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7/31/2019 Report DOE2

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CHAPTER 1 : INTRODUCTION

1.0 PROJECT BACKGROUND

Nowadays, pop corn can be made even without the popcorn maker. There

are other ways like using microwave and saucepan1. In this research, we are

focusing on making popcorn by using microwave. Microwave is used instead of

saucepan is due to the result of our brainstorming session which is guided by the

SMART analysis. Figure 1.1 show the result of SMART analysis between

saucepan and microwave. The end result while making popcorn is to have high

number of popped corn compare to the un-popped corn. A study2 in 1993 was

conducted to find out the factors that influence the number of popped corn.

However, the finding is concluded as a failure as there is no significance factors

are found. This study will benchmark the factors used by the previous study and

brainstorm among the members to identify the variables that might influence the

result of popped-corn.

S M A R TMicrowave / / / / /

Saucepan x / x x x

Figure 1.1: SMART analysis on Popcorn making

1.1 PROBLEM STATEMENT

There are lots of kernels in the market that come out with different packaging and

instruction on the packaging on how making the popcorn (Appendix1). The

problem is does the instruction given will produce the high number of popped

1Kookies, How to make popcorn without a popcorn maker, 2009,

(answers.yahoo.com/quation/index?qid=20090227105628AA0wdpW)2

Applying DOE to microwave popcorn, Mark. J. Anderson and Hank P. Anderson (1993)


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corn as wanted by the maker?. What is the right combination that can boost up the

number of popped-corn.

1.2 OBJECTIVE

1.2.1 Identify factors that affect the end result of the pop-corn making.

1.2.2 Recommending the best combination of factor in pop-corn making.

1.2.3 Develop the regression equation to predict the CTQ (Response variable)


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CHAPTER 2 PARAMETER SELECTION

2.0 FACTOR, LEVEL AND RESPONSE SELECTION

2.1 As mention in chapter 1, we benchmark the factors used in the previous study2

and brainstorm on the factors that might influence the yield of the popcorn. Figure 1.2

shows the Fishbone Diagram that sum up the output of the brainstorming session. After

that, we come out with Cause and Effect matrix (Figure 1.3) to identify and select the

factors.

Figure 1.2: The Fishbone Diagram


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Key

Process

Output

Variables

Business

Importance10

Rank 1

1

Process Step KPIVPoppe

d-Corn

R

ank

Process

Ste

ps

&

Key

Process

Input

Variables

1 Put Kernels on the Paper Bag Brand/Price 9 1

2Use of

Margarines9 1

3 Preheat 1 2

4 Tray Elevate 9 1

5 Setting on the microwave Temperature 9 1

6Time

Cooking9 1

7Surrounding

Temperature9 1

Reverse

Score10

Reverse

Rank1

Figure 1.3 : Cause and Effect Matrix

2.2 Two level will be used which are low (-) and high (+). The low level is believed

to give low impact while the high level is believed to give high impact. The level is set

based on information on given on the packaging (Appendix 1) and also the level stated by


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the previous study. The additional factor which is margarine is derived from the

information on simple.recipe (appendix 2) which required the used of oil to enhance the

yield of the corn. Figure 1.4 shows the parameters which include the factors and level

that will be used in this experiment.

FACTOR LEVEL (-) LEVEL (+)

TEMPERATURE

Medium High

TRAY

ELEVATE

MARGARINE

COOKING

TIME

2 Minute 3 Minute

PRICE/BRAND

Figure 1.4 Level set up

2.3 As a popcorn maker, the number of less un-popped is wanted by the maker. In

this study, the response or the CTQ will be the weight (g) of un-popped corn left after theprocess of popcorn making take place. In this experiment, the flour weight scale with the

resolution 0.01 is used. The small resolution is used in order to avoid human error while

reading the weight of the kernels. See Figure 1.5


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Figure 1.5 Weight Scale

Figure 1.6 The factors and response of Pop Corn Making

POP CORN MAKING

100gram corn bullets

Power supply

Weight of un-popped

corn (g)

Temperature

CookingTime

TrayElevate

Margarine

Brand/Price

Surrounding

heat


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CHAPTER 3 EXPERIMENTAL DESIGN

3.0 EXPERIMENTAL DESIGN

As mention in previous chapter, there are 6 factors chosen. Five (5) factors

that can be controlled while one (1) factor cannot be control which is surrounding

heat. Hence, to see is there any significant different on surrounding heat, we

decide to block the experiment by using day and night with the justification that

the heat on day is higher than night heat. In order to avoid the existing heat of

microwave affecting the reading of the next experiment, after every experiment,

the microwave is left to be cooled for 10 minutes.

The full experiment need to be conducted in this project is 25

= 32 which

derived from the formula of full factorial nk. To avoid any error on measuring and

experiment we decide to run the experiment with 3 replication and 3 repetitions.

