topic Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications? in Discussions

DSD Screening Replication

rich_bubb — Thu, 04 Jun 2015 17:40:57 GMT

In trying to try to control a visual quality characteristic, we are nearly ready to run a 4 factor (all are continuous variables, e.g., temperature, feed rate, etc.) DSD at 3 levels per factor. This will have us run 9 treatments for the DSD.

My question is: Should I run one or more replications on the DSD screening-type DOE?

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

louv — Thu, 04 Jun 2015 19:09:45 GMT

If you have the resources to consider replication of the design then perhaps I would challenge you to brainstorm additional factors above the 4 factors that you are considering. The true power of the DSD's manifests itself when one is considering many more factors than 4. Of course the answer to your question is yes there is an advantage to adding replicates in any design especially if your measurement system my in your case be subjective?

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

rich_bubb — Thu, 04 Jun 2015 19:32:12 GMT

LouV,

We've already narrowed down the likely factors, and performed a multi-vari analysis to be sure our production system (i.e., equipment & measurement systems) and setup/operators were not causing variation.

Now we're down to "incoming material variation" & "process parameters". And the incoming material isn't varying much.

Per our Cause & Effect diagram (the Ishikawa had 24 possibilities), we were able to get to the four most likely factors.

The DSD method was chosen because the # DSD runs (9) is less than the normal screening # runs (16), and because we can screen for multiple levels in one screening run.

I'm more interested in detecting factor curvature* vs. economical design.

* I'm pretty sure one or more of the factors exhibit curvature.

Don't know if this is very relevant, but I'm using JMP7.

Thanks for the help.

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

Ryan_Lekivetz — Thu, 04 Jun 2015 20:27:37 GMT

I would recommend considering the 6 factor DSD for the 13 runs (and drop the 2 extra factors).

The 9-run DSD can't fit the full RSM model for any subset 3 factors, while the 13-run design is able to. In JMP 12, we now use the 13-run as the default.

Good luck!

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

louv — Thu, 04 Jun 2015 20:31:41 GMT

RB,

Thanks for the further detail. My only concern would be that when you narrowed down the likely factors was it done in DOE fashion? Since some factors that may be important due to interactions may have been dismissed. I only harp on this point because after a long career of running DOE's my only regret in retrospect was not to include more factors in my initial design and I wanted to share that experience with you. Also, if appropriate, always remember to include water as a factor since its understanding is probably responsible for 50% of my raises over the course of my career. If you are using JMP 7 I am surprised that you are able to run the DSD but if so that's great.

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

rich_bubb — Fri, 05 Jun 2015 11:23:01 GMT

Ryan,

Thanks for the 6 factor DSD tip, & I'll look into it today.

Are you referencing this paper:

http://www.jmp.com/content/dam/jmp/documents/en/white-papers/definitive-screening.pdf

specifically the designs on page 5?

And by "dropping the 2 extra factors", do you mean dropping the two right-most columns of the m=6 design?

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

rich_bubb — Fri, 05 Jun 2015 11:44:04 GMT

LouV,

We narrowed down the factors available on the Ishikawa diagram (originally had 24 possibilities) to the most likely candidates through engineer's process knowledge + multi-voting. The multi-voting gave each team member 100 points to "assign" to factors based on their knowledge of the process under consideration. Then the highest voted factors were selected for DOE-Screening. Just after that I stumbled upon DSD. Brilliant stuff indeed!

I am not understanding your comment, "Also, if appropriate, always remember to include water as a factor since its understanding is probably responsible for 50% of my raises over the course of my career." We aren't using water in our process.

To build the 4 factor DSD, I made a (JMP7) Screening Design for the 4 factors. Then added one center point. After [Make Table], I manually changed two of the rows to match the DSD 'pattern'. However, since Ryan Lekivetz (see above) suggested using the 6 factor DSD, I am going to revisit the DSD design I was using and try to wrangle it (Augment Design?) into a 6 factor version DSD. Can JMP handle this?

Thanks for all the helpful information!

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

Ryan_Lekivetz — Fri, 05 Jun 2015 13:56:36 GMT

You are right on with the "dropping the 2 extra factors".

I would use the m=6 design rather than using Augment design, but I'm partial to the special structure of DSDs.

Lou makes a very good point about considering extra factors. If you use the DSD for 6 factors, you're getting to study 2 extra factors for "free". If there's truly no effect on those extra factors, it's simple enough to remove them from the model, but you may end of finding something you didn't expect.

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

rich_bubb — Fri, 05 Jun 2015 14:39:22 GMT

Ryan,

I got the m = 6 DSD screening (13 runs) finished & gave it to our process engineer. Hopefully he'll be able to start running the treatments soon. I think I got it copied into this message (see below).

Due to confidentiality considerations I have to rename Factor 3 & 4. And I sorted the table based on the Temp column since it take forever to adjust our furnace's temperature & wait for it to normalize... so technically it probably should be a Blocked Factor.

I will add "Y" data when I get it.

