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Context of Robust Design Don Clausing Fig. 1 © Don Clausing 1998
An automatic document handler (ADH) was developed at the SS level. When integrated into the total system there were many new problems. The TQM Problem Solving Process was used, and many problems were solved. However, at the Field Readiness Test (FRT) before entering production the reliability was 15X worse than acceptable. Fig. 2 © Don Clausing 1998 Case study
What should they do next? What should be done in the future to avoid the same dysfunctional path? What is the fundamental problem? Fig. 3 © Don Clausing 1998 Case study questions
Bomb alert! Concept Design Ready Technology Stream Too much dependence on reactive improvement Fig. 4 © Don Clausing 1998 FRT Produce
Improvement to avoid bombs TSTS SS I – PROACTIVE IMPROVEMENT REACTIVE IMPROVEMENT R: requirementsC: concept TS: total system SS: subsystemPP: piece parts Fig. 5 © Don Clausing 1998 IMPROVE MENT R C I TECHNOLOGY PP
Proactive improvement Yea, we think that proactive is good! Fig. 6 © Don Clausing 1998
What is wrong here? Concept Design Ready Technology Stream Fig. 7 © Don Clausing 1998 FRT Produce
Rework– how much is enough? Ready for Production Build/Test/Fix Produce Fig. 8 © Don Clausing 1998 Design Complete
Build/test/fix –why? Reactive problem solving – Too little – limited scope of solutions – Too late Design contains many unsolved problems Biggest problem is lack of robustness – System works well in favorable conditions – But is sensitive to noises – unfavorable conditions that inevitably occur Fig. 9 © Don Clausing 1998
Proactive problem solving Must shift from emphasis on build/test/fix Must address effects of noises – Erratic performance – Leads to delusionary problem solving; chases problem from one failure mode to another Fig. 10 © Don Clausing 1998
Noises Affect performance – adversely IPDT cannot control – examples: – Ambient temperature – Power-company voltage – Customer-supplied consumables Noises lead to erratic performance IPDT: Integrated product development team Fig. 11 © Don Clausing 1998
Failure modes Noises lead to failure modes (FM) One set of noise values leads to FM 1 Opposite set of noise values leads to FM 2 Simple problem solving chases the problem from FM 1 to FM 2 and back again, but does not avoid both FMs with the same set of design values – endless cycles of build/test/fix (B/T/F) Fig. 12 © Don Clausing 1998
Performance; favorable conditions Variation during Lab conditions FM 1 No No problem problem FM 2 © Don Clausing 1998 Fig. 13
Variation during Lab conditions Simple problem solving FM 1 No problem Initial proble m FM 2 © Don Clausing 1998 Fig. 14
Variation during Lab conditions Simple problem solving FM 1 No problem FM 2 © Don Clausing 1998 Fig. 15
Variation during Lab conditions Simple problem solving FM 1 No problem FM 2 © Don Clausing 1998 Fig. 16
Variation during Lab conditions Simple problem solved FM 1 No problem FM 2 © Don Clausing 1998 Fig. 17 Problem solved
Much more difficult problem FM 1 No problem FM 2 © Don Clausing 1998 Fig. 18 Initial problem Performance variation with factory and field noises
Simple solution FM 1 FM 2 © Don Clausing 1998 Fig. 19 Look, no problem!
