ReliaWiki - User contributions [en]

Template:WebNotes/DOEGage R

2015-04-28T23:03:51Z

Beth Ann Paape:

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The gage R&R folio (gage repeatability and reproducibility) is used to study the different variance components in a measurement process. The variances in a measurement process include the part to be measured, the operators who measure it, and the gage they used. The variance caused by operator and gage should be small compared to the part variation. Otherwise, better gages or more experienced operators should be used.

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| [http://reliawiki.com/index.php/Measurement_System_Analysis#Gage_Repeatability_and_Reproducibility_.28R.26R.29_Study the theory textbook...]
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| [http://www.weibull.com/hotwire/issue153/hottopics153.htm related article(s)...]
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Template:WebNotes/DOEGage R

2015-04-28T23:02:37Z

Beth Ann Paape:

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|{{Font|Gage R&R Folios|11|tahoma|bold|gray}}
The gage R&R folio (gage repeatability and reproducibility) is used to study the different variance components in a measurement process. The variances in a measurement process include the part to be measured, the operators who measure it, and the gage they used. The variance caused by operator and gage should be small compared to the part variation. Otherwise, better gages or more experienced operators should be used.

|}
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|-
| [[Image:Book blue.png]]
| [http://reliawiki.com/index.php/Measurement_System_Analysis#Gage_Repeatability_and_Reproducibility_.28R.26R.29_Study the theory textbook...]
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| [[Image:Articleblue.png]]
| [http://www.weibull.com/hotwire/issue153/hottopics153.htm related article(s)...]
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| [[Image:Bulbblue.png]]
| [http://help.synthesisplatform.net/doe9/gage_r&r_folio__example.htm application example(s)...]
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Template:WebNotes/DOEExpert Sheet

2015-04-28T21:48:50Z

Beth Ann Paape:

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The simulation worksheet contains a spreadsheet which can be used to share data between DOE++ and BlockSim. It provides a means of simulating DOE++ responses from BlockSim without ever running the experiment. DOE++ contains functionality to transfer data between standard designs and expert sheets. Factor values provided by DOE++ can be used to vary simulation variables in a BlockSim diagram and then simulation results provided by BlockSim can then be transferred back to DOE++ and used as response values.

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Template:WebNotes/DOESpreadsheet

2015-04-28T21:47:07Z

Beth Ann Paape:

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Spreadsheets in DOE++ are similar to regular Excel data sheets. You can use spreadsheets to record and analyze data by using any of the 130+ embedded functions (accessible via the Function Wizard). Special spreadsheets (called analysis reports) include items that have been added via the Report Generator window, which lets you copy any of the analysis results and plots from a standard design folio into the spreadsheet (e.g., you can copy the ANOVA table, regression information table, diagnostic plot, design evaluation, residual plots and effect plots).

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Template:WebNotes/DOESpreadsheet

2015-04-28T21:46:05Z

Beth Ann Paape:

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Spreadsheets in DOE++ are similar to regular Excel data sheets. You can use spreadsheets to record and analyze data by using any of the 130+ embedded functions (accessible via the Function Wizard). Special spreadsheets (called analysis reports) include items that have been added via the Report Generator window, which lets you copy any of the analysis results and plots from a standard design folio into the spreadsheet (e.g., you can copy the ANOVA table, regression information table, diagnostic plot, design evaluation, residual plots and effect plots).

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| [[Image:Bulbblue.png]]
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Template:WebNotes/DOESpreadsheet

2015-04-28T21:44:52Z

Beth Ann Paape:

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|{{Font|Spreadsheets in DOE++|11|tahoma|bold|gray}}
Spreadsheets in DOE++ are similar to regular Excel data sheets. You can use spreadsheets to record and analyze data by using any of the 130+ embedded functions (accessible via the Function Wizard). Special spreadsheets (called analysis reports) include items that have been added via the Report Generator window, which lets you copy any of the analysis results and plots from a standard design folio into the spreadsheet (e.g., you can copy the ANOVA table, regression information table, diagnostic plot, design evaluation, residual plots and effect plots).