Best regards,

Treat-ment #	Pattern	Temp.	Feed	Proprietary Factor 3	Proprietary Factor 4	"Y" rating aka undesirable visual quality characteristic
Low	-	540	5	100	0.7
Nom	0	560	10	300	1.3
High	+	580	15	500	1.7
1	-0+-	540	10	500	0.7
2	--0+	540	5	300	1.7
3	-++0	540	15	500	1.3
4	-+-+	540	15	100	1.7
5	----	540	5	100	0.7
6	0+--	560	15	100	0.7
7	0-++	560	5	500	1.7
8	0000	560	10	300	1.3
9	+0-+	580	10	100	1.7
10	++0-	580	15	300	0.7
11	+--0	580	5	100	1.3
12	+-+-	580	5	500	0.7
13	++++	580	15	500	1.7

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

louv — Fri, 05 Jun 2015 14:46:54 GMT

RB,

Sorry for the confusion about my "water" comment. Although water was not in many of my chemical processes it certainly reared it's head as a factor coming in with some of the raw materials or environment. With a such a low molecular weight versus some of the complex chemicals we were synthesizing knowing our sensitivity to water was important. So for instance if we were running our chemical process in Ethyl Acetate we would spike water in at low levels to understand it's impact at low levels. All of his may not apply to you but wanted to share some of life's experiences with you.

I'm with Ryan on adding two additional factors based on your multi-voting process since you will be running 13 runs and would gain information about them for free.

Lou V

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

Ryan_Lekivetz — Mon, 08 Jun 2015 18:27:16 GMT

Hope the experiment goes well!

In the future, if blocking for DSDs has been added in JMP 12 which will give you some extra flexibility in the number of blocks you can consider (as well as more balanced block sizes instead of 3 runs for the "center" block).

There have also been improvements to the way we construct DSDs compared to the original paper - I'm not sure if the DSD add-in works in JMP 7.

Cheers,

Ryan

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

rich_bubb — Thu, 11 Jun 2015 19:34:01 GMT

Table below has the results data (Original data then two replications).

Obviously the area around run 2 is where future experimentation will be focused.

RUN	Pattern based on m = 6	Temp.	Feed	Factor 3	Factor 4	Visual Rating 1=Best 5=Worst Signal-to-Noise (LIB)
						Y1	Y2	Y3	S/N	Rank
1	-0+-	540	10	500	0.7	2.6	2.1	2.0	-7.039	2
2	--0+	540	5	300	1.7	2.3	2.0	2.0	-6.464	1
3	-++0	540	15	500	1.2	3.6	3.6	2.1	-10.048	4
4	-+-+	540	15	100	1.7	4.8	4.8	4.0	-13.158	13
5	----	540	5	100	0.7	3.5	3.4	2.3	-9.868	3
6	0+--	560	15	100	0.7	4.4	4.5	4.0	-12.680	12
7	0-++	560	5	500	1.7	3.5	3.3	3.0	-10.299	5
8	0000	560	10	300	1.2	3.8	3.2	3.6	-10.985	7
9	+0-+	580	10	100	1.7	4.0	4.1	3.7	-11.903	11
10	++0-	580	15	300	0.7	3.7	3.4	3.8	-11.216	9
11	+--0	580	5	100	1.2	3.8	3.6	3.8	-11.445	10
12	+-+-	580	5	500	0.7	3.9	3.7	2.5	-10.688	6
13	++++	580	15	500	1.7	4.1	4.0	2.5	-11.146	8

NOTE: Color shading in Y1, Y2, & Y3 only used to indicate better (green), moderate (yellow-orange), and bad (red) visual-quality rating.

Rating method was to arranged all Y1, Y2 & Y3 samples side-by-side for comparison/evaluation purposes. We had to do this type of rating method because no one make a reasonably affordable gage (giving quantitative results) for this product's visual characteristic we're trying to reduce.

PS Thanks to all who provided help on this (LouV & Ryan Lekivetz).

PPS. Now I have ammo to justify getting JMP12... woohoo!

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

louv — Thu, 11 Jun 2015 20:36:34 GMT

RB,

I would give a try to (-,-,+) for Temp = 540,Feed = 5 and Factor 3 = 500. Factor 4 does not seem to impact response so choose the economical/ergonomical setting for that one.

Re: DSD Screening Replication: We are about ready to run a 4 factor (all continuous variables) at 3 levels/factor, for a total of 9 treatments. It is advisable to run one of more replications?

rich_bubb — Fri, 12 Jun 2015 20:29:29 GMT

LouV & Ryan,

In my follow-up series of experiments, I'm considering two options:

starting an Augmented Design sequence using the existing 13 runs and adding runs based on the Prediction Profiler's Maximize Desirability results and Grouping the New Runs into a Separate Block, and/or

hiding/excluding the worst X% results, and then starting an Augmented Design plan with the most skewed results not "clouding the picture"... and I need to explain the "hiding/excluding" wording. The worst results are remarkably visually unacceptable. Therefore the combination of settings that made them would, in hindsight, be Disallowed Combinations.

So the question I have is: Should I clear the design-space picture, exclude/hide really bad resulting runs, then Augment?

As a point of historical reference, a visual rating of "2" (1-5 scale, w/1=best, 5=worst ever), is better than about 95% of everything we've ever made. The 1-5 scale was set up with the understanding that a "3" was currently acceptable product, and anything rated < 3 would be considered an improvement.

Have a great weekend,