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Fig. 1 © Don Clausing 1998 Control and noise factors Don Clausing Red border: emphasized slides
Fig. 2 © Don Clausing 1998 The engineered system Noise Signal System Response Contro l factors
Fig. 3 © 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. 4 © Don Clausing 1998 Actual response Ideal respons e M1 SIGNAL M2 Effect of noises RESPONSE
Fig. 5 © Don Clausing 1998 Response depends on: Value of signal factor Values of control factors – Engineers can select values – Examples: dimensions, electrical characteristics Values of noise factors – Engineers cannot select values – Examples: temperature, part variations
Fig. 6 © Don Clausing 1998 Critical control parameters Strongly affect performance of the system IPDT can control (select) the value Complex systems have hundreds of critical control parameters Fault trees help IPDT to identify Note: IPDT is Integrated Product Development Team
Fig. 7 © Don Clausing 1998 Fault tree for paper feeder MISFEED JAM DAMAGE BIG SLUG OVERLAP LARGE FRICTION DIFFERENC E FAIL TO FEED SINGLE SHEET MULTIFEED WEAK SEPARATION SMALL WRAP ANGL E LOW RETARD FRICTION SMALL RETARD RADIUS INITIALWEAR WEAK BELT TENSION µ pp α 0 α t µ rp T R Identification of critical parameters – both control and noise
Fig. 8 © Don Clausing 1998 Critical parameters at base of tree Both control and noise parameters Example of control factors: – Roll radius – Belt tension Example of noise factor: paper-to-paper friction, µ p-p
Fig. 9 © Don Clausing 1998 Noises Affect performance – adversely IPDT cannot control – examples: – Ambient temperature – Power-company voltage – Customer-supplied consumables IPDT must apply large magnitudes of noise early in the development schedule
Fig. 10 © Don Clausing 1998 Role of control factors Values of control factors determine response Many combinations of control-factors values will give same value for response One of these combinations will give the least sensitivity to undesirable variations (noises) Improvement is achieved by searching through the combinations of control-factors values to find the one that gives best performance
Fig. 11 © 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. 12 © Don Clausing 1998 Three kinds of product noise Environment; e.g., ambient temperature Manufacturing – no two units of production are exactly alike Deterioration – causes further variations in the components of the system
Fig. 13 © Don Clausing 1998 Searching for robustness 1.Select one combination of control factors values 2.Test performance when a set of noise values are applied: y 1, y 2 … y n are performance values for n combinations of noise 3.Change values of control factors; repeat 2. 4.Search through control-factor space, testing each point with same set of noises
Fig. 14 © Don Clausing 1998 Introduction of noises Natural noises are often small in laboratory environment; therefore slow to learn effect Therefore we consciously introduce large magnitufes of noises to obtain quick evaluation of effect of noises Example: (1) high temperature, high humidity, (2) low temperature, low humidity Measure y 1 and y 2
Fig. 15 © Don Clausing 1998 Effect of noises, 1 & 2 Y A.GOOD PERFORMANCE Y 1 Y 2 B. BAD PERFORMANCE FAILURE MODE 1 FAILURE MODE 1 Y 1 FAILURE MODE 2 FAILUR MODE 2 Y2Y2
Fig. 16 © Don Clausing 1998 Understanding performance Case A: Is good performance due to: – Good system? – Small magnitude of noises? Winning approach: – Apply large magnitudes of noises 1 & 2 to create Case B – Then improve values of control factors; B A – Increase noises; repeat improvement, B A
Fig. 17 © Don Clausing 1998 Path to success NOISE 1 Y 1 NOISE 2Y 2 Y 2 -Y 1 1 C F SET 1 MODERATE5 MODERATE C F SET 2 MODERATE 14 MODERATE C F SET 2 ST RONG8 ST RONG C F SET 3 ST RONG 14 ST RONG 16 2 CON TROL FAC TOR SE T IMPROVEMENT PATH: CF1 CF2 CF3
Fig. 18 © Don Clausing 1998 Noises and failure modes Apply one noise, N i, for each failure mode Example: combination of resistances, capacitances, transistor characteristics, and temperature have strong effect on performance – Adjust values, N 1, to cause low voltage out, Y 1 – Adjust values, N 2, to cause high voltage out, Y 2 Then optimize control factors; minimize Y 2 -Y 1
Fig. 19 © Don Clausing 1998 Fundamental noise situation EFFECT OF ALL NOISES IN WORLD TYPICAL LAB VARIATION FORCE FAILURE MODES PUT IN NOISES FAILURE MODE 1 FAILURE MODE 2 Y
Fig. 20 © Don Clausing 1998 Robustness improvement Y PERFORMANCE VARIATION BEFORE IMPROVEMENT FAILUR E MODE 1 PERFORMANCE VARIATION AFTER IMPROVEMENT FAILURE MODE 2
Fig. 21 © 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, µp-p – 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. 22 © Don Clausing 1998 Noise strategy NOISE SOURCE INPUT NOIS E N IN SYSTEM STRATEGY OUTPUT NOISE N OUT HOLD N IN CONSTANT MINIMIZE N OUT NOT IMPORTANT SPECIFIC SOURCE MAGNITUDE OF N IN
Fig. 23 © Don Clausing 1998 Example of noise strategy Paper feeder failiure modes: Sheet 1 arrives too soon: paper jam Sheet 1 arrives too late: misfeed Sheet 2 arrives too soon: multifeed – Caused by large change in paper friction, µ p-p – Strategy for implementation during improvement?
