ExecutingRobustDesign - Rose

Transcript ExecutingRobustDesign - Rose

Executing Robust Design

Definition of Robust Design

Robustness is defined as a condition in which the product or process will be minimally affected by sources of variation.

A product can be robust: Against variation in raw materials Against variation in manufacturing conditions Against variation in manufacturing personnel Against variation in the end use environment ` Against variation in end-users Against wear-out or deterioration

LSL LSL Nom Nom Why We Need to Reduce Variation USL Low Variation; Minimum Cost USL High Variation; High Cost

Purpose of this Module

 To introduce a variation improvement investigation strategy – Can noise factors be manipulated?

 To provide the MINITAB steps to design, execute, and analyze a variability response experiment  To provide the MINITAB steps to optimize a design for both mean and variation effects

Objectives of this Module

At the end of this module, participants will be able to :    Identify possible variation effects from residual plots Create a variability response from replicates Identify possible mean and variance adjustment factors from noise-factor interaction plots  Use the MINITAB Response Optimizer to achieve a process on target with minimum variation

Strategies to Detect Variation Effects

 Passive Approach – Noise factors are

NOT

included, manipulated or controlled in the experimental design – Possible variation effects are identified through analysis of the variability of replicates from an experimental design  Active Approach – Noise factors

ARE

included in the experimental design in order to force variability to occur – Analysis is similar to the passive approach

The Passive Approach

 A factorial experiment is performed using Control factors. Noise factors are not explicitly manipulated nor is an attempt made to control them during the course of the experiment.

 Pros – – Simple extension of standard experimental techniques Does not require explicit identification of noise factors  Cons – – – Requires larger number of replicates than would typically be required to determine mean effects Requires “true” randomization and replication Requires that noise factors be “noisy” during the execution of the experiment

How to ensure that noise is noisy?

  Let excluded factors vary Compare noise factor variations prior to and within DOE – – – Monitor noise factor levels during normal process conditions Monitor noise factor variation during course of experiment Compare before/during levels  Run DOE over a longer period of time with : – – More replicates Full randomization

A Passive Example

A B

 A and B are control factors. Within each treatment combination, noise factors are allowed to naturally fluctuate. Within treatment variation is largely driven by this background noise.

Example output from a Passive Design

  The graphs at right illustrate the type of output which might be obtained from a Robust Parameter Design Experiment. Both are Main Effects plots with the top row showing the main effects of factors A and B on the mean and the bottom row showing the main effects of factors A and B on the

variation

Note that in this example the mean and variation can be adjusted independently of each other!

The Models



Our objective in performing a designed experiment is to develop a transfer function between the factors (X’s) and the Y. Thus far, we have only addressed the mean of Y. 

0 

1 

2 

2    Now we must also consider the variability of Y

s y

2 

0 

1 

2 

2    If our experiments are successful at identifying a variation effect, we now have an opportunity to simultaneously optimize both equations!

Example: A Passive Noise Experiment

 A design engineer has evaluated the output performance of a circuit design and performed an initial capability analysis of this design to determine if there is a problem with the mean and/or the variability. Stat > Quality Tools > Capability Analysis > Normal Y = Y1Initial; Lower Spec = 58; Upper Spec = 62

Design Capability Analysis

 Is there a problem?

Process Data LSL Target U SL Sample Mean Sample N 58 * 62 56.253

100 StDev (Within) 1.47217

StDev (O v erall) 1.5414

Process Capability of Y1Initial

LSL USL Within Overall Potential (Within) C apability C p C PL C PU C pk 0.45

-0.40

1.30

-0.40

O v erall C apability Pp PPL PPU Ppk C pm 0.43

-0.38

1.24

-0.38

* O bserv ed Performance PPM < LSL 860000.00

PPM > U SL PPM Total 0.00

860000.00

52.5

54.0

55.5

Exp. Within Performance PPM < LSL 882324.02

PPM > U SL PPM Total 47.35

882371.37

57.0

58.5

60.0

Exp. O v erall Performance PPM < LSL 871474.48

PPM > U SL PPM Total 96.33

871570.81

61.5

2 Full Factorial Experiment

  A 2 – 4 full factorial has been designed to determine if four factors have an effect on the mean and/or variability of voltage drop (Y1). There are five replicates for a total of 80 runs, with no center points or blocks.

