Mod 4 Robustness Sept 03

8/12/2019 Mod 4 Robustness Sept 03

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1 Visteon Corporation BB Mod #4 Robustness Rev 1.0 8/01

Robustness Methodology

Module

Black Belt TrainingModule #4


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Introduction

The robustness initiative was instituted to:

Improve thequalityof our products and

processes.

Lower our costto design and manufacture.

Reduce our overall development timeso we

can get new product to market faster.


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1. Quality and Cost

Objectives:

Define the relationship between

quality and cost

Recognize the value of meeting

target vs. specification


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Step Loss Function Examined

It is afallacythat:

A quality product is one that meets

specifications

Any product that is within specifications is

good; any product outside specifications is

bad Any value within the specification limits is

as good as any other


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Only when we have to scrap

or rework a product.

Only if a product that is

outside of specification gets

to the customer. (In that case,we would experience some

warranty cost.)

QUALITY CHARACTERISTICS

LOWER SPEC UPPER SPEC

SCRAP &REWORK

SCRAP &REWORK

LOSS INDOLLARS

Max

Min

Let's examine this from a customer viewpoint.

Anotherfallacy isthat we experience a dollar loss

Loss Function


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what is the difference between the product that is just

outside the specification limit and the one that is just

inside the limit?

... really, a minute difference.

TARGETLOWERLIMIT

UPPERLIMITVALUE

A B

From the customer viewpoint,

Loss Function


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Quality Loss Function

Whenever we vary from

the target value, we

experience both qualityloss and, hence, financial

loss.

The quality loss cost (loss

in dollars) increases

quadratically as we movefrom the target value.

LOSS INDOLLARS

Min

Max

Target


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If we were producing this distribution,

we would be experiencing some loss.

TARGETLOWERLIMIT

UPPERLIMITVALUE

Loss Function



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If we were producing this distribution, that has less

variance from target, the loss cost would be lower.

TARGETLOWERLIMIT

UPPERLIMITVALUE

Loss Function



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Robustness

methodologies will helpus achieve customer

targets and reduce

variance from the target.

Robustness Defined


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2. Robustness Defined

Objectives:

Define robustness

Discover robust and non-robust products

Recognize the parameters that affect the quality

characteristics of a product or process

Recognize where the robustness methodology isbest used


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The Robustness Definition

"Robustness is a state of insensitivity ...

...of the functional performance of a product or process ...

...to variations in raw material, manufacturing processes

and operating environment...

...over its intended useful life...

...at a cost that represents value."


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For Example...

As consumers, we expect our automobiles to start

every time, on the first turn of the key, regardless

of: Whether it's cold, hot, or humid

Altitude

Age of the car

Hot or cold start, etc.


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The Parameter(P) Diagram


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A number of parameters (factors) can influence the response of

the product or process.

NOTE: Parameter and factor are interchangeable terms.

P Diagram

Noise Facto rs

Contro l Factors

Signal Facto rs Respons eProduct or

Process


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Signal Factors

Factors that are set by the user or operator of the

product or process to achieve the intended target

value for the response.

Noise Factors

Control Factors

Signal Facto rs ResponseProduct or

Process


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You saw that customer requirements determine the response that is desired

of the product or process.

Fixed (Static) Same outcome every time the product or process

is used

Variable (Dynamic) Outcome is controlled by the operator or owner

Identifying the quality characteristic (response) that you want to make

robust to a certain condition is the starting point in robustness

methodology.

Product or

ProcessResponse

Signal / Response

Signal


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Signal Examples

RadioVolume Knob

Chip

Placement

Refrigerant

Charging

Process Response Signal Type

Decibels

X-Coordinate

Pounds of

Refrigerant

Dynamic

Static

Static


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Choose the setting that

optimizes the response

T

Static Signal


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Example: Spot Welding

Performance Measure: Nugget Size

Robust Performance: Variation Reduction

Target Attainment

size

Static with Nominal-the-Best Target


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Example: Spot Welding

Performance Measure: Pull Strength

Robust Performance: Variation Reduction

Maximize Strength

strength

Static with Larger-the-Best Target


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Example: Printed Wiring Boards

Performance Measure: Number of

Soldering Defects

Robust Performance:

Variation Reduction

Minimize Defects

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4

No. of Defects

Static with Smaller-the-Best Target


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Best Fit Line

X1 X2 X3 X4

Slope b

yResponse

xSignal

MSE

...

Best Fit Line

X1 X2 X3 X4

b Slope/Sensitivity

yResponse

xSignal

MSE = Variation

...

Choose the best control setting

that minimizes the overall

variation that will optimize

the response

Dynamic Signal


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Analyzing a Dynamic Signal

Slope (b):

Slope of the Best Fit Line

Mean Square Error (MSE):

Average of the square of the

distance from the response to the

best fit line.

