1. &
We Provide You Confidence in Your Product ReliabilityTM
Ops A La Carte / (408) 654-0499 / askops@opsalacarte.com / www.opsalacarte.com

2. Robust Design &
Reliability Engineering
Apr 4, 2012
Louis LaVallee
Sr Reliability Consultant

3. Ops A La Carte is a Professional Reliability Engineering firm focused on
providing you confidence in reliability throughout your product life cycle.
Mike Silverm Managing Partner Offices & labs
an
(408) 472-3889
mikes@opsalacarte.com
Louis LaVallee
Sr. Reliability Consultant
585-281-1882
Loul@opsalacarte.com
Website
www.opsalacarte.com
Dave Vaughan dave.vaughan@chart-ind.com

4. Who Are We?
– Ops A La Carte Santa Clara, CA
– Founded in 2001
– Named top 10 fastest growing, privately-held companies
in the Silicon Valley in 2006 and 2009 by the San Jose
Business Journal.
– Over 1400 projects completed in 10 years
– Over 500 Customers in over 30 countries
– Over 100 different industries, 6 main verticals
Clean Tech, Med Tech, Telecom, Defense, Oil/Gas,
Consumer product
– We conduct FREE monthly webinars.

5. • Ops Solutions – Ops provides end-to-end solutions that target the corporate
product reliability objectives
• Ops Individual “A La Carte” Consulting – Ops identifies and solves the
missing key ingredients needed for a fully integrated reliable product
• Ops Training – Ops’ highly specialized leaders and experts in the industry
train others in both standard and customized training seminars
• Ops Testing – Ops’ state-of-the-art
provides comprehensive testing services

6. Presenter: Dave Vaughan,
National Sales Manager, HALT/HASS Systems
Dave.Vaughan@chart-ind.com
HQ in Minnesota
• World’s Largest Cryogenic Equipment Supplier
• Building quality products since ‘ 63
• HALT/HASS chamber manufacturer since ‘ 97
• ISO 9001 Certified
• Only HALT/HASS supplier that can offer you a true Turn-key solution with
Tank, Pipe and Chamber.
• Hundreds of HALT/HASS chambers worldwide.

7. Upcoming Events For Ops a la Carte
•IPC Conference on Test & Inspection
•May 15-17, 2012, Costa Mesa, CA.
•Ops A La Carte’s Vijay Prasad will be giving a presentation at this conference on
"New Techniques for More Effective ESS".
•SMTA Conference on Soldering and Reliability
•May 15‐18, 2012, Toronto.
•Ops A La Carte's Peter Arrowsmith will be giving a presentation at this conference on
"Improving Product Reliability Using Accelerated Stress Testing".

8. Upcoming Events for Ops a la Carte
•MD&M East
•May 22‐24, 2012, Philadelphia, PA
•Ops A La Carte's Mike Silverman will be giving a 3 hour class on "Medical Reliability
Testing ‐ Identifying Testing Requirements Early.“
•Applied Reliability Symposium
•June 13-15, 2012, New Orleans, LA
•Ops A La Carte's Mike Silverman will be giving a presentation on "Calculating ROI When
Implementing a Design for Reliability Program".

9. Other Reliability Events
Certified Quality Engineer Preparation Class
Date(s): April 17 to May 29, 2012
Time: 6pm‐10pm one night a week, 7 weeks
Location: San Jose, CA and Webinar
http://www.opsalacarte.com/Pages/education/edu_10cqe.htm

10. Annual Reliability Symposium
May 7‐11, 2012 in Santa Clara, California and via
webinar
TRACK ONE ‐ DFX TOOLS
• Design for Reliability (DfR): May 7‐8
• Design for 6 Sigma (DfSS): May 9
• Design for Mechanical Reliability (DfMR): May 10
• Design for Warranty (DfW): May 11 morning
• Design for Software Reliability (DfS): May 11 afternoon
TRACK TWO ‐ REL TOOLS: ALT/DOE/RCA
• Design of Experiments (DOE): May 7‐8
• Best Accelerated Reliability Tests (BART): May 9‐10
• Root Cause Analysis (RCA): May 11

