design and analysys of ind. experiments

7/31/2019 Design and Analysys of Ind. Experiments

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Design and Analysis of

Industrial ExperimentsStatistica in azienda, Statistici in azienda

Padova Complesso Santa Caterina15 Giugno 2010


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ExternalTarantino-Leardi / Jun 2010

Design and Analysis of Industrial ExperimentsAgenda

Introduction of Tetra Pak

Statistics at Tetra PakStatistics support to PD process:

V-model approach

DOE at Tetra PakConsumer satisfaction case study

Process parameter optimization case study

Key messagesQuestions & Answer


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Processing solutions Packaging solutions Distribution solutions

Tetra Pak is a systems supplier of

Design and Analysis of Industrial Experiments

Tetra Pak Introduction


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Present in morethan 170 countriesacross 5 continents

42 packagingmaterial plants

11 R&D units

11 machineassembly plants

Tetra Pak is global and works locally




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Development & Engineering

No. of employees

Lund, Sweden 1031

Modena, Italy 466Stuttgart, Germany 19

Romont, Switzerland 26

Other locations 4




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2009Carton packaging material, mio packs 145,030

Distribution machines 1,113

Packaging machines 351

Processing units 1,699

Total world deliveries




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Machines in operation

Tetra Pak Group, January 2010

51,859 processing units

9,048 packaging machines

16,641 distribution machines




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Todays package portfolio




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V-model approach





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Statistics at Tetra Pak

Andmanyotherthings


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V-model approach





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CustomerCustomer

DefineDefine

SystemSystem

RequirementsRequirements

ConfirmConfirm

RequirementsRequirementsFulfilledFulfilled


CascadeCascade

IntegrationIntegration

DetailedDetailed

DesignDesign

ArchitectureArchitecture

DesignDesign

VerificationVerification

PhysicalPhysical

ValidationValidation

Integration Tests

Validation Tests

Unit Test

Module Test

Verify linesVerify lines

consistencyconsistencyState of the art?Market research & screening

Commissioning

SPC at customer site

Requirements validation Screening & system simulations

VVT Strategy

Preliminary assessments Screening & Virtual verification

VVT Plan & Robust Design

Modules verification:Screening & Optimization

Concept evaluation, trade studiesScreening and confirmation

Unit testing

Verify system requirements: optimization

Validate the systemOptimization

andRobustness verification

Confirmation runsCombined with SPC

Root cause analysis& continuous improvements

Screening

Robust Design

Design and Analysis of Industrial ExperimentsStatistical support to PD process: DoE within V-model

State of the art?Market research & screening

CommissioningSPC at customer site



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CustomerCustomerneedsneeds

DefineDefine

SystemSystem


ConfirmConfirm


FulfilledFulfilled


CascadeCascade


DetailedDetailed

DesignDesign


DesignDesign


PhysicalPhysical



consistencyconsistency

Reqsverifiable?

Ready toVerify, Validate

And test?

State of the art?

SystemVerified?

SystemValidated?

Risk scenarioWhat is in

what is out?

SystemConsistentlyOperating?

Remainingareas

for improvement& issues

Design and Analysis of Industrial ExperimentsStatistical support to PD process: Decision process


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V-model approach





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Consumer Satisfaction

of opening systemCase study


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ExternalTarantino-Leardi / Jun 2010Tetra Pak Internal

Tarantino/May 09

The aim of this study was to identifythe parameters that optimize theperformance and the customersatisfaction

Packaging types, dimensional,sensorial and sociological factorswere studied.

Design and Analysis of Industrial

ExperimentsDOE at Tetra Pak ConsumerSatisfaction case study


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This activity is part of the product test & consumersatisfaction activity during concept development phase.


DoE at Tetra Pak Consumer Satisfaction case study

1. A trade study furnishes the feasibilitytest cases that fit the targeted usagescenarios.

2. Instrumented mock-ups aremanufactured in order to exercise the

alternative opening systems3. A representative set of consumers

from the addressed population isselected.

4. A short training set is proposed to

every consumer and successive 5randomized openings.

5. Subjective satisfaction index andobjective opening performance areregistered and analyzed.


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FACTORS UNDER STUDY Dimensional

Sensorial: different grips

Competitors: Carton vs. Bottle

Sociological: age & gender

Dimensional: height &diameter


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Due to subjective evaluations and low cost of the single test a mixed fullfactorial testing with multiple mid-points was planned and executed in orderto eliminate risky confounding and assess single users biases.

