DESIGN OF EXPERIMENT

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DESIGN OF EXPERIMENT
INTRODUCTION
TO
TOOLS AND TECHNIQUES
TRAINING PROGRAMS DEVELOPED FOR DURACELL
CHINA BY SCENTIA INTERNATIONAL INC.
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DESIGN OF EXPERIMENT
(3) Day Session
SECTION ONE Introduction to Design of
Experiment Quality

• Purposes and Practices of DOE as linked to the
  use of quality tools

• Define how DOE is currently applied in
  manufacturing operations

• Identify key resources necessary to conduct
  successful DOE Programs

• Learn basic advantages and limitations of DOE
  process

• Identify specific goals for use of DOE in
  manufacturing processes

SECTION TWO Key Elements for Successful
Application of DOE

• Introduction to Experimentation

• Selection of criteria for design of DOE projects

• Learn how to choose sampling data collection
  alternatives

• Learn useful statistical tests for variables
  factors that effect DOE

Conduct a step by
step, in-class
experiment

• Relate the principles of

Taguchi
loss and analysis to DOE applications
SECTION THREE Select and design an in-house
DOE project

• Determine baseline criteria for your DOE project

• Define the experimental levels for your
  in-house DOE project

• Generate present a timeline and resource list
  for your DOE project

• Define specific goals for your selected DOE
  project

(2) Day Session
SECTION FOUR Review in-house DOE and advanced
topics

• Team summary review of experiment design,
  application, and results

• Analysis of common roadblocks experienced in
  DOE design and application

• Learn modeling methods for DOE and using DOE
  in problem solving

SECTION FIVE Learn how to improve
production operations

• Learn to direct product and process design
  using DOE

• Learn how to improve management of production
  operation product design

• Show how DOE can reduce variance in your
  manufacturing operations system

• Evaluate accuracy of the DOE results in
  seeking solutions to process problems

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Purposes and practices of Design of Experiments

• A Method to
• describe
• predict
• and control
• variables in a process in order to understand and
  improve the process or product.
• In order to improve a process or system one must
  first understand it. If it is not understood,
  then any change made to it is tampering. The
  effect then is almost always detrimental to the
  quality of the process and product.

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Purposes of DOE

• To gain knowledge about the effects of variables
  (factors) in a process
• Identify primary and secondary variables
• Determine optimum levels of variables
• Determine interactions of variables

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Purposes of DOE

• Identify key factors to design (or re-design) a
  process or product for better quality/efficiency.
• Identify critical design parameters (1.2)
• Provides a base to monitor measurements and tests
• Can help in Failure Modes and Effects Analysis
  (FMEA)

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Practices of DOE

• Uses statistical principles to obtain accurate
  results
• Yields more information than one-at-a-time
  experiments
• Statistically valid design gives statistically
  valid results
• Uses confidence testing to determine significance
  of effects

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Practices of DOE

• Leads to good engineering practice such as
• Highly structured and thoughtful design and
  implementation encourages good design practice.
• Provides a base to mathematically model a process
  using response surface methodology. (session 2)
• Promotes systematic and logical testing that
  validates and verifies intuition and experience.
• Encourages good decisions through documentation
  and data analysis.

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Practices of DOE

• DOE builds on basic quality tools
• Depends on an in-control process
• Control charts
• Histograms
• Flow Charts
• Identification of factors
• Cause and Effect diagrams
• Pareto charts and Check sheets
• Monitoring of changes made by DOE
• Control Charts
• Scatter plots

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Purposes and practices of Design of Experiments

• Summary
• DOE provides an mechanism for active change in
  the improvement process.
• A very effective high level tool that can be used
  in detail operations, sequential processes or
  overall systems.

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DOE in Manufacturing Injection Molding Example
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Tensile test Pareto Chart

• Effects of Molding Variables on Tensile Strength

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Interactions plot

• What are the interactions that would effect the
  process? Example of fiber length and fiber
  concentration

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Interactions Plot

• What are the interactions that would effect the
  process? Example of temperature and mold
  thickness.

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Interactions plot - 3-way example

• What are the interactions that would effect the
  process? Example of all three factors.

