Foredrag Bo Bergman

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Vlkomna!till workshop

Bo Bergman SKF Professor

OOOOualitySciencesOOOOualitySciences 1

till workshop

ROBUST KONSTRUKTIONSMETODIK FR KAD TILLFRLITLIGHET

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Tillfrlitlighet och variation

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Tillfrlitlighet, variation

och robusthet



robusthetBo BergmanSKF Professor Quality SciencesDivision of Quality SciencesChalmers University of TechnologySE-412 96 Gothenburg, SwedenPhone: +46 31 772 8180E-mail: [email protected]

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The Kano Model

CustomerSatisfaction

Expected

Attractive



Degree offulfilment

Must be

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History (industry)

AssemblyIntegrationSpecialisation

ProcessLearningVariation

OrganizationContinuous Improvement

JapanisationQuality Drivenorganisationdevelopemnt



Japanese Export

. . . . .

ManyDialects..Six SigmaLean

Quality Drivenorganizationdevelopment

S D

PA

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Demings Profound Knowledge +

Understanding Variation Not only handling and reduction

Psychology Not only individual but also organisation and social



Not only individual but also organisation and social

Knowledge Theory How knowledge determines what we can observe and interpret, and how new knowledge is created

Systems Thinking The Complexity Growth

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The World is full of Variation

Big Bang (from variation, a quantum fluctuation, and in variation)

Physical Reality (Thermodynamics, Statistical mechanics, Quantum Mechanics)

Biological Reality (Evolution: Replication and

Facts about the world:



Biological Reality (Evolution: Replication and Increased and reduced variation)

Humans and Human Artefacts (We find variation everywhere!)

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Reliability and Safety-

must be qualityCustomer

Satisfaction

Expected

Attractive



Degree offulfilment

Must be

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Why do we have failures?

Due to variation!



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Reliability in a World Full of Variation

Variation: For good and for bad



Without VariationNo World!Life is Variation!

Variation CreatesProblems:- Deviations- Disturbances- Noise

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early failure period

best period

wear-out period

z(t)

The Bathtub Curve



constant failure ratet

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Innervariation

early failure period

best period

wear-out period

z(t)

Manufacturingvariation

Usagevariation

Un-reliability due to Variation



variationDeteriorationconstant failure rate

t

Manufacturingvariation

variation

Production ProcessesUnder Statistical Control?

Usage Environment Under Statistical Control?

Usually NOT!!!

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A Critique of Reliability Theory Assumptions

Probability models under the assumption:

Processes under statistical control? Probably not!!!



Probably not!!!

Lagging indictors of reliability performance The design is created before testing

Usage feedback is even much later

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Back to Basics

Work with the failure mechanismsand their relations to Variation!



and their relations to Variation!

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Six Sigma:

VariationRed c



Reduction

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Chance vs Assignable causes of variation

Time Time Time



a process withassignable causes

a stable process a stable morecapable process

Processes Out of statistical In Statistical ControlControl

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Manufacturing controls process capabilitiesProcess

Capability

Engineering controlstolerances



defects

Lower tolerance limit

Upper tolerance limit

Quality Deficiency CostsExpensive components

Relation to Six Sigma

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),...,,,( 21= nxxxfy

DFSS and Six Sigma

DfSS



...

22

2

22

1

221

+

+

= xxyx

yx

y

Six Sigma

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Variation/Robust Design

Quality Loss L(y)

Quality Loss L(y)

Quality Loss L(y)

Quality Loss L(y)

a b



TargetValue

LTL UTLy

TargetValue

LTL UTLy

Target Value

LTL UTL y

Target Value

LTL UTL y

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P-diagram

Noise factors



ProductProcessSystemSignal

factors Control factors

Response

Ideally)(xfy = but

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Targeted Effects of Variation Reduction



The effects of variation focused in Design for Six Sigma programs;based on 25 responses.

Ida G?

