Robust design and reliability engineering synergy webinar 2013 04 10

1,662 views

Published on

Published in: Technology, Business

Robust design and reliability engineering synergy webinar 2013 04 10

  1. 1. This is the first of a series of four webinars beingput on by Ops A La Carte, ASTR, and ASQReliability DivisionEach webinar will also be presented as a full 2 hourtutorial at our ASTR Workshop Oct 9-11th, San Diego. Abstracts for presentations are due Apr 30. www.ieee-astr.org
  2. 2. &   Accelerated  Stress  Tes-ng  and  Reliability  Workshop   October  9-­‐11,  2013                San  Diego,  CA   Accelerating Reliability into the 21st CenturyKeynote  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  
  3. 3. Robust Design andReliability Engineering Synergy BYLou  LaVallee,  Senior  Reliability  Consultant,  Ops  A  La  Carte  
  4. 4. Agenda•  Introduction 5 min•  Robust Design 45 min•  Questions 10 min
  5. 5. 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.  
  6. 6. Upcoming Reliability WebinarsTitle:    Prognos-cs  as  a  Tool  for  Reliable  Systems  Author:    Doug  Goodman  of  Ridgetop  GroupDate: May 1, 2013, 11:30am PDThaps://www2.gotomee,ng.com/register/657949994  Location: WebinarElectronics are the keystone to successful deployment ofcomplex systems (50+ MPUs in an automobile). Large MTBFand Statistical Process Control and Centering methods are notsufficient alone for reliability due to “outliers” (e.g. Toyota Prius,Deepwater Horizon Drilling Rig, Boeing 787). Ridgetoptechnology exists to pinpoint degrading systems before theyfail; supporting operational readiness objectives and cost-saving Prognostics/Health Management (PHM) and ConditionBased Maintenance (CBM) initiatives.
  7. 7. Upcoming Reliability WebinarsTitle:    Accelerated  Reliability  Growth  Tes-ng  Author:    Milena  Krasich  of  Raytheon  IDS  Date: June 12, 2013, 8:30am PDThaps://www2.gotomee,ng.com/register/283538530  Location: WebinarThis  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.  
  8. 8. Webinar Stats•  This  is  our  27th  Webinar    •  (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.    
  9. 9. Registration Demographics•  For this webinar we have signed up – 200 Registrants – 17 Countries – 28 US States
  10. 10. Registration Question #1•  Have you ever practiced Robust Design Engineering?
  11. 11. Registration Question #2•  Would you say you follow the philosophy of Robust Design or Design for Reliability more?
  12. 12. Robust Design andReliability Engineering Synergy BYLou  LaVallee,  Senior  Reliability  Consultant,  Ops  A  La  Carte  
  13. 13. Agenda•  Background/Introduction 5 min•  Robust Design 45 min•  Questions 10 min
  14. 14. Robust  or  Just  Strong      Dangerous  
  15. 15. 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        
  16. 16. Abstract for full tutorial Robust Design (RD) Methodology is discussed forhardware development. Comparison is made with reliabilityengineering (RE) tools and practices. Differences andsimilarities are presented. Proximity to ideal function for robust design is presented andcompared to physics of failure and other reliability modelingand prediction approaches. Measurement selection is shownto strongly differentiates RD and reliability engineeringmethods When and how to get the most from eachmethodology is outlined. Pitfalls for each set of practices arealso covered. (This presentation is a preview of a largerpresentation to be delivered @ ASTR conference, October,San Diego)
  17. 17. Choice of Many Design Methods Interfaces AXD TRIZ QFD DFR PUGH DOE ROBUST DESIGN VA/VE DFSS 6σ CP/CS MNGMT
  18. 18. RD ≠ Reliability Life Tests P-diagram     Root cause Tolerance Design Expt  Layout   Analysis Ideal Function Physics of Failure DOE RCM Response  Tuning   Engineering   Maintainability CBM 6σ FlexibilityLean   Scienc Warranty e Robust Design Simulation ReliabilityQuality Tes,ng  Loss Math Models Reuse   FMECA transformability Planning HALT/HASS   S/N RSM   ADT Life prediction Redundancy   Online QC ALT Parameter design FTA Availability Generic Function RBD
  19. 19. Robust Design DefinitionA systematic engineering based methodology(which is part of the Quality Engineering Process)that develops and manufactures high reliabilityproducts at low cost with reduced delivery cycle.The goal of robust design is to improve RDproductivity and reduce variation while maintaininglow cost before shipment and minimal loss to societyafter shipment.Dr Taguchi , who died this year, always used to say “lets find a way toimprove reliability without measuring reliability”
