Control and noise factors Don Clausing Red border: emphasized slides ? Don Clausing 1998 16.881 Fig. 1 The engineered system Noise Signal System Response Control factors ? Don Clausing 1998 16.881 Fig. 2 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! ? Don Clausing 1998 16.881 Fig. 3 Actual response Ideal response Effect of noises RESPONSE M 1 SIGNAL M 2 ? Don Clausing 1998 16.881 Fig. 4 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 ? Don Clausing 1998 16.881 Fig. 5 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 ? Don Clausing 1998 16.881 Fig. 6 Fault tree for paper feeder FAIL TO FEED SINGLE SHEET MULTIFEEDMISFEED JAM DAMAGE BIG SLUG WEAK SEPARATION OVERLAP SMALL WRAP ANGLE INITIAL WEAR WEAK BELT TENSION LOW RETARD FRICTION LARGE FRICTION DIFFERENCE SMALL RETARD RADIUS ?μ pp α 0 α t μ rp T R Identification of critical parameters – both control and noise ? Don Clausing 1998 16.881 Fig. 7 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 ? Don Clausing 1998 16.881 Fig. 8 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 ? Don Clausing 1998 16.881 Fig. 9 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 ? Don Clausing 1998 16.881 Fig. 10 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 ? Don Clausing 1998 16.881 Fig. 11 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 ? Don Clausing 1998 16.881 Fig. 12 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 ? Don Clausing 1998 16.881 Fig. 13 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 ? Don Clausing 1998 16.881 Fig. 14 Effect of noises, 1 & 2 FAILURE MODE 2 FAILURE MODE 1 A. GOOD PERFORMANCE FAILURE MODE 2 FAILURE MODE 1 Y 1 Y 2 B. BAD PERFORMANCE Y Y Y 1 Y 2 ? Don Clausing 1998 16.881 Fig. 15 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; BnullA – Increase noises; repeat improvement, BnullA ? Don Clausing 1998 16.881 Fig. 16 Path to success CON TROL FAC TOR SE T NOISE 1 Y 1 NOISE 2 Y 2 Y 2 -Y 1 1C F SET 1 MODERATE 5 MODERATE 25 20 2C F SET 2 MODERATE 14 MODERATE 16 2 3C F SET 2 ST RONG 8 ST RONG 22 14 4C F SET 3 ST RONG 14 ST RONG 16 2 IMPROVEMENT PATH: CF1nullCF2 nullCF3 ? Don Clausing 1998 16.881 Fig. 17 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 ? Don Clausing 1998 16.881 Fig. 18 Fundamental noise situation EFFECT OF ALL FAILURE MODE 2 FAILURE MODE 1 NOISES IN WORLD TYPICAL LAB VARIATION Y FORCE FAILURE MODES PUT IN NOISES 16.881 ? Don Clausing 1998 Fig. 19 Robustness improvement FAILURE MODE 2 FAILURE MODE 1 PERFORMANCE VARIATION AFTER IMPROVEMENT Y PERFORMANCE VARIATION BEFORE IMPROVEMENT ? Don Clausing 1998 16.881 Fig. 20 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 ? Don Clausing 1998 16.881 Fig. 21 Fig. 22 ? Don Clausing 1998 16.881 INPUT NOISE N IN OUTPUT NOISE N OUT SYSTEM NOISE SOURCE STRATEGY ? HOLD N IN CONSTANT ? MINIMIZE N OUT NOT IMPORTANT ? SPECIFIC SOURCE ? MAGNITUDE OF N IN Noise strategy 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 16.881 Fig. 23 Introduce large ?μ p-p F N ?μ 1-2 ?μ p-p = ?μ 1-2 - ?μ 2-3 Sheet 2 ?μ p-p > 0 causes F N ?μ 2-3 sheet 2 to move ? Don Clausing 1998 16.881 Fig. 24 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 16.881 Fig. 25 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 16.881 Fig. 26 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 ? Don Clausing 1998 16.881 Fig. 27 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 ? Don Clausing 1998 16.881 Fig. 28 Optimization ? 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 16.881 Fig. 29 Critical parameter drawing for paper feeder WRAP ANGLE 45 o BELT: CONTACT: ANGLE: 0 TENSION: 15 NEWTON WIDTH: 50 MM VELOCITY: 250 MM/SEC DISTANCE: 12 MM ANGLE: 45 RETARD: PAPER STACK STACK FORCE: VELOCITY: 300 MM/SEC 0.7 LB GUIDE: MOUTH OPENING: 7 MM FRICTION: 1.0 RADIUS: 25 MM FRICTION: 1.5 Fig. 30 16.881 Optimized values of critical parameters guide the detailed design ? Don Clausing 1998 Robustness ? 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 16.881 Fig. 31 Signal/noise ratio ? Measure of deviation from ideal performance (noise here is N out ) ? 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 ? Don Clausing 1998 16.881 Fig. 32 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 ? Don Clausing 1998 16.881 Fig. 33 Examples of manufacturing noise ? Temperature variations ? Humidity variations ? Cleanliness variations ? Material variations ? Machine-tool variations ? Cutting-tool variations ? Don Clausing 1998 16.881 Fig. 34 Noise strategy for production ? Simply operate in normal manner during optimization of production robustness ? Don’t 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 ? Don Clausing 1998 16.881 Fig. 35 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 16.881 Fig. 36 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 ? Don Clausing 1998 16.881 Fig. 37