Resistor R33 (A) – – – Inductor L3 (B) Capacitor C23 (E) Capacitor C29 (F) Worksheet: “

Passive Design

”

Passive Analysis Roadmap - Part 1 (for Mean Only)

 Analyze the response of interest – – – Factorial Plots (Main Effects, Interaction) Statistical Results (ANOVA table and p-values) Residual Plots by factor  Reduce model using statistical results  Use the residuals plot to evaluate potential existence of variation effects – If residuals plot indicates a possible variation effect, go to Passive Analysis Roadmap - Part 2

Interaction Plot

 Based on the interaction plot, a few of the interactions may be significant. Check the statistical output for verification.

Interaction Plot (data means) for Y1

1 5 12.5

13.5

63 69

A B E

60.0

57.5

55.0

60.0

57.5

55.0

60.0

57.5

55.0

B 1 5 A 10 20 E 12.5

13.5

Stat > DOE > Factorial > Factorial Plots > Interaction Plot

Main Effects Plot

 The main effects plot indicates that factors B and E have the largest effects. Factor A also has a moderate positive effect. Factor F does not seem to be important. Let’s look at the results.

Main Effects Plot (data means) for Y1

A B 58 57 56 55 54 10 20 1 5 E F 58 57 56 55 54 12.5

13.5

63 69 Stat > DOE > Factorial > Factorial Plots > Main Effects Plot

Factorial Analysis

 A preliminary look at the statistical output of the experiment indicates factor F may not be significant. Did we make a mistake by including it in the experimental design?

Estimated Effects and Coefficients for Y1 (coded units) Term Effect Coef SE Coef T P Constant 55.991 0.2041 274.27 0.000

A 2.363 1.181 0.2041 5.79 0.000

B -3.192 -1.596 0.2041 -7.82 0.000

E 3.312 1.656 0.2041 8.11 0.000

F 0.463 0.231 0.2041 1.13 0.262

A*B 0.178 0.089 0.2041 0.43 0.665

A*E 0.002 0.001 0.2041 0.01 0.995

A*F -0.138 -0.069 0.2041 -0.34 0.737

B*E -0.933 -0.466 0.2041 -2.28 0.026

B*F -0.662 -0.331 0.2041 -1.62 0.110

E*F -0.007 -0.004 0.2041 -0.02 0.985

* Note that the 3-way and 4-way interactions are still in the model but not presented in the output above Stat > DOE > Factorial > Analyze Factorial Design

Reduce Model to Significant Terms

 Our final model indicates that factors A, B, and E are significant, along with interactions BE, BF, ABF, and BEF, using a p-value cut-off of 0.2

Estimated Effects and Coefficients for Y1 (coded units) Term Effect Coef SE Coef T P Constant 55.991 0.1946 287.74 0.000

A 2.363 1.181 0.1946 6.07 0.000

B -3.192 -1.596 0.1946 -8.20 0.000

E 3.312 1.656 0.1946 8.51 0.000

F 0.463 0.231 0.1946 1.19 0.239

B*E -0.932 -0.466 0.1946 -2.40 0.019

B*F -0.662 -0.331 0.1946 -1.70 0.093

A*B*F -0.513 -0.256 0.1946 -1.32 0.192

B*E*F 0.838 0.419 0.1946 2.15 0.035

S = 1.74046 R-Sq = 73.11% R-Sq(adj) = 70.08% Stat > DOE > Factorial > Analyze Factorial Design

The Role of Residual Plots in RD

  In Robust Parameter Design, the residual plots can show the possibility for a variation effect Remember from ANOVA and Regression, we stated one of the assumptions on the residuals was constant variance and we checked this via plots Stat > DOE > Factorial > Analyze Factorial Design Choose Graphs > Residuals vs Variables > A B E F

What Next?

