Abstract:
A method of automatically designing a plurality of experiments for analyzing at least one data set from a process to determine a relationship of a plurality of process factors of interest to a process output of interest. The method uses a computer to elicit input from a user to determine at least one characteristic of the data set including a quantity of the plurality of factors and whether one or more of the plurality of factors has greater than two levels, selects a design from a plurality of experiment designs based on established conventions for each of the plurality of experiment designs, the design applicable to the elicited at least one characteristic of the data set, and provides a design of experiments including a standard order of the experiments and a run order of the experiments, the design of experiments indicating the combinations of factors and levels for each experiment.

Description:
FIELD OF THE INVENTION 
     The invention relates to systems and methods for assisting a user in designing experiments. Specifically, the invention provides a wizard for guiding a user through the design of experiments. 
     BACKGROUND OF THE INVENTION 
     Design of Experiments (DOE) is used to analyze a process to determine which process inputs have the greatest impact on the process. The process inputs, referred to as factors (e.g., temperature, quantity, etc.), have different levels. DOE allows a comparison of how different levels for the factors impact the process output or response. DOE uses randomization and replication to improve the results of the experiment. A concept called blocking allows variable factors to be removed from the experiment (e.g., differences between workers on a first shift and a second shift). Experiments are then created using full or fractional factorial designs. 
     SUMMARY OF THE INVENTION 
     In one embodiment, the invention provides a method of automatically designing a plurality of experiments for analyzing at least one data set from a process to determine a relationship of a plurality of process factors of interest to a process output of interest. The method uses a computer to elicit input from a user to determine at least one characteristic of the data set including a quantity of the plurality of factors and whether one or more of the plurality of factors has greater than two levels, selects a design from a plurality of experiment designs based on established conventions for each of the plurality of experiment designs, the design applicable to the elicited at least one characteristic of the data set, and provides a design of experiments including a standard order of the experiments and a run order of the experiments, the design of experiments indicating the combinations of factors and levels for each experiment. 
     In another embodiment, the invention provides a non-transitory computer readable medium accessible by a computer processor. The computer readable medium includes a software program for automatically designing a plurality of experiments for analyzing at least one data set from a process to determine a relationship of a plurality of process factors of interest to a process output of interest. The software includes modules which elicit input from a user to determine at least one characteristic of the data set including a quantity of the plurality of factors and whether one or more of the plurality of factors has greater than two levels, which select a design from a plurality of experiment designs based on established conventions for each of the plurality of experiment designs, the design applicable to the elicited at least one characteristic of the data set, and which provide a design of experiments including a standard order of the experiments and a run order of the experiments, the design of experiments indicating the combinations of factors and levels for each experiment. 
     Other aspects of the invention will become apparent by consideration of the detailed description and accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram of a computer system for implementing a software program embodying the invention. 
         FIG. 2  is a spreadsheet for defining a design of experiments project. 
         FIGS. 3A and 3B  are an embodiment of the operation of a wizard for designing experiments. 
         FIG. 4  is a user interface screen displayed by the wizard. 
         FIG. 5  is a process specific help screen displayed by the wizard. 
         FIG. 6  is a process specific help screen displayed by the wizard. 
         FIG. 7  is a process specific help screen displayed by the wizard. 
         FIG. 8  is a process specific help screen displayed by the wizard. 
         FIG. 9  is a process specific help screen displayed by the wizard. 
         FIG. 10  is a process specific help screen displayed by the wizard. 
         FIG. 11  is a process specific help screen displayed by the wizard. 
         FIG. 12  is a process specific help screen displayed by the wizard. 
         FIG. 13  is a user interface screen displayed by the wizard. 
         FIG. 14  is a user interface screen displayed by the wizard. 
         FIG. 15  is a user interface screen displayed by the wizard. 
         FIG. 16  is a user interface screen displayed by the wizard. 
         FIG. 17  is a user interface screen displayed by the wizard. 
         FIG. 18  is a user interface screen displayed by the wizard. 
         FIG. 19  is a user interface screen displayed by the wizard. 