Each consumer opened 5 consecutive randomized mock-ups afterone or two training openings on the mid-points.


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DoE at Tetra Pak Consumer Satisfaction case studyThe two main responses evaluated are characterized by:

Opening force: objective, continuous, normally distributed

Consumer satisfaction: Subjective, semi-quantitative and comparative:

To determine the number of replicates in the experiment we used thepower function on the base of historical information in order to optimizingthe chances to identify at least one grade on the satisfaction scale.

The sampling so determined was more than sufficient to characterize the

opening force characteristics.

Min. Max satisfaction

0 1 2 3 4 5

X1X4X2 X0X3 X5


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Note: the runs are here not randomized but in reality they are. The responses areartificially changed for confidentiality reasons


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Bottles Carton

The green area inside the plot shows the range of diameter andheight where the criteria: appraisal 2.5-5 and a reasonable torque areboth satisfied. In the yellow one only one of the two responses is fitsthe criteria.This plot is used to find the best operating conditions for getting the

desired dimensions of the cap.


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The final configuration for the openingsystem design to achieve the target of thisstudy is:

Height: 20.35 mm

Diameter: 39.5 mm

Grip: G2 (not practically relevant)

Grip is not practically relevant and so it was

settled up to the state of the art withoutfurther developments.




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CustomerCustomer

DefineDefine

SystemSystem


ConfirmConfirm


FulfilledFulfilled


CascadeCascade


DetailedDetailed

DesignDesign


DesignDesign


PhysicalPhysical


Integration Tests

Validation Tests

Unit Test

Module Test


consistencyconsistency

Design and Analysis of Industrial ExperimentsStatistical support to PD process: DoE within V-model

State of the art?Market research & screening



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Process parameteroptimization

Case study


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InternalTarantino/Nov.09Tetra Pak Internal

Tarantino/May 09

The aim of this study was tooptimize the injection mouldingprocess parameters to producecaps according to the dimensions

identified in the previous study.In particular, cap-lid diameter and

cap total height were studied.

Design and Analysis of IndustrialExperiments

Process parameter optimization


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The injection moulding is a manufacturing process forproducing parts from thermoplastic material.


DOE at Tetra Pak Process parameter optimization

1. Granules of plastic powder are pouredor fed into a hopper

2. A heater heats up the tube and when itreaches a high temperature a screwthread starts turning.

3. A motor turns a thread which pushesthe granules along the heater sectionwhich melts then into a liquid.

4. The liquid is forced into a mould whereit cools into the desired shape (in thiscase a cap).

5. The mould then opens and the unit isremoved.


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INJECTION TIME: Time for the injection of the polymer into the mold cavity

INJECTION TEMPERATURE: Temperature at which the heater heats up the tube

HOLDING PRESSURE: Pressure applied by the screw to compensate the shrinkage of theplastic part

HOLDING TIME: Time at which the screw applied the holding pressure

COOLING TIME = Time to transform row plastic material into desired part



FACTORS UNDER STUDY


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543 bar407 barHolding pressure

1.2 s0.8 sHolding Time

1.1C0.9 CCooling temperature

250 C230 CInjection temperature

0.4 sec0.2 secInjection time

High level (+1)Low level (-1)

For each one of the factors, 2 levelswere studied, a high leveland a low level. We call these levels by 1 and +1 respectively(or just and +).


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Design and Analysis of Industrial ExperimentsDOE at Tetra Pak Process parameter optimization

3 additional midpoints wereadded.

They are used to learnsomething about non-lineareffect and to limit the effortof replications

-1 0 1

-1

0

1

With 5 factors and 16 runs wehave a resolution V factorialdesign, i.e. main effects would

be confounded with four-factorinteractions, and two-factorinteractions would beconfounded with certain three-factor interactions.


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0.020.010.00-0.01-0.02

1.0

0.8

0.6

0.4

0.2

0.0

Effect

Power

A lpha 0.05

StDev 0.002

# Factors 5# C orner Pts 16

# Blocks none

# Terms O mitted 0

C enter Points Yes

Term Included In Model

A ssumptions

1, 3

Ctr Pts Per Blk

Reps,

Power Curve for 2-Level Factorial Design

To determine the number of replicates in the experiment we used thepower function

Power function is a function of the probability to reject a certain

hypothesis

Significance level: 0.05,risk to reject the

hypothesis that the Effectis zero despite the fact thatit is. (Type I risk)

Relevant difference: If the effect

is 0.02 we want to detect it

Sample size

needed.