 After reducing the model, the “Residuals versus Factor F” plot still indicates that F contributes to a variation effect. This finding should encourage us to move further in the analysis of this data to create a variability response and analyze the data. Thus we move on to Part 2 of the roadmap.

Residuals Versus F

(response is Y1) Stat > DOE > Factorial > Analyze Factorial Design Choose Graphs > Residuals vs Variables > F 3 2 1 0 -1 -2 -3 63 64 65 66

67 68 69

Passive Analysis Roadmap - Part 2 (if Variation effect present)

  Create a Variability Response Analyze Variability – – – Factorial Plots (Main Effects, Interaction) Statistical Results (ANOVA table and p-values) Reduce model using statistical results  Compare main effects plots for mean and variability to determine which are Mean Adjustment Factors and which are Variance Adjustment Factors (or both)  Use the Multiple Response Optimizer to find optimal settings of the factors – – Mean on target Minimum variability  Perform a capability study / analysis on the resulting factor settings

Create a Variability Response

 We are now going to use the replications to make a new response in order to model the variability. Once we have modeled the variability, we can use the MINITAB Response Optimizer to find the settings of the control factors that will put Y on target with minimum variation.

 MINITAB makes this easy with a pre-processing of the responses in preparation for a variability analysis  You will see that MINITAB will use the standard deviation as the measure of variability, rather than the variance – the results are equivalent

Uses Natural Log (Standard Dev of Y)

  All of the statistical techniques that we are using to analyze this DOE assume that the data is symmetric (because we are testing for mean differences) Unfortunately, when we use a calculated standard deviation as a response, we do not meet this assumption because the sampling distribution of variances is expected to be skewed, hence the distribution of standard deviations would also be skewed Raw St Dev Ln (St Dev)

Create a Variability Response

Stat > DOE > Factorial > Pre-Process Responses for Analyze Variability

Create a Variability Response

 Worksheet should now contain the following new columns

Analyze the Variability

   Analysis of the variability will be essentially identical to the analysis for the mean Will select the “Terms” to estimate in the model Will use the Pareto of Effects “Graph” in order to facilitate the first model reduction Stat > DOE > Factorial > Analyze Variability

Analyze the Variability

TERMS GRAPHS

Pareto Chart of the Effects

 Because we don’t have any degrees of freedom for error, we must look at the Pareto of effects to decide which term to drop into the error and begin to reduce the model D A AC B AD C ABC BCD BD ABCD AB ACD CD BC ABD 0.0

0.130

0.2

Pareto Chart of the Effects

(Response is natural log of StDevY1, Alpha = 0.20) 0.4

0.6

Effect

0.8

1.0

1.2

Lenth's PSE = 0.0878313

1.4

F actor A B C D Name A B E F Drop ABD interaction first

Final Model for ln StDevY1

 Once the insignificant terms have been eliminated using a p value cut-off of 0.2, the reduced model is shown below Regression Estimated Effects and Coefficients for Natural Log of StDevY1 (coded units) Ratio Term Effect Effect Coef SE Coef T P Constant 0.0747 0.01013 7.38 0.002

A 0.5139 1.6718 0.2569 0.01013 25.37 0.000

B -0.2953 0.7443 -0.1477 0.01013 -14.58 0.000

E -0.1521 0.8589 -0.0760 0.01013 -7.51 0.002

F -1.3384 0.2623 -0.6692 0.01013 -66.08 0.000

A*B 0.0371 1.0378 0.0185 0.01013 1.83 0.141

A*E 0.4451 1.5606 0.2225 0.01013 21.98 0.000

A*F -0.2573 0.7731 -0.1287 0.01013 -12.71 0.000

B*F -0.0781 0.9249 -0.0390 0.01013 -3.86 0.018

A*B*E 0.1323 1.1414 0.0661 0.01013 6.53 0.003

B*E*F 0.0850 1.0887 0.0425 0.01013 4.20 0.014

A*B*E*F 0.0390 1.0398 0.0195 0.01013 1.93 0.126

R-Sq = 99.93% R-Sq(adj) = 99.75%

Interaction Plot for StDevY1

 The interaction plot indicates a moderately strong interaction between factors A & E and A & F

Interaction Plot (data means) for StDevY1

1 5 12.5

13.5

63 69

A B E

2 1 3 2 1 1 3 3 2 B 1 5 A 10 20 E 12.5

13.5

Where should factors A, B, E and F be set in order to minimize the variability in voltage drop, Y1?