|}
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| [http://help.synthesisplatform.net/doe9/index.htm#reports.htm V9 Help] or [http://help.synthesisplatform.net/doe10/index.htm#reports.htm V10 Help]
|-
| [[Image:Bulbblue.png]]
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Template:WebNotes/DOESide By Side Plot

2015-04-28T21:43:11Z

Beth Ann Paape:

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Template:WebNotes/DOEOverlay Plot None

2015-04-28T21:41:50Z

Beth Ann Paape:

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Template:WebNotes/DOEPlot

2015-04-28T21:39:24Z

Beth Ann Paape:

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| [http://reliawiki.org/index.php/DOE%2B%2B_Examples application example(s)...]
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Template:WebNotes/DOEPlot

2015-04-28T21:36:59Z

Beth Ann Paape:

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|{{Template: Plot Sheets}}

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| | [http://help.synthesisplatform.net/doe9/index.htm#plots.htm V9 Help] or [http://help.synthesisplatform.net/doe10/index.htm#plots.htm V10 Help]
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Template:WebNotes/DOEOptimization Dynamic Overlaid Contour Plot

2015-04-28T21:34:37Z

Beth Ann Paape:

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This is an extended version of the regular overlaid contour plot. In the regular plot, users can only vary two factors: one for the x-axis and the other for the y-axis. Other factors are held constant. In the dynamic overlaid contour plot, in addition to varying the factors for the x- and y-axes, users also can specify other factors to be either held constant or varied over a given range (using a sequence of plots).

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|-
| [[Image:Book blue.png]]
| [http://reliawiki.org/index.php/Response_Surface_Methods_for_Optimization#Analysis_of_the_Second_Order_Model the theory textbook...]
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| [[Image:Bulbblue.png]]
| [http://help.synthesisplatform.net/doe9/optimization_example.htm application example(s)...]
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Template:WebNotes/DOEOptimization Overlaid Contour Plot

2015-04-28T21:32:13Z

Beth Ann Paape:

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The overlaid contour plot is used when there are two or more responses. For each response, there is a requirement for its desired value. From an overlaid contour plot, you can locate the feasible range of the input factors that can meet the requirements of all the responses. You can choose one factor for the x-axis, another factor for the y-axis, and then specify the values of the other factors.

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|-
| [[Image:Book blue.png]]
| [http://reliawiki.org/index.php/Response_Surface_Methods_for_Optimization#Analysis_of_the_Second_Order_Model the theory textbook...]
|-
| [[Image:Bulbblue.png]]
| [http://help.synthesisplatform.net/doe9/optimization_example.htm application example(s)...]
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Template:WebNotes/DOEOptimization Optimal Solution Plot

2015-04-28T21:29:55Z

Beth Ann Paape:

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The optimal solution plot is an optimization tool for finding the best factor settings for the response, based on the fitted regression model. The requirement for a response can be either the larger the better (maximization), the smaller the better (minimization), or the closer to a specified value the better (target). You can optimize several responses at the same time.

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|-
| [[Image:Book blue.png]]
| [http://reliawiki.org/index.php/Response_Surface_Methods_for_Optimization#Analysis_of_the_Second_Order_Model the theory textbook...]
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| [[Image:Bulbblue.png]]
| [http://help.synthesisplatform.net/doe9/optimization_example.htm application example(s)...]
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Template:WebNotes/DOEGage R

2015-04-28T21:24:30Z

Beth Ann Paape:

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The gage R&R folio (gage repeatability and reproducibility) is used to study the different variance components in a measurement process. The variances in a measurement process include the part to be measured, the operators who measure it, and the gage they used. The variance caused by operator and gage should be small compared to the part variation. Otherwise, better gages or more experienced operators should be used.

|}
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{| border="0" align="left" cellpadding="0" cellspacing="3"
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| [http://help.synthesisplatform.net/doe9/index.htm#gage_r&r_folios.htm V9 Help] or [http://help.synthesisplatform.net/doe10/index.htm#gage_r&r_folios.htm V10 Help]
|-
| [[Image:Book blue.png]]
| [http://reliawiki.com/index.php/Measurement_System_Analysis#Gage_Repeatability_and_Reproducibility_.28R.26R.29_Study the theory textbook...]
|-
| [[Image:Articleblue.png]]
| [http://www.weibull.com/hotwire/issue153/hottopics153.htm related article(s)...]
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| [[Image:Bulbblue.png]]
| [http://help.synthesisplatform.net/doe9/gage_r&r_folio__example.htm application example(s)...]
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Template:WebNotes/DOEGage Agreement

2015-04-28T21:22:58Z

Beth Ann Paape:

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The gage agreement folio is used to compare the accuracy and precision of two gages. Two gages can have the same accuracy but different precision, the same precision but different accuracy, or different accuracy and precision. This folio is useful for answering questions such as "Can I use either of these gages without losing any accuracy or precision?"

