In Case There are Any Scientists or Engineers Out there...
 

Perhaps you are a scientist or engineer or know one or even a student? This may be of interest:

https://www.amazon.com/Designed-Experiments-Science-Engineering-Holloway/dp/1032854413/ref=sr_1_1?crid=3KBBRMJ684D7M&dib=eyJ2IjoiMSJ9.3Mj _lZLzPKIEkTaZPJuRHw.GB9rDK6soXiZGo4wEFumYePerny6Zk KFbzbngandEI8&dib_tag=se&keywords=designed+experim ents+for+science+and+engineering+by+michael+d+holl oway&qid=1718920658&sprefix=designed+experiments+f or+science+and+engineering+by+michael+d+holloway+% 2Caps%2C112&sr=8-1

http://forums.pelicanparts.com/uploa...1719331589.jpg

There is a story behind the cover art...

other titles in case anyone is interested:

https://www.amazon.com/s?k=michael+d+holloway&crid=3AWD8RUVY3E2F&sprefix= michael+d+holloway%2Caps%2C136&ref=nb_sb_noss_1

why would i want to buy a book on the worst part of engineering ... DOEs suck. DOEs mean you dont know what your doing and why its happening. bad news all round.

The topic might be interesting - I'm just not sure of that author :)

Congrats, Lubey!

Meh. Let us know when the 8th Edition is released.

Just kidding. Congrats, Mike. We miss you here.

I'm gonna wait for the movie ... in IMAX :D

I'd expect that this book (haven't seen but I'm interested) would go well past the first layer of screening tests. If you are digging into some identified mechanism I think that it gets more interesting and useful than your 6-S standard training.

One problem is that everything is estimates of estimates of a population and things get wonky when you deviate from standardized PDFs. Even more so when you're looking to understand the likelihood of rare things in the tails.

mostly the thing i am pointing out is if you are looking at all sorts of nonlinear, dependent and interaction effects in your system, like something you need a really good DOE to figure out ... then you dont really know how your system is working or why.

im less mocking DOEs, and more pointing out that when you need a good DOE, you are deep **** and not having fun. life is hard when you dont have a fundamental understanding of your system.

lubey, what part of Houston are you in?

The part that's a LOT closer to Dallas :)

Well, he can write a sentence in the English language, so there is that.

I know nothing about engineering or even what a DOE is but I know English when I see it, or don't.

Moved to town house in Mid-Town / the 'Tre' a few months ago...

Well, the upside is you just saved yourself $250!

Anyone that is in R&D or engineering that doesn't do designed experiments will waste a ton of time. These techniques are normally used with high-end development and refinement. A designed experiment allows you to look at several different variables without having to do a ton of trials. Here's a simplified decision tree to guide you in selecting a designed experiment:

1. Determine your research objective:
• Is it screening and identifying key factors?
• Are you looking to estimate the main effects and interactions?
• Do you want to build a response surface model and optimize the process?

2. Consider the number of factors involved:
• One factor: One-Factor-at-a-Time (OFAT): Simple but inefficient for exploring interactions.
• Two factors:
o Full Factorial: Efficient for exploring all combinations and interactions but can be costly for many factors.
o 2-Factor Central Composite Design (CCD): More advanced, allows for model building and optimization with curvature exploration.
• Three or more factors:
o 3-Factor Central Composite Design (CCD): More advanced, allows for model building and optimization with curvature exploration.
o Box-Behnken Design: Efficient for exploring quadratic terms without requiring as many runs as a full factorial.
o Plackett-Burman Design: Useful for screening many factors with limited resources, but only provides information about main effects.
o Fractional Factorial Design: Efficient for screening and identifying key factors, requiring fewer runs than a full factorial.
o Derringer Design: Useful for optimizing multiple responses simultaneously when interactions are important.

3. Analyze your budget and resource constraints:
• Limited resources: Consider Plackett-Burman, Fractional Factorial, or even OFAT if interactions are not a major concern. Choose smaller designs with fewer runs.
• Ample resources: Full factorial, CCD, or Box-Behnken designs can be beneficial for detailed analysis and model building.

4. Assess the expected relationship between factors and response:
• Linear relationship: Factorial, Box-Behnken, or Fractional Factorial designs might suffice.
• Non-linear relationship: Central Composite Design (Derringer Design) might be better to capture curvature and complex interactions.
• Unknown relationship: Start with a Fractional Factorial design (Plackett-Burman design) to identify key factors, then follow up with a more specific design based on the findings.

5. Consider experimental error and accuracy needs:
• High accuracy: Include replicates in your design, especially at the center point. Choose designs with inherent replication or error estimation capability.
• Initial exploration and rough estimates: Fewer replicates might be acceptable, depending on the tolerance for error.

i already paid off my student loans ;)

Well, I can't imagine it took you very long.