Stat > DOE > Factorial > Factorial Plots > Interaction Plot

Main Effects Plot for StDevY1

   Factor F has the largest effect on the variability. Increasing F should reduce variability. But what did the interaction plot show?

Factor A is the next strongest. Set A = 10. What did the interaction plot show?

Factors B and E are weak but what did the interaction plot show?

Main Effects Plot (data means) for StDevY1

A B 2.5

2.0

1.5

1.0

0.5

10 20 1 5 E F 2.5

2.0

1.5

1.0

0.5

12.5

13.5

63 69 Stat > DOE > Factorial > Factorial Plots > Main Effects Plot

Determine Mean & Variation Effects

  The graphs at right allow us to directly compare each factor’s singular effect on both the mean and variation Based on these graphs, – Factors B and E are Mean Adjustment Factors since they affect the mean with little or no effect on the variability – Factor F is a Variance Adjustment Factor since it affects the variability with little or no effect on the mean – Factor A appears to affect both mean and variability 58 57 56 55 54 2.5

2.0

1.5

1.0

0.5

Main Effects Plot (data means) for Y1

A B 10 20 1 5

Affects Both Affects Mean Main Effects Plot (data means) for StDevY1

A B 20 1 5 1.5

1.0

0.5

Main Effects Plot (data means) for Y1

E F 58 57 56 55 54 12.5

13.5

63 69

Affects Mean Affects Variation Main Effects Plot (data means) for StDevY1

E F 2.5

2.0

12.5

13.5

63 69

Quality Check: Status of Your Models

 Use the “Show Design” icon to check on the status of the analysis. You should make sure that the correct model has been fit for each response that you intend to specify in the response optimizer.

 As shown in this window, models have been fit for both the Y1 and StDevY1 responses

Multiple Response Optimizer

   Set the Weight for Y1 to 10 to ensure hitting 60, tight lower & upper range Read “first-guess” target & upper values for StDevY1 from interaction plots – Note that StDevY1 is in regular units here, NOT logged units!

For StDevY1, set weight low to protect against a bad first guess Stat > DOE > Factorial > Response Optimizer

Multiple Response Optimizer

 We use the multiple response optimizer to provide a stacked main effects plot. This plot allows us to interactively manipulate the values of each factor in the model and see the effect on both the mean and the variation.

Optimal D 0.93698

Hi Cur Lo A 20.0

[13.4878] 10.0

B 5.0

[1.0] 1.0

E 13.50

[13.50] 12.50

F 69.0

[69.0] 63.0

Y1 Targ: 60.0

y = 60.0001

d = 0.99884

StDevY1 Minimum y = 0.5725

d = 0.87896

 You can use the red sliders to tune each of the factors

Use the Equations to Confirm Y1

 Let’s use the model coefficients to predict and see that it matches (make sure to use un-coded)!

 From the optimized solution, A = 13.4878, B = 1, E = 13.5, F = 69

Y1 =

-22.0994 + 0.214670*

+ 0.242708*

- 3.26279* 0.000108988*

F B

* + 45.2619*

+ 0.0423718*

F B

+ 4.71125*

- 0.607676*

Y1 =

-22.0994 + 0.214670* 4.71125*

13.5

0.607676*

69 13.4878

+ 0.242708*

+ 45.2619*

- 3.26279*

13.5

+ + 0.000108988*

13.4878

0.0423718*

13.5

Y1 = 60.0001

, as seen in the optimizer window

Use the Equations to Confirm StDevY1

  Again, make sure to use the un-coded coefficients!

