The Design of Experiment (DOE) software was used for the RSM analysis. The most commonly used design methods in the field of civil engineering are central composite design (CCD) and Box–Behnken design (BBD). Design expert software has been utilized for developing the mix designs and optimizations. The central composite design technique is a very useful tool that provides statistical models, which help researchers to understand the interactions between the parameters that have been optimized and also helps them to optimize the effective parameters using the minimum number of experiments 26, 27. Central Composite designs (CCD) are based on 2-level factorial designs, augmented with center and axial points to fit quadratic models. Regular CCD’s have 5 levels for each factor. This can be modified by choosing an axial distance of 1.0 creating a Face-Centered, Central Composite design which has only 3 levels per factor. I would like to use a software to design the experiment. My supervisor recommend me 'MODDE Umetrics', but there is not a free version available (only the trial version and I need it for a long time).
- Central Composite Design software, free download 2012
- Class Composite Software
- Central Composite Design
The pyDOE package is designed to help thescientist, engineer, statistician, etc., to construct appropriateexperimental designs.
Hint
All available designs can be accessed after a simple import statement:
Capabilities¶
The package currently includes functions for creating designs for anynumber of factors:
- Factorial Designs
- General Full-Factorial (fullfact)
- 2-Level Full-Factorial (ff2n)
- 2-Level Fractional-Factorial (fracfact)
- Plackett-Burman (pbdesign)
- Response-Surface Designs
- Box-Behnken (bbdesign)
- Central-Composite (ccdesign)
- Randomized Designs
- Latin-Hypercube (lhs)
Installation and download¶
Important note¶
The installation commands below should be run in a DOS or Unixcommand shell (not in a Python shell).
Under Windows (version 7 and earlier), a command shell can be obtainedby running cmd.exe (through the Run… menu item from the Startmenu). Under Unix (Linux, Mac OS X,…), a Unix shell is available whenopening a terminal (in Mac OS X, the Terminal program is found in theUtilities folder, which can be accessed through the Go menu in theFinder).
Automatic install or upgrade¶
One of the automatic installation or upgrade procedures below might workon your system, if you have a Python package installer or use certainLinux distributions.
Under Unix, it may be necessary to prefix the commands below withsudo, so that the installation program has sufficient accessrights to the system.
If you have pip, you can try to installthe latest version with
If you have setuptools, you can try to automatically install orupgrade this package with
Manual download and install¶
Alternatively, you can simply download the package archive from thePython Package Index (PyPI) and unpack it. The package can then beinstalled by going into the unpacked directory(pyDOE-..), and running the provided setup.pyprogram with
or, for an installation in the user Python library (no additional accessrights needed):
or, for an installation in a custom directory my_directory:
Central Composite Design software, free download 2012
or, if additional access rights are needed (Unix):
You can also simply move the pyDOE-py* directorythat corresponds best to your version of Python to a location thatPython can import from (directory in which scripts usingpyDOE are run, etc.); the chosenpyDOE-py* directory should then be renamedpyDOE. Python 3 users should then run 2to3-w.from inside this directory so as to automatically adapt the code toPython 3.
Source code¶
The latest, bleeding-edge but working codeand documentation source areavailable on GitHub.
Contact¶
Any feedback, questions, bug reports, or success stores shouldbe sent to the author. I’d love to hear from you!
Credits¶
This code was originally published by the following individuals for use withScilab:
- Copyright (C) 2012 - 2013 - Michael Baudin
- Copyright (C) 2012 - Maria Christopoulou
- Copyright (C) 2010 - 2011 - INRIA - Michael Baudin
- Copyright (C) 2009 - Yann Collette
- Copyright (C) 2009 - CEA - Jean-Marc Martinez
- Website: forge.scilab.org/index.php/p/scidoe/sourcetree/master/macros
Much thanks goes to these individuals.
Class Composite Software
License¶
This package is provided under two licenses:
- The BSD License (3-Clause)
- Any other that the author approves (just ask!)
References¶
There is also a wealth of information on the NIST website about thevarious design matrices that can be created as well as detailed informationabout designing/setting-up/running experiments in general.
Related Topics: |
The data set used in this example is available in the example database installed with the software (called 'doe9_examples.rsr9'). To access this database file, choose File > Help, click Open Examples Folder, then browse for the file in the DOE sub-folder.
The name of the example project is 'Response Surface Method - Central Composite Design.'
A chemical engineer is interested in determining the operating conditions that maximize the yield of a process.* Two controllable variables influence process yield: reaction time and reaction temperature.
A central composite design with five center points and alpha = 1.414 is used to conduct the experiment. A full quadratic model is fitted to the data.
Designing the Experiment
The engineer uses DOE++ to design a central composite design then performs the experiment according to the design and enters the response values into DOE++ for further analysis. The design matrix and the response data are given in the 'Central Composite Design' folio. The following steps describe how to create this folio on your own.
- Choose Insert > DOE > Add Standard Design to add a standard design folio to the current project.
- ClickDesign Type in the folio's navigation panel, and then select Central Composite Response Surface Method in the input panel.
- Rename the folio by clicking the Experiment1 heading in the navigation panel and entering Central Composite Design for the Name in the input panel.
