Report

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 Why We Need to Reduce Variation LSL Nom USL Cost Low Variation; Minimum Cost Cost LSL Nom 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 Mean Y Variation Y 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. y b0 b1 x1 b 2 x 2 b12 x1 x 2 Now we must also consider the variability of Y s 2 y c 0 c1 x1 c 2 x 2 c12 x1 x 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? P r o c e s s C a pa bi lity o f Y 1 Initi a l LS L USL P ro ce ss D a ta W ith in LS L 58 T a rg e t * USL 62 S a m p le M e a n 5 6 .2 5 3 S a m p le N 100 S tD e v (W ith in ) 1 .4 7 2 1 7 S tD e v (O v e ra ll) 1 .5 4 1 4 O v er all P o te n tia l (W ith in ) C a p a b ility Cp C PL 0 .4 5 -0 .4 0 C PU 1 .3 0 C pk -0 .4 0 O v e ra ll C a p a b ility Pp PPL PPU 1 .2 4 P pk -0 .3 8 C pm 5 2 .5 O b se rv e d P e rfo rm a n ce P P M < LS L PPM > USL P P M T o ta l 8 6 0 0 0 0 .0 0 0 .0 0 8 6 0 0 0 0 .0 0 5 4 .0 5 5 .5 5 7 .0 5 8 .5 6 0 .0 E xp . W ith in P e rfo rm a n ce E xp . O v e ra ll P e rfo rm a n ce P P M < LS L P P M < LS L PPM > USL P P M T o ta l 8 8 2 3 2 4 .0 2 4 7 .3 5 8 8 2 3 7 1 .3 7 PPM > USL P P M T o ta l 8 7 1 4 7 4 .4 8 9 6 .3 3 8 7 1 5 7 0 .8 1 6 1 .5 0 .4 3 -0 .3 8 * 24 Full Factorial Experiment A 24 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. Inte r a c tio n P lo t ( da ta me a ns ) fo r Y 1 1 5 12.5 13.5 63 69 60.0 57.5 A 10 20 A 55.0 60.0 57.5 B 1 5 B 55.0 60.0 57.5 E 12.5 13.5 E 55.0 F 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. M a i n E f f e c ts P l o t ( da ta me a ns ) f o r Y 1 A 58 B 57 56 Mean of Y 1 55 54 10 20 1 E 58 5 F 57 56 55 54 1 2 .5 1 3 .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 Constant A B E F A*B A*E A*F B*E B*F E*F Effect 2.363 -3.192 3.312 0.463 0.178 0.002 -0.138 -0.933 -0.662 -0.007 Coef 55.991 1.181 -1.596 1.656 0.231 0.089 0.001 -0.069 -0.466 -0.331 -0.004 SE Coef 0.2041 0.2041 0.2041 0.2041 0.2041 0.2041 0.2041 0.2041 0.2041 0.2041 0.2041 T 274.27 5.79 -7.82 8.11 1.13 0.43 0.01 -0.34 -2.28 -1.62 -0.02 P 0.000 0.000 0.000 0.000 0.262 0.665 0.995 0.737 0.026 0.110 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 Constant A B E F B*E B*F A*B*F B*E*F Effect 2.363 -3.192 3.312 0.463 -0.932 -0.662 -0.513 0.838 S = 1.74046 Coef 55.991 1.181 -1.596 1.656 0.231 -0.466 -0.331 -0.256 0.419 SE Coef 0.1946 0.1946 0.1946 0.1946 0.1946 0.1946 0.1946 0.1946 0.1946 R-Sq = 73.11% T 287.74 6.07 -8.20 8.51 1.19 -2.40 -1.70 -1.32 2.15 P 0.000 0.000 0.000 0.000 0.239 0.019 0.093 0.192 0.035 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. R e s idua ls V e r s us F (r e s po ns e is Y1) Stat > DOE > Factorial > Analyze Factorial Design Choose Graphs > Residuals vs Variables > F S t a nd a r d iz e d R e s id ua l 3 2 1 0 -1 -2 -3 63 64 65 66 F 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 P a r e to C ha r t o f the E ffe c ts (R e s po ns e is na tur a l lo g