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|-
| [[Image:Book blue.png]]
| [http://reliawiki.com/index.php/Measurement_System_Analysis#Gage_Agreement_Study the theory textbook...]
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| [[Image:Bulbblue.png]]
| [http://help.synthesisplatform.net/doe9/gage_agreement_folio__example.htm application example(s)...]
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Template:WebNotes/DOELinearity and Bias

2015-04-28T21:21:14Z

Beth Ann Paape:

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The linearity & bias folio is used to study the accuracy of a measurement device, or gage. Users can use the study results to evaluate if a gage is adequate for measuring a product. The linearity and bias are compared to the product variation. If the linearity and bias are relatively large compared to the variation of a product, then a better gage should be used.

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|-
| [[Image:Book blue.png]]
| [http://www.reliawiki.org/index.php/Measurement_System_Analysis#Gage_Linearity_and_Bias_Study the theory textbook...]
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| [[Image:Bulbblue.png]]
| [http://help.synthesisplatform.net/doe9/linearity_&_bias_folio__example.htm application example(s)...]
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Template:WebNotes/DOEANOVA

2015-04-28T21:19:38Z

Beth Ann Paape:

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The one-way ANOVA folio is used to analyze existing data in order to compare the responses at different levels of a single factor. One-way ANOVA is the statistical method used in analyzing the results from a one factor design. If you need to plan an experiment for a one factor design, use the standard design folio instead.

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|-
| [[Image:Book blue.png]]
| [http://reliawiki.com/index.php/ANOVA_for_Designed_Experiments#Analysis_of_Single_Factor_Experiments the theory textbook...]
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| [[Image:Bulbblue.png]]
| [http://help.synthesisplatform.net/doe9/one-way_anova_folio__example.htm application example(s)...]
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Template:WebNotes/DOEMultiple Linear Regression

2015-04-28T21:17:03Z

Beth Ann Paape:

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The multiple linear regression folio is a general statistical tool that allows users to perform simple linear regression analysis. (If you wish to analyze existing data for the purposes of DOE, consider using the free form folio instead.) Multiple inputs (entered as quantitative predictor levels) and multiple outputs can be analyzed simultaneously.

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|-
| [[Image:Book blue.png]]
| [http://reliawiki.com/index.php/Multiple_Linear_Regression_Analysis the theory textbook...]
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| [[Image:Articleblue.png]]
| [http://www.weibull.com/hotwire/issue95/relbasics95.htm related article(s)...]
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| [http://help.synthesisplatform.net/doe9/multiple_linear_regression_folio__example.htm application example(s)...]
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Template:WebNotes/DOEFree Form Design

2015-04-28T21:15:07Z

Beth Ann Paape:

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The free form folio is used when users have historical data that will be analyzed in order to determine the relationship between input variables and output responses. Users can specify which columns are factors and which columns are responses. Both qualitative and quantitative factors can be analyzed, and interaction effects can be investigated. Optimization also can be conducted once the final model describing the relationship is found. This folio provides an easy and flexible way for conducting statistical analysis for existing data.

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Template:WebNotes/DOEMixture Data

2015-04-28T21:13:10Z

Beth Ann Paape:

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Mixture designs are used to determine the best proportions of components to use in a mixture. They can also help you determine the best process variable settings to use (e.g., temperature).

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| [[Image:Book blue.png]]
| [http://www.reliawiki.org/index.php/Mixture_Design the theory textbook...]
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Template:WebNotes/DOEMixture Data

2015-04-28T21:12:33Z

Beth Ann Paape:

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Mixture designs are used to determine the best proportions of components to use in a mixture. They can also help you determine the best process variable settings to use (e.g., temperature).