Oops! FM 1 FM 2 © Don Clausing 1998 Fig. 20 Look, no problem! New problem
Build/test/fix FM 1 FM 2 © Don Clausing 1998 Fig. 21 Look, no problem! New problem B/T/F chases problems from FM 2 to FM 1 – and back again
Build/test/fix FM 1 FM 2 © Don Clausing 1998 Fig. 22 B/T/F chases problems from FM 2 to FM 1 – and back again
Build/test/fix FM 1 FM 2 © Don Clausing 1998 Fig. 23 B/T/F chases problems from FM 2 to FM 1 – and back again
Build/test/fix FM 1 FM 2 © Don Clausing 1998 Fig. 24 B/T/F chases problems from FM 2 to FM 1 – and back again
Build/test/fix FM 1 FM 2 © Don Clausing 1998 Fig. 25 B/T/F chases problems from FM 2 to FM 1 – and back again
Build/test/fix FM 1 FM 2 © Don Clausing 1998 Fig. 26 B/T/F chases problems from FM 2 to FM 1 – and back again
Build/test/fix FM 1 FM 2 © Don Clausing 1998 Fig. 27 B/T/F chases problems from FM 2 to FM 1 – and back again
Build/test/fix FM 1 FM 2 © Don Clausing 1998 Fig. 28 B/T/F chases problems from FM 2 to FM 1 – and back again
Robustness solves problem FM 1 FM 2 Fig. 29 © Don Clausing 1998
Robustness makes money Robustness reduces performance variations Avoids failure modes Achieves customer satisfaction Also shortens development time – reduces build/test/fix Fig. 30 © Don Clausing 1998
Noises cause performance variations Noises are input variations that we cannot control They cause performance variations – Which cause failure modes – Lose customer satisfaction Example: temperature – affects performance of cars, chips, and many other products Fig. 31 © Don Clausing 1998
Three kinds of noises in products Environment – ambient temperature Manufacturing – no two units of production are exactly alike; machine-to-machine variation Deterioration – causes further variations in the components of the system Fig. 32 © Don Clausing 1998
Fig. 3# © Don Clausing 1998 Manufacturing noise in products Unit-to-unit variations Caused by noises in factory; e.g., – Temperature and humidity variations – Cleanliness variations – Material variations – Machine-tool and cutting-tool variations Factory can be made more robust; reduces one type of noise in product
Role of noises Traditional approach – Make product look good early – Keep noises small – Reactive problem solving does not explicitly address noises Proactive problem solving – Introduce realistic noises early – Minimize effect of noises – robustness Fig. 34 © Don Clausing 1998
Fig. 35 © Don Clausing 1998 Introduction of noises during development Product – Noises are often small in lab – Therefore must consciously introduce noises Factory – Noises naturally present during production trials – Operate in natural manner Dont take special care
Fig. 36 © Don Clausing 1998 Introduce product noises early Drive the performance away from ideal Do it early. Don't wait for the factory or customers to introduce noises IPDT needs to develop the skill of introducing these noises Management needs to design this into the PD process and check that it is done to an appropriate degree
Fig. 37 © Don Clausing 1998 Early introduction of noises goes against engineers culture of making product look good Two most important elements for success: – Early introduction of noises – Recognition that performance variation must be reduced – while noise values are large Cultural change
Problem prevention Concept Design Ready Introduce noises early Technology Stream ReduceVariations –then no Problems Fig. 38 © Don Clausing 1998
Fig. 39 © Don Clausing 1998 Integration of new technologies A1G1A1G1 B1B1 C1C1 F1F1 NEW TECHNOLOGY (NT) E 1 D1D1 A - G present new noises to NT – cause integration problems. Robustness enables smooth integration; minimizes build/test/fix.
Fig. 40 © Don Clausing 1998 Robust design Achieves robustness; i.e., minimizes effects of noises Proactive problem solving – robustness before integration Optimize values of critical design (control) parameters to minimize effects of noise parameters
The engineered system Signal System Response Control factors Noise Fig. 41 © Don Clausing 1998
Ideal response Want Ideal Response to Signal – usually straight-line function Actual response is determined by values of control factors and noise factors If noise factors are suppressed early, then difficult problems only appear late Introduce noises early! Fig. 42 © Don Clausing 1998
Fig. 43 © Don Clausing 1998 Actual response Ideal respons e Effect of noises M1M1 M2M2 SIGNAL RESPONSE
Fig. 44 © Don Clausing 1998 Robustness Keeps the performance (response) of the system acceptably close to the ideal function Minimizes effect of noise factors Key to proactive improvement
Fig. 45 © Don Clausing 1998 Purpose – to optimize the nominal values of critical system parameters; for example: – Capacitor is selected to be 100 pF – Spring is selected to be 55 N/mm Improves performance so that it is close to ideal – under actual conditions Parameter design
Signal/noise ratio Fig. 46 © Don Clausing 1998 Measure of deviation from ideal performance Based on ratio of deviation from straight line divided by slope of straight line Many different types – depends on type of performance characteristic Larger values of SN ratio represent more robust performance
Critical control parameters Strongly affect performance of the system IPDT can control (select) the value Fault trees help IPDT to identify Complex systems have hundreds of critical control parameters Fig. 47 © Don Clausing 1998 Note: IPDT is Integrated Product Development Team
Fig. 48 © Don Clausing 1998 Important noise strategy Not all sources of noise need to be used Identify key noise functional parameter; e.g. – Interface friction in paper stack – EM radiation in communications Specific source is not important Magnitude enables quick optimization – Specs on noise are not important – Worse noise in field is not important
Fig. 49 © Don Clausing 1998 Noise strategy NOIS E INPUT NOISE N IN SYSTEM OUTPUT NOISE N OUT SOURCE STRATEGY HOLD N IN CONSTANT MINIMIZE N OUT NOT IMPORTANT SPECIFIC SOURCE MAGNITUDE OF N IN
Fig. 50 © Don Clausing 1998 Successful noise strategy Enables quick optimization Provides best performance inherent in concept – Even when future noise sources change – Even when future noises are larger – Even when spec changes Performance is as robust as possible Future improvements will require new concept
Fig. 51 © Don Clausing 1998 Important steps in parameter design Define ideal performance Select best SN definition Identify critical parameters Develop sets of noises that will cause performance to deviate from ideal Use designed experiments to systematically optimize control parameters
Fig. 52 © Don Clausing 1998 Critical parameter drawing for paper feeder CONTACT: ANGLE: 0 DISTANCE: 12 MM WRAP ANGLE 45 o BELT: TENSION: 15 NEWTON WIDTH: 50 MM VELOCITY: 250 MM/SEC PAPER STACK VELOCITY: 300 MM/SEC STACK FORCE: 0.7 LB GUIDE: ANGLE: 45 MOUTH OPENING: 7 MM FRICTION: 1.0 RETARD: RADIUS: 25 MM FRICTION: 1.5 Optimized values of critical parameters guide the detailed design
Fig. 53 © Don Clausing 1998 Culture change Emphasize – Ideal function – Noise strategy – Parameter design Do it early! Be proactive!
