applying robust engineer in to 6sigma

8/14/2019 Applying Robust Engineer In to 6sigma

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Apply ing Robust

Engineer ing t o Six Sigm a

Elizabeth CudneyCQE, SSBB

December 5, 2005


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Agenda

Six Sigma Overview

Robust Design Overview

Taguchi System of Quality Engineering

Integrated Robust Engineering and Six Sigma

Case Study


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Six Sigm a Overview

Six Sigma is a customer focused continuousimprovement strategy and discipline that minimizesdefects and variation towards an achievement of 3.4defects per million opportunities in product design,production, and administrative processes.

It is focused on customer satisfaction and cost reductionby reducing variation in processes.

Six Sigma is also a methodology using a metric based

on standard deviation.

Six sigma targets aggressive goals.


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What is Six Sigm a?

Strategy to minimize variation towards the goal of 3.4defects per million.

A philosophy to promote excellence in all businessprocesses.

A 5 phase methodology for continuous improvement.

A statistic which describes the amount of variation in aprocess.

A tool to reduce or eliminate variation.


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What is Six Sigm a?

3.4 defects per million

Six Sigma is about. . .

Reducing cost

ROI Improving quality

Satisfying the customer

Designing better products

REDUCING VARIATION


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What is Six Sigm a?

Six Sigma is a customer focused continuous

improvement strategy and discipline thatminimizes defects and variation towards anachievement of 3 defects per million

opportunities in product design, production, andadministrative processes.

Focused on customer satisfaction and $ resultsby reducing variation in processes.

Methodology

Metric based on standard deviation.

Aggressive goals.


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Why Use Six Sigm a?

Increase capacity

Reduce cost

Improve yields

Reduce the impact of a Hidden Factory


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The Hidden Fac t ory

Each defect must be detected, repaired andplaced back in the process. Each defectcosts time and money.

Time, Cost, People

Inputs

Rework

Scrap

First Time YieldOperation Inspect

Hidden Factory

Pass

Fail

Increased Cost - Lost Capacity


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Six Sigm a Goals

Develop a world class culture

Develop leaders

Support long range objectives


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Six Sigm a Benefi t s

Stronger knowledge of products and processes

Reduction in defects

Increased customer satisfaction level that generatesbusiness growth and improves profitability

Increased communication and teamwork

Common set of tools


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Six Sigm a St ra t egy

Define

Measure

Analyze

Improve

Control

Define

Measure

Control

Improve

Analyze

6


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Define

Project selection

Tools: Voice of the Customer

Value Stream Mapping


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Measure

What processes are involved?

Who is the process owner?

Who are the team members?

Which processes are the highest priority toimprove?

What data supports the decision? (Metric)

How is the process performed? How is the process performance measured?

Is your measurement system accurate and

precise?


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Measure (Cont .)

What are the customer driven specifications for theperformance measures?

What are the improvement goals?

What are the sources of variation in the process?

What sources of variability are controlled and how?

Tools: Process Flow

Capability Analysis

Measurement System Analysis

5S


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Analyze

What are the key variables affecting the average and

variation of the performance measures? What are the relationships between the key variables

and the process output?

Is there interaction between any of the keyvariables?

Tools: Hypothesis Test

TAKT Time

Cause and Effect Diagram

Failure Mode and Effects Analysis (FMEA)


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Improve

What are the key variable settings that optimizethe performance measures?

At the optimal setting for the key variables, whatvariability is in the performance measure?

Tools: Multiple Regression

Design of Experiments

Kanban

Visual Management


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Contro l

How much improvement has the processshown?

How much time and/or money was saved?

Long term metric.

Tools: Process monitoring

Standard workSPC

Control charts


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Six Sigm a Levels

Identify the CTQs

Critical to Quality

Characteristics or

customer requirements

for a product or service

Define Defect

Opportunities

Any step in the process

where a defect could

occur in a CTQ

Look for Defects in

Products or Services

Count defects or

failures to meet CTQ

requirements

Arrive at DPMO

Defects Per Million

Opportunities

Convert DPMO to

Sigma Level

Use the sigma

table

Sigma

Level

Defects per

Million of

Opportunity

Z PPM

2 308,537

3 66,807

4 6,210

5 233

6 3.4


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Robust Design

A product is said to be robust when it isinsensitive to the effects of sources ofvariability, even though the sourcesthemselves have not been eliminated.

