Design of Experiments Group Presentation- Spring 2013

Optimization of Pipetting Parameters for a Robotic Liquid Handler

Kristi Ballard, Chuck Kemmerer, Thorsten Verch

14 April 2013

Temple QA/RA DOE Course

Problem Statement

• Pipetting steps have associated accuracy/bias and precision ranges

• Minimizing bias and %CV is key to analytical method performance

• Factors affecting pipet bias and precision of a robotic liquid handler were investigated for improvement opportunities

• Goal: Lowest Bias and Lowest %CV possible

Accuracy/Bias & Precision

• Bias can be accommodated through calibration

• Imprecision cannot be adjusted

Image: http://academics.wellesley.edu/Chemistry/Chem105manual/Appendices/uncertainty_analysis.html Created By: Adilia James '07 and Sarah Coutlee '07

http://academics.wellesley.edu/Chemistry/Chem105manual/Appendices/uncertainty_analysis.html





TECAN Robotic Liquid Handler

8 Independent syringes to aspirate/dispense

LiHa picks up tips and moves liquid from reservoirs into microwell plates

Dispense into microwell plates: Screening: 4 runs / plate Confirmation: 2 runs / plate

Artel Measurement System

• Measurement of a red dye in an undiluted blue background

• Calculates delivered volume by comparing measured concentration with expected concentration

• Lambert-Beer:

Image: J Biomol Screen July 12, 2012 , doi: 10.1177/1087057112453433

c: Concentration A: Absorbance (measured) e: Extinction Coefficient (known) d: Path length (known)

Selected Pipetting Parameters

Volume

Aspirate Speed ↑ Dispense Speed ↓

Aspirate Delay ↑ Dispense Delay ↓

Break-off Speed

Blow-out (Leading Airgap)

Retract Speed post Aspirate Retract Speed post Dispense

Pre-wet (Conditioning)

Held Constant: System Air Gap Trailing Air Gap Calibration Factors Pipet Height in Liquid Channels Tip Size Liquid Type / Viscosity (Volume)

Screening DOE 1

Objectives:

Comparison of different instruments

Evaluation of 11 factors

Experimental Design (Half)

• 12+12 Run Plackett-Burman fold-over 11 Factors

• 1 Block for robot

• Second half of the fold-over design was run on a separate day

RunOr

der

Asp

vol

Asp

spd

Asp

del

Asp ret

spd

Asp ld

air

gap

Trl air

gap

Cond

vol

Dis

spd Dis dly

Dis ret

spd

Dis bk-off

spd

1 200 50 50 5 10 10 yes 50 1000 60 20

2 200 150 50 60 10 0 yes 50 50 5 200

3 25 50 50 5 0 0 No 50 50 5 20

4 200 150 1000 5 10 10 No 500 50 5 20

5 25 150 50 5 0 10 yes 500 50 60 200

6 25 150 1000 60 0 10 yes 50 1000 5 20

7 25 50 50 60 10 10 No 500 1000 5 200

8 25 50 1000 60 10 0 yes 500 50 60 20

9 200 150 50 60 0 0 No 500 1000 60 20

10 200 50 1000 5 0 0 yes 500 1000 5 200

11 200 50 1000 60 0 10 No 50 50 60 200

12 25 150 1000 5 10 0 No 50 1000 60 200

• 8 channel replicates (pipets) / run 3 dispense replicates / channel 4 robots 12 runs x 3 replicates x 8 wells x 4 robots = 1152 data points!

Results

EVO150BBR0509BBR0508BBR0479

50

25

0

-25

-50

-75

Robot ID

% V

olu

me

Bia

s

Boxplot of % Volume Bias

• Difference in the mean bias between the robots depends on run

• Subset of instruments was used as representative

• Higher variability observed in one instrument

• Blocking by instrument was not used downstream in order to maintain a “worst case” scenario Data were averaged across instruments

BBR0509BBR0508

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Robot ID

Lo

g %

CV

Boxplot of Log %CV

BBR0509BBR0508

1.75

1.50

1.25

1.00

0.75

0.50

Robot ID

Lo

g %

Bia

s

Boxplot of Log %BiasDOE2 DOE1

Data Transformation

• Log transformed data distribution is closer to normal

• Log data used for all models

6040200-20

99.99

99

90

50

10

1

0.01

Residual

Pe

rce

nt

10987

60

45

30

15

0

Fitted Value

Re

sid

ua

l

50403020100-10

240

180

120

60

0

Residual

Fre

qu

en

cy

1100

100090

080

070

060

050

040

030

020

010

01

60

45

30

15

0

Observation Order

Re

sid

ua

l

Normal Probability Plot Versus Fits

Histogram Versus Order

Residual Plots for Absolute % volume bias

Results, Main Factor Model, Pareto

• Aspiration Volume has the largest effect for %CV → Not practical to improve process by volume. Required to pipette multiple volumes. *Use the small volume for future runs*

• Conditioning has the largest effect for %Bias → Do not condition during future runs.