However, the total experiment need to be run is 3(25) = 96 times which too many.

Hence, we decide to use fractional factorial with quarter experiment with 3

repeated measure and 3 replication which give 3(25-2) = 24 times of experiment.

The quarter is chose instead of half is due to below justification:

3.0.1 Limited resources

Conducting 96 experiments required lots of kernels, margarine,

paper bag and other resources which are a waste for the

experimenter. Beside, long time consuming will be required and

experimenter cannot fulfill it due to other commitment.

3.0.2 Capabilities/Durability of Machine (Microwave)

The capabilities of the microwave are limited to little extent.

Excess usage of the microwave might blow up the microwave.


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The fractional factorial is used to screen out important factors. Full factorial will

be conducted after unimportant factors is screened out so that the factors can be analyzed

without any confounded or aliased. Figure 1.7 shows the process flow of this experiment.

Figure 1.7 Flow Chart of Experiment

START

Fractional

Factorial (25-2)

Identify

Significance

Factors

Screen out

important factors

Conduct Full

Factorial (2k)

ANALYZE

Y

Re-level

N


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CHAPTER 4 CONDUCT THE EXPERIMENT

4.0 CONDUCTING THE EXPERIMENT

4.1 Fractional Factorial

4.1.1 The Table 4.1 below show the matrix diagram for fractional factorial, 25-2

been conducted. The experiment below is blocked with Day and Night.

It randomizely done. The result below represent the average from 3

repeated measure and 3 replications.

4.1.2 This 25-2 experiment is having Resolution III confounding which means

that:

4.1.2.1There is no main effect is confounded with another main effect

4.1.2.2Main effect are confounded with two-factors interaction

4.1.3 The confounding for this 25-2 popcorn experiment are as below:

4.1.3.1 I + ABD + ACE + BCDE

4.1.3.2 A + BD + CE + ABCDE

4.1.3.3 B + AD + CDE + ABCE

4.1.3.4 C + AE + BDE + ABCD

4.1.3.5 D + AB + BCE + ACDE

4.1.3.6 E + AC + BCD + ABDE

4.1.3.7 BE + CD + ABC + ADE


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CHAPTER 5 ANALYSIS

5.0 ANALYZING THE FRACTIONAL FACTORIAL EXPERIMENT

All the analyzing that is proven by Minitab is attached to this report as an appendix.

5.1 Normality Test

Normality test is used to make sure the distribution of the data gain from the

experiment by using Minitab 1.5.

Ho : The data is normally distributed

HA : The data is not normally distributed

Based on the result of the normality test, p-value is equal to 0.0850 which is

greater than 0.05, so the null hypothesis is fail to reject. Thus, the data is normally

distributed.


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78)/4

= 62.23

Table 4.2 : Main Effect of the Factors

Figure 1.8 Main Effect Plot

Based on the main effect plot, we identify three important factor which is the

Brand/Price, Cooking Time and Temperature. Full factorial experiment is

conducted through the use of these 3 factors. Before conducting the full factorial,

we test the significant of these three factors to ensure that the level is used

correctly. Table 4.3 shows the result of the significance test.


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Factors Hypothesis Result (paired t-test)

T =

; p < 0.05

reject the null

hypothesis

Brand/Price Ho : There is no significance

different between branded and low

brand of kernels towards the output

HA : There is significance

different between branded and low

brand of kernels towards the output

P = 0.221

Null hypothesis is failed

to reject as p is greater

than 0.05.

There is no significance

different on branded low

brand kernels

Temperature Ho : There is no significance

different between medium and high

temperature towards the output of

kernels


different medium and high

temperature towards the output of

kernels

P = 0.019

Null hypothesis is

rejected as p is less than

0.05.

There is significance

different on high and

medium temperature

Time Ho : There is no significance

different between 2 minutes and 3

minutes towards the output of

kernels


different between 2 minutes and 3minutes towards the output of

kernels

P = 0.338

Null hypothesis is fail to

reject as the p value is

greater than 0.05 .

There is no significance

different on 2 minutesand 3 minutes

Figure 4.3 Result of paired t-test on selected factors


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As the cooking time is not significant, we decide to increase the level for full

factorial experiment.

The significance of Day and Night also been measured to identify whether the

surrounding heat gives impact towards the yield of the corn by using two t-test.

The result of the two t-test is shown in the Table 4.4

Hypothesis Result two t-test

T =

; p


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Temperature

Cooking

Time3 minutes 1.5 minutes

Figure 4.5 Factors and Level of full factorial experiment.

5.3.1 The Cooking Time is once again taken as factors even the result in the

fractional factorial shows it is significance due to the reason on finding if

the popcorn can be make faster (low cooking time) with good amount of

unpopped corn. Hence, in this full factorial, we adjusted the level of Time

from 2 minutes and 3 minutes into 1.5 minutes to 3 minutes.