Source (Fowlkes and Creveling, 1995)


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Robust Design

Robust Design: Not just strong.Flexible! Idiot Proof! Simple! Efficient!A product/process that produces

consistent, high level performancedespite being subjected to a wide rangeof changing client and manufacturing

conditions.- Genichi Taguchi


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Taguc h i Sys t em of

Qual i t y Engineer ing

Dr. Taguchi, the father of QualityEngineering, introduced to the US in the1980s his approach for achievingrobustness in product design.

There are four steps to ensure robustproduct performance including

1) product parameter design,2) tolerance design,

3) process parameter design, and

4) on-line quality control.


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Product

Parameter

DesignTolerance

Design

Process

Parameter

Design

On-line

Quality

ControlNoises

Robust

System

Performance

Degradation

Quality

Loss


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The first and most important step in the TaguchiSystem of Quality Engineering (TSQE) is productparameter design which is the most important

action to design a robust system.

The concept of product parameter design is that

the final product is robust or insensitive to theincoming variations or noises.


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Robust design is achieved through athree step process:

1) Define the objective,

2) Define the feasible option, and3) Select the best option to meet the objective.

Robust design is measured using theSignal-to-Noise (SN) ratio.


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Pro jec t Desc r ip t ion

The project is to design a disk brakesystem as a variation attenuator.

The goal of the project is to design thesystem so that it provides on target

performance in the presence of all types ofvariation.


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Selec t Vehic le

Ford Explorer 2002


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Approach

Spend about 80% of your time inengineering analysis and planning and

about 20% actually running experimentsand evaluating the results.

- Dr. Genichi Taguchi


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Approach

Ideal Function

P-Diagram

FAST Diagram

Static Experiment

Dynamic Experiment


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Idea l Func t ion

Defines the desired input/outputrelationship.

Displays the ideal relationship between thesignal and the quality characteristic.


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Idea l Func t ion

EnergyTransformation

Input Energy

Pedal Force

Intended Function

Slow Downthe Vehicle


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Brak ing Sim ulat or


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P-Diagram

P-Diagram is used to display variousfactors that affect performance.

It graphically displays the anatomy of a

product


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P-Diagram

AutomotiveBraking System

Input Signal

Pedal Force(lbs)

Quality

Characteristic

SmoothDeceleration

Stopping DistanceStopping Time

Control Factors

Noise Factors

Variation in Vehicle Weight (GVWR)Brake TemperatureVariation in Brake Line Pressure

Variation in Initial VelocityVariation in Coefficient of Friction between Tire & Road

Number of Pistons per Brake (front)Caliper Piston DiameterBrake Booster RatioTire Diameter

Rotor DiameterPad Inner DiameterSubtended Angle


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Func t iona l Analys is Syst em

Tec hnique (FAST) Diag ram

Displays the functional relationshipbetween all elements of a product.

Distributes or flows the system (high

level) functions to lower levels.


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FAST Diagram

Slowlydecelerate

vehicle

Provide tires withgood

Stop rotation ofthe wheels

Emergencybrake

Force frombrake pedal

transferred to

the individualwheels

Provide inputsignal

Provide frictionbetween the

pads and rotor

Pressureexerted on themaster cylinder

pistons

Provide Vacuumassist from Engine

Providevacuumbooster

Apply Force toBrake Pedal

Provide fluidpressure inbrake lines

Provide Brake Pedal

Provide control valve

and

and/or

How?


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St a t ic Ex per im ent

Inner Array - 7 Control Factors

L18 (37)

Outer Array 5 Noise Factors

L8 (25

)


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Input Signal

Signal provided by the vehicle user

Pedal force (lbs)


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Cont ro l Fac t ors

Control factors are parameters selectedto control the performance of a product.

Should have a large effect on mean

performance and a small effect onvariation of mean performance.


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Cont ro l Fac t ors

7 Control Factors, L18 (37

)

Number of Wheel Pistons (front)

Caliper Piston Diameter Brake Booster Ratio Tire Diameter Rotor Diameter Annular Pad Inner Radius Subtended Angle


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Cont ro l Fac t ors

135120110Subtended AngleCF7

3.83.53.2Annular Pad Inner RadiusCF614.013.012.0Rotor Diameter

CF5

30.530.029.5Tire DiameterCF4

6.05.54.5Brake Booster RatioCF3

1.991.811.63Caliper Piston DiameterCF2

21Number of Wheel Pistons (front)CF1

HighMediumLowControl Factor


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Noise Fac t ors

Noise factors represent incomingvariation.

These are parameters that have a

strong effect on performance that wecan not or choose not to control.