o f S tDe vY1 , A lpha = 0 .2 0 ) 0.130 F a cto r N am e A A B B AC C E B D F D A AD Te r m C A BC BC D BD A BC D AB Drop ABD interaction first ACD CD BC A BD 0.0 0.2 0 .4 0 .6 0 .8 Effe c t Le nth's PS E = 0.0878 31 3 1.0 1 .2 1.4 Final Model for ln StDevY1 Once the insignificant terms have been eliminated using a pvalue cut-off of 0.2, the reduced model is shown below Regression Estimated Effects and Coefficients for Natural Log of StDevY1 (coded units) Term Constant A B E F A*B A*E A*F B*F A*B*E B*E*F A*B*E*F Effect Ratio Effect 0.5139 -0.2953 -0.1521 -1.3384 0.0371 0.4451 -0.2573 -0.0781 0.1323 0.0850 0.0390 1.6718 0.7443 0.8589 0.2623 1.0378 1.5606 0.7731 0.9249 1.1414 1.0887 1.0398 R-Sq = 99.93% Coef 0.0747 0.2569 -0.1477 -0.0760 -0.6692 0.0185 0.2225 -0.1287 -0.0390 0.0661 0.0425 0.0195 R-Sq(adj) = 99.75% SE Coef 0.01013 0.01013 0.01013 0.01013 0.01013 0.01013 0.01013 0.01013 0.01013 0.01013 0.01013 0.01013 T 7.38 25.37 -14.58 -7.51 -66.08 1.83 21.98 -12.71 -3.86 6.53 4.20 1.93 P 0.002 0.000 0.000 0.002 0.000 0.141 0.000 0.000 0.018 0.003 0.014 0.126 Interaction Plot for StDevY1 The interaction plot indicates a moderately strong interaction between factors A & E and A & F Inte r a c tio n P l o t ( da ta me a ns ) fo r S tD e v Y 1 1 5 12.5 13.5 63 69 3 A 10 2 A 20 1 3 B 1 2 B 5 1 3 E 12.5 2 E 13.5 Where should factors A, B, E and F be set in order to minimize the variability in voltage drop, Y1? 1 F 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? M a in E ffe c ts P l o t ( da ta me a ns ) fo r S tD e v Y 1 A 2 .5 B 2 .0 M e a n o f S t De v Y 1 1 .5 1 .0 0 .5 10 20 1 E 2 .5 5 F 2 .0 1 .5 1 .0 0 .5 1 2 .5 1 3 .5 63 69 Stat > DOE > Factorial > Factorial Plots > Main Effects Plot Determine Mean & Variation Effects A 58 M ain Ef f ec ts P lot (data m eans ) f or Y 1 B F 57 Me a n o f Y1 57 Me a n o f Y1 E 58 56 55 56 55 54 54 10 20 Affects Both 1 5 12.5 A 2.5 13.5 63 69 Affects Mean Affects Variation Affects Mean M ain Ef f ec ts P lot (data m eans ) f or StDev Y 1 M ain Ef f ec ts P lot (data m eans ) f or StD ev Y 1 B E 2.5 F 2.0 2.0 M e a n o f S t D e vY 1 – 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 M ain Ef f ec ts P lot (data m eans ) f or Y 1 M e a n o f S t D e vY 1 The graphs at right allow us to directly compare each factor’s singular effect on both the mean and variation Based on these graphs, 1.5 1.5 1.0 1.0 0.5 0.5 10 20 1 5 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 Hi D Cur 0.93698 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*A + 45.2619*B + 4.71125*E + 0.242708*F - 3.26279*B*E - 0.607676*B*F + 0.000108988*A*B*F + 0.0423718*B*E*F Y1 = -22.0994 + 0.214670*13.4878 + 45.2619*1 + 4.71125*13.5 + 0.242708*69 - 3.26279*1*13.5 0.607676*1*69 + 0.000108988*13.4878*1*69 + 0.0423718*1*13.5*69 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 = 15.4015 - 0.001778*A + 0.727714*B 0.927015*E - 0.0566905*F - 0.152626*A*B + 0.0533652*A*E - 0.00978998*A*F + 0.0242198*B*F + 0.00983241*A*B*E - 0.00282966*B*E*F + 0.0000317*A*B*E*F lnStDevY1 = 15.4015 - 0.001778*13.4878 + 0.727714*1 - 0.927015*13.5 - 0.0566905*69 0.152626*13.4878*1 + 0.0533652*13.4878*13.5 0.00978998*13.4878*69 + 0.0242198*1*69 + 0.00983241*13.4878*1*13.5 - 0.00282966*1*13.5*69 + 0.0000317*13.4878*1*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? P r o c e s s C a pa bility o f Y 1 F ina l LS L USL P ro ce ss D a ta LS L 58 T a rg e t * USL 62 S a m p le M e a n 6 0 .3 1 8 3 S a m p le N 100 S tD e v (W ith in ) 0 .1 2 0 0 8 4 S tD e v (O v e ra ll) 0 .1 1 5 9 5 5 W ith in O v er all P o te n tia l (W ith in ) C a p a b ility Cp 5 .5 5 C PL 6 .4 4 C PU 4 .6 7 C pk 4 .6 7 O v e ra ll C a p a b ility worksheet “passive capability” Stat > Quality Tools > Capability Analysis > Normal Y = Y1Final; Lower Spec = 58; Upper Spec = 62 Pp 5 .7 5 PPL 6 .6 6 PPU 4 .8 3 P pk 4 .8 3 C pm 58.30 58.85 59.40 59.95 60.50 61.05 61.60 O b se rv e d P e rfo rm a n ce E xp . W ith in P e rfo rm a n ce E xp . O v e ra ll P e rfo rm a n ce P P M < LS L 0 .0 0 P P M < LS L 0 .0 0 P P M < LS L 0 .0 0 PPM > USL 0 .0 0 PPM > USL 0 .0 0 PPM > USL 0 .0 0 P P M T o ta l 0 .0 0 P P M T o ta l 0 .0 0 P P M T o ta l 0 .0 0 * 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 24 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? P r o c e s s C a pa bi lity o f Y 4 Initi a l LS L USL P ro ce ss D a ta W ith in LS L 60 T a rg e t * USL 80 S a m p le M e a n 7 0 .3 2 1 S a m p le N 100 S tD e v (W ith in ) 4 .0 0 6 3 8 S tD e v (O v e ra ll) 3 .9 7 6 6 9 O v er all P o te n tia l (W ith in ) C a p a b ility Cp 0 .8 3 C PL 0 .8 6 C PU 0 .8 1 C pk 0 .8 1 O v e ra ll C a p a b ility Pp 0 .8 4 PPL 0 .8 7 PPU 0 .8 1 P pk 0 .8 1 C pm 60 64 68 72 O b se rv e d P e rfo rm a n ce E xp . W ith in P e rfo rm a n ce P P M < LS L P P M < LS L 4 9 9 5 .4 6 P P M < LS L 7 8 4 8 .2 0 PPM > USL 0 .0 0 PPM > USL 1 0 0 0 0 .0 0 PPM > USL P P M T o ta l 1 0 0 0 0 .0 0 P P M T o ta l 1 2 8 4 3 .6 6 76 E xp . O v e ra ll P e rfo rm a n ce P P M T o ta l 4 7 2 4 .4 0 7 4 6 7 .8 7 1 2 1 9 2 .2 8 80 * 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 24-1 fractional design (Res IV) was rejected because the 2-way interactions are of great interest in this experiment A 24 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 P a r e to C ha r t o f the E ffe c ts (r e s po ns e is Y4, A lpha = .2 0 ) 0.0 8 F a cto r N am e A A B B CD C C C D G D BD B Te r m A A BD AD A BC BC AB A BC D BC D ACD AC 0 5 10 15 20 Effe c t Le nth's PS E = 0 .05 62 5 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 Constant A B C G A*B A*G B*C B*G C*G A*B*C A*B*G A*B*C*G Effect 5.987 9.787 14.612 21.387 -0.038 -0.087 -0.062 -18.038 16.638 0.062 0.137 0.037 Coef 70.081 2.994 4.894 7.306 10.694 -0.019 -0.044 -0.031 -9.019 8.319 0.031 0.069 0.019 SE Coef 0.006250 0.006250 0.006250 0.006250 0.006250 0.006250 0.006250 0.006250 