In article <95nuk5$8df$[EMAIL PROTECTED]>,
[EMAIL PROTECTED] wrote:
> Thanks very much for your helpful response.
> 1) My factors are continous. I have multiple responses. Some are
> continous and some are categorical. I need to optimize my resonses.
> The main region that they are interested is for A between-35 and 95
> and for B between 900 and 1750.
> In addition they want to run couple of points outside of this region,
> as there is reason to believe that it will optimize the response.
> These are B=2000 and A is any pt between 65 and 95, say 80.
> Also, they want to run the combination A=35 and B=1650.
> Also, would like to include A=90 and B=1650.
> Also, would like to include A=105 and B close to 1325.
> these points are not totally fixed. If I can get close to it that will
> work.
> Everything else looks like flexible. I'll be able to run the
experiment
> 21 times. I can include replications. Will replication on some runs
and
> not on other destroy orthogonality?
>
> I'm not sure how to set this up.
> I appreciate your help very much.
> SH. Lee
> In article <[EMAIL PROTECTED]>,
> [EMAIL PROTECTED] wrote:
> > Flash response:
> >
> > 1) Are the levels fixed by some characteristic of the process?
> they
> > look continuous, and you could do much better if they were, and you
> > could select different intermediate levels.
> >
> > 2) the number of levels can be what you want of it. Some good
> > response surface designs use 5 levels. some use more.
> >
> > 3) Factor B levels are equally spaced, which is good. Factor A
> > levels are not evenly spaced. A full factorial will not give you a
> > 'clean' design - Without doing the math, I don't believe it will be
> > orthogonal, even if you did do all the combinations.
> >
> > 4) what are you going to do with the results of this experiment?
> If
> > you wish to build a model of the system behavior, then a full
> factorial
> > type approach is a waste of your effort, time, and experimental
runs.
> >
> > 5) Suggest you look at a Response model, with maybe 3-5 levels in
> > both factors, but using a proper RSM type design. If you do it
> > properly, you can avoid a single 'corner' point and recover it
> > mathematically.
> >
> > 6) I'd also ask if you have hard reason to believe that a RSM
type
> > model, which will get you quadratic terms in a model, is in fact
> worth
> > doing (financial/your time costs) the first time out? If little
> prior
> > information is available, it would probably be better to do a
> simpler,
> > 2-level factorial first, if at all possible. Doing this will teach
> you
> > a great deal [that you probably don't already know]. Your choice
> here,
> > but remember - most people overestimate their knowledge level :)
> >
> > 7) You haven't discussed the response yet. Please spend some
time
> > thinking about that, too.
> >
> > More later, if this helps at all. Let me know.
> >
> > Jay
> >
> > [EMAIL PROTECTED] wrote:
> >
> > > Hi,
> > >
> > > I have two factors A and B and I want to run a DOE to study my
> response.
> > > My factor B is at 3 levels; (900, 1450 and 2000) , my factor A is
> at 4
> > > levels 35, 65, 80 and 105.
> > > First of all is it right to have one factor at 4 levels. I have
> > > encountered situations where the factors are either at 2 levels
or 3
> > > levels.?
> > > This will require me to have 12 runs for a full factorial, right?
> > > Also, I do not want to run only the level 35 of factor A with the
> level
> > > 900 of factor B. If I remove the combination 35, 1450 and 35,
2000;
> > > I'll have only 10 runs and the resulting design space will not be
> > > orthogonal. How do I tackle this problem?
> > > Is there a different design that you would suggest.
> > > Thanks for your help.
> > > SH Lee
> > >
> > >
> > > Sent via Deja.com
> > > http://www.deja.com/
> > >
> > >
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> > >
> >
> > --
> > Jay Warner
> > Principal Scientist
> > Warner Consulting, Inc.
> > 4444 North Green Bay Road
> > Racine, WI 53404-1216
> > USA
> >
> > Ph: (262) 634-9100
> > FAX: (262) 681-1133
> > email: [EMAIL PROTECTED]
> > web: http://www.a2q.com
> >
> > The A2Q Method (tm) -- What do you want to improve today?
> >
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>
> Sent via Deja.com
> http://www.deja.com/
>
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