dfss-materialdevelopment

7/23/2019 DFSS-MaterialDevelopment

1/18

GE Research &Development Center

_____________________________________________________________

Application of Six Sigma Quality Tools toHigh Throughput and Combinatorial

Materials Development

J.N. Cawse and R. Wroczynski

2001CRD055, April 2001

Class 1


2/18

Copyright 2001 General Electric Company. All rights reserved.


3/18

Corporate Research and Development

Technical Report Abstract Page

Title Application of Six Sigma Quality Tools to High Throughput and Combinatorial MaterialsDevelopment

Author(s) J.N. Cawse Phone (518)387-6095R. Wroczynski 8*833-6095

Component Characterization and Environmental Technology Laboratory

ReportNumber 2001CRD055 Date April 2001

Numberof Pages 12 Class 1

Key Words Six Sigma, combinatorial, high throughput, screening, Design for Six Sigma, quality,experimentation, chemistry, materials development

As our ability to generate large numbers of experiments has accelerated, it has become more

important to ensure that the quality of each process step and individual sample is as high as possible.There are too many samples for each data point to be checked individually, and too many process steps

for human oversight. To achieve a reasonable level of quality in the information from a high throughput

experimental system, the principles of modern Six Sigma manufacturing must be applied. The quality of

the end product is then ensured by the quality of each individual process step and the robustness of final

quality to variation in the process steps. Design for Six Sigma tools are particularly appropriate in this

effort.

Manuscript received March 13, 2001


4/18

Application of Six Sigma Quality Tools to High Throughput and

Combinatorial Materials Development

James N. Cawse and Ronald Wroczynski

Introduction

Background

Over the past ten years, the new research technology called Combinatorial Chemistry or High

Throughput Screening has seen exponential growth. This technology a set of techniques for creating amultiplicity of compounds and then testing them for activity has been widely adopted in the

pharmaceutical industry over the past few years. Virtually every major drug manufacturer is now using

these techniques as the cornerstone of their research and development program. In the pharmaceutical

industry, libraries of 1000 to 1,000,000 distinct compounds are routinely created and tested for biological

activity. This is now practical because of the

convergence of low-cost computer systems,

reliable robotic systems, sophisticated molecular

modeling, statistical experimental strategies, anddatabase software tools.

In the last four years, this technology has

expanded to materials design problems outside

the drug field1. Major chemical companies have

entered this arena, either by themselves or in

concert with a company such as Symyx2, which

specializes in new technologies for combinatorialmaterials discovery. Initial work has focussed on

development of robotic sample preparation,

reactors and sensors. Some of this equipment is

becoming available commercially. With the use of

this equipment, we have found that astonishing

increases in the throughput of experimentation

are possible (Figure 1).

Need For Quality

As our ability to generate large numbers of experiments has accelerated, it has become more important to

ensure that the quality of each process step and individual sample is as high as possible. There are too

many samples for each data point to be checked individually, and too many process steps for human

oversight. To achieve a reasonable level of quality in the information from a high throughput experimental

25,000

15,000

10,000

5,000

0

Oct-9

7

NumberofReactions

Dec-9

7

Feb-98

Apr-9

8

Jun-98

Aug-98

Oct-9

8

Dec-9

8

Feb-99

Apr-9

9

Jun-99

20,000Combinatorial Reactor

Conventional Reactors

Figure 1. GE experience with high-throughput screening

of a catalyst system.


5/18

chemicals in making up the stock solutions for a run. An error in any weighing will affect 96 samples,

however, so any weighing defect will result in 96 sample defects. Therefore this step has 96 x 5 = 480

opportunities for defects. We assume that any human operation has 0.6% (6000 dpmo) chances of being

defective4, so the total number of defects potentially arising from this process are 480 x 6000 x 10-6= 2.88

defects/array. Other sources add to this as shown in Figure 2, which is a highly abbreviated total from a

more complex system. It is easy to see that the number of defects/array can quickly become quite large.

Table 1.A Six Sigma glossary.

Process Step Source Defect dpmo # times DPU

Make Stocks Chem. Supplier offspec chemicals 1000 480 0.48

Technician weigh chemicals 6000 480 2.88Make Samples Robot poor mixing 3000 96 0.29

Heatup Time 1500 96 0.15

Temp. Control Temp. 5000 96 0.48

Cooldown Time 1500 96 0.15

Gas Chromatograph Peak Identif ication 100 96 0.01Analyze Samples

GC Endpoint Detection 10000 96 0.96

GC Peak Integration 500 96 0.05

React Samples

Total Defects 5.45

Figure 2. Estimating the total number of defects in an experimental unit:

a 96-well sample array. dpmo = defects per million operations; #times =

the number of times that operation will be performed on that unit; DPU

= defects per unit = #times*dpmo/1000000.