5.3.2 The same step is repeated. Figure 1.9 shows the matrix diagram for full

factorial experiment and its result (gram). 23

= 8 experiment is runs with

three (3) repeated measures and 3 replications. The figure 1.9 shows the

average of the results.

Exp

No

Run

Order

Brand/Price Cooking

Time

Temperature CTQ (g)

1 4 ACI II 3 min High 18.0

2 6 ACI II 3 min Medium 62.5

3 2 ACI II 1.5 min High 99.0

4 8 ACI II 1.5 min Medium 100.0

5 1 TESCO 3 min High 58.5

6 7 TESCO 3 min Medium 71.5

7 3 TESCO 1.5 min High 90.0

8 5 TESCO 1.5 min Medium 100.0

Figure 1.9 Matrix Diagram of Full Factorial


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5.3.3 Normality Test

Normality test is used to ensure that the result is normally distributed.

Ho : The data is normally is distributed.

HA : The data is not normally distributed.

The p-value is equal to 0.164 which is greater than 0.05. Thus, null hypothesis is fail to

reject which means that the data is normally distributed.

5.3.4 Main Effect of Full Factorial

Main effect plot for full factorial is develop to identify the effect of each

factor towards the yield of the kernels. Figure 1.10 shows the main effect

of full factorial.

FACTORS MAIN EFFECT

Price/Brand (+) = (18 + 62.5 + 99.0 +

100) / 4

= 69.88

(-) = (58.5 + 71.5 + 90.0

+ 100)/4

(+)(-)

= 69.8880

= -10.12


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= 80

Temperature (+) = ( 18 + 99.0 + 58.5

+ 90.0)/4

= 66.37

(-) = (62.5 + 100 + 71.5

+ 100) / 4

= 83.5

(+)(-)

= 66.37-83.5

= -17.13

Cooking Time (+) = (18 + 62.5 + 58.5 +

71.5)/4

= 52.63

(-) = (99.0 + 100 + 100 +

90)/4

= 97.25

(+)(-)

= 52.63- 97.25

= - 44.63

Figure 1.10 Main Effect Plot of full factorial

5.3.5 Significance Testing of Main Effect


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Factors Hypothesis Result (paired t-test)

T =

; p < 0.05

reject the null

hypothesis

Brand/Price Ho : The branded kernels gives no

influenced to the numbers of un-

popped corn

HA : The branded kernels is

influencing the result of un-popped

corn.

P = 0.731

The p value is greater

than 0.05 so the null

hypothesis is fail to

reject.

The branded kernels

give no influence to the

yield of popped corn.

Temperature Ho : The high or low temperature

producing the equal number of

unpopped corn

HA : The high temperature give high

number of popped-corn compare to

the medium

P = 0.572

Null hypothesis is fail to

reject as the p value is

greater than 0.05.

Thus, there is no

difference in the number

of un-popped corn with

the use of high or

medium temperature.

Cooking

Time

Ho : The 3 minutes cooking time gives

same amount of 1.5 minutes un-

popped corn

HA : The 3 minutes cooking time

popped different from 1.5 minutes.

P = 0.038

Null hypothesis is

rejected as the p value is

less than 0.05.

Thus, the 3 minutes and1.5 minutes cooking

time produced different

unpopped corn amount

Table 4.6 The significance test for full factorial experiment.


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Based on the significance test conducted on the main effect plot, Time factor is

the only factor that gives significant value in this experiment. To be compare with

the fractional factorial, temperature is the significant one, however due to any

confounding; it turns to be not significant in the full factorial.

5.3.6 Interaction Plot of Full factorial

5.3.6.1Interaction plot is develop to measure or identify the interaction

between the factors. Figure 1.11 shows the interaction plot of full

factorial experiment between the three factors.

Brand

(A)

Cooking

Time (B)

Temperature

(C) CTQ (Y) AB*Y AC*Y BC*Y

ACI II

(+)

3 min

(+)

High

(+)

18 18 18 18

ACI II

(+)

3 min

(+)

Medium

(-)

62.5 62.5 - 62.5 -62.5

ACI II

(+)

1.5 min

(-)

High

(+)

99.0 - 99.0 99.0 - 99.0

ACI II

(+)

1.5 min

(-)

Medium

(-)

100 -100 -100 100

TESCO

(-)

3 min

(+)

High

(+)

58.5 -58.5 -58.5 58.5

TESCO

(-)

3 min

(+)

Medium

(-)

71.5 - 71.5 71.5 -71.5

TESCO

(-)

1.5 min

(-)

High

(+)

90.0 90.0 -90.0 -90.0

TESCO

(-)

1.5 min

(-)

Medium

(-)