Asp Speed

Asp delay

Asp Retract Speed

Break-off Speed

Asp lead/Disp Trail Air Gap

Dispense Trailing Air Gap

Disp Delay

Disp Speed

Retract Speed

Target Volume (uL)

Conditioning

43210

Te

rm

Standardized Effect

2.030

DOE1 with Volume, Pareto, Main Factors Only(response is Log %Bias, Alpha = 0.05)

Disp Delay

Retract Speed

Disp Speed

Asp Speed

Dispense Trailing Air Gap

Asp Retract Speed

Break-off Speed


Conditioning

Asp delay

Target Volume (uL)

76543210

Te

rm

Standardized Effect

2.030

DOE1 with Volume, Pareto, Main Factors Only(response is Log %CV, Alpha = 0.05)

Log % Bias Log % CV

Results, Effects

• Positive conditioning mean high bias and high %CV → Do not condition during future runs.

• Blocked for volume

• Main effects only

Screening DOE 2

Objectives:

Confirmation of DOE1

Improved Experimental Design

Remove Run Block

Experimental Design (Half)

• 12+12 Run Plackett-Burman fold-over 9 Factors

• Low volume only (20 mL)

• 8 channel replicates (pipets) / run 3 dispense replicates / channel 2 robots 24 runs x 3 replicates x 8 wells x 2 robots = 1152 data points!

Run Asp Spd Asp del

Asp Retr

Spd

Asp ld/Dsp

Trl Air Gap Cond Disp Spd Disp Del Ret Spd

BkOff

Spd

1 50 1000 60 10 no 50 50 60 20

2 250 1000 60 10 no 500 1000 5 20

3 250 50 5 10 no 50 1000 60 200

4 50 50 60 10 yes 50 1000 5 200

5 50 50 60 0 yes 500 1000 60 20

6 250 1000 60 0 yes 50 50 5 200

7 250 50 60 0 no 500 50 60 200

8 50 1000 5 10 yes 500 50 60 200

9 50 1000 5 0 no 500 1000 5 200

10 50 50 5 0 no 50 50 5 20

11 250 1000 5 0 yes 50 1000 60 20

12 250 50 5 10 yes 500 50 5 20

Results, Main Effects Model, Pareto

• Conditioning & Dispense Speed have the largest effect for both responses

Log % Bias Log % CV

Asp Retract Speed

Disp Delay

Asp Speed

Retract Speed

Asp delay

Break-off Speed


Disp Speed

Conditioning

3.02.52.01.51.00.50.0

Te

rm

Standardized Effect

2.024

Pareto Chart of the Standardized Effects(response is Log %CV, Alpha = 0.05)

Disp Delay

Asp delay

Break-off Speed

Asp Retract Speed

Retract Speed


Asp Speed

Disp Speed

Conditioning

2.01.51.00.50.0

Te

rm

Standardized Effect

2.024

Pareto Chart of the Standardized Effects(response is Log %Bias, Alpha = 0.05)

Results, Second Order Model, Pareto

• Conditioning has the largest effect for both responses

• Many interactions → Follow up with a resolution V design

Log % Bias Log % CV

CG

ABAH

ADAC

BJ

AEAHBF

BCAJAF

BEDF

AGE

3.02.52.01.51.00.50.0

Te

rm

Standardized Effect

2.052

A A sp Speed

B A sp delay

C A sp Retract Speed

D A sp lead/Disp Trail A ir Gap

E C onditioning

F Disp Speed

G Disp Delay

H Retract Speed

J Break-off Speed

Factor Name

DOE2, Pareto(response is Log %CV, Alpha = 0.05)

ABAC

GB

AE

BFJCH

AHDA

BCADAF

AJBEAG

FE

2.52.01.51.00.50.0

Te

rm

Standardized Effect

2.052

A A sp Speed

B A sp delay

C A sp Retract Speed

D A sp lead/Disp Trail A ir Gap

E C onditioning

F Disp Speed

G Disp Delay

H Retract Speed

J Break-off Speed

Factor Name

DOE2, Pareto(response is Log %Bias, Alpha = 0.05)