Again, A = 13.4878, B = 1, E = 13.5, F = 69

lnStDevY1 =

0.927015*

0.0533652* 15.4015 - 0.001778*

- 0.0566905*

- 0.00978998*

0.00983241*

0.0000317*

F A

- 0.152626* - 0.00282966*

+ 0.727714*

+ + 0.0242198*

F A

* +

lnStDevY1 =

0.152626* 15.4015 - 0.001778* - 0.927015*

13.5

13.4878

- 0.0566905* *

0.00978998*

13.4878

0.00983241*

13.4878

13.5

+ 0.0000317*

13.4878

13.5

69 13.4878

+ 0.0533652* + 0.0242198*

* + 0.727714*

1 13.4878

13.5

+ - 0.00282966*

13.5

69 lnStDevY1 =

-0.5577

StDevY1 = e -0.5577

= 0.5725

, as seen in the optimizer

Final Design Capability Analysis

 Did we achieve our objectives?

Process Capability of Y1Final

LSL Process Data 58 Target U SL Sample Mean Sample N * 62 60.3183

100 StDev (Within) 0.120084

StDev (O v erall) 0.115955

LSL worksheet “

passive capability

” Stat > Quality Tools > Capability Analysis > Normal Y = Y1Final; Lower Spec = 58; Upper Spec = 62 O bserv ed Performance PPM < LSL PPM > U SL 0.00

PPM Total 0.00

0.00

58.30

58.85

59.40

59.95

60.50

61.05

61.60

Exp. Within Performance PPM < LSL PPM > U SL 0.00

PPM Total 0.00

0.00

Exp. O v erall Performance PPM < LSL PPM > U SL 0.00

PPM Total 0.00

0.00

USL Within Overall Potential (Within) C apability C p 5.55

C PL C PU C pk 6.44

4.67

O v erall C apability Pp PPL PPU Ppk C pm 5.75

6.66

4.83

Remember the Two Strategies?

 We just reviewed the Passive Approach – Noise factors are

NOT

included, manipulated or controlled in the experimental design – We analyzed the variability of replicates from an experimental design  Now we will look at the Active Approach – Noise factors

ARE

included in the experimental design in order to force variability to occur – We will see that the analysis is similar to the passive approach

The Active Approach

   A factorial experiment is performed using Control AND Noise factors in the same experiment. Analysis can be performed by characterizing Control*Noise interactions only or by moving forward to analyze the variability by dropping the noise factors into the error term.

Pros – – – – Simple extension of standard experimental techniques Guarantees noise in the Noise factors Provides for flexibility in analysis methods Can allow for reduced replication Cons – – Requires ability to manipulate and control Noise factors Optimal designs for minimization of unneeded effects (noise by noise interactions) can be difficult to create

Example: An Active Noise Experiment

   3 control factors 1 noise factor 2 4 full factorial design  Two Approaches to Analysis – Use only interpretation of interaction plots to choose settings of the control factors to minimize effect of noise – Model the variability by dropping the noise factors into the error and analyze like the passive approach

Active Analysis Roadmap – Plots Only

      Create and execute with noise included as a factor Analyze the response of interest – – – Factorial Plots (Main Effects, Interaction) Statistical Results (ANOVA table and p-values) Reduce model using statistical results Review Interactions Plot – Interpret the interaction plots to look for evidence of variation effects Review Main Effects Plot (if applicable) Use the Multiple Response Optimizer to find the optimal settings of the factors such that the mean is on target – Will force in settings obtained from the interaction plots Perform a capability study / analysis on the resulting factor settings

Example: An Active Noise Experiment

  An engineer is interested in improving the stability and robustness of a filtration product Review the capability of the current performance to determine the opportunity to apply robust design techniques Stat > Quality Tools > Capability Analysis > Normal Y = Y4Initial; Lower Spec = 60; Upper Spec = 80

Design Capability Analysis

 What conclusions can you draw from this graph?