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DOE in Manufacturing

• Composites injection molding example
• Better able to manage the process for cost,
  time and quality
• Better able to predict the effects of adjustments
  of the process
• Can better meet customer requirements

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DOE in manufacturing

• The process in now easier to manage in the system
  for
• Production amounts
• Production times
• Inventory/material required
• Relationship to other operations

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Identify resources necessary for successful DOE

• Remember DOE is a prevention cost activity
• More difficult to measure savings
• Training and planning are critical and on-going

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Identify DOE resource needs

• Management support necessary
• Time and money for training
• Need management perspective to see impact on the
  whole system
• Downtime, if necessary of process
• Material/product costs (25-30 on experiment 1)
• Knowledge and expertise
• Statisticians (if any) can be of help
• Experts involved in, or related to, the process
  being tested

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Identify DOE resource needs

• A method to help determine time and material
  resource needs
• What product/process will be targeted and why?
• How much time/material will be necessary to
  conduct experiment 1? (then multiply by four)
• What will be the effect on production?
• When will be the best time to conduct the
  experiment
• Production schedule personnel
• External conditions that may affect results
• How much time to analyze and document the
  experiment

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Advantages and limitations of DOE

• Advantages
• Statistical foundation yields a lot of
  information for relatively low cost
• Provides main, secondary and interaction effects
  of factors being tested
• Basic designs can be conducted and evaluated
  without significant statistical knowledge or
  expertise
• Much more information than obtained from
  one-at-a-time experimentation
• Pro-active tool for directing improvements

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Advantages and limitations of DOE

• Limitations
• Not valid if process is not in-control or
  statistically stable
• Requires more analytical skill than basic quality
  tools
• Sometimes is used on processes when simpler tools
  would suffice
• Care must be take not to confuse results, for
  example
• Maintain homogeneity in material
• External conditions should be as stable as
  possible
• Requires good planning and documentation

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Identifying specific goals for DOE

• Ask Why am I using this tool on this problem?
• This will help identify the desired response
  variable (the goal) for the experiment and ensure
  DOE should be used.
• Ask How will improving the desired response
  variable improve the process?
• Validates the need for conducting the experiment

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Identifying specific goals for DOE

• Ask How will improving the targeted process
  affect the system?
• Protects against sub-optimization of a process at
  the expense of the system
• Ask Can I maintain constant external
  conditions?
• Helps to prevent introduction of special causes
  that would corrupt the analysis
• If the external conditions cannot be maintained
  constant then they can be planned for

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Identifying specific goals for DOE

• Ask What are the main factors to be considered
  in this experiment?
• Using a Cause and effect diagram will help answer
  this question
• All who are part of the process (management to
  line people) should be involved
• Ask Do I have baseline information for a
  benchmark?
• If no baseline has been obtained this should be
  the first goal of data collection

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End Section One
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Main Tab Page for Section Two
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Introduction to the experimental method
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Introduction to the experimental method
The Factorial Pattern Row A B C D 1 2 -
__________k1 3 - 4 - - ________
__k2 5 - 6 - - 7 - - 8 - - -
__________k3 9 - 10 - - 11 - -
12 - - - 13 - - 14 - - - 15 - - - 16
- - - -__________k4
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Introduction to the experimental method

• How the factors and interactions are tested
  (refer to the factorial pattern on the previous
  page)
• Factor A against desired response in rows 1 2
  (also rows 3 4)
• Rows 1 8 together show effect of C on effect of
  B and effect of A (3-way)
• Rows 1-4 show the change of A at high B and at
  low B (2-way)
• Software does all this automatically

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Introduction to the experimental method

• Summary
• Full factorials cover full design space
• Full factorials are easy to lay out - repeating
  pattern in standard order
• Multiple factors are tested and interactions

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Design of the DOE

• Cause and effect diagram (right side)
• Review section 1 item 5
• Determine how to measure the response variable

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Design of the DOE

• Screening designs (more later)
• Cause and effect diagram (left side)
• Historical data as baseline

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Design of the DOE

• Blocking
• Planning for known variation (noise) that may be
  inherent in the experimental sample. For example
• two replications from same lot
• 1/2 of design in lot A and 1/2 in lot B
• Randomization
• To avoid time trends and other variation not
  known
• Never run in sequence order
• Avoids the effects of hidden variable
• Validates statistical conclusions
• Use tables or numbers from hat

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Design of the DOE

• Replication
• Measure experimental variability
• Improve estimate of effects
• Determine if change in factor levels is special
  (induced) or common cause variation

• A replication of a run is an independent and
  random application of the run including set up.
• A repeat is application of a run without a new
  setup.

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Data Collection

• Strive for homogenous material
• Same lots etc.
• Reduce effects of time through randomization or
  new setup (example chemical bath degradation)
• Use blocking and randomization to minimize
  external effects

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Data Collection

• Record keeping
• Emphasize integrity and accuracy of documentation
• Resisting estimating (let the data perform its
  function)
• Note special conditions in the data collection
  sheet
• Always include as much information as possible -
  name, time, date, location, operator, shift, etc.