         FIG. 20  is a user interface screen displayed by the wizard. 
         FIG. 21  is a user interface screen displayed by the wizard. 
         FIG. 22  is a user interface screen displayed by the wizard. 
         FIG. 23  is a user interface screen displayed by the wizard. 
         FIG. 24  is a user interface screen displayed by the wizard. 
         FIG. 25  is a user interface screen displayed by the wizard. 
         FIG. 26  is a user interface screen displayed by the wizard. 
         FIG. 27  is a design of experiments summary screen generated by the wizard. 
         FIG. 28  is a design of experiments response entry screen generated by the wizard. 
         FIG. 29  is a portion of analysis results spreadsheet generated by the software program. 
         FIG. 30  is a portion of analysis results spreadsheet generated by the software program. 
         FIG. 31  is a portion of analysis results spreadsheet generated by the software program. 
         FIG. 32  is a portion of analysis results spreadsheet generated by the software program. 
         FIG. 33  is a portion of analysis results spreadsheet generated by the software program. 
         FIG. 34  is a portion of analysis results spreadsheet generated by the software program. 
     
    
    
     DETAILED DESCRIPTION 
     Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. 
       FIG. 1  illustrates a system for performing DOE according to an embodiment of the present invention. The system includes a general purpose computer  100 . The computer  100  provides a platform for operating a software program that guides a user through the design of experiments and then analyzes the results of the experiment. In the system identified, data and program files are input to the computer  100 , which reads the files and executes the programs therein. Some of the elements of the computer  100  include a processor  105  having an input/output (IO) section  110 , a central processing unit (CPU)  115 , and a memory module  120 . In one form, the software program for DOE is loaded into a non-transitory computer readable medium such as a memory  120  and/or a configured CD ROM (not shown) or other storage device (not shown). The software program includes instructions that are executed by the processor  105 . The IO section  110  is connected to a keyboard  125  and an optional user input device or mouse  130 . The keyboard  125  and mouse  130  enable the user to control the computer  100 . IO section  110  is also connected to a monitor  135 . In operation, computer  100  generates the user interfaces identified in  FIGS. 4-34  and displays those user interfaces on the monitor  135 . The computer also includes a CD ROM drive  140  and a data storage unit  145  connected to IO section  110 . In some embodiments, the software program for DOE may reside on the storage unit  145  or in memory unit  120  rather than being accessed through the CD ROM drive using a CD ROM. Alternatively, CD ROM drive  140  may be replaced or supplemented by a floppy drive unit, a tape drive unit, a flash drive, or other data storage device. The computer  100  also includes a network interface  150  connected to IO section  110 . The network interface  150  can be used to connect the computer  100  to a local area network (LAN), wide are network (WAN), internet based portal, or other network  155 . Any suitable interface can suffice, including both wired and wireless interfaces. Thus, the software may be accessed and run locally as from CD ROM drive  140 , data storage device  145 , or memory  120 , or may be remotely accessed through network interface  150 . In the networked embodiment, the software may be stored remote from the computer  100  on a server or other appropriate hardware platform or storage device. 
     In one embodiment, the software is an add-in running in Microsoft® Excel®. A user loads the software onto the computer  100 , and when the user starts up Excel® a menu selection for the add-in appears on a menu bar. By clicking through the menu selection and any submenus, the user is provided with a DOE menu. In some embodiments, the DOE menu provides the user with four choices: a DOE planning worksheet, a design wizard, a run default analysis, and a run custom analysis. Clicking the DOE planning worksheet opens a new worksheet  200  ( FIG. 2 ). The worksheet  200  includes a plurality of cells for defining experiments. The worksheet  200 , while optional, assists a user in planning experiments by having the user provide all the information that will be needed to design the experiments. In addition, spaces are provided for information that is useful for implementing the experiments (e.g., the process owner&#39;s name, the objective of the experiments, etc.). 