S/N i = 10 log b2

MSE{ }

Signal to Noise Ratio


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Example: Modular Alternator

Performance Measure: Output DC for 500-

6000 rpm

Ideal function: Varying output

with respect to inputRobust Performance:

Variation Reduction

Ideal function attainment

0

10

20

30

40

50

500 1500 2500 3500 4500 5500 6500 7500

Speed

Current

Dynamic with Ideal Function


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Noise Factors

Noise Factors

Control Factors

Signal Factors ResponseProduct or

Process

Factors that cannot be controlled or the engineer

decides not to control.

We want to make the response insensitive to noise


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Categories of Noise Factors

External: Environment in whicha product/process works and theload to which it is subjected.

Unit-to-unit variation: Inevitablevariation in a manufacturing

process leads to variation in theproduct from unit to unit.

Deterioration: Individualcomponents may deteriorate

causing a decrease inproduct/process performance astime passes.

Noise Factors

Control Factors


Process


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Examples of Noise by Category

External

Environment(Customer Usage Profiles)

Temperature, humidity, vibration

Operator usage

Unit-to-Unit Variation

Manufacturing variation (Process Capability)

Differences in torque on a fastener

Differences in diameter from machining

Raw material variation

Deterioration

Wear over time (Reliability)


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Control Factors

Noise Factors

Contro l Facto rs


Process

Factors that can be freely specified by the engineer.

May be set at varying levels.

Control factors are specified by the engineer.


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Examples of Control Factors

Injection Molding Process

Material Type

Dimensions

Injection Time

Hold Pressure

Can you name others?


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Desired Response: Fixed Suspension Stiffness (Static)

Suspension

Response

FixedSuspension

Stiffness

Control Factors

Suspension Type(Hydraulic/Pneumatic)Coil Properties

Placement and Orientation on VehicleInitial StiffnessSize of Shock

Noise Factors

Road ConditionVehicle Load*

Temperature

Wear Over Time

Vehicle Speed*

Contamin ants (Dirt /Salt)

* Noise factors are now considered Signal

factors because they are inputs used to

adjust the target response

P Diagram for Suspension


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P Diagram for Braking

Desired Response: User Adjustable Stopping (Dynamic)

Signal(s)

Force onBrake Pedal

Response

StoppingDistance

Control Factors

Pad MaterialCaliper Type

Rotor ThicknessPad Area

Noise Factors

Road ConditionVehicle LoadTemperature

Wear Over Time

Vehicle Speed

Brake

Stopping Distance

R b t C B A hi d


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Robustness Can Be Achieved

Using a Three-step Approach

1. Concept Design

3. Tolerance Design

2. Parameter Design


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1. Concept Design

Various architectures and technologies areexamined before selecting one that will achievethe desired function of the product or processand will meet the customer's requirements

The alternatives selected through conceptdesign can affect the sensitivity to noise factorsand reducing cost

Involves innovation


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*Statistical Experimental Design techniques, Signal-to-Noise ratios, and

their application to robustness will be covered in detail in another course.

One tool for achieving th e object ives of parameter

design is Stat ist ical Exper imental Design.*

In p arameter design, the results o f an exper iment d esign

are oft en measu red in terms of th e Signal-to-Noise* ratio

(S/N), which is an ind icator of robustness .

Parameter design achieves robustnessw ith no inc rease in the

produ ct /manufactur ing cost .

2. Parameter Design


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3. Tolerance Design

Tolerances are tightened and higher-grade materials are specified as required.

Trade-offs are made between the reduction in quality loss due to performancevariation and the increase in product cost.

Tolerance design increasesthe product/manufacturing

cos tand should be done only when parameter design has

not suff ic ient ly reduc ed var iat ion.

Production

Cost

Quality

Loss


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The Traditional ApproachTraditionally, most engineering

time is spent on concept and

tolerance design, and a small

percentage on parameter design.

Impact o f Tradit ional

Approach

Relying on tolerance

design makes productsmore expensive to

manufacture.

Relying on improved

concept design requires

breakthroughs that aredifficult to schedule and

require greater lead time.

Traditional Approach


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Robustness Methodology

Best used early in the design/development cycle.

Applies to all areas in product development cycle.

Ensures the functional

performance of the

product/process is insensitive

to noise.

Decreases manufacturingprocess variation.

Product/Process Design

Improves new technologies,

concepts, and designs before

they are put on the "bookshelf."

Ensures that they are usable in a

wide range of applications andenvironments.

Technology


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Why the robustness initiative exists

Management's commitment to the initiative

The education and training process that will be used

The relationship between quality and cost

Basic robustness definitions and concepts

When and where the robustness methodology is used

The relationship between the robustness methodology andparameter design

Formulation steps for parameter design

Summary

Mod 4 Robustness Sept 03

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