop Test
Analysis of a Circuit Board
• Determine Temperature distribution and stresses due
to CTE Mismatch.
• Determine dynamic response due to frequency
response and random excitation
• Determine acceleration response and stresses due to
Drop Testing

IC Leads

PWB
Connector

April 9,9, 2008

Circuit Board Demonstration

Thermal
Analysis Structural
Warping
Modal
Determine Temperature Determine Stresses Analysis
Distribution in board due to CTE mismatch

Evaluate stress
and deformation

Determine Response
Levels due to input PSD
Frequency
Determine stresses Response
due to Drop Testing
Analysis

Drop Random
Test Vibration

April 9,9, 2008

Summary - MD Nastran Workflow
• MD Nastran eliminates two software applications from the process.
Step 1 Ref: MSC Software

Thermal Analysis

Step 2
MD Nastran

Structural Warping

Step 3
Modal Analysis

Step 4
Frequency Response Analysis

Step 5
Random Vibration

Step 6
Drop Test

April 9,9, 2008

References – Thermal & Cooling
• Electronic Cooling Solutions
http://www.ecooling.com/references.html
• Electronics Cooling
http://www.electronics-cooling.com
• Coolingzone
http://www.coolingzone.com/library.php

April 9,9, 2008

Probabilistic Design System (PDS)
• Essentially a DOE using finite element analysis
• Key variables are specified with some sort of
distribution (e.g., Gaussian distribution, etc.)
• Series of simulations are analyzed varying the
parameters within the specified distributions
• Terms such as “stochastic analysis” are also
used

April 9,9, 2008

• Tools like Engineous iSight can be used to drive
other applications and finite element codes to
give a PDS
• ANSYS includes PDS capabilities in the
Workbench
• MSC Adams Insight provides excellent DOE
and Response Surface capabilities
• MSC.Software MD-Nastran is adding stochastic
and DOE capability for multi-disciplinary
applications

April 9,9, 2008

Sources of Uncertainty in Engineering
Physical uncertainty
Material properties
F F
Boundary and initial conditions
Assembly and manufacturing imperfections
Environmental loads
Geometry

Non-physical uncertainty
Modelling errors (material model, element type, etc.)
Choice of method (linear, non-linear, etc.)
non-
Solver
Computer
Engineer

April 9,9, 2008

Definition of a Stochastic Problem
Model

Random response

Multiple executions
Single computer run
are a Simulation is an Analysis

April 9,9, 2008

Introduction to Stochastics

April 9,9, 2008

Variation in Design
• Traditional Simulation • Reality
– “Deterministic” – No two examples of a
– All parameters are known product are exactly alike
precisely – No two tests will produce
– One solution – uniquely the same results
determined – It’s not always obvious why
– Evaluation of whether a some samples pass
design is “good” is usually acceptance tests while
based on nominal others fail
conditions – Simulations from two
different engineers will give
slightly different results

April 9,9, 2008

How Do Designers Deal with this
Variation?
• By trying to eliminate it.
• Make reality behave like a deterministic simulation rather
than making the simulation a better reflection of reality
– Tight tolerances
– Elaborate process specifications
– Statistical quality control
– Conservatism factors
– Scrap parts that show “too much” variation
– Expensive and time consuming tests to determine “real world”
performance
• The result?
– Reduces but doesn’t eliminate variation
– Can be very expensive
April 9,9, 2008

The Alternative
• Make simulations vary like reality
• Perform multiple simulations, each with slightly
different parameter values that reflect the
variations that occur in actual manufacturing and
use
• Multiple simulations provide a distribution of
outcomes, rather than a single outcome
• Evaluate which variations have a large impact
on performance and reliability and should be
controlled and which have a small impact and
can be allowed to vary over a wider range
• This is the idea behind stochastic simulation
April 9,9, 2008

Stochastic
• sto·chas·tic
1 : involving a random variable <a
stochastic process>
2 : involving chance or probability <a
stochastic model of radiation-induced
mutation>
• Source: Merriam-Webster's Medical
Dictionary, © 2002 Merriam-Webster, Inc.

April 9,9, 2008

Why Stochastics Makes Sense for
Electronics Applications
(and many other applications!)