25050

0.9

0.8

0.7

100050 605

100

0.9

0.8

0.7

y esno 50050

100050

0.9

0.8

0.7

605 20020

A sp Speed

Me

an

A sp delay A sp Retract Speed

A sp lead/Disp Trail A ir Gap C onditioning Disp Speed

Disp Delay Retract Speed Break-off Speed

Main Effects Plot for Log %CVData Means

Main Effects

Largest Effects (Minimize Bias and %CV)

• Trailing Airgap (high)

• Conditioning (low)

• Dispense Speed (high)

25050

1.1

1.0

0.9

100050 605

100

1.1

1.0

0.9

y esno 50050

100050

1.1

1.0

0.9

605 20020

A sp Speed

Me

an




Main Effects Plot for Log %BiasData Means

100050 yesno 100050 20020

1.0

0.8

0.61.0

0.8

0.61.0

0.8

0.61.0

0.8

0.6

Asp Speed

Asp delay

Conditioning

Disp Delay

Break-off Speed

50

250

Speed

Asp

50

1000

Asp delay

no

yes

Conditioning

50

1000

Delay

Disp

Interaction Plot for Log %CVData Means

Interaction Effects

Largest Effects (Minimize Bias and %CV)

• Trailing Airgap (high)

• Conditioning (low)

• Dispense Speed (high)

100050 yesno 100050 20020

1.10

0.95

0.80

1.10

0.95

0.80

1.10

0.95

0.80

1.10

0.95

0.80

Asp Speed

Asp delay

Conditioning

Disp Delay

Break-off Speed

50

250

Speed

Asp

50

1000

Asp delay

no

yes

Conditioning

50

1000

Delay

Disp

Interaction Plot for Log %BiasData Means

Asp

Speed

Asp

Delay

Conditioning

Disp

Delay

Break-off

Speed

Asp

Speed

Asp

Delay

Conditioning

Disp

Delay

Break-off

Speed

Effect Comparison – DOE1 vs. DOE2

Factor

% Bias

DOE1

% Bias

DOE2

% CV

DOE1

% CV

DOE2

Aspiration Speed 0 0 0 0

Aspiration Delay 0 0 (+) 0

Aspiration Retract

Speed 0 0 (+) 0

Asp lead/Disp Trail Air

Gap 0 0 (-) (-)

Conditioning (+) (+) (+) (+)

Dispense Speed 0 (-) 0 (-)

Dispense Delay 0 0 0 0

Retract Speed (+) 0 0 0

Break-off Speed 0 0 (-) 0

Confirmation Run

Set-Up

• 3 Liquid Classes: 1. Best conditions from DOE runs 2. Best conditions predicted by effects model 3. Original settings

• 2 calibration modifications: 1. Original 2. Reduced off-set

• 2 Volumes: 1. 20 mL 2. 100 mL

• 6 dispense replicates / channel • 8 channels • 6 * 8 = 24 data points per liquid class & volume

Results (Total Averaged Data)

% Bias % CV

• No improvement in %Bias

• Much tighter %CV for settings predicted by the model

• Single outlier in original liquid class No solution was added to the wells (robotic failure?)

Results (Outlier Excluded)

OldDOE Prediction-OffsetDOE PredictionDOE Data-OffsetDOE Data

10.0

7.5

5.0

2.5

0.0

-2.5

-5.0

Liquid Class

Av

era

ge

% B

ias

Boxplot of Average % Bias, outlier excluded

OldDOE Prediction-OffsetDOE PredictionDOE Data-OffsetDOE Data

700

600

500

400

300

200

100

0

-100

-200

Liquid Class

Av

era

ge

%C

V o

f b

ias

Boxplot of Average %CV of bias, outlier excluded

% Bias % CV

• No improvement in %Bias

• Much tighter %CV for settings predicted by the model

Conclusions - Strategy -

• No significant difference between robots (?) Liquid classes can be transferred

• Optimization at 20 mL can be transferred to larger volumes

• Bias may be addressed with calibration Liquid class can be optimized for precision

• Placket-Burman design is efficient to screen a number of factors and reduce the total. Fold-over design helps understanding main factors

• Even with a limited model, improvements can be achieved

• Numerous interactions require a fractional factorial design for complete modeling

Conclusions - DOE Analysis -

• Response may need log-transformation

• Bias and Precision are driven by different factors with some overlapp

• Most factors interact. Few stand-alone main effects.