     Once the spreadsheet  200  has been completed, the user selects the design wizard function.  FIGS. 3A and 3B  show the operation of an embodiment of a DOE wizard. As shown in  FIG. 4 , the wizard provides a plurality of navigation buttons  405 - 430  and a progress bar  435  for each screen. Clicking an exit button  405  exits out of the wizard, deleting all previously entered data. Clicking a help button  410  opens a process specific help window with instructions for the particular portion of the DOE presently displayed by the wizard ( FIGS. 5-12 ). Clicking a reset button  415  takes the user back to the start of the wizard, erasing all previously entered data. Clicking a back button  420  takes the user one screen back in the wizard. Clicking a next button  425  moves the user to the next screen. Clicking a finish button  430  causes the software to design the experiments based on the data entered into the wizard. The back button  420  is not available on a first screen  440 . The next button  425  is only available when all necessary data has been entered for a particular screen of the wizard (the wizard provides default values in certain instances that do not need to be modified and some of the requested information is optional and need not be entered). The finish button  430  is only available when on the final screen of the wizard after all the necessary data has been entered. The progress bar  435  provides an indication of how far the user has progressed through the wizard. 
     The wizard provides two modes: (1) question and answer mode or (2) DOE map mode (see screen  440 ). The question and answer mode provides a high level of guidance to the user, asking questions for each step of design. The DOE map allows a user with more experience to select the type of experiments directly. 
     Referring back to  FIG. 3A , if the user selects the question and answer mode (step  450 ), the wizard asks the user for the number of levels for each factor (step  455 ). The selections include only two levels for each factor or at least one factor having more than two levels ( FIG. 13 ). If the user selects the DOE map mode (step  450 ), the user is presented with a map ( FIG. 14 ) showing the available experiments and the criteria for each. The user selects a design of experiments from the map (step  460 ). 
     If the user selects the only two levels per factor option in the question and answer mode (step  455 ) or selects the full factorial, high resolution fractional factorial, or low resolution fractional factorial experiments in the DOE map mode (step  465 ), the wizard continues with requesting the user to enter the number of factors to be used in the experiments (step  470 ) (see  FIG. 15  for the question and answer mode and  FIG. 16  for the DOE map mode). If the user selected five or more factors (step  472 ) the wizard prompts the user to select whether the experiments are for screening or testing ruggedness (step  473 ) (if the user selects four or less factors, this selection input is not provided). Screening is an economical experiment designed to examine a large number of possible factors to determine which of the factors might have the greatest effect on the outcome of the test (generally using high resolution fractional factorial design). Testing for ruggedness is an economical experiment designed to establish the ruggedness of a process to a large number of factors. This often involves the destructive testing of parts and typically only evaluates possible main effects (generally using low resolution fractional factorial design). 
     Next the user is presented with a screen having boxes for entering a name, a type, a unit type, a first level, and a second level for each of the factors chosen at step  470  (step  475 ) ( FIG. 17A ). The user is able to select the type of each factor as “categorical” or “numerical.” A categorical factor has a discrete number of values based on categories or groups. For example, a factor for location could be limited to east and west. A numerical factor has the possibility of a range of numeric values. For example, a factor for temperature theoretically has an infinite number of values within a range. 
     Next, the user is presented with a grid ( FIG. 18 ). In the question and answer mode, the grid allows the user to select the number of runs to be executed for the experiments (step  480 ). This essentially selects the design of experiments from the full factorial, high resolution fractional factorial, or low resolution fractional factorial experiment designs. The grid allows the user to select the number of runs for the previously entered number of factors. For example, in  FIG. 18 , the user had selected five factors, and is able to choose runs of  8 ,  16 , or  32  (highlighted in the rectangle). In the DOE map mode, the user previously selected the type of experiments. Therefore, only one selection is available in the grid. When the type of experiments is selected, a verification  482  of the type of experiments is shown below the grid ( FIG. 19 ). In some embodiments, the wizard allows only a low resolution fractional factorial design when the user selects ruggedness testing. In some embodiments, the wizard only allows a high resolution fractional factorial design when the user selects screening. 
