This is the first of a series of four webinars being put on by Ops A La Carte, ASTR, and ASQ
Reliability Division
Each webinar will also be presented as a full 2 hour tutorial at our ASTR Workshop Oct 9-11 th , San Diego.
Abstracts for presentations are due Apr 30.

Accelerated  Stress  Tes-ng  and  Reliability  Workshop
October  9-­‐11,  2013                San  Diego,  CA
Accelerating Reliability into the 21 st Century
Keynote  Presenter  Day  1:    Vice  Admiral  Walter  Massenburg
Keynote  Presenter  Day  2:    Alain  Bensoussan,    Thales  Avionics
CALL  FOR  PRESENTATIONS:     We  are  now  Accep,ng  Abstracts.
Email  to:   don.gerstle@gmail.com
.
Guidelines  on  website   www.ieee-­‐astr.org
For  more  details,  click  here  to  join  our  LinkedIn  Group:
IEEE/CPMT  Workshop  on  Accelerated  Stress  TesIng  and  Reliability

BY
Lou  LaVallee,  Senior  Reliability  Consultant,  Ops  A  La  Carte

•   Introduction
•   Robust Design
•   Questions
5 min
45 min
10 min

Lou  LaVallee,  CRE,  Senior  Reliability/Quality  Consultant
•   Lou  has  over  30  years  of  experience  as  a  quality  and  reliability  engineer.
•   Lou  has  a  strong  technical  background  in  physics,  engineering  materials/
polymer  science  and  a  solid  grounding  in  consumer  product  design,
development,  and  delivery.
•   His  comprehensive  background  includes  electronic  films  ,  robust  design,  modeling  &   analy,cs,  cri,cal  parameter  management,  six  sigma  DFSS  &  DMAIC,  op,miza,on  of  product   quality/reliability,  experimental  design,  reliability  test  methods,  and  design  tool  development   and  deployment.
•   He  successfully  managed  systems  engineering  groups  for  development  of  ink  jet  print  heads   at  Xerox  Corp.
•   Mr.  LaVallee  has  held  other  technical  management  posi,ons  in  manufacturing  technology,   engineering  excellence  (trained  several  thousand  engineers  worldwide).  He  also  managed   the  robust  engineering  center  at  Xerox  for  10  years,  managed  a  high  volume  prin,ng  product   quality  and  reliability  group,  and  worked  extensively  with  high  volume  prin,ng  product   service  organiza,on.
•   He  has  strong  valida,on  experience  of  design  quality  and  reliability  through  product  reviews   and  customer  interac,on
•   Mr.  LaVallee  holds  a  Bachelor  of  Science  degree  in  Physics  (BS),  and  an  MS  from  the
University  of  Rochester  in  materials/polymer  engineering.
•   He  holds  several  U.S.  patents  involving  fluidics  and  engineering  design  processes.
•   Mr.  LaVallee  is  an  ASQ  cer,fied  reliability  engineer.  Lou  works  in  the  upstate  New  York  area.

Upcoming Reliability Webinars
Title:    Prognos-cs  as  a  Tool  for  Reliable  Systems
Author:    Doug  Goodman  of  Ridgetop  Group
Date: May 1, 2013, 11:30am PDT haps://www2.gotomee,ng.com/register/657949994
Location: Webinar
Electronics are the keystone to successful deployment of complex systems (50+ MPUs in an automobile). Large MTBF and Statistical Process Control and Centering methods are not sufficient alone for reliability due to “outliers” (e.g. Toyota Prius,
Deepwater Horizon Drilling Rig, Boeing 787). Ridgetop technology exists to pinpoint degrading systems before they fail; supporting operational readiness objectives and costsaving Prognostics/Health Management (PHM) and Condition
Based Maintenance (CBM) initiatives.

Title:    Accelerated  Reliability  Growth  Tes-ng
Author:     Milena  Krasich  of  Raytheon  IDS
Date: June 12, 2013, 8:30am PDT haps://www2.gotomee,ng.com/register/283538530
Location: Webinar
This  webinar  will  cover  the  following:
1)  Reliability  Growth  Test  overview/objec,ves
2)  Explain  tradi,onal  Reliability  Growth  test  methodology
3)  Show  shortcomings  of  the  tradi,onal  methods
4)  Show  principles  of  the  Physics  of  Failure  test  methodology
5)  Show  how  the  Reliability  growth  test  based  on  PoF  is  constructed
6)  Show  how  the  expected  stresses  are  applied  and  accelerated
7)  Show  reliability  measures
8)  Show  advantages  of  the  test  PoF  test  design  and  accelera,on
9)  Show  achieved  considerable  test  cost  reduc,on.