April 9,9, 2008

Electronics Applications
• Examples:
– Computer equipment and components
– Cell phones and communications equipment
– Biomedical devices
– Appliances
– Automotive and aerospace components
– Manufacturing equipment
• Typically have large production volume
• Emphasis on miniaturization
• High cost of failure (liability, warranty)
• Operate in a variety of frequently harsh and
uncontrolled environments

April 9,9, 2008

Design Objective for Electronics
Devices
• The design objective for electronics
devices is typically a probabilistic one
• For example, mean time between failure
(MTBF) or probability of failure (POF)
• Translating deterministic results to
probabilistic terms is difficult at best
• Probabilistic design objectives require
probabilistic design techniques
April 9,9, 2008

Business Value
• Stochastics provides:
– Time to Market
• Efficient design space surveys
• Shorten development time by enabling engineers to analyze
results, not run tests
– Reduce warranty/risk
• Greater understanding of key parameters in the design
• Design for manufacture
– Improve perceived quality/robustness
• Fewer returns
• Fewer complaints
• Less warranty costs

April 9,9, 2008

Stochastic Crash Analysis

April 9,9, 2008

MSC.ADAMS/Insight
• Insight is a framework for performing stochastic
simulation
• Insight was designed to be flexible enough in its
implementation that it can work with virtually any
simulation or analysis tool
• When coupled to MSC.SimManager, a web-
based portal for simulation management, it can
provide efficient stochastic simulations to an
entire enterprise

April 9,9, 2008

How Does It Work?
• The user defines parameters (called “factors”) which vary
and the way they vary (a list of potential discrete values,
a probability distribution, etc.)
• The user defines responses, i.e. – dependent variables
that vary as a result of the variation in the input factors
• The user defines how the input factors and output
responses are related to the simulation model
• Insight then performs multiple simulations.
– Each simulation has a set of input factors whose values are
automatically selected based on a variety of methods
– The responses of each simulation are collected
– Essentially “virtual tests” of the design
• The result is a statistical sampling of inputs and the
resulting outputs

April 9,9, 2008

What Does Insight Provide?
• The selection of factors for each stochastic simulation
can be based on Latin Hypercube or Monte Carlo
methods
– Other non-stochastic virtual experimental design methods,
such as Full Factorial, Central Composite, Box-Behnken,
Placket Burman, Sweep Study, D-Optimal, and Design of
Experiments (DOE) are also available
• Statistical analysis of the variation in responses to the
variation in the inputs
• Response surfaces that represent the relationship
between factors and responses of the system as curve-
fit polynomials
• Statistical data that can be exported to tools such as
Excel, Visual Basic, and MATLAB
• Export results to a web server

April 9,9, 2008

Typical Output Using Insight’s Web Interface

Includes
response
surface data.
“+” and “-”
buttons enable
the user to
interactively
change values
of factors see
their effect on
the responses.

April 9,9, 2008

What are the Benefits?
• Response surfaces allow a user to select values for
factors needed to achieve a desired response
• Statistical analysis identifies which factors have the
greatest affect on responses and can provide the
greatest benefit
– Relax tolerances on design parameters that have little affect
– Tighten tolerances on those that have a greater affect
– Assess the probability of a specific outcome (e.g. – “stress will be
greater than allowable”)
• Physical tests can be more rapidly correlated by
comparison to the results of “virtual” tests
• Designs can be automatically improved using “stochastic
design improvement” (SDI) techniques

April 9,9, 2008

What are the Applications?
• Drop Testing
– Vary impact energy and orientations, material
properties at high strain rates
• Ball Grid Array Durability
– Vary thermal input, ball size and shape, load on
individual balls in the array, solder material properties
at temperature
• Mechanical Systems Reliability
– Vary manufacturing tolerances, actuator forces,
freeplay
• Controller Algorithm Development
– Vary gains, phase lags, sensor accuracy

April 9,9, 2008

An Example:
BGA-Ball Grid Array

April 9,9, 2008

The Problem
• Fatigue analysis of a single solder ball
• Loads come from a less detailed model of a ball
grid array. This is the most severely loaded ball
in the array.
• Shape and size of solder balls varies. For very
small solder balls, even small variations can
have significant impact on strain and hence
fatigue life
• Fatigue methodology for solder under plastic
deformation is semi-empirical and subject to
significant scatter