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Robust Design Methodology

Sources of Variation



ResultsPRODUCTor

PROCESS

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Failure Experiences and RemediesThe Growth of Reliability Engineering

Early Problems Elevators in mines; Rail Road Accidents; Fatigue Problems; Rocket Problems (fortunately); Electronics Problems (esp. in the US Navy); etc.

Aircraft Safety and Availability Improvements based on a serious feedback process

Life Cycle Cost based Acquisitions



Life Cycle Cost based Acquisitions Defence Industry, Process Industry

Competitiveness Automobile Industry AC equipment producers (Garvin, 1988)

Today, most industries have been forced to realise the problem

Warranty costs often as high as 50% of the Development costs

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Aim of Reliability efforts

Causes Find Estimate Reduce Eliminate


OOOOualitySciencesOOOOualitySciences 23Consequences

Fault

Reduce Eliminate

Find Estimate Reduce Eliminate

ExperienceFeed-back

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Stress & StrengthDemand and Capacity

Stress Strength

Probability density


OOOOualitySciencesOOOOualitySciences 24Strength/Stress

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Failure Mode Avoidance

Lusser (in the 1950-ties) Robert Lusser

The V1 rocket

Lussers Law



Lussers Law

Starfighter F104 (widowmaker)

Missile development criteria

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Reliability, Stress, and Strength



Lusser, 1955

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FMEA Failure Mode and Effects analysis Physics of Failure



Physics of Failure

Clausing (Xerox/MIT) Operating Window

Pat OConnor Taguchi Davis (Ford)

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Failure Mode Avoidancein Robust Design Methodology

Ideal Function

Response



Signal

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Ideal Function

Response



Signal

S/N ratio An Engineering

Measure of Reliability?

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FMEA Failure Mode and Effects analysis Physics of Failure



Physics of Failure

Clausing (Xerox/MIT) Operating Window

Pat OConnor Taguchi Davis (Ford) Frame: DfSS e.g Park, Creveling et al. .

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P-diagram

Noise factors



ProductProcessSystemSignal

factors Control factors

Response

Ideally but

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Product representation as a System of P-Diagrams



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Robust Design

System design

Decide on the products characteristics so that the requirements are fulfilled and it can be produced easily. Creative Robustness should be looked for!



easily. Creative Robustness should be looked for! Parameter Design

Find a set-up of the construction parameters that make the product independent of disturbances.

Tolerance Design

Decide on tolerances, but strive for the target value

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Creative solutions: some illustrations

The self aligning bearing

A Creative Reliability

Improvement



Improvement

1907

1995

Sven Wingquist

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Inspiration

Creative yesterday commonplace today

Replacing the chain with a wire



DFA - solutions

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Poka-Yoke Principles

1. Make it easier for the person to do the right thing than the wrong thing

2. Make mistakes obvious to the person immediately so that some



2. Make mistakes obvious to the person immediately so that some correction can be made on the spot

3. Allow the person to take corrective action or stop before any irreversible step occurs

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How to create a robust design?

y

y



xx0

y0

x1

X1 results in less variation in y

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Transfer function

1. Is the transfer function known to the experimenter?

? ? ?)*,,( NCNCfy =



1. Is the transfer function known to the experimenter?

2. Is it possible to use Design of Experiments to estimate the transfer function ?

3. Is the transfer function possible to estimate by use of simulation?

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Pump design transfer function known

Tubing

Flow rate (F) (l/min)

Transfer function:

F = (3.141 x R2 x L - B) N

One wayvalve



Piston

F = (3.141 x R2 x L - B) N

R = Piston radius (dm)L = Stroke length (dm)B = Back flow (l)N = Motor speed (rpm)

Customer requirement: F=100.75l/min

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Pump design

Factors Nominal value Standard Deviation

Radius 0.2-0.8 dm 0.001

Stroke length 0.2-0.8 dm 0.002MA

K

E



Stroke length 0.2-0.8 dm 0.002

Back flow 0.001-0.004 l 0.00005 0.00002

N (rpm) 50-100rpm 2 1

Low cost High cost

(Inlet Valve)

B

U

Y

(Electrical motor)

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The tolerance design approach