•
th
•   (see  Ops  site  for  past  webinar  topics/content  at   hap://www.opsalacarte.com/Pages/resources/resources_techpapers.htm#webinars
•   We  run  these  webinars  once  a  month
•   We  partner  with  other  companies
•   We  partner  with  socie,es
(IEEE,  ASQ,  and  others  for  broader  reach)
•   This  webinar  is  brought  to  you  by  Ops,  ASTR,  and  ASQ
Reliability  Division.
•   All  past  webinars  are  archived  on  our  site   www.opsalacarte.com/Reliapedia.

•   For this webinar we have signed up

•

•

BY
Lou  LaVallee,  Senior  Reliability  Consultant,  Ops  A  La  Carte

•   Robust Design
•   Questions
45 min
10 min

Robust  or  Just  Strong    &  Dangerous

Polling  Ques,on  1:
For  engineering  ac,vi,es  in  hardware  development,  when  do  you   typically    start  to  act  on  design  robustness  and  reliability  concerns.
a)    When  field  problems    and  customer  complaints  begin.
b)    When  system  and  subsystem  DVT  tests  indicates    hardware
failures
c)    When  technology  readiness  tests  indicate  hardware  failures
d)    When  concepts  and  architecture    are  being  selected

Abstract for full tutorial
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. (This presentation is a preview of a larger presentation to be delivered @ ASTR conference, October,
San Diego)

Choice of Many Design Methods & Interfaces
AXD
TRIZ
QFD
DFR
PUGH
VA/VE
DOE
ROBUST
DESIGN
CP/CS MNGMT
DFSS 6
σ

≠
Quality
Loss
S/N
P-diagram
Tolerance Design transformability
Expt  Layout
Lean
6
σ
Ideal Function
Response  Tuning
Flexibility
Robust Design
Reuse
RSM
Online QC
Parameter design
DOE
Engineering
Scienc e
Simulation
Math Models
Planning
ADT
Life Tests
Root cause
Analysis
RCM
Maintainability
ALT
Physics of Failure
FTA
CBM
Reliability
Warranty
FMECA
Life prediction
Tes,ng
HALT/HASS
Redundancy
Availability
Generic Function RBD

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.
Dr Taguchi , who died this year, always used to say “lets find a way to improve reliability without measuring reliability”

Defini-ons
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”

Variation is the Enemy of Robustness & Reliability
•   Search for root cause & eliminate it
•   Screen out defectives (scrap and rework, HASS)
•   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 reduce the power , component derating
•   Correct design mistakes (e.g. putting diodes in backwards, … )

6
σ
Fundamental Concept
Y=f(X)+e
Ø   Response Y
Ø   Dependent
Ø   Output
Ø   Effect
Ø   Symptom
Ø   Monitor
Ø   X
1
, X
2
, … ,X
N
Ø   Independent
Ø   Input
Ø   Cause
Ø   Problem
Ø   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 most important.

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
New build jump
Cumulative test time

Robustness
Growth
Factors Can be changed today
S/N time
Factors Can be changed in
1 week
S/N
Factors Can be changed in
2 weeks
S/N time
Competition at launch
Robustness gains time

Progression of Robustness to Ideal Function Development
LSL USL
Zero Defects
C pk
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.

Polling  ques,on  2
Have  you    ever  used  robust  design  methods  for  hardware   development  ?
a)    Yes,  it  is  a  part  of  our    group’s  engineering  culture
b)    Yes,  but  mostly  on  high  risk  issues
c)    Yes  but  mostly  on  low  risk  issues
d)    No  we  do  not  see  any    advantage  over  tradi,onal   build-­‐test-­‐fix  methods

Taxonomy of Design Function --P Diagram
Input signals
M i
Main Function
Y=f(x)+ ε
Useful
Output
Harmful
Output
Noise
Factors
Control
Factors

Force  F
Simple  Metal    Helical  Compression    Spring
Force  vs  Displacement  ideal  Func,on
0,0
Ideally,    all  points  fall  on  dashed  line   passing  through  origin.
Noise  factors  add  varia,on
Varia,on  may  exceed    tolerable   limits

Simple Helical Spring Design
Y =
M +
Input signal
X
Main Function
F=-kX+e
Useful
Output F
Zero Point Proportional Ideal Function
Forc e
N
Ideal (Hooke’s Law)
Actual with Noise Factor effect
0,0   Displacement X (mm)

Response
Transformability & Robustness Improvement
Before and After Improvement
Response
0,0
N
1
N
1
N
2
N
2
M           signal
0,0 M       signal
Minimizing the effects of noise factors on transformation of input to output . Improves robustness & reliability. Sensitivity increase (tuning) can be used for power reduction, which also improves reliability. Tuning to different spring constants enabled

Typical Failure Modes and Failure
Causes for Mechanical Springs
TYPE OF SPRING/
STRESS CONDITION
FAILURE MODES FAILURE CAUSES
- Static (constant deflection or constant load)
- Load loss
- Creep
-Compression Set
- Yielding
- Parameter change
- Hydrogen embrittlement
- Cyclic (10,000 cycles or more during the life of the spring)
- Fracture
- Damaged spring end
- Fatigue failure
- Buckling
- Surging
- Complex stress change as a function of time
- Corrosive atmosphere
- Misalignment
- Excessive stress range of reverse stress **
- Cycling temperature
…
- Dynamic (intermittent occurrences of a load surge)
- Fracture
- Fatigue failure
- Surface defects
- Excessive stress range of reverse stress
- Resonance surging