     Next, if available for the particular design chosen, the user is prompted to select whether to use blocks for the test or not (step  485 ) ( FIG. 20 ). If the user elects to use blocks, the wizard prompts the user to enter the number of blocks to use, the blocking factor name, and the name of each block (step  490 ) ( FIG. 21 ). 
     In the next screen ( FIG. 22 ), the user is provided with data about the chosen experiments including the power  500 . The wizard also shows how many replicates are required to raise the power to 80%  505  and 90%  510 . The wizard prompts the user to enter the number of replicates the user desires (step  515 ). If at step  485 , the user had selected not to enter blocks, the wizard moves directly to the replicates screen ( FIG. 22 ) (step  520 ). If the user selects two or more replicates after having not selected blocks (step  525 ), the wizard asks whether the user wishes to run the replicates in blocks (step  530 ) ( FIG. 23 ). 
     On the next screen ( FIG. 24 ), the user is prompted to select the number of responses for the experiments and is then able to enter a name and type of units for each response (step  535 ). Finally, the user is prompted to enter a significance level (step  540 ). Once the significance level is selected, the finish button is highlighted and the user clicks on the finish button to design the experiments (step  545 ). 
     If in the question and answer mode at step  455  the user selected more than two issues or in the DOE map mode the user selected full factorial, the wizard prompts the user to enter the number of factors (step  550 ). If the user selects five or more factors (step  552 ) the wizard prompts the user to select whether the experiments are for screening or testing ruggedness (step  553 ) (if the user selects four or less factors, the selection input is not provided). 
     Next the wizard prompts the user for the number of levels for each factor (step  555 ), and the factor and level information (step  560 ) ( FIG. 25 ). Where three or more levels are used, unlike the factor information for factors with only two levels, the wizard limits the type of factor to categorical. A confirmatory notification  562  indicates this to the user (see  FIG. 17B ). 
     Next, in  FIG. 26 , the wizard prompts the user for the number of replicates (step  565 ), and if the replicates are greater than one, allows the user to select blocking (step  570 ). The user is then prompted to select the number of responses for the experiments and is then able to enter a name and type of units for each response (step  575 ). Finally, the user is prompted to enter a significance level (step  540 ). Once the significance level is selected, the finish button is highlighted and the user clicks on the finish button to design the experiments (step  545 ). 
       FIGS. 27 and 28  are a sample of a design of experiments. The experiments have three factors, using full factorial experiments with one replication and one response. The experiments were run and response data entered into the table. Once all the experiments have been run, and the results entered into the table, the user selects a run default analysis option from a pull-down menu. The system runs the analysis and provides the results of the analysis in several forms shown in a response worksheet ( FIGS. 29-34 ). 
     The results are given in an effect table  600 , an ANOVA table  605 , a recommended model  610 , a half normal plot  615 , a Pareto chart  620 , a normal probability plot  625 , a versus fits plot  630 , a versus order plot  635 , a histogram  640 , a plurality of main effect plots  645 - 655 , a plurality of interaction plots  660 - 665 , and a cube plot  670 . For the main effect plots  645 - 655 , the interaction plots  660 - 665 , and the cube plot  670 , a pull-down menu  675  allows the user to determine which factor or interaction to show in each graph. 
     The software analyzes the data, and determines which factors and interactions are significant. The software then highlights the significant factors and interactions and produces a recommended model. For example, the half normal plot highlights (e.g., by color and symbol) the factors or interactions that are significant. In the example shown, factors A and B and interaction AB are significant and are shown as golden squares. The other factors and interactions are shown as blue diamonds. Each of the factors and interactions are also labeled in the chart. Thus a user can quickly identify the significant factors by simply viewing the plot. 
     Similarly, in the Pareto chart, the factors and interactions that are significant are grouped together and shown in gold, while the less significant factors and interactions are shown in blue. 
     A recommended model (in the example: Response 1=0.7*A+1.825*B+2.2*AB+13.825) is automatically generated by the software, providing a user who is not fluent in the analysis with an optimum model or saving a fluent user the time needed to generate the model. 
     Various features and advantages of the invention are set forth in the following claims.