LSL Target U SL Process Data 60 * 80 Sample Mean Sample N 70.321

100 StDev (Within) 4.00638

StDev (O v erall) 3.97669

LSL

Process Capability of Y4Initial

USL Within Overall Potential (Within) C apability C p 0.83

C PL C PU C pk 0.86

0.81

O v erall C apability Pp PPL PPU Ppk C pm 0.84

0.87

0.81

* O bserv ed Performance PPM < LSL PPM Total 0.00

PPM > U SL 10000.00

10000.00

60 64 Exp. Within Performance PPM < LSL 4995.46

PPM > U SL PPM Total 7848.20

12843.66

68 72 76 Exp. O v erall Performance PPM < LSL 4724.40

PPM > U SL PPM Total 7467.87

12192.28

Example: An Active Noise Experiment

 The device contains several control factors from which three were identified as DOE candidates – – – Pressure (A) Concentration (B) Stir Rate (C)  Ambient Temperature was identified as being significant, but not economically controllable – Temperature would not change appreciably during the time in which it would take to execute a three factor experiment – Decided to include it as a factor in the design to force it to change – Call this Factor G

The Experimental Design

 A 2 4-1 fractional design (Res IV) was rejected because the 2-way interactions are of great interest in this experiment   A 2 4 full factorial design was used Because of time constraints, only 1 replicate was performed    The variables are listed below: – – – – A = Pressure B = Concentration C = Stir Rate G = Temperature The data is in worksheet “

Active Design

” Open worksheet “

Active Design

” within “

Robust Design.mpj

”

Factorial Analysis

 This DOE is an unreplicated, 4-factor full factorial – We need to create the “Pareto of Effects” chart

Pareto Chart of the Effects

(response is Y4, Alpha = .20) 0.08

D BD CD C B A ABD AD ABC BC AB ABCD BCD ACD AC 0 5 10

Effect

15 20 F actor A B C D Name A B C G Lenth's PSE = 0.05625

Stat > DOE > Factorial > Analyze Factorial Design

Fit the Reduced Model

 Based on the p-values, the A*G and B*G and C*G interactions are important. Since we have identified some important control*noise interactions, the next step is to examine the interaction plots.

Estimated Effects and Coefficients for Y4 (coded units) Term Effect Coef SE Coef T P Constant 70.081 0.006250 11213.00 0.000

A 5.987 2.994 0.006250 479.00 0.000

B 9.787 4.894 0.006250 783.00 0.000

C 14.612 7.306 0.006250 1169.00 0.000

G 21.387 10.694 0.006250 1711.00 0.000

A*B -0.038 -0.019 0.006250 -3.00 0.058

A*G -0.087 -0.044 0.006250 -7.00 0.006

B*C -0.062 -0.031 0.006250 -5.00 0.015

B*G -18.038 -9.019 0.006250 -1443.00 0.000

C*G 16.638 8.319 0.006250 1331.00 0.000

A*B*C 0.062 0.031 0.006250 5.00 0.015

A*B*G 0.137 0.069 0.006250 11.00 0.002

A*B*C*G 0.037 0.019 0.006250 3.00 0.058

Stat > DOE > Factorial > Analyze Factorial Design

Interaction Plot

 The interaction plot show the valuable interactions available to the designer. Let’s take a closer look at the strongest ones.

Interaction Plot (data means) for Y4

1 5 150 200 -1 1

A B C

75 50 100 75 50 100 75 50 100 B 1 5 A 10 20 C 150 200

Stat > DOE > Factorial > Factorial Plots > Interaction Plot

Interaction Plot – A Closer Look

  The two interaction plots at right indicate that both B and C can be exploited to desensitize Y4 to the noise variable G – If B is set at its high level, the slope of the G effect line is minimized – If C is set at its low level, the slope of the G effect line is also minimized To minimize output variation due to noise in ambient temperature (G), the above two settings should be controlled in the design 100 80 60 40 100 80 60 40

Interaction P lot (data means) for Y4

-1 -1

G G

1 1 C 15 0 20 0 B 1 5

Main Effects Plot

 Since factor A is not involved in a significant interaction and its main effect is significant, we should take a look at its main effect plot to see if there is some potential value in controlling A

Main Effects Plot (data means) for Y4

73 72 71 70 69 68 67 10 20

 This plot indicates that factor A has about +/- 3 units of control over the nominal value of Y4. Thus, if manipulating one of the other factors takes the mean value off target, this factor could be used to exert some control over the mean value of Y.