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Results will be no better than the quality of
data you obtain and record. Complete accuracy
and integrity in the data is critical
Data Collection
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Statistical tests for DOE variables and factors

• Calculating effects
• A effect is the difference in the averages
• Main effects
• E(A)?A - ?A-
• Interaction effects
• E(AB)1/2(?A - ?A-)B - (?A - ?A-)B-

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Statistical tests for DOE variables and factors

• Standard deviation of experiment runs
• Se?(?Si2/k)
• Standard deviation of effects
• SeffSe ?(4/N)
• where N in the total number of trials
• T-statistic
• using t-tables (appendix) and degrees of freedom
  where
• degrees of freedom
• d.f.( of observations per run-1) X ( of runs)
• See example with in class experiment

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Statistical tests for DOE variables and factors

• Charting the results
• Pareto charts of results with significance limits

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Statistical tests for DOE variables and factors

• Interaction plots
• parallel lines mean no interaction
• normal lines mean strong interaction

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Statistical tests for DOE variables and factors

• Cube Plots
• Shows the effects at planes and corners of the
  factorial

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In class experiment

• Determine the effects of
• Heat treatment
• Vendor
• Size
• on the durability of paper clips.
• Will use 3 factors at 2 levels as defined by the
  experiment sheet

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In class experiment

• Groups of four people with the following tasks
  assigned
• Test engineer _________________
• Runs the test (bends the clips)
• Analyst ______________________
• Leads the team in calculating results
• Recorder_____________________
• Ensures all data is properly recorded
• Lead engineer_________________
• Ensures runs and treatments are performed in
  proper order and form

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In class experiment
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In class Experiment
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In class experimentTime order plot of data
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In class experimentSummary Table for
experimental results
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In class experiment
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In class experiment

• Calculate effects
• Calculate t-ratios
• Construct Pareto Chart of effects
• Construct Cube plot
• Construct interaction charts
• See appendix for chart blanks

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End Section Two
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Main Tab Page for Section Three
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Review of Taguchi loss and DOE applications

• Deviation from target is loss
• Loss can be calculate for point or sample
• Loss at a point is L(X) k(x-m)2
• Loss of the sample set is Lks2 (in per unit)
• kA/ª2 (A is the cost of failure at the spec
  limit, ª2squared distance from target to failure
  value)
• xmeasured value
• mtarget value
• sstandard deviation of the sample

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Review of Taguchi loss and DOE applications

• DOE builds a model to identify both product and
  process target values
• Robust design (product or process) will include
  DOE experiments.
• Testing existing target values on products or
  process using DOE and historical data

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Group design of a DOE

• Flight time of a paper helicopter
• Customers have been complaining about the short
  flight times of PHL (Paper Helicopter Ltd.)
  helicopters. Management want your group to
  redesign the helicopters to increase flight times.

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Group design of a DOEHelicopter standard design
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Group design of a DOE

• Factors that may affect flight time
• Approved possible modifications
• Factor Standard Allowed change
• Paper type Copier Heavy colored
• Paper clip none yes
• Taped body no yes (3 inches)
• Taped wing joint no yes
• Body width 75mm 100mm
• Wing length 75mm 100mm
• Each team must select only three factors

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Group design of a DOE

• Responsibilities and duties
• Lead engineer________________
• Final decision on prototype
• Test Engineer________________
• Final say on conducting tests
• Assembly Engineer____________
• Final say on building issues
• Recorder____________________
• Leads data collection and analysis

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Group design of a DOE
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Group design of a DOE Time order plot of data
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Group design of a DOE Summary Table for
experimental results
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Group design of a DOE
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Your own DOE project

• Determine baseline for the project
• Is the process in-control?
• If not then special causes must be eliminated
  before proceeding
• Do you have historical data available?
• If not then determine data collection method and
  collect data
• If the process is in-control then proceed. If not
  see step 1.

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Determine factors and levels for your DOE

• Use a cause and effect diagram with others in
  involved to determine factors to be tested
• Use only three factors

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Determine factors and levels for your DOE

• To determine levels
• To be done with others involved with, expert in,
  or affected by the process.
• What is the current baseline level?
• What levels would those involved suggest be the
  edges of the operating range (or just ones all
  want to test)
• Document the levels and the reason for the
  selected level

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Generate Time line

• Using a Gantt chart or project timeline determine
  the following task milestone dates
• Finalize project design
• Determine necessary resources
• Conduct experimental runs
• Analyze data
• Present to group and instructor (July 26th)

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Generate DOE resource list

• A method
• What product/process will be targeted and why?
• How much time/material will be necessary to
  conduct experiment 1? (then multiply by four)
• What will be the effect on production?
• When will be the best time to conduct the
  experiment
• Production schedule personnel
• External conditions that may affect results
• How much time to analyze and document the
  experiment

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Identifying goals for your DOE

• Why am I using this tool on this problem?
• How will improving the desired response variable
  improve the process?
• How will improving the targeted process affect
  the system?

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Identifying goals for your DOE

• Can I maintain constant external conditions?
• What are the main factors to be considered in
  this experiment?
• Should have been completed already
• Do I have baseline information for a
  benchmark?
• Should have been completed already

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Good luck and see you in July

• If you have questions feel free to email
  Professor Hawks at
• hawksv_at_byu.edu

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End Section Three