First Design Piston Radius R =0.4 dm Stroke length L=0.4 dm Back flow B=0,002 l (low cost) Motor speed N=50rpm (low cost)

The target is 10 l/min, but 3 sigma process



Motor speed N=50rpm (low cost)

Tightening the specifications of the motor (the high cost type) gives better performance 5 sigma process

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A robust design approachThe effect of the factors on

the mean and the variance of the flow

V

a

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c

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(

f

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)

R B NL0 .2

0

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8

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0

1

5

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R B NL0 .2

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A robust design approach

Set R and L as low as possible, i.e. R=L=0,2dm Use low cost back flow (B) Bring the flow rate to target (F=10 l/min) by adjusting N



The resulting performance is:

Almost a 5 sigma process!

As N100, keep R low and increase L until F=10 l/min

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Manufacturing process of composite material

y bending strenght response variable

A curing temperatureB pressureC holding time

control factors (process variables)

D proportion of hardener

y = f (A,B,C,D,E,F,G,H)?

Composite material experiment: transfer function unknown



Four different process conditions Eight batches of raw material

D proportion of hardenerE thermo-plastic contentF proportion of epoxyG material ageingH process type

noise factors

?

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Experimental designD E F G H-1 -1 -1 1 -1 20751 -1 -1 1 1 2117-1 1 -1 -1 1 22211 1 -1 -1 -1 2227-1 -1 1 -1 1 22011 -1 1 -1 -1 2179-1 1 1 1 -1 19881 1 1 1 1 1858-1 -1 -1 1 -1 18291 -1 -1 1 1 1978-1 1 -1 -1 1 21111 1 -1 -1 -1 2205-1 -1 1 -1 1 2127

A B C 1 -1 1 -1 -1 2106

Process variables (control factors)A Curing temperatureB PressureC Holding time

Incoming material (noise factors)D Proportion of hardenerE Thermo-plastic contentF Proportion of epoxy Process

Product



A B C 1 -1 1 -1 -1 2106-1 -1 1 -1 1 1 1 -1 18701 -1 -1 1 1 1 1 1 1879-1 1 -1 -1 -1 -1 1 -1 22451 1 1 1 -1 -1 1 1 2242

-1 1 -1 -1 1 22451 1 -1 -1 -1 2258-1 -1 1 -1 1 22061 -1 1 -1 -1 2207-1 1 1 1 -1 20531 1 1 1 1 2188-1 -1 -1 1 -1 22191 -1 -1 1 1 2145-1 1 -1 -1 1 21741 1 -1 -1 -1 2265-1 -1 1 -1 1 22411 -1 1 -1 -1 2187-1 1 1 1 -1 22081 1 1 1 1 2181

F Proportion of epoxyG Material agingH Type of process

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-1

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1

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-1 5 0 -1 0 0 -5 0 0 5 0 1 0 0 1 5 0

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-1 5 0 -1 0 0 -5 0 0 5 0 1 0 0 1 5 0

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-1 5 0 -1 0 0 -5 0 0 5 0 1 0 0 1 5 0

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B

G

BG

Identification of location effects



-3

-2

Contrasts

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Contrasts

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Contrasts

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G

Location effects B, G and BG was determined to be active based on engineering knowledge and the normal plots

Process factors Factors and interactionsassociated with incoming material

Interactions between process factorsand incoming material factors

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Model

( )( , ) 2132 72 65 462132 72 46 65y B G B G BG

B B G= + + =

+ +



( )2132 72 46 65B B G+ + B 1.4

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Conclusions

The storage time of the incoming material (G) is causing variation in the bending strength of the composite



bending strength of the composite material.

If the pressure (B) is set at high level the bending strength is made insensitive to the storage time.

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Robust Testing

TheDesign

Variation of

Noise factors

N1 N2 . Nn



DesignNoise factors

Evaluate the Design

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Design reviews

good design Robust



good designgood discussion good dissection

RobustDRBFM*

Design Review

*Design Review Based on Failure Mode

Documents

Foredrag Bo Bergman