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Template:WebNotes/DOERobust Data

2015-04-28T21:09:02Z

Beth Ann Paape:

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Robust design is also called Robust Parameter Design. It is used to determine the best settings of input factors in order to make a product or process robust against certain noise factors. Noise factors are factors that are difficult or impossible to control (such as environmental factors).

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Template:WebNotes/DOEExtreme Vertex

2015-04-28T21:07:28Z

Beth Ann Paape:

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Extreme vertex designs are used when both lower and upper bound constraints on the components are used, or when linear constraints are added to several components. These constraints define the feasible region of the experiment, which is usually not a simplex. All the vertices of the feasible region will be included in the experiment.

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Template:WebNotes/DOESimplex Axial

2015-04-28T21:06:19Z

Beth Ann Paape:

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Simplex axial design is usually used in a screening experiment where first degree models are to be fitted. Most of the runs in a simplex axial design are inside the simplex, and all the runs are on the axial of each component, which is the imaginary vertical line extending from the base point to the vertex of that component.

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Template:WebNotes/DOESimplex Centroid

2015-04-28T21:04:20Z

Beth Ann Paape:

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A simplex centroid design only includes the centroid points. Therefore, in any given run, the values of all non-zero components are the same. For a design with a degree of centroid of ''m'', centroids of spaces up to ''m'' dimensions are added in the experiment. The center of a face is the centroid of a 2-dimensional space.

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Template:WebNotes/DOESimplex Centroid

2015-04-28T21:03:22Z

Beth Ann Paape:

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A simplex centroid design only includes the centroid points. Therefore, in any given run, the values of all non-zero components are the same. For a design with a degree of centroid of ''m'', centroids of spaces up to ''m'' dimensions are added in the experiment. The center of a face is the centroid of a 2-dimensional space.

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Template:WebNotes/DOESimplex Lattice

2015-04-28T21:03:10Z

Beth Ann Paape:

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A {''q'', ''m''} simplex lattice design has ''q'' components, and each component can take ''m''+1 equally spaced values from 0 to 1. m is the called the ''degree of the design''. More runs will be included in designs with higher degree values, and the obtained data can be used to fit a higher order polynomial model.

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Template:WebNotes/DOESimplex Lattice

2015-04-28T21:01:51Z

Beth Ann Paape:

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A {''q'', ''m''} simplex lattice design has ''q'' components, and each component can take ''m''+1 equally spaced values from 0 to 1. m is the called the ''degree of the design''. More runs will be included in designs with higher degree values, and the obtained data can be used to fit a higher order polynomial model.

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Template:WebNotes/DOESimplex Lattice

2015-04-28T20:58:58Z

Beth Ann Paape:

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A {''q'', ''m''} simplex lattice design has ''q'' components, and each component can take ''m''+1 equally spaced values from 0 to 1. m is the called the ''degree of the design''. More runs will be included in designs with higher degree values, and the obtained data can be used to fit a higher order polynomial model.

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Template:WebNotes/DOESimplex Lattice

2015-04-28T20:56:27Z

Beth Ann Paape:

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A {''q'', ''m''} simplex lattice design has ''q'' components, and each component can take ''m''+1 equally spaced values from 0 to 1. m is the called the ''degree of the design''. More runs will be included in designs with higher degree values, and the obtained data can be used to fit a higher order polynomial model.

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|-
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Template:WebNotes/DOEOne Factor

2015-04-28T20:54:55Z

Beth Ann Paape:

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A one factor design is used to determine whether the values of a response are the same at several different levels of a qualitative factor. For example, a factor could be temperature and the levels could be "low," "medium" and "high." Or the factor could be vendor and the levels could be several different vendors.

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|-
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Template:WebNotes/DOEBox Behnken RSM

2015-04-28T20:53:15Z

Beth Ann Paape:

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A Box-Behnken RSM design is used when the few important factors (<5) have been identified from a previous factorial design. It does not include combinations with all the factors at their highest or lowest level. Therefore, it is often used when such combinations are prohibited due to safety concerns or test equipment constraints. This design can be used to determine the optimal settings for each factor.