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Noise Fac t ors

5 Noise Factors, L8 (25)

Variation in Vehicle Weight (GVWR)

Brake Temperature

Variation in Brake Line Pressure

Variation in Initial Velocity

Coefficient of Friction between Tire and Ground


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Noise Fac t ors

0.850.4Coefficient of Friction betweenTire and GroundE

5% (63 mph)-3% (58 mph)Variation in Initial VelocityD

-5%-2%Variation in Brake Line PressureC500 F40 FBrake TemperatureB

2% (4176 lb)-2% (4012 lb)Variation in Vehicle WeightA

Level 2Level 1Noise Factor


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Qual i ty

Charac ter is t ic

Quality characteristic measures the useful

output of the system.

Smooth deceleration

Stopping distance (GVWR)

Federal Motor Vehicle Safety Standard(FMVSS) 135

Nominal-the-Best (NTB) Signal-to-Noise Ratio


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St a t ic Ex perim ent

Cross-Array Form at

Inner ArrayL18

Outer ArrayL8

PerformanceArray

PerformanceStatistics

NoiseStatistics


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Fac t or Ef fec t s Plo t S/N

Factor Leve l Effect Plot- Factor S/N vs. Level

19.0

19.1

19.2

19.3

19.4

19.5

19.6

19.7

19.8

19.9

20.0

Control factor Level (1, 2 and 3)

S/N

(dB

)

CF1

CF2

CF3

CF4

CF5

CF6

CF7


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Fac t or Ef fec t s Plo t Mean

Factor Level Effect Plot - Mean vs. Level

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

Control factor Level (1, 2 and 3)

Mean,

ft

CF1

CF2

CF3

CF4

CF5

CF6

CF7


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ANOVA Cont ro l Fac t or

Cont r ibu t ion t o S/N

ANOVA-Control Factor Contribution to S/N

Annular Pad Inner

Diameter

0.46%

Subtended Angle 0.00%

Tire Diameter

0.01%

Number of Pistons per

Brake (front)

82.18%

Caliper Piston Diameter

17.36%

Brake Booster Ratio 0.00%

Rotor diameter

0.00%

1

2

3

4

5

6

7


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Cont r ibut ion t o Mean

ANOVA-Control Factor Contribution to Mean

Subtended Angle

0.00%Tire Diameter

8.50%


Caliper Piston Diameter

14.43%Number of Pistons (front)

66.84%

Annular Pad Inner

Diameter

0.37%

Rotor Diameter

9.85%

1

2

3

4

5

6

7


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Dynamic Ex per iment

Cross-Array Form at

Control FactorsInner Array

L18

M1 M2 M3N1N2 N1N2 N1N2

Outer Array

PerformanceArray

PerformanceStatistics

F t Ef f t Pl t


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Fac t or Ef fec t s Plo t

S/N

Control Factor Effect Plot

S/N vs. Level

-32.10

-32.00

-31.90

-31.80

-31.70

-31.60

-31.50

Level (1, 2 & 3)

S/N

,dB

CF1

CF2

CF3

CF4

CF5

CF6

CF7

F t Ef f t Pl t


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Mean

Control Factor Effect PlotMean vs. Level

0.00

50.00

100.00

150.00

200.00

250.00300.00

350.00

400.00

Level (1, 2 & 3)

Mean,

ft

CF1

CF2CF3

CF4

CF5

CF6

CF7

F t Ef f t Pl t


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Be ta

Dynamic Estimaged Beta Value

R2 = 0.9977

0

50

100

150

200

250

300

350

400

450

0 20 40 60 80 100 120 140 160

Pedal Force (lbs)

StoppingDistance(ft)

N1

N2

Avg

Linear (Avg)

y = 432.1 - 1.026x

ANOVA C t l F t


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Cont r ibu t ion t o S/N

ANOVA-Control Factor Contributions to S/NTire diameter

0.01%

Pad Inner Diameter

0.44%

Number of Pistons per Brake

(front)

82.11%

Caliper piston diameter

17.44%

Rotor Diameter

0.00%

Brake Booster Ratio 0.0% Subtended Angle 0.00%

1

2

3

4

5

6

7

ANOVA C t l F t


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Cont r ibut ion t o Mean

ANOVA-Control Factor contribution to Mean

Rotor diameter

12.52% Pad Inner Diameter 0.39%

Number of Pistons per Brake

(front)

71.41%

Caliper Piston Diamter 15.40%


Tire Diameter

0.26% Subtended Angle

0.00%

1

2

3

4

5

67


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Quest ions???

Thank you!

applying robust engineer in to 6sigma

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