麻省理工学院：《Robust System Design》Control and noise factors

Control and noise factors Don clausing Red border: emphasized slides C Don Clausing 1998 人 16881 Fig. 1

Control and noise factors Don Clausing Red border: emphasized slides © Don Clausing 1998 16.881 Fig. 1

The engineered system N olse Signal System Response Control factors C Don Clausing 1998 人 16881 Fig 2

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, the en difficult problems only appear late Introduce noises early C Don Clausing 1998 人 16881 Fig 3

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 00.0u2 Effect of noises 1 SIGNAL M2 C Don Clausing 1998 人 16881 Fig 4

Actual response Ideal response Effect of noises RESPONSE M1 SIGNAL M2 © Don Clausing 1998 16.881 Fig. 4

R esponse 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 C Don Clausing 1998 人 16881 Fig 5

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 C Don Clausing 1998 人 16881 Fig 6

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 MISFEED MULTIFEED JAM DAMAGE BIG WEAK OVERLAP SLUG SEPARATION ArGe SMALL LOW WEAK SMALL FRICTION WRAP RETARD BELT RETARD DIFFERENCEI ANGLE FRICTION TENSION RADIUS INITIAL WEAR μ T R pp Identification of critical parameters -both control and noise C Don Clausing 1998 人 16881 Fig. 7

Fault tree for paper feeder FAIL TO FEED SINGLE SHEET MISFEED MULTIFEED JAM DAMAGE BIG SLUG WEAK SEPARATION OVERLAP SMALL WRAP ANGLE INITIAL WEAR WEAK BELT TENSION LOW RETARD FRICTION LARGE FRICTION DIF FERENCE 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,up-p C Don Clausing 1998 人 16881 Fig 8

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, µp-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 C Don Clausing 1998 人 16881

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 C Don Clausing 1998 人 16881 Fig. 10

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