  20. 20. Defini-ons  Robustness is… “The ability to transform input to output as closely toideal function as possible. Proximity to ideal function ishighly desirable. A design is more robust if ratio of usefulpart to harmful part [of input energy ] is large. A designis more robust if it operates close to ideal, even whenexposed 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”
  21. 21. 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,…)
  22. 22. 6σ Fundamental Concept Y=f(X)+e Ø Response Y Ø X1, X2,…,XN Ø Dependent Ø Independent Ø Output Ø Input Ø Effect = Ø Cause Ø Symptom Ø Problem Ø Monitor Ø ControlIn reliability engineering for example , Y is the continuousstochastic variable (time-to-failure) and f(x) is the failuremechanism, or mechanistic model . In RD, smoothtransformability between input and output is most important.
  23. 23. Reliability GrowthHistorically, the reliability growth process has been treatedas, a reactive approach to growing reliability based onfailures “discovered” and fixed during testing or, mostunfortunately, once a system/product has been delivered toa customer. This reactive approach ignores opportunities togrow reliability during the earliest design phases of asystem or product. Delayed fix MTBF jump New build jump Cumulative test time
  24. 24. RobustnessGrowth S/NFactors Can be changed today time S/NFactors Can be changed in 1 week time S/N Competition at launchFactors Can be changed in 2 weeks Robustness gains time
  25. 25. Progression of Robustness to Ideal Function Development A   B   C   LSL USL Zero Defects Cpk Static S/N Dynamic S/N RatioWhen a product’s performance deviates from target, its qualityis considered inferior. Such deviations in performance causelosses to the user of the product, and in varying degrees to therest of society.
  26. 26. 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                    
  27. 27. Taxonomy of Design Function --P Diagram Useful Input Output Main Function signals Y=f(x)+ε Mi Harmful Output Noise Control Factors Factors
  28. 28. Spring  Example  
  29. 29. Simple  Metal    Helical  Compression    Spring            Force  vs  Displacement  ideal  Func,on  Force  F   Ideally,    all  points  fall  on  dashed  line   passing  through  origin.   Noise  factors  add  varia,on   Varia,on  may  exceed    tolerable   0,0   Displacement  limits      x    (mm)  
  30. 30. Simple Helical Spring Design Useful Input signal Main Function Output F X F=-kX+e Y = βM + ε Zero Point Proportional Ideal Function Forc Ideal (Hooke’s Law) e N Actual with Noise Factor effect 0,0   Displacement X (mm)
  31. 31. Transformability Robustness Improvement Before and After ImprovementResponse 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 robustness reliability. Sensitivity increase (tuning) can be used for power reduction, which also improves reliability. Tuning to different spring constants enabled
  32. 32. Typical Failure Modes and Failure Causes for Mechanical Springs TYPE OF SPRING/ FAILURE MODES FAILURE CAUSES STRESS CONDITION - Load loss - Static (constant - Parameter change - Creep deflection or constant - Hydrogen embrittlement -Compression Set load) - Yielding - Fracture - Damaged spring end - Corrosive atmosphere- Cyclic (10,000 cycles or - Fatigue failure - Misalignment more during - Buckling - Excessive stress range of the life of the spring) - Surging reverse stress ** - Complex stress change - Cycling temperature as a function of time … - Dynamic (intermittent - Surface defects - Fracture - Excessive stress range of occurrences of - Fatigue failure a load surge) reverse stress - Resonance surging
  33. 33.                                      Ideal  Func-on    Failure  Modes          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.                      
  34. 34. 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   Improvements  in    Product/ op-miza-on  ,    not  dysfunc,on.     Process  Varia,on   Reduced  complexity  of  design   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   Beaer   degrada,on.     Good    5.  Develop  robustness  against  noise   LFL   Target   UFL   factor  ‘,me’  –  not  a  life  test  
  35. 35. Useful Input signal Main Function Output M Y=f(x)+ε Main function is to transform input signal to useful output.Energy transformation takes many different forms, (but usuallynot 2nd order polynomials, as in RSM) Common Ideal Function Forms: Y = M +ε Y = [β + β * (M * − M * )]M + ε Y = βM + ε Y = 1 − e − βM + ε Y − Y0 = β ( M − M 0 ) + ε Y = β M x + ε Y = α + βM + ε YY = (R + jX )(R − jX ) ) + ε Y = β M 1M 2 + ε M1 ( Y =α + β M − M +ε ) Y =β +ε M2 ...
