The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs - PowerPoint PPT Presentation

Loading...

PPT – The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs PowerPoint presentation | free to download - id: 76b12f-NzNiZ



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs

Description:

... Bell Labs & Nobel Laureate-Physics Teaching Statistics to Engineers, Science Editorial, ... – PowerPoint PPT presentation

Number of Views:48
Avg rating:3.0/5.0
Slides: 28
Provided by: Mathem58
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs


1
The Essentials of 2-Level Design of
Experiments Part I The Essentials of Full
Factorial Designs
  • Developed by Don Edwards, John Grego and James
    Lynch Center for Reliability and Quality
    Sciences Department of Statistics University of
    South Carolina 803-777-7800

2
Part I The Essentials of Full Factorial Designs
  • Some Motivation and Background
  • Two Important Advantages of Factorial
    Experiments
  • The Essentials of 2-Cubed Designs
  • Full Factorial Designs

3
I.1 Some Motivation Arno Penzias Chief Scientist
and VP for Research, Bell Labs Nobel
Laureate-Physics Teaching Statistics to
Engineers, Science Editorial, June 2 1989
  • Statistical Tools Are Needed In Industry
  • Competitive Position Demands It
  • Optimizing Complex Technological Manufacturing
    Processes Requires It

4
I.1 Some Motivation
  • Leaders In Quality
  • Use Statistics At All Process Stages For Quality
    and Optimization Purposes
  • Provide The Necessary Statistical Training To Do
    This

5
I.1 Some Motivation QS9000
  • QS9000 required that The supplier shall
    demonstrate knowledge in Design of Experiments
    (DOE) and use it as appropriate.

6
I.1 Some Motivation Examples of DOE Applications
  • NCR has used factorial designs in a variety of
    situations, e.g., to analyze computer performance
    and to compare different soldering methods.
  • Sara Lee Hosiery Division has used simple designs
    in a number of settings. Several have resulted
    in considerable annual savings.

7
I.1 Some Motivation Examples of DOE Applications
  • Ohio Brass has conducted several fractional
    factorial designs which have had significant
    impact. One study resulted in an annual savings
    of 25K by modifying an existing process and
    avoided a capital investment of a 1/4 to 1/2
    million dollars in new equipment. Another
    enabled them to reduce the dimensions of two key
    components which resulted in annual savings of
    50K.

8
I.1 Some Motivation Examples of DOE Applications
  • Michelin has used designs to determine
    maintenance programs for some of their machinery.

9
I.1 Background Why Should You Use DOE?
  • Intelligent Decisions Should Be Based On
    "Informed Observation And Directed
    Experimentation" (George Box)
  • It is consistent with the Scientific Method which
    is fundamental to the quality management
    philosophy (The Deming-Shewhart PDSA Cycle)
  • DOE is a formalism that forces you to organize
    your thoughts (so you don't do things haphazardly)

10
I.1 Background Why Should You Use DOE?
  • DOE Concentrates Your Efforts
  • Screening designs aid in identifying the
    vital/critical factors that may affect the
    (process) response of interest
  • More refined design techniques determine the
    factor levels that optimize the response

11
I.1 Background Why Should You Use DOE?
  • DOE Concentrates Your Efforts
  • DOE helps you to understand how factors affect
    the process. This knowledge helps to choose
    factor settings that are cost effective but dont
    compromise quality (constrained optimization).

12
I.1 Background Quality Management Philosophy
  • Some Tenets Related to These Components
  • All processes have variation
  • Different types of variation
  • e.g., common cause system verses special causes
    being present
  • Management needs predictable/stable processes to
    make decisions (process needs to be in control,
    i.e., a common cause variation system)

13
I.1 Background Quality Management Philosophy
  • Implications for DOE
  • The smaller the effects you are trying to detect
    relative to the background variation, the more
    replication you need or a different design
    (blocking)
  • Data from an out-of-control process is suspect

14
I.1 Background Contrasting SPC and DOE
  • Statistical Process Control (SPC)
  • SPC is used to determine if a process is in
    control
  • An Out-of-Control process that is brought into
    control is not process improvement (Juran)

15
I.1 Background Contrasting SPC and DOE
  • Design of Experiments (DOE)
  • A methodology useful for determining
  • what factors may affect a response
  • what factor settings are feasible
  • SPC Lets You Listen to the Process DOE Allows
    You to Converse With It
    William Hunter

16
I.1 Background Experimentation
  • Experiment
  • A series of trials or tests which produce
    quantifiable outcomes.
  • Quantifiable Outcome
  • Some Outcome Measurement of Interest
  • Response Variable (y)

17
I.1 Background Examples of Responses
  • Yield
  • Viscosity
  • Computer Performance
  • Breaking Strength of Fiber
  • Smoothness of Polyurethane Sheets
  • Bowing of a Molding
  • Chain Length in Polymer
  • Number of Flaws

18
I.1 Background Responses- Bowing of a Molding
  • Three Moldings
  • Top - Most Severe Bowing
  • Bottom - Flat, No Bowing

19
I.1 Background Responses- Bowing of a Molding
True versus Substitute Quality Characteristics
  • The Displacement D
  • Substitute Quality Characteristic for Bowing
  • Measurable

20
I.1 Background Factors
  • Experimental (Variable) Conditions That May
    Affect the Response.
  • A. Flow rate of a raw material
  • B. Process temperature
  • C. Presence/Absence of a Catalyst
  • D. Raw Material Supplier (e.g. 1,2, or 3)

21
I.1 Background Factors
  • Factors May Be
  • Continuous (A and B Above)
  • Discrete (C and D Above)

22
I.1 Background First Motivation To Experiment
  • To Improve The Response.....
  • Optimize average response
  • Minimize variability in response
  • Minimize susceptibility to uncontrollable noise
    factors

23
I.1 Background Best Motivation
  • To Understand The Response! (George Box)
  • Levels of Understanding
  • Which?
  • How?
  • Why?

24
I.1 Background Levels of Understanding An
Example - Yellowfin Tuna Growth
  • Traditional Theoretical Growth Models Allow For
    Only One Point of Inflection (Two Growth Stages)

25
I.1 Background Levels of Understanding How
Stage An Example - Yellowfin Tuna Growth
  • Lowess Fit Suggests
  • Two Points of Inflection
  • Rethink Theory

26
I.1 Background Levels of Understanding How
Stage An Example - Yellowfin Tuna Growth
  • More Pronounced In The Atlantic Ocean Yellowfin
    Tuna

27
I.1 Background Levels of Understanding Why
Stage An Example - Yellowfin Tuna Growth
About PowerShow.com