Overall Six Sigma Approach

Critical to Quality (CTQ). Any product or service characteristic that satisfies a key customerrequirement or process requirement.

Defect opportunities. Every operation and every subsystem component is considered to bea potential source of one or more defects.

Defects per million opportunities (DPMO). As actual or statistically probable defects arefound, they are divided by the opportunities. The ratio is multiplied by 1,000,000 to

accentuate the importance of even small numbers of defects.

Unit. An appropriate subdivision of the total effort is defined as a unit. In the present case,a single 96-well sample array was chosen.

Defects per Unit (DPU). To calculate the total number of potential defects in the unit, eachDPMO is multiplied by the number of times it can occur in a single unit. The results aretotaled to give the DPU.

Z. The standard metric for measuring process capability. ZLTis based on the overallstandard deviation and average output. ZSTrepresents the process capability with special

cause variation removed.


6/18

IDOV Step 1: Identify

The critical tasks in the Identify step are identifying the customers (all of them), determining their Critical

to Quality (CTQ) parameters, and generating a seamless connection from those parameters to

controllable elements of the actual experimental process.

CTQ Determination

Step 1 in CTQ determination is iteration of all the customers of the new process. This customer listing is

organized in a structure called the Application Channel (Figure 4), progressing from the most outside

the customer who actually pays money for a product to the most inside the people who are working

on the process. Typically there are three to five customer organizations. Within each of those

organizations, the people whose roles are critical to making a good judgment of the CTQs must be

determined. Once those people are found, the technique of customer needs mapping (CNM)5can be used

to determine the CTQs.

Identify

Design

Optimize

Validate

Q

ualitybyDesign

Identify customer CTQ's Perform CTQ flowdown Analyze measurement system capability

Generate/validate system/subsystem models Build variance predictions (parts, process, performance)

Roll-up variance for all subsystems Capability flow-up

Identify the gaps Low DPU of subsystem

Use DoE on prototypes to find the critical few X's Perform parameter and tolerance design Generate purchase and manufacturing specs

Confirm that pilot builds match predictions Mistake proof the process Refine models and process characterization database Document the effort and results

Figure 3.Outline of the DFSS methodology used for high throughput

screening and combinatorial experimentation.

End UserGE BusinessManufacturingOperation

Final customer forcatalyst, not process

GE BusinessPilot Plant Team

Interface ofdevelopment withend user

GE Corporate Researchand Development CatalystDevelopment Team

Validation of combinatorialdevelopment process andlaboratory scale test ofcandidates

GE Corporate Researchand DevelopmentCombinatorial Team

Development of process forcatalyst development andgeneration of catalyst leadsfor further investigation


7/18

Flowdown of CTQs by Quality Function Deployment

The CTQs determined by customer needs mapping are typically somewhat subjective and a bit fuzzy.Developing these into actionable process items is called CTQ flowdown and is typically done using the

Quality Function Deployment technique.6 A typical interrelationship of the houses of the QFD for a

material or catalyst development project is shown in Figure 5.

The first house (Figure 6) captures the critical to quality needs of the external customer and how the

Business unit expects to fulfill those customer needs. The primary customer CTQs become the rows

(Whats) of the first house of the QFD; the measurements become the columns (Hows). Using

the typical QFD process 6,7, importance levels are assigned (1 to 5, 5 is most important) to the external

Customer CTQs based on the CNM. The relationship of the CTQ's to the Hows (Business

Product Deliverables) is then defined by assigning high, medium, low (9,3,1) values to the cells based

on the impact that the measurements have on the CTQs. The products of the cell values with the

importance values of the CTQ's is determined and totaled for each column. These totals thus gives

the overall impact that each product deliverable has on meeting the Customer CTQs.

The Whats from the first house then become the Hows of the second house and similarly

importance values are assigned to them. The second house (Figure 6) is then used to establish the

relationship for How the materials, catalyst, or process can be varied to deliver the product changes

required by the Business to effect the Customer CTQs.

The third house identifies the variables that can or must be probed in the combinatorial space to be

able to screen the materials, catalyst, or process for desired changes

Finally, the fourth house relates to the actual design of the high throughput system (reagent

preparation, reactor conditions, product isolation and work-up, chemical analysis, and data storage and

data analysis) needed in order to deliver the desired capability of the combinatorial chemistry system.