© Don Clausing 1998 Fig. 24 Introduce large µ p-p F N µ 1-2 Sheet 2 µ p-p = µ µ 2-3 µ p-p > 0 causes sheet 2 to move F N µ 2-3
© Don Clausing 1998 Fig. 25 Noises for multifeeds Make µ p-p large, 0.1 Will cause many multifeeds; enable quick improvement Ignore – Paper brands – Customer usage, etc. Concentrate on creating paper stack with large µ p-p
© Don Clausing 1998 Fig. 26 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
© 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. 27
© Don Clausing 1998 Critical control-factor range IPDT judges best nominal value Then select larger value and smaller value – feasible but significant changes Gives 3 values for each of 13 parameters 3 13 =1,594,323 candidate sets of values Must then choose trials to systematically probe 1,594,323 candidates Fig. 28
© Don Clausing 1998 Optimization Fig. 29 Use designed experiments to select 27 out of 1,594,323 control-factor options Subject each option to same set of noises Select option that gives best SN ratio Enter selected values for critical control factors into critical parameter drawing; become requirements for detailed design
© Don Clausing 1998 Fig. 30 Critical parameter drawing for paper feeder Optimized values of critical parameters guide the detailed design CONTACT: ANGLE: 0 DISTANCE: 12 MM BELT TENSION: 15 NEWTON WIDTH: 50 MM VELOCITY: 250 MM/SEC 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 WRAP ANGLE 45 o
© Don Clausing 1998 Robustness Fig. 31 Keeps the performance (response) of the system acceptably close to the ideal function Optimizes values of control factors to minimize effect of noise factors Key to proactive improvement
© Don Clausing 1998 Signal/noise ratio Measure of deviation from ideal performance (noise here is Nout) Based on ratio ofdeviation 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 Fig. 32
© Don Clausing 1998 Manufacturing Machine-to-machine variation is one of three types of noise that affects product Machine-to-machine variations can be reduced by making production more robust N out for production is one type of N in for product Fig. 33
© Don Clausing 1998 Examples of manufacturing noise Temperature variations Humidity variations Cleanliness variations Material variations Machine-tool variations Cutting-tool variations Fig. 34
© Don Clausing 1998 Noise strategy for production Simply operate in normal manner during optimization of production robustness Dont take special care Would reduce magnitude of normal noises Assure that every trial is done in normal manner – realistic noises are present and the part variation is typical of actual production Fig. 35
© Don Clausing 1998 Fig. 36 Tolerance design Select economical precision Determines typical variation relative to optimized nominal value Primary task is selection of production process (or quality of purchased component) -determines variation of production Then put tolerance on drawing
© Don Clausing 1998 Fig. 37 Summary; control & noise basics Control factors are systematically varied through large ranges seeking best combination Noise values are set at large values to enable quick improvement Tolerance design selects variation range to be used during production Clarify these three variations in your mind; you will be well on your way to Master RD
Context of Robust Design Don Clausing Fig. 1 © Don Clausing 1998.
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