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“How To Design Your Model”. DOE Software. This EASY TO USE software has all the major experimental designs (general ANOVA, two-level full and fractional factorials, three-level factorials, several RSM designs, mixture designs, and much more).
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DOE Software • This EASY TO USE software has all the major experimental designs (general ANOVA, two-level full and fractional factorials, three-level factorials, several RSM designs, mixture designs, and much more). • More and more features are added with each new version of the software. • It has powerful graphical tools and it has been featured in several well-known texts on DOE. Apparently it has been WIDELY USED IN INDUSTRY. • The web site provides excellent DOE resources and one can download a 30-day full version from the web site. Design of Experiment www.statease.com
DOE Wisdom www.launsby.co • This software also claims that it offers PRACTICALITY and EASE-OF-USE that is IDEAL FOR BEGINNERS but with the computing power demanded by advanced users. • It basically offers VERY SIMILAR FEATURES TO DESIGN-EXPERT with the addition of the Pareto chart and a few other minor differences. • One can DOWNLOAD A 15-DAY FULL VERSION of the • software for the web site. DOEpack www.pqsystems.com • This standalone software available also FEATURES THE USUAL CLASSICAL AND TAGUCHI DESIGNS with a wide selection of SCREENING DESIGNS, PROCESS OPTIMIZATION DESIGN AND ANALYSIS TOOLS. • It has good graphical tools and an intuitive user interface
This standalone DOE software is developed by the well-known SAS Institute, developers of the SAS program. • This is also powerful software and provides design choices for almost every situation. Perhaps the additional feature that is not available in the other software is that the user can perform custom designs which give the experimenter the most flexibility. • Obviously, this feature is ONLY USEFUL FOR THE EXPERT USER. Limited time free download of the software is also available from the JMP web site. www.jmp.com
Why Goes For Experimental Design? 1 2 EFFICIENCY Get more info from fewer experiments FOCUSINGCollect only info that really needed • is a useful complement to multivariate data analysis. • with designed experiments there is a better possibility of • testing the significance of the effects and the relevance • of the whole model .
DOE Flowchart? 1 4 7 Set your objective Select your design Fit & Diagnose Model 5 8 2 Develop your strategy Interpret Model Select Input Factors Will be explain next semester 9 6 3 Confirm Model Run Experiment Select Output Response
Building a Experimental Design ! Your Final Year Project Titles Example: Studies Of Potential Parts Of Streblus asper (Kesinai) For Protease Production 1 To screen and identify the POTENTIAL PLANT PARTS of PROTEASE PRODUCTION from Streblus asper by using Design of Experiment (DOE). DEFINE THE OBJECTIVEof the investigation: e.g. “sort out important variables” or “find the optimum conditions
Building a Experimental Design ! 3 Weight of your sample (g) SELECT OUTPUT RESPONSE that will be measured to describe the outcome of the experimental runs (response variables), and examine their precision 2 SELECT INPUT FACTORS that will be controlled during the experiment (design variables), and their levels or ranges of variation. Enzyme Activity (unit/mL) Ratio (mol) (Buffer : Sample) Time of Extraction (minutes)
Building a Experimental Design ! 4 SELECT YOUR DESIGN CHOOSEamong the available standard designsthe one that is compatible with the objective, number of design variables and precision of measurements, and has a reasonable cost
Screening Design The Screening designer supplies a list of popular screening designs for 2 or more factors. Screening factors can be continuous or categorical with two or three levels. The list of screening designs also includes designs that group the experimental runs into blocks of equal sizes where the size is a power of two.
1 WELCOME!
Choose The Experimental Design 3 4
The choices appear in color on your screen. White squares symbolize full factorials requiring 2k runs for k (the number of factors) from 2 to 9. The other choices are colored like a stoplight: GREEN FOR GO, YELLOW FOR PROCEED WITH CAUTION, and RED FOR STOP, which represent varying degrees of resolution: ≥ V, IV, and III, respectively. This design builder offers full and fractional two‐level factorials for 2 to 21 factors in powers of two (4, 8, 16…) for up to 512 runs. For a quick overview of these color codes, press the screen tips button (or select Tips, Screen Tips) and click topic 1: “What type of information do you want?”
Insert your range here! Make sure the range is not too small or too big 8 Insert your factors and unit 7 9
Choose number of response in your study 10 11 Insert your response here! 12
Actual Values!!!! 14 Insert your experimental data here List of experiment that you design before will be shown here 13 6 5 Run Experiment Develop your strategy
You’ve put in some work at this point so it is a good time to save it. The quickest way of doing this is to press the standard save icon. But you can also go to the File menu and select Save As. Type in the name of your choice for your data file. Then click Save.
For Plackett Burman Design minimum factor is 11. If your factors is less than 11, you add dummies factors to complete it. Next step is the same as previous.
Response Surface Design Response Surface Methodology (RSM) is an experimental technique invented to find the optimal response within the specified ranges of the factors. These designs are capable of fitting a second order prediction equation for the response. The quadratic terms in these equations model the curvature in the true response function. If a maximum or minimum exists inside the factor region, RSM can find it. In industrial applications, RSM designs involve a small number of factors. This is because the required number of runs increases dramatically with the number of factors. The Response Surface designer in JMP lists well-known RSM designs for two to eight continuous factors. Some of these designs also allow blocking
CENTRAL COMPOSITE design, combines a two-level fractional factorial and two other kinds of points: Fractional factorial point Axial (or star) points, for which all but one factor set at zero (midrange) and one factor set at outer (axial) values. Axial Point Centre Point Center points, for which all the factor values are at the zero (or midrange) value. TheBOX-BEHNKENdesign, is an alternative to central composite designs. One distinguishing feature of the Box-Behnken design is that there are ONLY THREE LEVELS PER FACTOR. Another important difference between the two design types is that the BOX-BEHNKEN DESIGN HAS NO POINTS AT THE VERTICES OF THE CUBE defined by the ranges of the factors. This is sometimes useful when it is desirable to avoid these points due to engineering considerations. The price of this characteristic is the higher uncertainty of prediction near the vertices compared to the Central Composite design.
1 Insert your optimize factor and your range here Number of run 2
Insert list of response involved in your optimization here 3
Actual Values!!!! 5 Insert your experimental data here List of experiment that you design before will be shown here 4 6 5 Run Experiment Develop your strategy