April 9,9, 2008

The Approach
• Create a parametric model of the solder ball
using MSC.Patran software
– Height, diameter, the size of small fillets at the top
and bottom of the solder ball, the load on the solder
ball, the yield strength of the solder
• Use Insight to define normal distributions for
each of these parameters
• Conduct 128 finite element simulations with
MSC.Marc using Monte Carlo methodology to
sample the design space
• Determine the statistical distribution of plastic
strain, and hence, fatigue life

April 9,9, 2008

The Process
Define
Define Parametric Define Responses Monte Carlo
Distributions for
Model of Interest Sampling
Parameters

MSC.Patran FEA
Response
Surfaces

MSC.Marc FEA

Factor/
Response
Correlations
Database of
Factor and
Response Values

Outcome
Probabilities

April 9,9, 2008

Solder Ball – Parameterized Geometry

Fillet Height
Height

Diameter

April 9,9, 2008

Solder Ball
Finite Element Model

April 9,9, 2008

Solder Ball – Total Strain

April 9,9, 2008

Solder Ball – Plastic Strain

April 9,9, 2008

Work Space – Inputs and
Responses (only 36 out of 128
displayed)

April 9,9, 2008

Work Space Review

April 9,9, 2008

Influence of Input Variables on
Max. Plastic Strain

April 9,9, 2008

Work Space Correlations
(Pearson Correlation)

April 9,9, 2008

Work Space Correlations
Input vs. Responses

April 9,9, 2008

Work Space Scatter Plot
Diameter vs. Max. Displacement

April 9,9, 2008

Work Space Scatter Plot
Mesh Factor vs. Max. Plastic Strain

April 9,9, 2008

with ANSYS FEA

April 9,9, 2008

Stress Results
Max.Principal Stress Hoop Stress

April 9,9, 2008

Rank Order Correlation
Sensitivities & Histogram of Stress
– Wrist Pin ID has the greatest
influence on the fatigue stress

April 9,9, 2008

Probabilistic Simulations using
ANSYS to DFSS
• Introduction
• An Example
• Motivation
• Features
• Benefits
• Probabilistic Methods
• Probabilistic Results/Interpretation
• Summary
Ref: 2004, ANSYS DFSS Workshop

April 9,9, 2008

Introduction
Purpose of a Probabilistic Design System (PDS)

Input
Input ANSYS
ANSYS Output
Output
Material Properties Deformation
Geometry Stresses / Strains
Boundary Conditions Fatigue, Creep,...

It’s a reality that input
parameters are subjected to
scatter => automatically the
output parameters are
uncertain as well!!
April 9,9, 2008

Introduction
Purpose of Probabilistic Design System (PDS)

ANSYS PDS
ANSYS PDS

Questions answered with probabilistic design:

• How large is the scatter of the output parameters?
• What is the probability that output parameters do not
fulfill design criteria (failure probability)?
• How much does the scatter of the input parameters
contribute to the scatter of the output (sensitivities)?

April 9,9, 2008

An Example
Example: Lifetime of Components !!!
Random input Finite-Element Random output
variables Model parameters
Material • LCF lifetime
• Strength • Creep lifetime
• Material
• Corrosion lifetime
Properties
• Fracture mechanical lifetime
Loads •…
• Thermal
• Structural Evaluate reliability of products !
Geometry/
Tolerances Evaluate quality of products !

Boundary Evaluate warranty costs !
Conditions
• Gaps To evaluate is the first step
• Fixity
to improvement !

April 9,9, 2008

Motivation
Influence of Young’s Modulus and Thermal Expansion
Coefficient on thermal stresses:
thermal = E · · T

Deterministic Approach:
Emean and mean => evaluate expected value: expect

Probabilistic Approach:

P( thermal >
1.05 expect) P( thermal > 1.10 expect)
‘E’ scatters ±5% 16% (~1 out of 6) 2.3% (~1 out of 40)
‘E’ and ‘ ‘ scatter ±5% 22% (~1 out of 5) 8% (~1 out of 12)

April 9,9, 2008

Scatter in Material Properties
and Loads
Property SD/Mean %
Metallic materiales, yield 15 Source: Klein, Schueller
et.al. Probabilistic Approach
Carbon fiber composites, rupture 17 to Structural Factors of
Safety in Aerospace.
Metallic shells, buckling strength 14 Proc. CNES Spacecraft
Structures and Mechanical
Junction by screws, rivet, welding 8 Testing Conf., Paris 1994