It is here that the CTQs of the Combinatorial sub-systems are mapped onto the hows or variables

in the HTS factory (Figure 7).

The fully developed QFD assigns importance and impact values to the relationships of items in theWhats (rows) and Hows (columns) within each house. As a result it links (flowdown) the Customer

CTQs to items further down in the process. This ultimately determines the design of the high throughput

Business ProductDeliverables

(HOW s)

House#1

CustomerNeeds

CustomerWants

(WHATs)

Catalyst,Materials, Process(HOW s)

House#2

ProductctDeliverables

(WHATs)

HTS SystemCharacteristics

(HOW s)

House#3

sVariables

)

HTS Variables(HOW s)

Houses

tem


8/18

Importance

Qu

ality

Produ

ct

IncreaseCapacity

@L

ow

estCost

TotalCost

Env

iron

.HealthSafety

New polymer is the same as standard

Lowest cost polymer

Total

House 1

External CustomerExpectation

5

4

h

I

49

I

h

41

I

h

41

I

m

17

Imp

ortance

SideProdu

ctContent

InvestmentCosts

Manu

facturingThrou

ghpu

t

ResinColor

Quality Product

Increase Capacity @ Lowest Cost

Total

House 2

Business ProductDeliverables

5

4

h

h

86

I

h

78

m

h

56

h

I

54

ResinColorStability

ResinHy

drostability

ResinMeltFlow

CappingLev

el

Var

iableCosts

ReactionResidu

als

ResinManu

facturability

Total

h

I

h

I

54

h

I

54

m

m

40

I

m

30

m

I

I

I

22

285

156

54 26

4 I h I I I I I m m I I 92Total Cost

1 I I I I I I I I I m h 21

Business Product Deliverables

Polymer Reaction Measurables

Environ. Health and Safety

Figure 6.First and Second House QFD relating customer CTQs with measurable product and process deliverables.

DataAnalysis

Preparation Chemical

ReactionAnalytical

PreparationChemicalAnalysis

Database System

Importance

Design Space Choice

Accuracy

Total

HTS Variables

CombiChem Factors

5

3

2Design Space Coverage

2

2

Reproducibility

Scaleability

126 72 70 70 5858

h h m m

MonomerStability

Reage

ntVolatility

MonomerRatio

Overa

llVariability

h

h

m

h h

I

h

Temp.

Accuracy

MonomerPurity

Liquid

VolumeDelivery

Slurry

Delivery

DiessolutionProcess

SamplePlacementAccuracy

Post-reactionReaction

I

h

I

h

h m I I I I

h h h h h h

h m

42 42 40 38 3034

m I m I m I

SamplingRates

Chrom

atographyPeakPicking

Secon

daryAnalysisAccuracy

SampleTracking

Cataly

stChoice

MonomerReactivity

SolventChoice

OutlierID

PSStandardsforRelativeMw

Temp.

Uniformity

DataS

torage(Long-Term)

m

m

I

h

I

m

I

h

I

I

I

I

m I I I I h

m m h I h I

28 28 28 28 24 24 24 2 424 20

Order

ofAddition

Temp

Profile

ReactionTime

Autom

ationofDataAnalysis&Storage

PreparationTime

Cataly

stDelivery

SolventRemovalProcedure

Cataly

stConcentration

N2Flo

wRate

Liquid

HandlerDeliverytoDSC

Total

230

582

112

380

156

I

54

I

I

h

I

h

54

I

I

h

I

h

54

I

I

h

I

h

54

I

I

h

I

h

54

I

I

h

I

h

54

I

I

h

I

h

54

I

I

h

I

h

54

I

I

h

I

h

54

I

I

h

I

h

42

m

m

m

m

m

I

m

I

m

m

I

m

I

m

m

I

m

I

m

m

I

m

m

m

I

I

m

I

m

I

I

m

I

m

I

I

m

I

m

I

I

m

I

I

m

I

m

I

m

I

I

m

I

I

I


9/18

screening (HTS) system (the combinatorial factory). Conversely, once the HTS system is in place, its

statistical capability to identify new leads can be related back (flowup) to the ability to reliably deliver

the Customer CTQs.