• Confounded models may not adequately predict performance

• Blocking for factors that are not going to be optimized further can enhance analysis of the remaining factors

Path Forward

• Characterization DOE and eventually a Response Surface DOE with reduced number of factors

• Repeat the study for a protein solution (2-5% BSA)

• Improve experimental design based on experience with this study:

– No conditioning

– Focus on lowest volume

– Consider a fractional factorial design with less replication for screening

Back-ups

DOE1 – Residual Plots

0.500.250.00-0.25-0.50

99

90

50

10

1

Residual

Pe

rce

nt

1.21.00.80.60.4

0.50

0.25

0.00

-0.25

-0.50

Fitted Value

Re

sid

ua

l

0.40.20.0-0.2-0.4

24

18

12

6

0

Residual

Fre

qu

en

cy

454035302520151051

0.50

0.25

0.00

-0.25

-0.50

Observation Order

Re

sid

ua

l



DOE1, Residual Plots for Log %Bias

DOE2 – Residual Plots

0.500.250.00-0.25-0.50

99

90

50

10

1

Residual

Pe

rce

nt

1.20.90.60.3

0.50

0.25

0.00

-0.25

-0.50

Fitted Value

Re

sid

ua

l

0.40.20.0-0.2-0.4-0.6

8

6

4

2

0

Residual

Fre

qu

en

cy

454035302520151051

0.50

0.25

0.00

-0.25

-0.50

Observation Order

Re

sid

ua

l



DOE2, Residual Plots for Log %CV

0.500.250.00-0.25-0.50

99

90

50

10

1

Residual

Pe

rce

nt

1.41.21.00.80.6

0.50

0.25

0.00

-0.25

-0.50

Fitted Value

Re

sid

ua

l

0.40.20.0-0.2-0.4

12

9

6

3

0

Residual

Fre

qu

en

cy

454035302520151051

0.50

0.25

0.00

-0.25

-0.50

Observation Order

Re

sid

ua

l



DOE2, Residual Plots for Log %Bias

Comparison of DOE1 & 2, Main Effects

15050

1.0

0.9

0.8

100050 605 100

100

1.0

0.9

0.8

y esno 50050 100050

605

1.0

0.9

0.8

20020

A sp Speed

Me

an

A sp delay A sp Retract Speed A sp lead/Disp Trail A ir Gap

Dispense Trailing A ir Gap C onditioning Disp Speed Disp Delay

Retract Speed Break-off Speed

DOE1, Main Effects Plot for Log %BiasData Means

25050

1.1

1.0

0.9

100050 605

100

1.1

1.0

0.9

y esno 50050

100050

1.1

1.0

0.9

605 20020

A sp Speed

Me

an




DOE2, Main Effects Plot for Log %BiasData Means

Comparison of DOE1 & 2, Main Effects

15050

0.5

0.4

0.3

100050 605 100

100

0.5

0.4

0.3

y esno 50050 100050

605

0.5

0.4

0.3

20020

A sp Speed

Me

an

A sp delay A sp Retract Speed A sp lead/Disp Trail A ir Gap

Dispense Trailing A ir Gap C onditioning Disp Speed Disp Delay

Retract Speed Break-off Speed

DOE1, Main Effects Plot for Log %CVData Means

25050

0.9

0.8

0.7

100050 605

100

0.9

0.8

0.7

y esno 50050

100050

0.9

0.8

0.7

605 20020

A sp SpeedM

ea

nA sp delay A sp Retract Speed



DOE2, Main Effects Plot for Log %CVData Means

DOE1, Results, Second Order Factor Model

• Aspiration Volume has the largest effect for %CV → Not practical to improve process by volume. Required to pipette multiple volumes. *Use the small volume for future runs*

• Conditioning has the largest effect for %Bias → Do not condition during future runs.

ACBC

ABDLEF

ALJH

AJAD

KBCAGAFAKAEAH

AG

43210

Te

rm

Standardized Effect

2.064

K Retract Speed

L Break-off Speed

A Target V olume (uL)

B A sp Speed

C A sp delay

D A sp Retract Speed

E A sp lead/Disp Trail A ir Gap

F Dispense Trailing A ir Gap

G C onditioning

H Disp Speed

J Disp Delay

Factor Name

DOE1 with Volume, Pareto(response is Log %Bias, Alpha = 0.05)

ABAH

JAE

KALACBCAJAKAFHBFDLE

AGAD

GCA

76543210

Te

rm

Standardized Effect

2.064

K Retract Speed

L Break-off Speed

A Target V olume (uL)