Bond insert, axial load 12
Honeycomb, tension 16
Honeycomb, shear, compression 10
Honeycomb, face wrinkling 8
Launch vehicle , thrust 5
Transient loads 50
Thermal loads 7.5
Deployment shock 10
Acoustic loads 40
Vibration loads 20
April 9,9, 2008

Motivation
±5-100%
Materials,
Bound.-
Cond.,
Loads, ...
Thermal
Analysis
CAD FEM
Geometry
CFD LCF ±??%
± 0.1-10%
Structural
FEM
Materials,
Analysis Materials
Bound.-
Cond., ... Materials, ±30-60%
Bound.-
±5-50%
Cond.,
±5-100% Loads, ...

April 9,9, 2008

PDS Benefits
Deterministic Analysis: Probabilistic Analysis:
• Only provides a YES/NO answer • Provides a probability and reliability
(design for reliability)
• Safety margins are piled up • Takes uncertainties into account in a
“blindly” (worst material, maximum realistic fashion => This is closer to
load, … worst case) reality => Over-design is avoided
1 worst case assumption =10-2
2 worst case assumptions =10-4
...
=> Leads to costly over-design • “Tolerance stack-up” is included
• Only “as planned“, “as is” or the (design for manufacturability)
worst design

April 9,9, 2008

PDS Benefits
Deterministic Analysis: Probabilistic Analysis:
• Sensitivities do not take range of • Range/width of scatter is “built-in” into
scatter or possibilities into account probabilistic sensitivities
• Sensitivities do not take
interactions between input • Interactions between input variables
variables into account (second are inherently taken care of
order cross terms)

April 9,9, 2008

PDS Benefits
Illustration of the Benefits of
Probabilistic Analysis over Deterministic Analysis
Probabilistic Analysis

Deterministic Analysis

April 9,9, 2008

ANSYS PDS Features
• Works with any ANSYS analysis model
• Static, dynamic, linear, non-linear, thermal, structural, electro-
magnetic, CFD ..
• Allows large number random input and output
parameters
• 10 statistical distributions for random input parameters
• Random input parameters can be correlated
• Probabilistic methods:
• Monte Carlo - Direct & Latin Hypercube Sampling
• Response Surface - Central Composite & Box-Behnken
Designs
April 9,9, 2008

ANSYS PDS Features
• Use of distributed, parallel computing techniques for
drastically reduced wall clock time
• Comprehensive probabilistic results
• Convergence plots, histogram, probabilities, scatter
plots, sensitivities, ...
• State-of-the art statistical procedures to address the
accuracy of the output data
• Confidence intervals

April 9,9, 2008

Probabilistic Methods
Monte Carlo Simulation: Fully Parallel
Perform numerous analysis runs based
on sets of random samples, and then
evaluate statistics of derived responses.

• Direct (Crude) Sampling Monte Carlo (DIR)
• Latin Hypercube Sampling Monte Carlo (LHS)
• User defined (USR)

April 9,9, 2008

Monte Carlo Simulation Method Scheme:
Simulation of input Statistical analysis of
parameters at output parameters
random locations

X1 X2 X3

Repetitions = Simulations

ANSYS
ANSYS

April 9,9, 2008

Finite Element Runs for Monte Carlo
For Monte Carlo Simulation the number of simulations does not
depend on the number of random input variables, but on the
probabilistic result you are looking for:

• For assessment of the statistics of output parameters (Mean, sigma)
Nsim 30 … 100
• For histogram and cumulative distribution function
Nsim 50 … 200
• For assessment of low probabilities P (tails of the distribution)
Nsim 30/P … 100/P

April 9,9, 2008

– Response Surface Methods:

Fully Parallel
Select specific observation points for
each random variable, run analyses,
establish response surface for each
response parameter, perform Monte
Carlo Analysis on Response Surface.
• Central Composite Design (CCD)
• Box-Behnken Matrix (BBM)
• User defined (USR)

April 9,9, 2008

Response Surface Methods Scheme:
Simulation of input parameters Statistical analysis of
at specific locations output parameters

X1 X2 X3

Evaluate input
Evaluate input Monte Carlo Simulations
Monte Carlo Simulations
parameter values
parameter values on Response Surface
on Response Surface
DOE Repetitions = Simulations Response Surface Fit
Response Surface Fit

ANSYS
ANSYS

April 9,9, 2008

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