7/28/2019 10 Errors in DoE

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dpncanada.com June/July 200726 Design Product News

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Info Card 38

Feature: Data Acquisition

The ten most common designed experiment errors

dpncanada.com June/July 2007

By Jeff Hybarger

When I rst learned about

designed experiments in 1986,I really liked the act that using

these techniques I had the potential tosolve production problems rom the qual-ity department.

My rst experiment was a very expen-sive ailure. I spent one day to runsamples, two weeks to measure, and twoweeks or manual analysis. I denitely bito more than I could chew.

The important thing was that I ana-lyzed my ailure and ound that planningis everything with a designed experiment.I the planning is done properly, you willlearn something. Lets take a look atten tips or avoiding the most common

designed experiment mistakes.

Get good sotware and learn to useit properly. Ive tried about hal adozen dierent programs o all priceranges. Some will actually help you

pick the wrong actors and some donot have residual analysis included.Make sure that the equipment theexperiment is going to run on iscalibrated and all preventive main-tenance is up-to-date. Its rustratingto optimize a process only to lose itater calibration. You need to startrom scratch and cant make assump-tions rom the rst experiment.Do not run too narrow o a rangerom low to high or your actors.I you do, it will appear as i keyactors do not aect the process. Inreality, they do not aect the processin the range you selected.

Do not run too wide o a range rom

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low to high or your actors. I yourun too wide a range, you may ndthat some combinations o actorsdo not yield usable results. Beore theexperiment review the matrix and

nd the two worst cases by using

subject matter knowledge. Run thesesamples rst. I there are problems,tighten up the ranges o the actors. Ininjection molding, I like to run rangesjust inside o shorting or fashing the

parts.The sample size or each run andthe number o runs needs to be largeenough to detect the size o partchanges you think are signicant. Iyou do not use large enough samples,you will not detect changes that real-ly occurred. There are ormulas thatassist in determining the optimumnumber o samples.Factors that are not included in theexperiments matrix cant be touchedduring the experiment. Changinganything that is not in the matrixadds actors that are not accountedor. Stay on the production foor to

keep a close eye on the machine set-tings.Experimental design run orders arenot the easiest order to run the exper-iment in. It would be easier or thepeople running the experiment tochange the order. Again, keep on thefoor and keep an eye on things.Ive experienced two kinds o mea-surement error in experiments thatcan lead to poor results. The rst isgage error. Gage error studies needto be complete beore running theexperiment. Error should be under20% to give good results. The secondis having more than one person mea-

sure a dimension or rate attributes.Data entry is always an issue. I alwayshave the inspectors enter the data ina spreadsheet. Standard deviationsrom all runs can be compared. I anystandard deviation looks signicantlyhigher, check or obvious data entryand measurement error. I the partswere numbered by run and part, theindividual part can be re-measuredand entered into the spreadsheet.Ater analysis, verication run(s) needto be completed. Do not ever basetool work o o predicted sotwarevalues. Run the optimized processand worst case runs i applicable,

measure the parts and then maketooling changes.

I these tips are ollowed, a good 90%o designed experiment ailures can beavoided. The important thing is to learnrom the successes and ailures o eachexperiment.

Je Hybarger is a DOE practitioner withmany years o engineering experience inmanuacturing (hybargerconsulting@char-ter.net). It frst appeared in the December2006 stateaser newsletter rom Stat-Ease,Inc. (statease.com).

Info Card 327

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26 Design Product News

Design for 2 to 21 factors where each factor is varied over 2 levels. Good software and methods yield good results.