11. Webinar Demographics
Registrants Approx 200
US States 30
Countries 13

12. Survey Question #1
Do You Currently Practice Design for Robustness?
Yes 41%
No 37%
I Don't Know 22%

13. Survey Question #2
Do You Differentiate Design for Robustness from
Design for Reliability when designing a product?
Yes 21%
No 48%
I Don't Know 30%

14. Abstract
Robust Design (RD) Methodology is discussed for hardware
development. Comparison is made with reliability engineering (RE)
tools and practices. Differences and similarities are presented.
Proximity to ideal function for robust design is presented and
compared to physics of failure and other reliability modeling and
prediction approaches . Measurement selection is shown to
strongly differentiates RD and reliability engineering methods
When and how to get the most from each methodology is outlined.
Pitfalls for each set of practices are also covered.

15. AXD
TRIZ
QFD
DFR
PUGH
Many
Design
methods &
Interfaces DOE ROBUST DESIGN
VA/VE
DFSS6
CP/CS MNGMT

16. RD  Reliability
Life Tests
P-diagram
Root cause Analysis
Tolerance Design Layout POE
Ideal Function POF
DOE RCM
Tuning
Engineering Maintainability CBM
6 Flexibility
Lean Science Warranty $
Robust Design Simulation Reliability
Quality Testing
Loss Models
Reuse FMECA
transformability HALT/HASS
Planning
S/N RSM Life prediction Redundancy
Online QC ADT
ALT
Parameter design FTA
Availability
Generic Function RBD

17. Robust Design Definition
A systematic engineering based methodology
(which is part of the Quality Engineering Process)
that develops and manufactures high reliability
products at low cost with reduced delivery cycle.
The goal of robust design is to improve R&D
productivity and reduce variation while
maintaining low cost before shipment and minimal
loss to society after shipment.

18. Robustness is…
“The ability to transform input to output as closely to
ideal function as possible. Proximity to ideal function is highly
desirable. A design is more robust if ratio of useful part to
harmful part of input energy is large. A design is more robust
if it operates close to ideal, even when exposed to various
noise factors, including time”
Reliability is…
“The ability of a system, subsystem, assembly, or
component to perform its required functions under stated
conditions for a specified period of time”

19. RD Noise Factor Definitions:
Factors that disturb the function, which are difficult to control, or
too expensive to control.
 External noise factors include temperature, humidity, dust,
vibration, contaminants,…
 Unit-to-unit variation from manufacturing processes.
 Deterioration, as time passes.
Selection of noise factors and levels is a prediction of how device
will be used downstream. Noise factors are used for upstream,
midstream and sometimes downstream experimentation.

20. HALT & Reliability

21. Polling question 1
How would you classify highly accelerated life testing
(HALT )?
A. Robust design activity
B. Reliability activity
C. Neither
D. Both

22. ANSWER: B
Robust Design HALT
Quantitative Qualitative
‘Customer from hell ‘ prediction , Highly elevated stress levels for
with compound noise factors Temp, Vibration , voltage
Energy efficiency maximization None
Transformability of input to output None
Ideal function driven Failure mode driven
Cost & Quality Loss driven Warranty costs, Service costs
Upstream Technology Design-Build-test-fix-test-fix
reproducibility downstream HALT Calculator

23. Test Time Compression with HALT/HASS

24. Highly Accelerated Life Test (HALT)
• Stresses product beyond specifications
• Gathers information on Product Limitations
• Focuses on Design Weakness & Failures
• 6 DoF Vibration
• High Thermal Rate of Change
• Loosely Defined ‐ Modified “On the Fly”
• Not a “Pass/Fail” Test

25. Case Study #1
This was a board from an instrument panel. The corner of
the inductor housing and lead are broken. This happened
during combined temp + vibration at 50 Grms & 100C.

26. Case Study #2
This is from a board used in an industrial power supply. The
transformer windings broke causing an arc. This happened during
combined temp + vibration at 30 G’s & 80C.

27. Case Study #3
• Largest Test Equipment manufacturer for the
semiconductor industry
• Over $10 million dollars in warranty claims per year
• Implemented HALT/HASS as part of their Product
Development Process
• Company wide deployment
• Result: After 2‐3 years, warranty claims were
reduced to $500K.