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|-
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Template:WebNotes/DOECentral Composite RSM

2015-04-28T20:51:46Z

Beth Ann Paape:

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A central composite RSM design is used when the few important factors (<5) have been identified from a previous factorial design. It is used to determine the optimal settings of these important factors in order to obtain a desired value for one or more responses. Quadratic terms are also included in the regression models.

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Template:WebNotes/DOETaguchi OA Factorial

2015-04-28T20:50:13Z

Beth Ann Paape:

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Taguchi OA factorial design is a special type of fractional factorial design. It has many pre-defined design tables, traditionally called "arrays." Users can choose a design table that best fits their needs. Taguchi OA design has two types of arrays: Taguchi Single Level Array (where all the factors have the same number of levels) and Taguchi Mixed Level Array (where factors can have different numbers of levels).

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Template:WebNotes/DOEGeneral Full Factorial

2015-04-28T20:48:16Z

Beth Ann Paape:

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In a general full factorial design, each factor can have a different number of levels. Both qualitative and quantitative factors can be analyzed. This design is used when the number of factors is small (<5) and the number of total runs in the experiment is not large. Otherwise, a fractional factorial design such as two level fractional factorial or Taguchi OA should be used.

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Template:WebNotes/DOEPlackett Burman Factorial

2015-04-28T20:40:23Z

Beth Ann Paape:

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Placket-Burman design is a special type of two level factorial design (where each factor must have exactly two levels). The number of runs in a Placket-Burman design is always a multiple of 4, such as 4, 8, 12, 16, 20, etc. For a design with a number of runs of n, it can be used to study the effect of up to n-1 factors. Placket-Burman design is a highly fractional factorial design.

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Template:WebNotes/DOETwo Level Factorial

2015-04-28T20:37:31Z

Beth Ann Paape:

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Two level factorial design is used to screen out the vital few important factors from many factors that are run at two levels each. The number of runs in this design is always a power of 2, such as 4, 8, 16, 32, etc. Only a small number of factors significantly affect the output of a process or the performance of a product. Two level factorial designs allow you to identify the vital factors quickly and efficiently.

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Template:WebNotes/RGAPlot

2015-04-23T23:35:43Z

Beth Ann Paape:

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Template:WebNotes/RGAAnalysis Workbook

2015-04-23T23:30:26Z

Beth Ann Paape:

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Template:WebNotes/RGADuane

2015-04-23T23:18:52Z

Beth Ann Paape:

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The Duane model is based on an observation by J.T. Duane. While plotting test data from generators, hydro-mechanical devices and aircraft jet engines, Duane observed that the cumulative failure rate vs. cumulative test time was linear on a log-log plotting paper. Therefore, he proposed the use of a learning curve to model changes in the system’s reliability over time. The parameters of the Duane model are estimated using least squares.
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Template:WebNotes/RGAGeneral Spreadsheet

2015-04-23T23:16:26Z

Beth Ann Paape:

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The general spreadsheet gives you the ability to generate data and results, and to create customized plots and graphs to display these results. The general spreadsheet has over 140 built-in functions, including complete in-cell formula support and cell references. You can automatically link results from different analyses into one general spreadsheet.
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Template:WebNotes/RGACrow AMSAA Fleet

2015-04-23T23:09:30Z

Beth Ann Paape:

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Fleet analysis is similar to repairable systems analysis, except the fleet of systems is considered, and the models are applied to the fleet failures rather than to the system failures. In other words, repairable systems analysis models the number of system failures versus system time, whereas fleet analysis models the number of fleet failures versus fleet time. The Crow-AMSAA model for fleet data can be used as an alternative to the power law model for repairable systems when the latter does not provide a good fit for the data.
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|-
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Template:WebNotes/RGACrow AMSAA Developmental

2015-04-23T23:07:08Z

Beth Ann Paape:

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In "Reliability Analysis for Complex, Repairable Systems" (1974), Dr. Larry H. Crow noted that the Duane model could be stochastically represented as a Weibull process, allowing for statistical procedures to be used in the application of this model in reliability growth. This statistical extension became what is known as the Crow-AMSAA (NHPP) model. This method was first employed by the U.S. Army Materiel Systems Analysis Activity (AMSAA). It is frequently used on systems when usage is measured on a continuous scale. It can also be applied for high reliability, a large number of trials and one-shot items. Test programs are generally conducted on a phase-by-phase basis. The Crow-AMSAA model is designed for tracking the reliability within a test phase, rather than across test phases.
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Template:WebNotes/RGACrow AMSAA Developmental

