![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”](https://image.slidesharecdn.com/robustdesignandreliabilityengineeringsynergywebinar-20130410-130412192015-phpapp02/75/Robust-design-and-reliability-engineering-synergy-webinar-2013-04-10-21-2048.jpg)
![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 usually
not 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 ...](https://image.slidesharecdn.com/robustdesignandreliabilityengineeringsynergywebinar-20130410-130412192015-phpapp02/75/Robust-design-and-reliability-engineering-synergy-webinar-2013-04-10-36-2048.jpg)
![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.](https://image.slidesharecdn.com/robustdesignandreliabilityengineeringsynergywebinar-20130410-130412192015-phpapp02/75/Robust-design-and-reliability-engineering-synergy-webinar-2013-04-10-44-2048.jpg)
Uploaded byASQ Reliability Division
Robust design and reliability engineering synergy webinar 2013 04 10
This webinar discusses robust design and reliability engineering. It is presented by Lou LaVallee, a senior reliability consultant from Ops A La Carte. The webinar will provide a 45-minute presentation on robust design principles followed by a 10-minute question and answer session. Registration demographics show over 200 attendees from 17 countries and 28 US states registered. The webinar is part of a monthly series hosted by Ops A La Carte, ASTR, and ASQ Reliability Division to discuss reliability topics.
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Robust design and reliability engineering synergy webinar 2013 04 10
- 2. This is thefirst 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 9-11th, San Diego. Abstracts for presentations are due Apr 30. www.ieee-astr.org
- 3. & Accelerated Stress Tes-ng and Reliability Workshop October 9-‐11, 2013 San Diego, CA Accelerating Reliability into the 21st 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
- 4. Robust Design and ReliabilityEngineering Synergy BY Lou LaVallee, Senior Reliability Consultant, Ops A La Carte
- 5. Agenda • Introduction 5 min • Robust Design 45 min • Questions 10 min
- 6. 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.
- 7. 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 cost- saving Prognostics/Health Management (PHM) and Condition Based Maintenance (CBM) initiatives.
- 8. Upcoming Reliability Webinars 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.
- 9. 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.
- 10. Registration Demographics • Forthis webinar we have signed up – 200 Registrants – 17 Countries – 28 US States
- 11. Registration Question #1 • Have you ever practiced Robust Design Engineering?
- 12. Registration Question #2 • Would you say you follow the philosophy of Robust Design or Design for Reliability more?
- 13. Robust Design and ReliabilityEngineering Synergy BY Lou LaVallee, Senior Reliability Consultant, Ops A La Carte
- 14. Agenda • Background/Introduction 5 min • Robust Design 45 min • Questions 10 min
- 15. Robust or Just Strong Dangerous
- 16. 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
- 17. Abstract for fulltutorial 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)
- 18. Choice of ManyDesign Methods Interfaces AXD TRIZ QFD DFR PUGH DOE ROBUST DESIGN VA/VE DFSS 6σ CP/CS MNGMT
- 19. 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 Flexibility Lean Scienc Warranty e Robust Design Simulation Reliability Quality 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
- 20. Robust Design Definition Asystematic 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 RD 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”
- 21. 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”
- 22. Variation is theEnemy 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,…)
- 23. 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 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.
- 24. Reliability Growth Historically, thereliability 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 MTBF jump New build jump Cumulative test time
- 25. Robustness Growth S/N Factors Can be changed today time S/N Factors Can be changed in 1 week time S/N Competition at launch Factors Can be changed in 2 weeks Robustness gains time
- 26. Progression of Robustnessto 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.
- 27. 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
- 28. Taxonomy of DesignFunction --P Diagram Useful Input Output Main Function signals Y=f(x)+ε Mi Harmful Output Noise Control Factors Factors
- 29.
- 30. 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)
- 31. Simple Helical SpringDesign 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)
- 32. Transformability RobustnessImprovement Before and After 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 robustness reliability. Sensitivity increase (tuning) can be used for power reduction, which also improves reliability. Tuning to different spring constants enabled
- 33. Typical Failure Modesand 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
- 34. 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.
- 35. 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
- 36. 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 usually not 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 ...
- 37.
- 38. Automotive Brake Example One Signal Factor Y 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*
- 39. Braking Ideal Function=Y=βMM* 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 (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
- 40. Measurement System IdealFunction Y=βM+e M=true value of measurand Y=measured value Auto Steering Ideal function Y=βM+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=βM/M*+e M=Load M*=Cross sectional area Fuel Pump Ideal Function Y=βM Y=Fuel volumetric flow rate M=IV/P current, voltage, backpressure
- 41. 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
- 42. Comparison Robust Design Reliability Focus on design transfer functions, Focus on design dysfunction, failure modes, ideal function development failure 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 verification testing requirement root cause analysis and countermeasures Orthogonal array testing, Design of Life tests, accelerated life tests, highly Experiments 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, acceleration reducing sensitivity to noise and factors amplifying sensitivity to signal Actively change design parameters Design-Build-test-fix cycles for reliability to improve insensitivity to noise growth factors, and sensitivity to signal factors
- 43. Robust Design Reliability Failure inspection only with Design out failure mechanisms. verification testing of improved Reduce variation in product strength. Reduce functions 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 Identify Increase design margins, HALT functional limit, spec limit, control HASS testing to expose design weaknesses. limit, 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 Fitting distributions to stochastic failure time energy relate measures data Time compression by stress application Compound noise factors largest HALT HASS highly accelerated testing to stress. Reduce variability to noise reveal design vulnerabilities and expand factors by interaction between noise margins. Root cause exploration and and control factors, signal and noise mitigation factor.
- 44. Summary • RD methodsand 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.
- 45. 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
- 46. 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
- 47. 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 ?
- 48. 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?
- 49. 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)
- 50. After signing offthe webinar, you will be asked to take a quick 3 minute survey If you fill out survey, you will receive slides and webcast of broadcast.
- 51. Contact Informa-on Ops A La Carte, LLC Mike Silverman Managing Partner (408) 472-‐3889 mikes@opsalacarte.com www.opsalacarte.com
- 52. Questions Thank you foryour attention. What questions do you have?