28. • What HALT/HASS does for your Company
– Your Product’s Reliability
• New Products
– Expedite the Development of New Products
– Understand the existing product margins (and widen them)
– ‘Ruggedize’ the product and remove design related weaknesses
• Mature Products
– Warranty Concerns
– Find Manufacturing Defects/ Initiate Process Changes
– Look for Continuous Product Improvement
Defective products create unexpected costs for producers and
financial losses for disappointed consumers

29. Harmful Variation & Countermeasures
• Search for root cause & eliminate it
• Screen out defectives(scrap and rework)
• Feedback/feed forward control systems
• Tighten tolerances (control, noise, signal factors)
• Add a subsystem to balance the problem
• Calibration & adjustment
• Robust design (Parameter design & RSM)
• Change the concept to better one
• Turn off or turn down the power
• Correct design mistakes (putting diodes in backwards)

30. Sales Points for Robust design
Enabled Reasoning
Flexible technology to avoid having to reoptimize or
reinvent a new design each time
Fewer experiments to obtain required information.
Improved reproducibility of experiments and measurements
Improved energy efficiency through dynamic S/N ratios &
adjustment factors
Experimental planning to reduce ad hoc / trial & error testing
Bringing Improvement objective to experimentation rather than just
characterization
Preventing non-robust designs from going downstream creating lots of
trouble
Smoother subsystem integration With coarse and fine tuning factor
identification

31. Enabled Reasoning
Early input to reliability planning to reduce excessive testing time
Reduced design complexity for simpler software, simpler
compensation systems, & reduced
information content
Identification of good tuning factors for when process drifts or when system
designer does subsystem integration
Capturing design engineering for rapid product variants and future
knowledge platform of products
Rapid maturation of critical parameters and supporting
critical specifications
Early identification of technology limitations
Inspection of engineering knowledge- to prevent downstream problems
Identification of good measures To enable consistently correct decisions

32. 6 Fundamental Concept
Y=f(X)+e
Response Y X1, X2,…,XN
Dependent Independent
Output Input
Effect = Cause
Symptom Problem
Monitor Control
In reliability engineering for example , Y is the continuous
stochastic variable (time-to-failure) and F(x) is the failure
mechanism, or mechanistic model . In RD, smooth transformability
between input and output is considered.

33. Reliability Growth
Historically, the reliability growth process has been treated as, a reactive
approach to growing reliability based on failures “discovered” and fixed
during testing or, most unfortunately, once a system/product has been
delivered to a customer. This reactive approach ignores opportunities to
grow reliability during the earliest design phases of a system or product.
Delayed fix
jump
MTBF
New build
jump
Cumulative test time

34. Robustness Growth
S/N
Factors Can be changed
today
time
S/N
Factors Can be changed in 1
week
time
S/N
Benchmark Target
Factors Can be changed in 2
weeks
Robustness gains
time

35. Progression of Robustness to Ideal Function Development
A B C
LSL USL
Zero Defects
Cpk
Static S/N
Dynamic S/N Ratio
When a product’s performance deviates from target, its quality is
considered inferior. Such deviations in performance cause losses to
the user of the product, and in varying degrees to the rest of
society.

36. Useful
Input signals Output
Main Function
Mi Y=f(x)+
Harmful
Output
Noise Control
Factors Factors
Taxonomy of Design Function --P Diagram

37. Polling Question 2
Which is a more difficult problem for engineers?
a. Tuning the mean value of a device or process output
b. Controlling the variation of a device or process output
c. Both have the same degree of difficulty
d. Tuning and variation control are just manufacturing
problems

38. Spring Example

39. Simple Helical Spring Design
Useful
Input signal Main Function Output
X F=-kX+e
Y  M   Zero Point Proportional Ideal Function
Force Ideal (Hooke’s Law)
N
Actual
0,0 Displacement X (mm)

40. Transformability &Robustness Improvement
Response Response
N1 N1
N2
N2
0,0 M signal 0,0 M signal
Minimizing the effects of noise factors on transformation of input to output
improves reliability. Sensitivity increase can be used for power reduction .
Noise factor here might be fatigue cycles, or stress in one or two directions, or …

