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7/28/2019 Presentasi Isiem dsg
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
TEACHING DESIGN OF EXPERIMENT FORINDUSTRIAL STATISTICS LABORATORY CLASS
Dedy [email protected] / [email protected]
Jakarta, 2007
mailto:[email protected]:[email protected]:[email protected]:[email protected] -
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Problem :
Books on design of experiments (DOE) havemany exercises at the end of chapters that givestudents practise in the analysis of completed experiments, but students often receive littleexperience in DOE
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Objective :
to share some ideas about teaching DOE by giving examples of simple experiments for laboratory class that integrates practice in
designing realistic experiments, running theexperiments, and also practice analyzing data insuch a way that is easy to learn, fun, challenging,and memorable.
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Where do the ideas come from ?
Antony J, N Capon. Teaching Experimental Design Techniques toIndustrial Engineers. Int. J. Engng Ed . Vol. 14, No. 5, , 1998 pp.335343
Antony J, N Capon. Some key things industrial engineers shouldknow about experimental design. Logistics Information Management Volume 11 Number 6 1998 pp. 386 392Hunter WG. 101 Ways to Design an Experiment, or Some Ideas
About Teaching Design of Experiments . 1975.http://williamghunter.net/articles/101doe.cfm Lye LM. Tools and toys for teaching design of experimentsmethodology . 33rd Annual General Conference of the CanadianSociety for Civil Engineering. 2005Martinez-Dawson R. Incorporating Laboratory Experiments in anIntroductory Statistics Course. Journal of Statistics Education Volume
11, Number 1, 2003.http://www.amstat.org/publications/jse/v11n1/martinez-dawson.html
http://williamghunter.net/articles/101doe.cfmhttp://williamghunter.net/articles/101doe.cfm -
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Some examples of simple experiments from Lye LM(2005), Mackisack M (1994) and Hunter WG (1975):
No Response or output Factors or input variables
1 taste (maximize), un- popped kernels
(minimize) of Microwave popcorns
brand, time, power, height (on bottom or raised)
2 virus scan time RAM cache, program size, operatingsystem
3 time to boil water pan type, burner size, cover, amount of water, lid on or off, size of pan
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
No Response Factors
4 distance paper aeroplaneflew
design, paper weight, angle
5 blending time for soy beans blending speed, amount of water,temperature of water, soaking time
before blending6 height of cake oven temperature, length of heating, amount
of water
7 length of rubber band before it broke
brand of rubber band, size, temperature
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Industrial Statistics LaboratoryIndustrial Eng. Dept Trisakti UniversityLab. Form for DOE Module :
Names:____________________ Date:_______________ Period:______________ Purpose of experiment :
Hypothesis:
Materials:
Procedures:
Results and Analysis using MINITAB :
Conclusion:
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Case 1 : Pop corn experiment
For example, we may want to investigate theinfluence of pop corn brands on the proportion of un-popped kernels (minimize). We use
completely randomize design or without blockingof experimental unit for this single factor experiment. There are tree levels for brand (A, Band C) and tree replications for each lavel. We
use one hundred kernels for each trial and 3,5minutes to make pop corn on stove.
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
We wish to test hypotheses about thetreatment means, and our conclusion willapply only to the factor levels considered inthe analysis
Ho : 1 = 2 = . = a H1 : i j for at least one pair (i,j)
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Randomization using Minitab :
Run order Un-popped kernels
proportion
Brand C
Brand C
Brand B
Brand A
Brand C
Brand B
Brand ABrand A
Brand B
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Picture 1. Three brands of pop corn
Picture 2. Processing of pop corn
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
The results of experiment :
Picture 3. Popped and un-
popped kernelsfrom tree brands
Run order
Un-poppedkernels
proportion
Brand C 0,04
Brand C 0,05
Brand B 0,11
Brand A 0,00
Brand C 0,08
Brand B 0,13
Brand A 0,03
Brand A 0,03Brand B 0,08
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Minitab Output :One-way ANOVA:Pooled StDev = 0,02134Source DF SS MS F PBrand 2 0,011356 0,005678 12,46 0,007Error 6 0,002733 0,000456Total 8 0,014089
S = 0,02134 R-Sq = 80,60% R-Sq(adj) = 74,13%Individual 95% CIs For Mean Based onPooled StDev
Level N Mean StDev ---+---------+---------+---------+------Brand A 3 0,02000 0,01732 (-------*-------)Brand B 3 0,10667 0,02517 (-------*------)Brand C 3 0,05667 0,02082 (------*-------)
---+---------+---------+---------+------
0,000 0,040 0,080 0,120
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Interpreting the Results :
The small p-values for the brand (p =0.007) that lower than ( 0.05) suggestthere is significant effect of brand onproportion of un-popped kernels. Individual95% confidence interval for mean of threebrand suggest that brand A has significantly
difference with brand B.