Question: What is thesize of difference in theresponse that we want

to be able to detect(practical relevance)

With such lowvariations in the

experiment 1replicate per runis good enough


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RESPONSESFACTORSRUN

39.50320.299475112400.319

39.49920.301475112400.318

39.49920.301475112400.317

39.71320.4025431.21.12500.416

39.46320.2994071.21.12500.215

39.57820.3044071.21.12300.414

39.58920.4005431.21.12300.213

39.57520.2894071.20.92500.412

39.57620.3845431.20.92500.211

39.71220.3915431.20.92300.410

39.45920.2844071.20.92300.2939.40220.2154070.81.12500.48

39.40320.3045430.81.12500.27

39.53220.3085430.81.12300.46

39.28720.2114070.81.12300.25

39.52820.2965430.80.92500.44

39.28420.1944070.80.92500.23

39.40720.2024070.80.92300.42

39.40220.2935430.80.92300.21

DHHolding pressureHolding timeCooling timeInj. TempInj. Time

Note: the runs are here not randomized but in reality they are. The responses areartificially changed for confidentiality reasons


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Randomizing the order of the runs is usually good.

It is some kind of insurance that our conclusions will not be

affected by uncontrolled variation of the test environment.

but randomization is not always easy or even possible

Drawbacks with randomization:

Some factors are hard and time consuming to change

The number of changes of factor levels might in itself be timeconsuming

It might get difficult to keep track of the experiments.



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0.40.30.2

39.60

39.55

39.50

39.45

39.40

250240230 1.11.00.9

1.21.00.8

39.60

39.55

39.50

39.45

39.40

543475407

Inj. Time

Mean

Inj. Temp Cooling time

Holding time Holding pressure

Corner

Center

Point Type

Main Effects Plot for DData Means

0.40.30.2

20.350

20.325

20.300

20.275

20.250

250240230 1.11.00.9

1.21.00.8

20.350

20.325

20.300

20.275

20.250

543475407

Inj. Time

Mean

Inj. Temp C ooling time

Holding time Holding pressure

Corner

Center

Point Type

Main Effects Plot for HData Means


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250240230 1.11.00.9 1.21.00.8 543475407

20.4

20.3

20.220.4

20.3

20.220.4

20.3

20.220.4

20.3

20.2

Inj. Time

Inj. Temp

Cooling time

Holding time

Holding pressure

0.2 Corner

0.3 Center

0.4 Corner

Time

Inj.

Point Type

230 Corner

240 Center

250 Corner

Temp

Inj.

Point Type

0.9 Corner

1.0 Center

1.1 Corner

time

Cooling

Point Type

0.8 Corner

1.0 Center

1.2 Corner

time

Holding

Point Type

Interaction Plot for HData Means


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250240230 1.11.00.9 1.21.00.8 543475407

39.6

39.5

39.4

39.6

39.5

39.4

39.6

39.5

39.4

39.6

39.5

39.4

Inj. Time

Inj. Temp

Cooling time

Holding time

Holding pressure

0.2 Corner

0.3 Center

0.4 Corner

TimeInj.

Point Type

230 Corner

240 Center

250 Corner

Temp

Inj.

Point Type

0.9 Corner

1.0 Center

1.1 Corner

time

Cooling

Point Type

0.8 Corner

1.0 Center

1.2 Corner

time

Holding

Point Type

Interaction Plot for DData Means


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180160140120100806040200

99

95

90

80

70

60

50

40

30

20

10

5

1

Standardized Effect

Percent

A Inj. T ime

B Inj. Temp

C C ooling time

D Holding time

E H olding pressure

F a ct or N am e

Not Significant

Significant

Effect Type

AE

E

D

A

Normal Plot of the Standardized Effects(response is D, Alpha = 0.05)

200150100500

99

95

90

80

70

60

50

40

30

20

10

5

1

Standardized Effect

Percent

A Inj. T ime

B Inj. Temp

C C ooling time

D Holding timeE H olding pressure

F a ct or N am e

Not Significant

Significant

Effect Type

DE

E

D

C

A

Normal Plot of the Standardized Effects(response is H, Alpha = 0.05)

