presentasi isiem dsg

7/28/2019 Presentasi Isiem dsg

1/30

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]


2/30


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


3/30


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.


4/30


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


5/30


6/30


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


7/30


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


8/30


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:


9/30


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.


10/30


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)


11/30


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


12/30


Picture 1. Three brands of pop corn

Picture 2. Processing of pop corn


13/30


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


14/30


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


15/30


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.


16/30


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.


17/30


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


18/30


Picture 4. Small pan, medium

pan and glasscover

Picture 5. Medium pan with cover on

stove


19/30


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


20/30


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


21/30


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)


22/30


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 .


23/30


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.


24/30


Our products :Wooden toy train


25/30


Picture 7. Materials and equipmentsof paintingexperiment

Picture 8. Preparation of experimental unit


26/30


Picture 9. Dipping method for painting

Picture 10. Spraying methodfor painting


27/30


easy to learn, fun, challenging, and memorable.


28/30


Picture 11. Drying


29/30


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.


30/30


presentasi isiem dsg

Documents