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Case 2 : Time to boil water experiment
For example, we may want to investigate theinfluence of pan size and cover on the time inminute to boil water. We use general full factorialdesign and completely randomize design or without blocking of experimental unit. Single.There are two-level for each factor and 3replications for each combination. Dimensions of small pan is 14 cm for diameter and 10 cm for height. Dimensions of medium pan is 18 cm for diameter and 11 cm for height. Volume of water is600 ml.
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Randomization using Minitab :RunOrder pan size cover time (minutes)
1 medium no2 small yes
3 small yes
4 small no
5 medium no
6 medium yes
7 medium yes
8 small yes
9 small no
10 medium yes
11 medium no
12 small no
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Picture 4. Small pan, medium
pan and glasscover
Picture 5. Medium pan with cover on
stove
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
The results of experiment :
RunOrder pan size cover time(minutes)
1 medium no 3.92
2 small yes 3.07
3 small yes 3.00
4 small no 3.53
5 medium no 3.63
6 medium yes 3.83
7 medium yes 3.20
8 small yes 3.22
9 small no 3.75
10 medium yes 3.9811 medium no 3.50
12 small no 3.60
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Minitab Output :General Linear Model: time (minutes) versus pan size, coverFactor Type Levels Valuespan size fixed 2 small, mediumcover fixed 2 yes, noAnalysis of Variance for time (minutes), using Adjusted SS for
TestsSource DF Seq SS Adj SS Adj MS F Ppan size 1 0.29768 0.29768 0.29768 4.90 0.058
cover 1 0.22141 0.22141 0.22141 3.65 0.093pan size*cover 1 0.20021 0.20021 0.20021 3.30 0.107Error 8 0.48560 0.48560 0.06070Total 11 1.20489S = 0.246374 R-Sq = 59.70% R-Sq(adj) = 44.58%Unusual Observations for time (minutes)
time
Obs (minutes) Fit SE Fit Residual St Resid7 3.20000 3.67000 0.14224 -0.47000 -2.34 R
R denotes an observation with a large standardized residu
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
M e a n o f t
i m e
( m i n u
t e s )
mediumsmall
3.70
3.65
3.60
3.55
3.50
3.45
3.40
3.35noyes
pan size cover
Main Effects Plot (data means) for time (minutes)
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Interpreting the Results :
The small p-values for the pan size (p =0.058) and cover (p = 0.093) that lower than (0.10) suggest there is enoughsignificant effect of pan size and cover ontime to boil water. Interaction of pan sizeand cover is not significant. Mean plot of
rensponse suggests that small and cover (yes) give lower time to boil water .
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Case 3 : Painting experiment
We may want to investigate the influence of paintingmethods (dipping, spray and brush) and brand of paint onvisual quality using 10 point scale. As a standard for point10, we use sample product from PT. Safira Tumbuh
Berkembang (wooden toys producer). General fullfactorial design and completely randomize design used inthis experiment. There are tree levels for painting methodsand two levels for paint brands and three replications for each combination. This experiment is part of
Manufacturing Industrial Design Lab. in IndustrialEngineering Department Trisakti University. The endproduct is wooden toy train.
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Our products :Wooden toy train
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Picture 7. Materials and equipmentsof paintingexperiment
Picture 8. Preparation of experimental unit
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Picture 9. Dipping method for painting
Picture 10. Spraying methodfor painting
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
easy to learn, fun, challenging, and memorable.
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
Picture 11. Drying
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University
CONCLUSIONS
1. This paper has shown the benefit of employing asystematic approach to simple experimentation usingDOE, rather than utilising a trial and error approach
2. The paper has also illustrated some simple experiments
that can be used as a powerful teaching and learningtool in industrial statistics laboratoy.
3. These simple experiments will form a student foundationfor studying DOE for the wider application in real-lifesituations or using other techniques of DOE like
response surface, taguchi or mixture experiments.
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Industrial Statistics Laboratory Industrial Engineering DepartmentTrisakti University