BD

AD

BE

BC

AB

CD

AE

AC

CE

B

DE

A

C

D

E

200150100500

Term

Standardized Effect

4.3

A Inj. Time

B Inj. Temp

C C ooling time

D Holding time

E H olding pressure

F a cto r N am e

Pareto Chart of the Standardized Effects(response is H, Alpha = 0.05)

AB

BD

AD

BE

BC

CE

AC

CD

B

C

DE

AE

A

E

D

180160140120100806040200

Term

Standardized Effect

4.3

A Inj. T ime

B Inj. Temp

C Cooling time

D Holding time

E H olding pressure

F a cto r N am e

Pareto Chart of the Standardized Effects(response is D, Alpha = 0.05)


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D i d A l i f I d i l E i


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Holding time

Holdingpressure

1.21.11.00.90.8

540

520

500

480

460

440

420

Inj. Time 0.2

Cooling t ime 0.9

Hold Values

>

< 20.20

20.20 20.24

20.24 20.2820.28 20.32

20.32 20.36

20.36

H

Contour Plot of H vs Holding pressure, Holding time

Holding time

Holdingpressure

1.21.11.00.90.8

540

520

500

480

460

440

420

Inj. Time 0.2

Cooling time 0.9

Hold Values

>

< 39.30

39.30 39.35

39.35 39.40

39.40 39.45

39.45 39.50

39.50 39.55

39.55

D

Contour Plot of D vs Holding pressure, Holding time

D i d A l i f I d t i l E i t


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The white area inside the plotsshows the range of holding time

and holding pressure where thecriteria for both responsevariables are satisfied.

This plot is used to find the best

operating conditions for gettingthe right height and the rightdiameter of the caps

Holding time

Holdingpressure

1.21.11.00.90.8

540

520

500

480

460

440

420

Inj. Time 0.2

Cooling time 0.9

Hold Values

20.3

20.38

H

Contour Plot of H

Holding time

Holdingpressu

re

1.21.11.00.90.8

540

520

500

480

460

440

420

Inj. Time 0.2

Cooling time 0.9

Hold Values

39.3

39.55

D

Contour Plot of D

D i d A l i f I d t i l E i t


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Design and Anal sis of Ind strial E periments


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The final configuration for the injectionmoulding process to achieve the target ofthis study is:

Injection time: 0.21 sec

Cooling time: 1.10 sec

Holding time: 1 sec

Holding pressure: 407

The injection temperature is unimportantand so it was settled up at 230 C



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Agenda



Statistics support to PD process:V-model approach



Key messages

Questions & Answer


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Key messages DoE increases value of the overall system life-cycle planned activity

Careful preliminary tests maximise the successas a part of proper planning.

DoE design is easy but proper planning,randomization, preparation and execution is

another game. DoE is not the Panacea to clarify all theuncertanties characteristics of the system duringthe development.

DoE complements very well with the majority ofthe other statistical techniques.


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Agenda



Statistics support to PD process:V-model approach



Key messages

Questions & Answer


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Question & Answer

Who should you contact


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Who should you contact

Pietro Tarantino

Expert Advisor

D&E - Packaging Technology

Engineering ExcellenceSystems Engineering Methodology

[email protected]+39 059898389

To know more and keep updated

Carlo LeardiExpert Advisor

D&E - Carton Value

Systems EngineeringSystems Engineering Validation

[email protected]+39 059898389

www.tetrapak.com

Th k f i !


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Thank you for attention!

Turning used cartons into an asset


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Turning used cartons into an asset

Collected cartons Repulping

Pulp

Poly/Al

Products

Products

TP1137, JH/200903

Separating paperboard from plastic and aluminium

Recycled cartons a valuable asset


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Recycled cartons a valuable asset

Trays Household tissue

Paper bags

Egg cartons

Cardboard

Envelopes Paper cores Plasterboard liner

Frozen food boxes Industrial tissue Office paper

Dry food boxes

TP1138, JH/200903

Raw material for a wide range of new products

Recycling a growing industry


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Recycling a growing industry

33% of beverage cartonsrecycled in EU (2008)

18.7% Tetra Pak cartonsrecycled world-wide (2009)

We actively supportincreased recycling andconsumer awareness

TP1113, JH/201002

Ensuring efficient re-use of valuable resources

design and analysys of ind. experiments

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