41. Analysis of Variance ANOVA
Resolution of the quantity of work which various types of sources
have performed on the target characteristic. All factors affect
variation. (screening?)
In physics, the quantity of work is proportional to the product of a
generalized force and a generalized displacement.
Energy is always the product of a pair of variables e.g. Force and
distance for mechanical , voltage and charge, pressure and volume,
absolute temperature and S, magnetic potential and magnetic
flux, chemical potential and quantity of material, moment and
angular rotation, etc.
RD’s focus is on elements of energy transfer

42. Comparison experiment between two helical spring suppliers A1
and A2. Two replicates are measured for each supplier.
Calculate S/N ratio for each supplier’s springs
Supplier
Signal factor M1=30 M2=60 M3=90
R1 65N 136 208 A1
R2 74 147 197 A1
Total 139 283 405
R1 61 135 201 A2
R2 71 145 208 A2
Total 132 280 409

43. Robustness S/N ratio ANOVA Calculation
S T   y11  y11  ...  y kr0
2 2 2
Total Sum of Squares
df  kr0 Total Degrees of freedom
E ( S  )  r0 r  2   2 Expected value of 
r  M 12  M 22  ...  M k2 Power of signal
S 
1
M 1 y1  M 2 y 2  ... M k y k 2 Sum of squares for slope 
r0 r
df  1 Degrees of freedom for 
S e  ST  S  Sum of squares for error
df  kr0  1 Degrees of freedom for error
Se
Ve 
kr0  1 Error Variance
1
r0 r
S   Ve  Power of signal to change useful output
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
  10 log 10  Power of Noise to change harmful output
Ve

44. S/N Ratio Calculation & Comparison for Two Suppliers A1 & A2
S T  65 2  74 2  ...  197 2  131879
df  6
 
r0 r  2  30 2  60 2  90 2  25 , 200
( M 1 y1  M 1 y1  M 1 y1 ) 2 (30  139  60  283  90  405 ) 2
S    131657 .14
r0 r 25200
df  1
S e  S T  S   131879  131657 .14  221 .86
df  5
S e 221 .86
Ve   44 .37
5 5
1
131657 .14  44 .37 
A  10 log 25200  10 log .1177   9 .29 dB
1
44 .37
A   7 .79 dB
2
Supplier A2 better by 1.5 dB

45. Useful
Input signal Main Function Output
M Y=f(x)+
Main function is to transform input signal to useful output. Energy
transformation takes many different forms, (but usually not 2nd order
polynomials, as in RSM)
Common Ideal Function Forms:
Y  M  Y     * M *  M * M  
Y  M   Y  1  e  M  
Y  Y0   ( M  M 0 )   Y   M x  
Y    M  
YY  R  jX R  jX )   
Y  M 1M 2  
M1 
Y    M  M  
Y  
M2 ...

46. Measurements Discussion

47. Measurements
• One of the most important distinctions between
robust design (RD) activities and reliability engineering
(RE) is what is being measured during experimentation.
Time-to-failure(TTF) , typically used in reliability
testing, is a continuous stochastic variable with an
associated probability distribution, e.g. Weibull, normal,
lognormal,
Rarely has TTF been used for robust design , other
than for isolated verification testing. RD focus is on
smooth energy transformation of input to output

48. Typical Reliability Measures
• Life data
• Time-to-failure
• Time between failures
• Survival Count
• Event times
• Degradation of performance (of items below)
Typical Robust Design Measures
• Force, Current
• Pressure Voltage
• Velocity Intensity
• Temperature RPM
• Weight Angular velocity
• Distance Magnetic potential
• Thickness Time relative to ideal
• Torque Luminous Flux
• Volume …
• Flow rate

49. RD Criteria for Measurement
 Validity first
 Continuous variable (richest analytics)
 Completeness
 Fundamental, disaggregated
 Practical, affordable
 Monotonic with control factors
 Related to basic energy transfer mechanism
 Measurement Capability High
 Engineering Focus
Picks up information about dysfunction
Open loop
Try not to measure ‘ideally zero ‘ quantities

50. Lack of monotonicity of ‘misregistration’ measurement
Stack 1 Stack 2
X
X
Stack 3 Stack 4
X X
Four paper stacks with same X. Same number indicates different
problems and different required countermeasures. Yield?