100.0 100 100 100

Interaction Effect

= -

58.5/4

= -14.63

= -22.5 / 4

= - 5.63

= -46.5/4

= -11.63

igure 1.11 Interaction Effect


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Interaction Factors

BrandTime (AB) (+) (+) (18 + 62.5)/2 = 40.25

(-) ( 99 + 100) / 2 = 99.5

(-) (+) (58.5 + 71.5) / 2 = 65

(-) ( 90 + 100 ) / 2 = 95

BrandTemperature (AC) (+) (+) (18 + 99)/2 = 58.5

(-) ( 62.5 + 100) / 2 = 81.25

(-) (+) (58.5 + 90) / 2 = 74.25

(-) ( 71.5 + 100 ) / 2 = 85.75

TimeTemperature (BC) (+) (+) (18 + 58.5)/2 = 38.25

(-) ( 62.5 + 71.5) / 2 = 67

(-) (+) (99 + 90) / 2 = 94.5

(-) ( 100 + 100 ) / 2 = 100


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Figure 1.12 Interaction Plot Effect

5.3.7 Figure 1.12 shows the interaction of the factors with each other. Based on

the graph, the interaction of Brand and Cooking Time give strong

interaction effect. ANOVA will be conducted to identify the significanceof the factors interaction.

5.3.8 ANOVA

5.3.8.1Two Way ANOVA is used to identify the significance of every

interaction. Table 4.7 shows the result of the significance test.


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Factors Hypothesis Result (Two Way ANOVA), Minitab 1.5

BrandTime Ho : The interaction

between brand and time

give no significance

different to yield of

popped-corn

HA : The interaction

between brand and time

give significance different

towards the yield of

popped corn

P = 0.285

Null Hypothesis is fail to reject as the p

value is greater than 0.05.

Thus, the interaction between brand and

time is having no significance different

towards the CTQ.

BrandTemperature Ho : The interaction

between brand and

temperature give no

significance different to

yield of popped-corn

HA : The interaction

between brand and

temperature give

significance different

towards the yield of

popped corn

P = 0.831

Null hypothesis is fail to reject as the p

value is greater than 0.05.

Thus, the interaction between brand and

temperature is having no significance

different towards the CTQ

TimeTemperature Ho : The interaction

between temperature and

time give no significance

different to yield of

popped-corn


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CHAPTER 6 CONCLUSION AND RECOMMENDATION

6.0 Based on the analysis done in previous chapter, we conclude that the Cooking Time

factor is the important and significance one.

Figure 1.10 Main Effect Plot of Full Factorial

Based on figure 1.10, the circle one is the wanted number of un-popped corn as it

less. Referring to this figure, lowest amount (weight) of un-popped corn is comes with

the use of ACI II brand, High temperature with 3 minutes cooking times. This is due to

the main effect plot, we can see that by using positive (+) level on brand which is ACI II

(in this study), the CTQ or the result of the un-popped corn is lower than the negative (-)

level. It same goes to the temperature and cooking time factor. The (+) level gives less

amount of un-popped corns compare to the (-) level.

However, as been analyzed before, the only factor that is significance is Time. Hence,

to come out with the same result, (less amount of un-popped corn) but more

economically and saving the cost, we propose to use the (-) of brand, in this study,

TESCO is the one. This is for the reason that the two brands is not significance. Thus,


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pop corn maker can still have the less number of un-popped corns with low branding

kernels but it should come with the high temperature and time.

We come out with regression to predict the amount of un-popped corn. 10 samples

are taken by using TESCO brand and 50gram of kernels. We use High temperature. We

stop the data or experiment once the kernels started to burnt. We repeated the measuring

for 3 times. The table 4.8 shows the average of the data gain from 3 repeating measures.

Exp Num Weight Temperature Time Result

1 50g High 0.5 min 50g

2 50g High 1 min 49.5 g

3 50g High 1.5 min 49.5 g

4 50g High 2.0 min 46.2 g

5 50g High 2.5 min 45 g

6 50g High 3.0 min 34.5 g

7 50g High 3.5 min 34.5 g

8 50g High 4 min 30.0 g

9 50g High 4.5 min 20.7 g

10 50g High 5 min 20.7 g

Mean 38.06

SD 11.60

Table 4.8 Samples for Regression development

We calculate the regression sample by using Minitab 1.5. The result are shown below.


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The R square is equal to 92.6% or 0.926 which shows that the correlation between time

and the CTQ (weight of un-popped corn) is 92.6 %. The R adjusted shows that the

success of the model or formula is 91.6 % or it also can be said that the formula has

accounted for 91.6% of the variance in the criterion variable.

The formula of the regression is

We are 95 % confidence that by using the obtained data as shown in table 4.8, the mean

will be within 30.87 to 45.25. ( Figure 1.13)

Figure 1.13

95% CI =

= 38.06 1.96 (

)

= 38.06 1.96 ( 7.19)

= 30.87. ; 45.25


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