  36. 36. Ideal  Func,on  Examples  
  37. 37. Automotive Brake Example One Signal FactorY Ideal Y Observe d Ideal Function=Y=βM Y=Torque Generated M=Master cylinder Pressure M M Two  Signal  Factors   Ideal Function=Y=βMM*Y Ideal Y Observe d Y=Torque Generated M=Master cylinder Pressure M*= Pad surface area MM* MM*
  38. 38. Braking Ideal Function=Y=βMM* M*=Pad surfaceareaControl factors: M=master cylinderpressure, Raw Materials Raw material prep process parameters Pad manufacturing process parameters Dimensions, …Symptoms side effects (ideally zero) : 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
  39. 39. Measurement System Ideal Function Y=βM+e M=true value of measurand Y=measured valueAuto Steering Ideal function Y=βM+e M=steering wheel angle Y=Turning radiusCommunication system ideal function Y=M+e M=signal sent Y=signal receivedCantilever beam Ideal Function Y=βM/M*+e M=Load M*=Cross sectional areaFuel Pump Ideal Function Y=βM Y=Fuel volumetric flow rate M=IV/P current, voltage, backpressure
  40. 40. 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      
  41. 41. Comparison     Robust  Design   Reliability    Focus on design transfer functions, Focus on design dysfunction, failure modes,ideal function development failure times, mechanisms of failureEngineering focus, empirical models, Mechanistic understanding, science orientedGeneric Models , statistics approachOptimization of functions with Characterization of natural phenomena withverification testing requirement root cause analysis and countermeasuresOrthogonal array testing, Design of Life tests, accelerated life tests, highlyExperiments planning accelerated tests, accelerated degradation survival tests,Multitude of Control, noise, and Single factor testing, some multifactor testing .signal factor combinations for Fixed design with noise factors, accelerationreducing sensitivity to noise and factorsamplifying sensitivity to signalActively change design parameters Design-Build-test-fix cycles for reliabilityto improve insensitivity to noise growthfactors, and sensitivity to signalfactors
  42. 42. Robust  Design   Reliability    Failure inspection only with Design out failure mechanisms.verification testing of improved Reduce variation in product strength. Reducefunctions the effect of usage/environment.Synergy with axiomatic design Simplify design complexity for reliabilitymethodology including ideal design, improvement. Reuse reliable hardwareand simpler designHierarchy of limits including Identify Increase design margins, HALT functional limit, spec limit, control HASS testing to expose design weaknesses.limit, adjustment limits Temperature vibration stressors predominateMeasurement system and response Time-to-failure quantitative measurementsselection paramount supported by analytic methodsIdeal function development for Fitting distributions to stochastic failure timeenergy relate measures data Time compression by stress applicationCompound noise factors largest HALT HASS highly accelerated testing tostress. Reduce variability to noise reveal design vulnerabilities and expandfactors by interaction between noise margins. Root cause exploration andand control factors, signal and noise mitigationfactor.
  43. 43. 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.
  44. 44. Ques,ons?  •  Slides  will  be  made  available  on  ASQ  website    •  E-­‐Mail  ques,ons    comments  to   Loul@opsalacarte.com  •  Thanks  for  your  ,me  and  aaen,on  
  45. 45. Poll  Ques-on  #4  Do  you  think  you  will  be  able  to  come  to  ASTR  Oct  9-­‐11  in  San  Diego  ?   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  
  46. 46. Poll  Ques-on  #5  If  you  answered  “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  ?  
  47. 47. Poll  Ques-on  #6  If  you  are  considering  joining  ASTR  via  webinar   a)  Would  you  want  streaming  video  of  audience  and   presenter  ?   b)  Would  you  want  webconference  only  ?   c)  Would  you  want  both?  
  48. 48. Our  Next  FREE  Webinar  will  be  on  May   1st  on       Prognos-cs  as  a  Tool  for     Reliable  Systems     haps://www2.gotomee,ng.com/register/657949994     (special  ediIon  presented  through  ASTR/ASQ  RD)  
  49. 49. After signing off the webinar, youwill be asked to take a quick 3minute surveyIf you fill out survey, you will receiveslides and webcast of broadcast.
  50. 50. Contact  Informa-on   Ops  A  La  Carte,  LLC     Mike  Silverman   Managing  Partner   (408)  472-­‐3889   mikes@opsalacarte.com   www.opsalacarte.com  
  51. 51. QuestionsThank you for your attention.What questions do you have?

×