- Rename the response by clicking Response 1 in the navigation panel and entering Yield in the input panel.
- Specify the number of factors by clicking the Factors heading in the navigation panel and choosing 2 from the Number of Factors drop-down list in the input panel.
- Define each factor by clicking it in the navigation panel and editing its properties in the input panel.
- First factor:
- Name: Time
- Units: min
- Factor Type: Quantitative
- Level 1 (Low): 80
- Level 2 (High): 90
- Second factor:
- Name: Temperature
- Units: F
- Factor Type: Quantitative
- Level 1 (Low): 170
- Iron man 3 vostfr uptobox movies online. Level 2 (High): 180
- Click the Additional Settings heading. In the input panel, set the following properties:
- Click the Design Summary heading to make sure that you have entered all settings correctly. The complete Design Summary is shown next.
- Finally, click the Build icon on the control panel to create a Data tab that allows you to view the test plan and enter response data.
Analysis and Results
The data set for this example is given in the 'Central Composite Design' folio of the example project. After you enter the data from the folio, you can specify the settings for the analysis by doing the following:
Note: To minimize the effect of unknown nuisance factors, the run order is randomly generated when you create the design in DOE++. Therefore, if you followed these steps to create your own folio, the order of runs on the Data tab may be different from that of the folio in the example file. This can lead to different results. To ensure that you get the very same results described next, show the Standard Order column in your folio, then click a cell in that column and choose Sheet > Sheet Actions > Sort > Sort Ascending. This will make the order of runs in your folio the same as that of the example file. Then copy the response data from the example file and paste it into the Data tab of your folio.
- Make sure all main effects and interactions will be considered. To do this, click the Select Terms icon on the control panel.
In the window that appears, select the All Terms check box then click OK.
- On the Analysis Settings page of the Control Panel, select to use Individual Terms in the analysis.
- Click the Calculate icon.
- The results in the Analysis Summary area on the main effects A and B, as well as the quadratic effects AA and BB, are significant.
Central Composite Design
- To view a plot comparing the standardized effect of each term, click the Plot icon.
Then choose Pareto Charts - Regression from the Plot Type drop-down list. The following plot appears.
The vertical blue line in the plot marks the critical value determined by the risk level specified on the Analysis Settings page of the control panel. If the bar goes past the blue line, then the effect is considered significant.
From these results, effects A, B and AA and BB will be included in the reduced model. In fact, you could also include term AB in the model. From the Pareto chart, you can see that AB is only slightly below the critical value. The inclusion or exclusion of AB is a personal decision that should be made based on the knowledge of the experiment and the statistical results. For this example, only A, B, AA and BB will be included in the model.
Optimization
The results for the reduced model (which only includes the terms that were found to be significant) are given in the 'Reduced Model' folio. The following steps describe how to create this folio on your own.
- Right-click the 'Central Composite Design' folio in the current project explorer. Then choose Duplicate from the shortcut menu.
- Right-click the new, duplicated folio in the current project explorer and choose Rename from the shortcut menu. Change the name to 'Reduced Model.'
- Open the 'Reduced Model' folio. Then click the Select Terms icon on the control panel.
- In the Select Terms window that appears, click the Select Significant Effects icon to select only the significant effects to calculate the new model, as shown next, then click OK.
- Click the Calculate icon.
- In the Analysis Summary area on the control panel, click the Detailed Summary link to view detailed results from the analysis.
- In the Analysis Summary window, select the Regression Table check box on the Available Report Items panel. The coefficients for the model will appear in the Regression Table as shown next.Adobe audition 3 0 free download - Adobe Audition, Adobe Illustrator CS6, Adobe Media Encoder CC 2015.0.1, and many more programs. Adobe Audition 3.0Comes with Adobe Bridge CS3Key: 1137-1004-8571-6848-7845-8029Might work on Windows 10. Tested on Windows XP, Vista, 7 and 8.1To Install, go. Jan 15, 2013 Users who bought a copy of Adobe Audition and other CS2 software can still download a copy from Adobe's servers and get a valid installation serial key here. Record and mix Adobe Audition 3.0 is. Adobe audition 3.0 free full version for mac.
- Click the Design - Optimization icon on the control panel.
- In the Response Settings window that appears, use the settings shown next and click OK. These settings indicate that you want to maximize the Yield response, with values above 80 being considered 100% desirable and values below 75 being undesirable.
- An Optimization folio will appear with a plot showing the single solution that is found.
- You can choose Optimization > Solutions > View Solutions or click the icon on the control panel to see the solution in numerical format.
The optimum settings for factors A and B are shown next.
You also can use the surface plot to visually identify the optimal settings for factors A and B.
- Return to the Analysis Plot tab of the 'Reduced Model' folio and click the Surface Plot icon in the control panel.
The Surface Plot window will open, as shown next.
Conclusions
From the surface plot, you can see that the maximum yield occurs at Time = 86.8 and Temperature = 176.3° F, which is the same as the result from the optimization. The predicted maximum yield is 80.1861. Keep in mind that it is necessary to conduct an experiment using these settings to confirm this conclusion.
* Montgomery, D. C. Design and Analysis of Experiments, 5th edition, John Wiley & Sons, New York, 2001.
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