The next key step in utilizing the QFD is to focus on the design of the HTS system. This is an iterative

process linking the Customer CTQs with the chemical analysis system and the reactor design

specifications. Simply stated, both the analysis and the reaction system have to be sufficiently capable to

allow identification of leads or hits above the composite noise of the systems. However, the ability to

perform certain high throughput analyses is a function of the type of reaction and reactor employed and,

similarly, the reaction system can be configured to more easily allow rapid in-line or off-line analysis. This

iterative process occurs early in the HTS design process and fully utilizes the interactive matrix of

variables and responses delineated by the QFD process.

To fully understand the interaction of the analysis and reaction systems, a complete process map is

constructed for the steps involved in running the HTS factory. Figure 8 depicts a generalized process map

including the design and operation stages of a HTS project. These generalized process steps can be sub-

divided into more detailed actions or steps. Figure 9 highlights an example of detailed steps that

correspond to Prepare Arrays in Figure 8. The detailed process steps that are unnecessary or unwieldy

can be changed or eliminated. The remaining steps in the process map can then be linked to the variables

in the HTS house of the QFD. Process steps related to those elements of the HTS house which have the

greatest impact on the Customer CTQ's are then the focus of significant quality evaluation and refinement

to reduce sources of variability.

IDOV Step 2: DesignThe key Six Sigma activities in the Design stage include setting the specifications for the system and

flowing up the variance from the subsystems to the total system.

Process Design

Analytical SystemDesign

Decide on Systemto Study

PreworkProcess

S P

FactoryDesign

FactoryProduction

Inputs

ChemicalSystem

InputsIdeas,Chemicals

DetermineDesign Required

ConstructPrototype

Validate Safetyand Operation

Define Key Analysisand Analysis Precision

Needed

Evaluate andValidateMethod

Plan and PrioritizeExperimentalUniverse

Plan CombinatorialStrategy forExploration

PurchaseChemicals

SynthesisSafety,

Solubility, andCompatibility Tests

Define StockSolutions andCoat Mixtures


10/18

Specifications

Unlike a conventional factory, a high throughput experimental system has only information as its product,

so the specifications are on the quality of that information. Therefore, all specifications are expressed in

terms of variance (or, more conventionally, standard deviation). Specifications are not internally

determined; they are externally agreed upon by the customer and the process team, and should flowlogically from the measurements of the critical customer parameters (CTQs). In our catalyst

development case, the critical parameters were accurate and rapid identification of catalyst leads. In

addition, the desired catalyst activity was more than double the current level. From that, we agreed that a

25% increase in activity would represent a useful lead, and that we desired a 95% probability of correctly

detecting that lead.

CTQs Specification

Accurate lead identification 95% probability of correctly detecting Rapid lead identification a 25% increase in catalyst activity

>100% increase in catalyst activity

This specification is on the whole system: the major program of the Design effort is to flow that

specification down to the process subsystems then flow the variances of the subsystems back up to the

Transportarray

to oven

Removesolvent

Securepipette

tips

Charge initiatorto array

with replicator

Transportarray to

vac oven

Removesolvent

Weighcatalystfor stock

Addsolvent

for stock

Chargecatalysts toHTS array

Removesolvent

Secure tipsto 96 wellreplicator

Charge reagentsto array with

replicator

Weighreagents for

stocks

Dissolvereagents for

stocks

DataAnalysis

Preparation Chemical

ReactionAnalytical

PreparationChemicalAnalysis

Database System

Figure 9.Detailed process map for array preparation.


11/18


12/18

Replicate Stocks

Replicate Reaction Runs

Replicate Samples

Replicate Analyses

Figure 11. A typical process in making a combinatorial or high throughput array.

Figure 12.A hierarchical experimental design for studying a nested system.

Defect AccountingIn defect accounting, the entire system and each subsystem is process mapped in detail. Each step is

examined by the Six Sigma Black Belt and the process expert for potential sources of defects. These can

be classified by type:

Parts. The quality level of components or raw materials (such as chemicals)

Process. The quality level of each unit operation (e.g. weighing, fluid transfer, or reaction).

Performance. The overall quality of subsystem performance vs specifications when all elements are

working correctly but normal variance is present.

Software. The quality of any software specifically written for the system.

The outcome of the process is illustrated in Figure 2, which shows a small part of a high throughput

screening system. Even with this small fraction of the overall opportunities for defects, we predicted 5.6

DPU.

Process Step

1 Make Stocks

2 MakeArray

3 React Array

4 Analyze Array

Factors

Stock solution factors:Amount of chemical

Sample factors:Amounts addedTotal volume

Reactor factors:Temperature

Reaction time Pressure Gas composition

Analysis factors: Peak identification settings

Integration settings


13/18

IDOV Step 3: Optimize

Optimization of the high variance processes identified in the previous steps is done by the standard Six

Sigma Measure-Analyze-Improve-Control (MAIC) process. The following cases illustrate the particularproblems of high throughput screening and the quality level we have been able to achieve through MAIC

methodology.

