Robust design and reliability engineering synergy webinar 2013 04 10
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
& 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: firstname.lastname@example.org. 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
Robust Design andReliability Engineering Synergy BYLou LaVallee, Senior Reliability Consultant, Ops A La Carte
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 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.
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.
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.
Registration Demographics• For this webinar we have signed up – 200 Registrants – 17 Countries – 28 US States
Registration Question #1• Have you ever practiced Robust Design Engineering?
Registration Question #2• Would you say you follow the philosophy of Robust Design or Design for Reliability more?
Robust Design andReliability Engineering Synergy BYLou LaVallee, Senior Reliability Consultant, Ops A La Carte
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 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)
Choice of Many Design Methods Interfaces AXD TRIZ QFD DFR PUGH DOE ROBUST DESIGN VA/VE DFSS 6σ CP/CS MNGMT
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
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”
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”
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,…)
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.
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
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
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.
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 Useful Input Output Main Function signals Y=f(x)+ε Mi Harmful Output Noise Control Factors Factors
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)
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)
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
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
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.
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
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 ...
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*
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
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
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 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
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.
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.
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
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
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 ?
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?
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)
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.
Contact Informa-on Ops A La Carte, LLC Mike Silverman Managing Partner (408) 472-‐3889 email@example.com www.opsalacarte.com
QuestionsThank you for your attention.What questions do you have?