51. Science & Engineering Attitude
• In science, understanding nature, searching for an accurate
relational equation, and uncovering patterns and symmetries
are most important. Reduction of error between observed
characteristic value and the relational equation prediction is
paramount. This method is completely correct as a scientific
objective, i.e. searching for a formula which correctly expresses
observational data.
• Engineering attitude however must consider profitability,
excellence, efficiency of R&D. Low cost competitive designs
which can operate with small variation even when subjected
to combinations of worst predicted customer usage conditions,
manufacturing tolerances, and degradation processes are
required.
RD is more engineering oriented while Reliability (POF) is more science oriented!

52. Marginal Means Plot from L9 Experiment Data
Response or 4 Factors @ 3 levels
Sensitivity or
S/N ratio
15% 45% 5% 15%
Grand avg
Factors
A B C D
Combination A2B2C2D2 ~ Mean
A3B1C3D3 = max response
A1B3C1D1 = min response
Error % ~20%

53. Ideal Function Examples

54. Automotive Brake Pad Example
Y Ideal Y Reality
M=Master cylinder Pressure
Y=Torque Generated
M
M
Y Ideal Y Reality Adjustment signal M*=
Pad surface area
Y=torque generated
Ideal Function=Y=MM*
MM* MM*

55. Braking Ideal Function=Y=MM*
M*=Pad surface area
Control factors: M=master cylinder pressure,
Raw Materials
Raw material prep process parameters
Pad manufacturing process parameters
Dimensions, …
Symptoms / side effects:
Brake Noise
Part Breakage
Wear
Vibration,
squealing … GM Working on this one for many years!
Noise factors:
Temperature/humidity variability
Deterioration and aging
Brake fluid type and amount
Manufacturing variability
Raw materials lot-to-lot & within lot
Variability in process parameter settings

56. Numerical Control Machine (NC)
Ideal Function: Transform programmed dimensions into product
dimensions
A generic test piece rather than an actual part, can be designed. The test piece
can be easier to measure accurately. It also can evaluate multiple levels of signal
factor.
Lots of dimensions
to measure by CMM~67
level signal factor
Control Factors: Speed, Feed rate, Tool Material, Tool travel, Tool
stiffness, cut Depth, tool angle, …
Noise Factors: Material Hardness, tool wear, Lubricant age, Lubricant
condition,…

57. Generic test piece Transformability
Hardness 1
Ideal Hardness 2
Function
Part Dimensions
Y=M+e
v
M1
M67
NC machine Intended dimension(mm)

58. Measurement System Ideal Function
Y=M+e
M=true value of measurand
Y=measured value
Auto Steering Ideal function
Y=M+e
M=steering wheel angle
Y=Turning radius
Communication system ideal function
Y=M+e
M=signal sent
Y=signal received
Cantilever beam Ideal Function
Y=M/M*+e
M=Load
M*=Cross sectional area
Fuel Pump Ideal Function
Y=M
Y=Fuel volumetric flow rate
M=IV/P current, voltage,& backpressure

59. Laser welding for Lap Joint Ideal Function
Y=MM*+e
M=displacement
M*=Length
Y=measured shear force
Windshield wiper Ideal Function
Y=M+e
Y = measured arrival time for wiper to reach a fixed point
M=Theoretical, ideal time for which system was designed 1/rpm
of the motor
Metal Stamping ideal function
Y=M+e
Y=thickness change (t0-t)
after stamping
M=intended height change
Minimize 

60. Adhesive film ideal function W
Y=M+e
Y=peel strength
M=contact width
Maximize 
Roll Mill ideal function
Y=M+e
Y=Thickness change
M=reduction ratio
Maximize 
Determine the limiting value for thinness.
More robust condition would enable thinner

61. POF

62. POF Underpinnings
Knowledge of how things fail and the root causes of
failures enables engineers to identify and avoid
unknowingly creating inherent potential failure
mechanisms in new product designs and solve problems
faster
“Knowing how & why things fail is equally important
to understand how & why things work”
POF is applicable to the entire product life cycle

