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Unsure of statistical tool

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  • #31767

    eric
    Participant

    Hello FriendsThis one of those situations where you have the answer in front of you but can’t see it.I have 10 diff mold on 10 diif stations. Some times we have mold 1 on station 1 and the scrap is 2% and sometimes we have the same mols on station 9 and the scrap is 2.6% . What tool can can I use to determine statistically what is the scrap driven factor, the mold or the station. Thanks for your time.

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    #84120

    taufiq
    Member

    Dear,
    you can use ANOVA or DOE.

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    #84121

    Robert Butler
    Participant

      As you describe it your concern is the apparent difference in a particular mold behavior at only two out of your 10 different stations.  If this is really the case then you have a two variable two level design.
            Mold               Station
            #1                    #1
           Other Mold       #1
           #1                     #9
           Other Mold       #9
       Where the “Other Mold” is either a mold that you “know” doesn’t seem to vary or is just a mold picked at random from your group of molds.  Replicate one of the design points to provide an estimate of error and you can check for the effect of mold, station, and mold x station interaction.  If you can’t afford to run a single replicate you can use the mold x station term as your estimate of error.
      This design will tell you about mold and station differences but unless you have evidence that suggests mold #1 and the other mold and station #1 and station #9 are representative of all of the other stations and molds in your system the results of the above investigation should only be applied to the particular subset of molds and stations that you used.
     

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    #84122

    eric
    Participant

    Thanks for your response. I think I need to clarify that the apparent difference is not on ly with a particular mold behavior at only two out of the 10 different stations. The situation is the behavior of the ten molds on the ten stations. Does that make sense?

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    #84125

    Robert Butler
    Participant

      Yes, ten molds and ten stations makes sense and certainly makes things more interesting.  Your problem could be viewed as a two way ANOVA with the variables being stations and molds.  If you have to view every mold and every position as distinctly different from one another then the “simplest” matrix becomes a 10 x 10 array with the response of each mold recorded at each station. If you have prior data that you can trust you may be able to fill in the matrix, identify the blank spaces and then run the missing combinations.  You could run an ANOVA on this array (it is possible to run an ANOVA with only one measurement per cell).  Assuming that you had sufficient data, the biggest problem would be the trust that you would have to put in your prior data-if the data covered a long period of time you would have to worry about process shifts unrelated to mold or position that would wind up either being attributed to mold or position or would mask their respective effects.  Of course, if you don’t have this data then you are faced with the formadible and time consuming task of taking 100 measurements.
      If you have acceptable prior data from some positions and some molds which would permit you to assemble a two way array of data on a subset of molds and positions you could run an analysis on this data.  While this wouldn’t answer the question about every mold and every position it would help you understand the magnitude and significance of differences in means and variances that you could quantify. It would aid in quantifying when an apparent difference is really a difference. Since we are assuming that every mold and every position is unique, it would also act as a guide for further work.
      If you have selection criteria that permit you to group your molds and stations into classes, the problems outline above shrink and the data gathering and analysis becomes easier.  For example, if your 10 molds can be grouped into one of 4 distinct groups and your stations can be grouped in a similar fashion then you could proceed with a much reduced matrix of molds and positions. In such a case you would randomly pick a representative mold and position for each grouping and gather data on these combinations.
      If you haven’t already, I would urge you to give serious thought to possible classes of molds and positions.  I used to work in the plastics industry and I know that there are many ways to classify molds. 
     While all of the above has been couched in terms of ANOVA the data can also be analyzed using regression methods.

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    #84128

    eric
    Participant

    Thanks Robert I can see the light now.
     
     
     
     
     

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