B A sp Speed

C A sp delay

D A sp Retract Speed

E A sp lead/Disp Trail A ir Gap

F Dispense Trailing A ir Gap

G C onditioning

H Disp Speed

J Disp Delay

Factor Name

DOE1 with Volume, Pareto(response is Log %CV, Alpha = 0.05)

DOE2 – ANOVA of log %CV

Analysis of Variance for Log %CV (coded units)

Source DF Seq SS Adj SS Adj MS F P

Main Effects 9 2.15259 2.15259 0.23918 1.73 0.116

Asp Speed 1 0.00360 0.00360 0.00360 0.03 0.873

Asp delay 1 0.02056 0.02056 0.02056 0.15 0.702

Asp Retract Speed 1 0.00006 0.00006 0.00006 0.00 0.983

Asp lead/Disp Trail Air Gap 1 0.46353 0.46353 0.46353 3.35 0.075

Conditioning 1 1.01453 1.01453 1.01453 7.34 0.010

Disp Speed 1 0.61547 0.61547 0.61547 4.45 0.042

Disp Delay 1 0.00050 0.00050 0.00050 0.00 0.952

Retract Speed 1 0.00440 0.00440 0.00440 0.03 0.859

Break-off Speed 1 0.02993 0.02993 0.02993 0.22 0.644

Residual Error 38 5.25335 5.25335 0.13825

Lack of Fit 14 2.57768 2.57768 0.18412 1.65 0.136

Pure Error 24 2.67567 2.67567 0.11149

Total 47 7.40594

Unusual Observations for Log %CV

Obs StdOrder Log %CV Fit SE Fit Residual St Resid

40 40 1.33029 0.59572 0.16971 0.73456 2.22R

R denotes an observation with a large standardized residual.

Estimated Coefficients for Log %CV using data in uncoded units

Term Coef

Constant 1.03053

Asp Speed 0.000086644

Asp delay -4.35743E-05

Asp Retract Speed -0.00004191

Asp lead/Disp Trail Air Gap -0.0196538

Conditioning 0.145383

Disp Speed -5.03271E-04

Disp Delay -6.78313E-06

Retract Speed -0.00034799

Break-off Speed 0.000277455

Factorial Fit: Log %CV versus Asp Speed, Asp delay, ... Estimated Effects and Coefficients for Log %CV (coded units)

Term Effect Coef SE Coef T P

Constant 0.7983 0.05367 14.87 0.000

Asp Speed 0.0173 0.0087 0.05367 0.16 0.873

Asp delay -0.0414 -0.0207 0.05367 -0.39 0.702

Asp Retract Speed -0.0023 -0.0012 0.05367 -0.02 0.983

Asp lead/Disp Trail Air Gap -0.1965 -0.0983 0.05367 -1.83 0.075

Conditioning 0.2908 0.1454 0.05367 2.71 0.010

Disp Speed -0.2265 -0.1132 0.05367 -2.11 0.042

Disp Delay -0.0064 -0.0032 0.05367 -0.06 0.952

Retract Speed -0.0191 -0.0096 0.05367 -0.18 0.859

Break-off Speed 0.0499 0.0250 0.05367 0.47 0.644

S = 0.371815 PRESS = 8.38208

R-Sq = 29.07% R-Sq(pred) = 0.00% R-Sq(adj) = 12.27%

DOE2 – ANOVA of log %Bias

Analysis of Variance for Log %Bias (coded units)

Source DF Seq SS Adj SS Adj MS F P

Main Effects 9 1.23678 1.23678 0.137420 1.22 0.312

Asp Speed 1 0.08264 0.08264 0.082638 0.73 0.397

Asp delay 1 0.00415 0.00415 0.004153 0.04 0.849

Asp Retract Speed 1 0.02543 0.02543 0.025431 0.23 0.637

Asp lead/Disp Trail Air Gap 1 0.06964 0.06964 0.069644 0.62 0.436

Conditioning 1 0.49750 0.49750 0.497495 4.42 0.042

Disp Speed 1 0.49657 0.49657 0.496573 4.41 0.042

Disp Delay 1 0.00262 0.00262 0.002625 0.02 0.879

Retract Speed 1 0.04862 0.04862 0.048619 0.43 0.515

Break-off Speed 1 0.00961 0.00961 0.009605 0.09 0.772

Residual Error 38 4.27884 4.27884 0.112601

Lack of Fit 14 1.78955 1.78955 0.127825 1.23 0.316

Pure Error 24 2.48929 2.48929 0.103721

Total 47 5.51562

Unusual Observations for Log %Bias

Obs StdOrder Log %Bias Fit SE Fit Residual St Resid

40 40 1.53174 0.85781 0.15316 0.67393 2.26R

44 44 1.74424 1.07701 0.15316 0.66723 2.23R

48 48 1.72926 1.01078 0.15316 0.71848 2.41R

R denotes an observation with a large standardized residual.