63. Physics of Failure and FEA
Where do the stresses exceed the strength of the materials?
Finite Element Analysis
(FEA) is a traditional
engineering technique for
predicting the responses of
structures and materials to
applied loads such as force,
temperature, vibration and
displacements. NASTRAN,
ABACUS, …

64. Describe Design Collect Fit Predict
RSM

65. Response Surface Methods (RSM)
Plateaus & Some Robustness
Requires construction of a mathematical model via response
surface methods (RSM) Typical model is a second order
polynomial model of the form:
Y   0  1 X 1   2 X 2  11 X 12   22 X 2  12 X 1 X 2  
2
The model approximates the unknown true response function
by the first few terms of a Taylor’s series expansion . RSM has
recently been enhanced by inclusion of Propagation of error
(POE) methodology.
‘s are unknown coefficients to be estimated from
experimental data.  is an unknown calculable error .

66. Propagation of Error & RSM
Variation from factor A setting is
transmitted to variation in response Y.
How much variation depends on
Y nonlinearity of relationship and input
variation.
sensitivity
2
k
 y 
      xi 2   resid
2
Y  x 
2
i 1  i 
Y   0   1 A1   11 A12
A1 A2
Yˆ  15  25 A1  . 7 A12
Factor A
y
 25  1 . 4 A
A
Y  25  1 . 4 A1  
2 2
A  2
resid

67. Central Composite Design
RSM Graphics
Plateau

68. Robust Design Reliability
Focus on design transfer functions, ideal Focus on design dysfunction, failure modes, failure
functions times, mechanisms of failure
Engineering focus, empirical models, Mechanistic understanding, science oriented
Generic Models , statistics approach ,
Optimization of functions with Characterization of natural phenomena with root
verification testing requirement cause analysis and countermeasures
Orthogonal array testing, Design of Life tests, accelerated life tests, highly accelerated
Experiments planning tests, accelerated degradation tests, survival
Multitude of Control, noise, and signal Single factor testing, some multifactor testing .
factor combinations for reducing Fixed design with noise factors, acceleration
sensitivity to noise and amplifying factors
sensitivity to signal
Actively change design parameters to Design-Build-test-fix cycles for reliability growth
improve insensitivity to noise factors,
and sensitivity to signal factors

69. Robust Design Reliability
Failure inspection only with verification Design out failure mechanisms.
testing of improved functions Reduce variation in product strength. Reduce the
effect of usage/environment.
Synergy with axiomatic design Simplify design complexity for reliability
methodology including ideal design, improvement. Reuse reliable hardware
and simpler design
Hierarchy of limits including functional Identify & Increase design margins, HALT & HASS
limit, spec limit, control limit, testing to flesh out design weaknesses.
adjustment limits Temperature & vibration stressors predominate
Measurement system and response Time-to-failure quantitative measurements
selection paramount supported by analytic methods
Ideal function development for energy Fitting distributions to stochastic failure time data
relate measures Time compression by stress application
Compound noise factors largest stress. HALT & HASS highly accelerated testing to
Reduce variability to noise factors by reveal design vulnerabilities and expand margins.
interaction between noise and control Root cause exploration and mitigation
factors, signal and noise factor.

70. Summary
• RD methods and Reliability methods both have functionality at their
core. RD methods attempt to optimize the designs toward ideal function,
diverting energy from creating problems and dysfunction. Reliability
methods attempt to minimize dysfunction through mechanistic
understand and mitigation of the root causes for problems.
• RD methods actively change design parameters to efficiently and cost
effectively explore viable design space. Reliability methods subject the
designs to stresses, accelerating stresses, and even highly accelerated
stresses, [to improve time and cost of testing]. First principle physical
models are considered where available to predict stability.
• Both RE and RD methods have strong merits, and learning when and
how to apply each is a great advantage to product engineering teams.

71. Thanks for you kind attention 
• Presentation .PDF will be available shortly on
www.opsalacarte.com
• Send comments and questions to
Loul@opsalacarte.com

72. APPENDIX

73. Upcoming Reliability Webinars
Our next FREE Webinar will be on: June 6, 2012
Which topic would you like to see?
1. Choosing the right Accelerated Life Test
2. Getting the most out of your FMEA
3. ROI for Design for Reliability
4. ROI for HALT Calculator
Send answer to : mikes@opsalacarte.com