If  data  remain  close  to  ideal  func-on,  even  under  predicted   stressful    usage  condi-ons,  and    there  is  no  way  for  failure  to   occur  without    affec-ng  func-onal  varia-on  of    the  data,    then   moving  closer  to  ideal  func-on  is  highly  desirable.
For  example,  spring  fracture,  if  it  did  occur  would     drama-cally  change  force-­‐deflec-on  (F-­‐D)    data  and  inflate   data  varia-on.    Similarly,  for  yielding,  F-­‐D  results  would     change  and  inflate  the  varia-on.    Other  failure  modes    would     follow  in  most  cases.

Reliability  Improvement  with  Robust  Design  early  in  design  cycle
1.       Power  reduc-on   by  enabling  changes  in  sensi,vity
β
to  input  power  without   increasing  sensi,vity  to  noise
σ
.    (Higher  signal-­‐to-­‐noise  ra,o)
Higher
β
with  lower
σ .
2.     Reducing  varia-on   of  useful    and  harmful    output.    Prevent  ing  overlap  of  stress  PDF   with    strength    limits,    and  keeping  distribu,on    away  from  failure  limits
3.
Focus  on   energy  related  response   op-miza-on  ,     not  dysfunc,on.
Reduced  complexity  of  design
Improvements  in    Product/
Process  Varia,on
Best
4.
A  product  produced  off  target  is   inferior  to  one  produced   close  to   target ,  and  is  more  likely  to  have  later   reliability  issues  due  to  driq  and   degrada,on.
Beaer
Good
5.
Develop  robustness  against  noise   factor  ‘,me’  –  not  a  life  test
LFL
Target
UFL

Input signal
M
Main Function
Y=f(x)+ ε
Useful
Output
Main function is to transform input signal to useful output.
Energy transformation takes many different forms, (but usually not 2 nd order polynomials, as in RSM)
Common Ideal Function Forms:
Y = M + ε
Y
Y = β M + ε
Y
=
=
[
β
1 −
+ β
* ( M * e − β M
− M
+ ε
* ) ]
M + ε
Y
Y
− Y
0
= α
= β ( M
+ β M
− M
0
) + ε
+ ε
Y = β M x
+ ε
Y
Y
=
=
β M
1
M
2
β
M
1
M
2
+ ε
+ ε
Y
Y Y
=
=
α
( R
+ β
+ jX )( R −
( M − M )
+ jX
ε
) ) + ε
...

Y
Y
M
Automotive Brake Example
One Signal Factor
Ideal
Y
Observe d Ideal Function=Y= β M
Y=Torque Generated
M=Master cylinder
Pressure
M
Ideal
Two  Signal  Factors
Y Observe d
Ideal Function=Y= β MM*
Y=Torque Generated
M=Master cylinder
Pressure
M*= Pad surface area
MM*
MM*

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

Measurement System Ideal Function
Y=
β
M+e
M=true value of measurand
Y=measured value
Auto Steering Ideal function
Y=
β
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= β M/M*+e
M=Load
M*=Cross sectional area
Fuel Pump Ideal Function
Y=
β
M
Y=Fuel volumetric flow rate
M=IV/P current, voltage,& backpressure

Polling  ques,on  3
Has  your  organiza,on    ever  used  both  robust  design  methods   and  design  for  reliability  in  the  same  program?
a)    Yes,    we  have  used  both
b)      No,  only  used  RD
c)    No,  only  used  DFR
d)    No,  used  neither

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

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

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.

a)   Yes  and  I  plan  on  submitng  an  abstract  by
April  30   b)   Yes  I  will  come  but  will  not  be  submitng  an   abstract   c)   Maybe,  I  will  check  it  out   d)   No  because  I  have  no  ,me   e)   No  because  I  have  no  travel  budget

a)   Would  you  consider  joining  by  webinar  if  it  were  free  ?   b)   Would  you  consider  joining  by  webinar  if  it  was  <  $50  ?   c)   Would  you  consider  joining  by  webinar  if  it  was  $50-­‐100  ?   d)   Would  you  consider  joining  by  webinar  if  it  was  $100-­‐150  ?

a)   Would  you  want  streaming  video  of  audience  and   presenter  ?   b)   Would  you  want  webconference  only  ?   c)   Would  you  want  both?

st
haps://www2.gotomee,ng.com/register/657949994

s

Ops  A  La  Carte,  LLC
Mike  Silverman
Managing  Partner
(408)  472-­‐3889   mikes@opsalacarte.com
www.opsalacarte.com