Estimated Coefficients for Log %Bias using data in uncoded units

Term Coef

Constant 1.03024

Asp Speed 0.000414925

Asp delay -1.95826E-05

Asp Retract Speed 0.00083700

Asp lead/Disp Trail Air Gap -0.00761819

Conditioning 0.101806

Disp Speed -4.52052E-04

Disp Delay 0.000015568

Retract Speed 0.00115731

Break-off Speed -1.57179E-04

Factorial Fit: Log %Bias versus Asp Speed, Asp delay, ... Estimated Effects and Coefficients for Log %Bias (coded units)

Term Effect Coef SE Coef T P

Constant 0.9755 0.04843 20.14 0.000

Asp Speed 0.0830 0.0415 0.04843 0.86 0.397

Asp delay -0.0186 -0.0093 0.04843 -0.19 0.849

Asp Retract Speed 0.0460 0.0230 0.04843 0.48 0.637

Asp lead/Disp Trail Air Gap -0.0762 -0.0381 0.04843 -0.79 0.436

Conditioning 0.2036 0.1018 0.04843 2.10 0.042

Disp Speed -0.2034 -0.1017 0.04843 -2.10 0.042

Disp Delay 0.0148 0.0074 0.04843 0.15 0.879

Retract Speed 0.0637 0.0318 0.04843 0.66 0.515

Break-off Speed -0.0283 -0.0141 0.04843 -0.29 0.772

S = 0.335561 PRESS = 6.82718

R-Sq = 22.42% R-Sq(pred) = 0.00% R-Sq(adj) = 4.05%

% Bias Interaction Plots: DOE1 vs. DOE 2

100050 yesno 100050 20020

1.10

0.95

0.80

1.10

0.95

0.80

1.10

0.95

0.80

1.10

0.95

0.80

Asp Speed

Asp delay

Conditioning

Disp Delay

Break-off Speed

50

250

Speed

Asp

50

1000

Asp delay

no

yes

Conditioning

50

1000

Delay

Disp

Interaction Plot for Log %BiasData Means

100050 yesNo 100050 20020

6

0

-6

6

0

-6

6

0

-6

6

0

-6

aspiration speed*

aspiration delay

conditioning volume**

dispense delay

Dispense Break-off Speed***

50

150

speed*

aspiration

50

1000

delay

aspiration

No

yes

volume**

conditioning

50

1000

delay

dispense

Interaction Plot for Average Run Bias (%)Data Means

• 2nd Level interactions are confounded in the Plackett Burman Design

• Results from both DOE studies indicate interactions are significant

• A screening study with clear 2-level interactions is needed to learn more

• Mostly similar effects. Differences may be due to:

DOE 1 run with volume as a factor

DOE 2 had a reduced number of factors (9) from DOE (11)

DOE1 DOE2

% Bias Interaction Plots: DOE1 vs. DOE 2

• 2nd Level interactions are confounded in the Plackett Burman Design

• Results from both DOE studies indicate interactions are significant

• A screening study with clear 2-level interactions is needed to learn more

• Mostly similar effects. Differences may be due to:

DOE 1 run with volume as a factor

DOE 2 had a reduced number of factors (9) from DOE (11)

DOE1 DOE2

100050 yesNo 100050 20020

102

96

90

102

96

90

102

96

90

102

96

90

aspiration speed*

aspiration delay

conditioning volume**

dispense delay

Dispense Break-off Speed***

50

150

speed*

aspiration

50

1000

delay

aspiration

No

yes

volume**

conditioning

50

1000

delay

dispense

Interaction Plot for Average Run Accuracy (%)Data Means

100050 yesno 100050 20020

1.0

0.8

0.61.0

0.8

0.61.0

0.8

0.61.0

0.8

0.6

Asp Speed

Asp delay

Conditioning

Disp Delay

Break-off Speed

50

250

Speed

Asp

50

1000

Asp delay

no

yes

Conditioning

50

1000

Delay

Disp

Interaction Plot for Log %CVData Means

Design of Experiments Group Presentation- Spring 2013

Science

Transcript of Design of Experiments Group Presentation- Spring 2013