0.006250 0.006250 0.006250 0.006250 0.006250 T 11213.00 479.00 783.00 1169.00 1711.00 -3.00 -7.00 -5.00 -1443.00 1331.00 5.00 11.00 3.00 P 0.000 0.000 0.000 0.000 0.000 0.058 0.006 0.015 0.000 0.000 0.015 0.002 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. Inte r a c tio n P lo t ( da ta me a ns ) fo r Y 4 5 1 200 150 1 -1 100 A 10 75 A 20 50 100 B 1 75 B 5 50 100 C 150 75 C 200 50 G Stat > DOE > Factorial > Factorial Plots > Interaction Plot Interaction Plot – A Closer Look Int e r a c t io n P lo t ( d a t a m e a ns ) f o r Y 4 100 B 1 5 Me an 80 60 40 -1 1 G 100 C 150 200 80 Me an 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 60 40 -1 1 G 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 M a i n E f f e c ts P l o t ( d a ta m e a ns ) f o r Y 4 73 Mean of Y 4 72 71 70 69 68 67 10 20 A 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! P r o c e s s C a pa bility o f Y 4 V a li d LS L USL P ro ce ss D a ta LS L 60 T a rg e t * USL 80 S a m p le M e a n 6 7 .8 9 8 S a m p le N 100 S tD e v (W ith in ) 2 .0 6 5 8 7 S tD e v (O v e ra ll) 2 .1 5 0 4 2 W ith in O v er all P o te n tia l (W ith in ) C a p a b ility Cp 1 .6 1 C PL 1 .2 7 C PU 1 .9 5 C pk 1 .2 7 O v e ra ll C a p a b ility Pp 1 .5 5 PPL 1 .2 2 PPU 1 .8 8 P pk 1 .2 2 C pm 60 O b se rv e d P e rfo rm a n ce 63 66 E xp . W ith in P e rfo rm a n ce P P M < LS L 0 .0 0 P P M < LS L PPM > USL 0 .0 0 PPM > USL P P M T o ta l 0 .0 0 P P M T o ta l 6 5 .9 0 0 .0 0 6 5 .9 0 69 72 75 E xp . O v e ra ll P e rfo rm a n ce P P M < LS L PPM > USL P P M T o ta l 1 1 9 .9 7 0 .0 1 1 1 9 .9 8 78 * 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 Hi D Cur 0.00000 Lo A 20.0 [15.0] 10.0 B 5.0 [5.0] 1.0 C 200.0 [150.0] 150.0 A 20.0 [18.80] 10.0 B 5.0 [5.0] 1.0 C 200.0 [150.0] 150.0 G 1.0 [-0.0076] -1.0 Y4 Targ: 70.0 y = 67.7506 d = 0.00000 New Hi D Cur 0.99934 Lo Y4 Targ: 70.0 y = 69.9987 d = 0.99934 Worksheet “active design” Stat > DOE > Factorial > Response Optimizer 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 P r o c e s s C a pa bil ity o f Y 4 R e duc e d LS L USL P ro ce ss D a ta LS L 60 T a rg e t * USL 80 S a m p le M e a n 7 0 .2 9 0 2 S a m p le N 100 S tD e v (W ith in ) 2 .0 9 1 0 3 S tD e v (O v e ra ll) 2 .2 0 5 8 3 W ith in O v er all P o te n tia l (W ith in ) C a p a b ility Cp 1 .5 9 C PL 1 .6 4 C PU 1 .5 5 C pk 1 .5 5 O v e ra ll C a p a b ility Pp 1 .5 1 PPL 1 .5 6 PPU 1 .4 7 P pk 1 .4 7 C pm 60 O b se rv e d P e rfo rm a n ce 63 66 E xp . W ith in P e rfo rm a n ce 69 72 75 E xp . O v e ra ll P e rfo rm a n ce P P M < LS L 0 .0 0 P P M < LS L 0 .4 3 P P M < LS L 1 .5 4 PPM > USL 0 .0 0 PPM > USL 1 .7 1 PPM > USL 5 .3 7 P P M T o ta l 0 .0 0 P P M T o ta l 2 .1 4 P P M T o ta l 6 .9 1 78 * 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 Copyright 2003 Cummins, Inc. All Rights Reserved. Copyright 2000-2002 Sigma Breakthrough Technologies, Inc. Used with permission. DFSS-57