Taguchi design of experiments ppt

Design of Experiments
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Design of Engineering Experiments
Part 1 – Introduction
Chapter 1, Text

Why is this trip necessary? Goals of the course
An abbreviated history of DOX
Some basic principles and terminology
The strategy of experimentation
Guidelines for planning, conducting and analyzing experiments
Introduction to DOX

An experiment is a test or a series of tests
Experiments are used widely in the engineering world
Process characterization & optimization
Evaluation of material properties
Product design & development
Component & system tolerance determination
“All experiments are designed experiments, some are poorly designed, some are well-designed”
Engineering Experiments

Reduce time to design/develop new products & processes
Improve performance of existing processes
Improve reliability and performance of products
Achieve product & process robustness
Evaluation of materials, design alternatives, setting component & system tolerances, etc.
Some of the objectives

Four Eras in the History of DOX

The agricultural origins, 1918 – 1940s
R. A. Fisher & his co-workers
Profound impact on agricultural science
Factorial designs, ANOVA
The first industrial era, 1951 – late 1970s
Box & Wilson, response surfaces
Applications in the chemical & process industries
The second industrial era, late 1970s – 1990
Quality improvement initiatives in many companies
Taguchi and robust parameter design, process robustness
The modern era, beginning circa 1990
Taguchi’s Method

For quality improvement

Robust parameter design
Making processes insensitive to difficult-to-control variables
Making products insensitive to variation transmitted from components
Determining the variable levels to meet required mean and variability requirements
Notes
Different opinions between engineers and statisticians
There were substantial problems with his experimental strategy and methods of data analysis
The Basic Principles of DOX

Randomization
Running the trials in an experiment in random order
Notion of balancing out effects of “lurking” variables
Replication
Sample size (improving precision of effect estimation, estimation of error or background noise)
Replication versus repeat measurements?
Blocking
Dealing with nuisance factors
Strategy of Experimentation

“Best-guess” experiments
Used a lot
More successful than you might suspect, but there are disadvantages…
One-factor-at-a-time (OFAT) experiments
Sometimes associated with the “scientific” or “engineering” method
Devastated by interaction, also very inefficient
Statistically designed experiments
Based on Fisher’s factorial concept
Factorial Designs

In a factorial experiment, all possible combinations of factor levels are tested
The golf experiment:
Type of driver
Type of ball
Walking vs. riding
Type of beverage
Time of round
Weather
Type of golf spike
Etc, etc, etc…
Factorial Design

Factorial Designs with Several Factors

Factorial Designs with Several Factors
A Fractional Factorial

Planning, Conducting & Analyzing an Experiment

Recognition of & statement of problem

Choice of factors, levels, and ranges

Selection of the response variable(s)

Choice of design

Conducting the experiment

Statistical analysis

Drawing conclusions, recommendations

Planning, Conducting & Analyzing an Experiment

Get statistical thinking involved early
Your non-statistical knowledge is crucial to success
Pre-experimental planning (steps 1-3) vital
Think and experiment sequentially