Case 1: Analytical System Gage Reliability and Reproducibility

The analytical system is the heart of any high throughput experimental operation, so it must operate at the

highest level of reliability for the operation to succeed. There are too many samples per day for the

operator or scientist to scan the raw data (spectra, chromatograms, etc) for accuracy and consistency. In

our catalyst system, the analysis was a rapid gas chromatogram. The MAIC process improved its

capability immensely:

Measure: one subsystem CTQ was peak

identification. A gas chromatograph integration

system identifies components by selecting the

largest peak inside a set retention time window.

(Figure 13.) Therefore the consistency of peak

retention times is critical to the overall system

capability.

Analyze: the initial Six Sigma analysis (Figure

14) of the chromatographic relative retention

time capability showed that the capability of the

system was far too low. The small groups in the

histogram were identified as single day

measurements. There was therefore severe day

to day drift (Zlt), but even the individual groups

indicated poor within-day capability (Zst).

Improve: Designed experiments on the numberand type of internal standards optimized the

system (Figure 15). The Zst improved to 27 and the Zlt to 12 far more than six sigma! Missed peak

identification from this source can be estimated to be in the parts per trillion.

counts

50000

40000

30000

20000

10000

1 1.5 2 2.5

Peakidentification

window

Peak

retention timevariation

Figure 13.The critical parameters in chromato-

graphic peak identification.

ZST1.8

ZLT0.6

LSL USL

Short Term

Long Term

LSL USL

Short Term

Long Term

ZST27

ZLT12


14/18

Control: Careful choice of the internal standards allows diagnosis of potential deterioration in

chromatographic behavior by tracking their relative areas using control charts.

Robot System Mixing Capability

Robotic pipettors are a mainstay of high throughput experimentation. They were primarily developed for

the medical and pharmaceutical industries so they are optimized for operation with water. In our system,

we planned to combine several different stock solutions made with a nonaqueous solvent and mix them by

repetitive aspirate and release operations. This was specified by the manufacturer as an effective mixing

technique. Using MAIC we found otherwise:

Measure: the subsystem CTQ was mixture uniformity. The initial specification was set at 20%

variation around the nominal, as measured by six consecutive chromatographic samples from a single

vial.

Analyze: the variation of the system was found to be more than 30% (Figure 16)

Improve: DOE on the pipette control parameters showed that the variation could not be reduced to

specifications at any available settings! A second experiment comparing alternative mixing methods

showed that a miniature magnetic stirring bar in each vial was the only way to achieve adequate

mixing (Figure 17).

Control: Mistake-proofing the procedure for putting the stirring bars in the vials was important. New

stirring bars for each experiment proved necessary to minimize cross contamination.

Mixture Sample

MixRatios

ZST7.7

ZLT7.5

LSL USL

Short Term

Long Term

Figure 16. The variation in composition of six repetitive measure-

ments taken on 58 samples after mixing by aspiration.


15/18

Reactor Temperature Control

In any materials development program that involves

the rate of a chemical reaction as a critical

parameter, temperature control and uniformity are

often under appreciated. Figure 18 shows the effect

on reaction rate variance caused by a small (2C)temperature variability. With MAIC we:

Measure: Since catalyst TON (Turn Over

Number) is directly related to reaction rate,

temperature was identified as a CTQ. A 2C

change in temperature was found to cause a

20% change in reaction rate in our system, so 2

C variation was set as the specification. Some

immediate low-hanging fruit was discovered

when it was found that the reactor temperature

was controlled with a low quality, easily broken

thermocouple (standard wire error 1.1-2.9C).That was replaced with a platinum resistance

thermometer (deviation 0.35C) 12.

Analyze: Measurement of the actual sample

temperatures showed relatively small sample to

sample variation but large swings caused by the

temperature control system (Figure 20) caused

by the crude control system and the thick walls

of the high pressure reactor.

Improve: Careful examination of the data

shown in Figure 19 showed that the within-

reactor temperature control was quite good (Zst= 4.5, as shown by the closeness of the lines in

the Figure). The critical problem was the

temperature control system, which suffered

severe lags because of the reactor wall

thickness. A control system upgrade followed by

optimization of its parameters reduced the temperature swings to less than 1.5C. We also modifiedthe sample holder to improve gas circulation within the autoclave.