Stat > DOE > Factorial > Factorial Plots > Main Effects Plot

New Settings : Capability Analysis

   B was set to 5, C was set to 150, A was left at nominal (15) It appears that the changes to B & C were successful in reducing variation in Y4 but the mean is now off target Use factor A to adjust back to target!

Process Capability of Y4Valid

LSL Target U SL Process Data 60 * 80 Sample Mean Sample N StDev (Within) 67.898

100 2.06587

StDev (O v erall) 2.15042

LSL USL Within Overall Potential (Within) C apability C p 1.61

C PL C PU C pk 1.27

1.95

1.27

O v erall C apability Pp PPL PPU Ppk C pm 1.55

1.22

1.88

1.22

* O bserv ed Performance PPM < LSL PPM Total 0.00

PPM > U SL 0.00

0.00

60 63 66 Exp. Within Performance PPM < LSL 65.90

PPM > U SL 0.00

PPM Total 65.90

69 72 75 Exp. O v erall Performance PPM < LSL 119.97

PPM > U SL 0.01

PPM Total 119.98

How much should we shift factor A?

   Set up the response optimizer to target 70. The lower and upper limits are not important since we will manually manipulate this.

Set factor B=5 and factor C=150. The optimizer indicates a nominal Y4=67.7, very close to that observed in the validation study.

Finally, slowly slide the bar for factor A to the right while observing the predicted value of Y4. This indicates that a nominal setting of A=18.8 should achieve Y4=70.

New D 0.00000

Hi Cur Lo Y4 Targ: 70.0

y = 67.7506

d = 0.00000

New D 0.99934

Hi Cur Lo Y4 Targ: 70.0

y = 69.9987

d = 0.99934

A 20.0

[15.0] 10.0

A 20.0

[18.80] 10.0

B 5.0

[5.0] 1.0

B 5.0

[5.0] 1.0

Worksheet “

active design

”

Stat > DOE > Factorial > Response Optimizer C 200.0

[150.0] 150.0

C 200.0

[150.0] 150.0

G 1.0

[-0.0076] -1.0

G 1.0

[-0.0076] -1.0

New Setting for A : Capability Analysis

 When we shift factor A to this new nominal value, we succeed at shifting the response to put it on target without degrading the variation

Process Capability of Y4Reduced

LSL Process Data 60 Target U SL Sample Mean * 80 70.2902

Sample N 100 StDev (Within) 2.09103

StDev (O v erall) 2.20583

LSL USL Within Overall Potential (Within) C apability C p C PL C PU C pk 1.59

1.64

1.55

O v erall C apability Pp PPL PPU Ppk C pm 1.51

1.56

1.47

* O bserv ed Performance PPM < LSL PPM Total 0.00

PPM > U SL 0.00

0.00

60 63 66 Exp. Within Performance PPM < LSL PPM Total 0.43

PPM > U SL 1.71

2.14

69 72 75 Exp. O v erall Performance PPM < LSL PPM Total 1.54

PPM > U SL 5.37

6.91

Summary

       Variation improvement strategies can take two forms: – – Passive Approach Active Approach The choice of which strategy to use depends on the ability to control or manipulate noise factors (at least for the duration of the experiment) Standard full and fractional designs can be used Variation effects must be calculated using replications A log transform of the variability response is automatically used to minimize the effects of asymmetry in the variance distribution The response optimizer can be used to simultaneously optimize both mean and variability responses A validation study must be made at the end of a Robust Parameter Design study

Objectives Revisited

At the end of this module, participants should be able to :    Identify possible variation effects from residual plots Create a variability response from replicates Identify possible mean and variance adjustment factors from noise-factor interaction plots  Use the MINITAB Response Optimizer to achieve a process on target with minimum variation  Complete validation capability studies

DFSS-57