Fig. 54 © Don Clausing 1998 Improvement activities Robust design – minimize variation – Parameter design – optimization of nominal values of critical design parameters – Tolerance design – economical precision around the nominal values Mistake minimization Three activities requiring very different approaches
Fig. 55 © Don Clausing 1998 Tolerance design Select economical precision Determines typical machine-to-machine variation around optimized nominal value Primary task is selection of production process (or quality of purchased component) – determines variation of production Then put tolerance on drawing
Fig. 56 © Don Clausing 1998 Mistake Minimization Mistakes are human errors – Diode is backwards – Cantilevered shaft has excessive deflection Mistake minimization approach: – Mistake prevention – Mistake elimination
Fig. 57 © Don Clausing 1998 Summary of improvement activities Robust design – Parameter design – optimization of nominal values of critical design parameters – Tolerance design – economical precision around the nominal values Mistake minimization
Fig. 58 © Don Clausing 1998 Planning for improvement –schedule Accept only robust technologies Complete optimization early – Critical parameter drawing displays requirements for detailed design – Detailed design objective is to make low-cost design that achieves optimized nominal values Do tolerance design during detailed design Also plan mistake minimization
Fig. 59 © Don Clausing 1998 Technology development Concept Design Prepare Produc e IMPROVE ROBUSTNESS CREATIV E WORK REJECT Technology Stream STRATEGY ROBUSTNESS SELECT C D R
Fig. 60 © Don Clausing 1998 Robust design timing Concept Design Ready SPD TD SVT PPD Produce QC Technology Stream PD PD – PARAMETER DESIGN, NEW PRODUCT & PROCESS TECHNOLOGIES SPD – SYSTEM (PRODUCT) PARAMETER DESIGN TD – TOLERANCE DESIGN SVT – SYSTEM VERIFICATION TEST PPD – PROCESS PARAMETER DESIGN QC – ON LINE QUALITY CONTROL (FACTORY FLOOR)
Fig. 61 © Don Clausing 1998 Inspection for robustness Have noises been applied? Have all failure modes been exercised? Has optimization made the failure modes more difficult to excite? Has head-on comparison been made with benchmark? – Same set of noises applied to both – Our system (or subsystem) has better robustness
Fig. 62 © Don Clausing 1998 Mistake minimization Concept Design Ready KNOWLEDGE-BASED ENGINEERING PROACTIVE CONCURRENT ENGR. REUSABILITY PROBLEM SOLVING PROCESS Produce Technology Stream PROACTIVE REACTIVE
Fig. 63 © Don Clausing 1998 Quality and reliability Robust design plus mistake minimization is the effective approach to the improvement of quality/reliability - usually also leads to the lowest total cost Q & R are not separate subjects – manage robust design and mistake minimization and Q & R are the result
Fig. 64 © Don Clausing 1998 Summary Early development of robustness is key to proactive improvement – Early application of noises – Optimize robustness – avoid all failure modes Supplement with tolerance design and mistake minimization
Fig. 65 © Don Clausing 1998 Benefits of robust design Shorter time to market Customer satisfaction – performance closer to ideal Reduced manufacturing cost Flexible integration of systems – responsiveness to the market
Fig. 1 © Don Clausing 1998 Control and noise factors Don Clausing Red border: emphasized slides.
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