Control: Once the optimized control system was in place, the system was set on an automaticprocedure which minimized the variability. An ongoing program of SPC samples in the process has

shown acceptable performance.

Validate

PercentVariation

Reaction Temperature

ActivationEnergy (Kcal)

16%

12%

8%

4%

0%

120 180240

3005

10

20

Figure 18.Rate variation caused by a 2 C

temperature difference across a reactor array as a

function of reaction temperature and Arrhenius

activation energy.

TC Locations

TC 1TC 2TC 3TC 4TC 5

ManualTC readings

12

34

5

Actual Run Time11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00

TempC

102

100

98

96

94

92

90

88

86

84

82

2 C Spec

Figure 19. The actual variation in temperature

within the high pressure autoclave as measured

by precision thermocouples.


16/18

being identified as leads for more detailed

traditional evaluation. Out of these leads, 90%

were confirmed as high activity catalysts with

lower side product formation in larger-scale

laboratory experiments. Figure 20 shows the

distribution of catalyst with various activity and

side-product formation. The current catalyst

performance is shown along with some of the

confirmed HTS leads highlighted by arrows.

Conclusion

In two years, we have successfully used this high

throughput screening technology to explore >2000

unique materials combinations in over 10,000

different ratios. Many hits were discovered and

several patents filed. The overall benefits to the

catalyst development program were:

A broad, proprietary technology position has

been developed for this process

New systems with attractive economic and environmental properties have been identified

Wide exploration has been made less risky. We have ended the moth around the flame syndrome, in

which only modest variations were made on a known good system because wild exploration was

too expensive.

From this we have identified the key learnings from application of DFSS (Designfor Six Sigma) to combi-

natorial and high throughput experimental systems:

Link and prioritize multiple complex steps with Quality Function Deployment

Pay special attention to the Gage Repeatability and Reproducibility of the analytical system

Focus on the variance of subsystems to reduce the overall system variance

Roll up subsystem variance to estimate system variance

Attack variance using advanced Design of Experiments tools.

References:

(1) Liu, D. R.; Schultz, P. G. Generating New Molecular Function: A Lesson From Nature.Angew. Chem. Int. Ed

1999, 38, 36-54.(2) Symyx Technologies.http://www.symyx.com, (April 3)

(3) Harry, M. J. The Nature of Six Sigma Quality; Motorola University Press: Schaumburg, IL, 1988.

(4) The quality of routine manual operations (e.g . restaurant bills, prescription writing) tends to cluster at 4

Sigma or 6000 DPMO: Harry, M. J. The Vision of Six Sigma: A roadmap for Breakthrough; Sigma Publishing

Company: Phoenix, AZ, 1994, p19.26.

(5) J J M I J Q li C l H db k 4 h d J J M Ed M G Hill N Y k 1988

ActivitySid

eProd

uct

Frequency

0

5

10

15

20

25

30

35

40

45

50

CurrentCatalyst

Figure 20.Distribution of catalyst activity and side

product production for an oligomerization catalyst.

The small yellow arrows indicate leads which were

confirmed upon scaleup.


17/18

Distribution List 2001CRD055

Corporate Research and Development

Research Circle

Niskayuna, NY 12309

M. Brennan

D. Buckley

J. Carnahan

J. CawseG. Chambers

B. Chisholm

K. Cueman

D. Dietrich

N. Doganaksoy

T. Early

V. Eddy-HelenekW. Flanagan

J. Flock

J. Hallgren

C. Hansen

B. Johnson

T. Jordan

R. Kilmer

T. LeibJ. Lemmon

R. Mattheyses

R. May

C. Molaison

GE Plastics5th and Avery Streets

Parkersburg, WV 26101

A. Berzinis

W. Morris

D. Olson

B. Pomeroy

R. PotyrailoE. Pressman

T. Repoff

C. Sabourin

A. Schmidt

R. Shaffer

K. Shalyaev

O. Siclovan

J. Silva

G. Soloveichik

J. Spivack

C. Stanard

J. Teetsov

J. Webb

J. Wetzel

D. WhisenhuntB. Williams

R. Wroczynski

GE Plastics

Highway 69 South

Mt. Vernon, IN 47620

S. Cooper

J. DeRudder

D. Whalen


18/18

J.N. Cawse Application of Six Sigma Quality Tools to High Throughput and Combinatorial 2001CRD055

R. Wroczynski Materials Development April 2001

dfss-materialdevelopment

Documents