2015-04-23T23:06:15Z

Beth Ann Paape:

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In "Reliability Analysis for Complex, Repairable Systems" (1974), Dr. Larry H. Crow noted that the Duane model could be stochastically represented as a Weibull process, allowing for statistical procedures to be used in the application of this model in reliability growth. This statistical extension became what is known as the Crow-AMSAA (NHPP) model. This method was first employed by the U.S. Army Materiel Systems Analysis Activity (AMSAA). It is frequently used on systems when usage is measured on a continuous scale. It can also be applied for high reliability, a large number of trials and one-shot items. Test programs are generally conducted on a phase-by-phase basis. The Crow-AMSAA model is designed for tracking the reliability within a test phase, rather than across test phases.
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Template:WebNotes/RGACrow Extended Repairable

2015-04-23T22:58:44Z

Beth Ann Paape:

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The Crow Extended model for repairable systems can be used to analyze data from operational testing. In this type of testing, the final product has to be fielded (it is not a developmental test). Therefore, only delayed fixes can be applied, and the configuration is fixed with design changes kept to a minimum.
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Template:WebNotes/RGACrow Extended Developmental

2015-04-23T22:56:04Z

Beth Ann Paape:

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In reliability growth analysis, the Crow-AMSAA (NHPP) model assumes that the corrective actions for the observed failure modes are incorporated during the test (test-fix-test). However, in actual practice, fixes may be delayed until after the completion of the test (test-find-test) or some fixes may be implemented during the test while others are delayed (test-fix-find-test). At the end of a test phase, two reliability estimates are of concern: demonstrated reliability and projected reliability. The Crow Extended model allows for a flexible growth strategy that can include corrective actions performed during the test, as well as delayed corrective actions.
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Template:WebNotes/RGACrow Extended Fleet

2015-04-23T22:51:19Z

Beth Ann Paape:

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Fleet analysis is similar to repairable systems analysis, except a fleet of systems is considered, and the models are applied to the fleet failures rather than to the system failures. In other words, repairable systems analysis models the number of system failures versus system time, whereas fleet analysis models the number of fleet failures versus fleet time. The main motivation for fleet analysis is to enable the application of the Crow Extended model for fielded data. In many cases, reliability improvements might be necessary on systems that are already in the field. These types of reliability improvements are essentially delayed fixes (BD modes) of the Crow Extended model.
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Template:WebNotes/RGACrow Extended Continuous

2015-04-23T22:31:02Z

Beth Ann Paape:

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The Crow Extended - Continuous Evaluation (3-parameter) model is an extension of the Crow Extended model. It is designed for practical testing situations where we need the flexibility to apply corrective actions at the time of failure or at a later time during the test, either at the end of the current test or during a subsequent test phase. This 3-parameter model is free of the Crow Extended model's constraint where the test must be stopped and all BD modes must be corrected at the end of the test. The failure modes can be corrected during the test or when the testing is stopped for the incorporation of the fixes, or even not corrected at all at the end of the test phase. Based on this flexibility, the end time of the test is also not predefined, and it can be continuously updated with new test data.
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Template:WebNotes/RGALloyd-Lipow

2015-04-23T22:27:38Z

Beth Ann Paape:

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Lloyd and Lipow (1962) considered a situation in which a test program is conducted in ''n'' stages. Each stage consists of a certain number of trials of an item undergoing testing, and the data set records successes and failures. All tests in a given stage of testing involve similar items. The results of each stage of testing are used to improve the item for further testing in the next stage. The Lloyd-Lipow model can be applied to the discrete data types (with the exception of mixed) and the reliability data type.
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Template:WebNotes/RGAStandard Gompertz

2015-04-23T22:26:49Z

Beth Ann Paape:

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The standard Gompertz reliability growth model is often used when analyzing success/failure data and reliability data obtained in developmental reliability growth programs. The standard Gompertz model is most applicable when the reliability data follow a concave shape.
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