The
challenge facing corporations is to cut design and development
time while producing low-cost quality products that are ready
to perform. Many organizations are being challenged to cut
delivery time in half or more.
Demanding
that people work harder is not the solution. Providing proper
tools aids people in working more efficiently and effectively.
Experimental Design is a premier tool in helping
meet these challenges. By understanding and applying Experimental
Design techniques, scientists, researchers, and engineers
typically obtain a 50% reduction in the time required to conduct
tests. This translates into enhanced understanding of technologies,
reduced design and development time, and decreased costs.
This
course is geared toward the technician, engineer, scientist
or manager who wants to understand how to conduct simple,
but powerful, designed experiments without becoming statisticians.
The course does not require previous experience with DOE or
statistical methods. It does more then simply give the student
the "theory" behind design of experiments. Numerous
examples and "hands-on" techniques are used to help
make the learning experience enjoyable. After completing this
course, students will be able to set up and analyze their
own designed experiments.
This
course contains seven modules. An abbreviated course outline
is below. For a more detailed outline, please contact us at
Course
Outline
DOE
Definition
Process
Diagram
Inputs
and Outputs
Responses
Factors
Examples
Factor
Levels
Tree
Diagram
Full
Factorial Design
Design
Matrix
Orthogonal
Design
Data
Collection
Analysis
Pareto
Chart
Main
Effects Plot
Hitting
a Target
Contour
Plot
Fractional
Factorial Designs
Interactions
Aliasing
Fractional
Factorial Example
Factors
Worksheet
Data
Entry
Pareto
Chart
Analysis
of Variance
Squared
Multiple R
P
(2 tail)
F
Ratio
P
Value
Optimizing
Several Responses
Mountain
Bike Tire Example
Factors
and Responses
Factor
Levels
Design
Matrix
Potential
Data Collection Problems
Measurement
Errors
Errors
In Conducting the Experiment
Wrong
assumptions regarding interactions
Process
Changes
Extrapolation
Desirability
Functions
Other
Design Types
Plackett-Burman
Design
Taguchi
Designs
L4,
L8, L9, L18, L12
Signal
To Noise Ratio
Smaller
Is Better
Larger
Is Better
Nominal
Is Better
Modeling
Designs
Box-Behnken
Central
Composite
D-optimal
Designs
Types
of Factors
Affect
the average
Affect
the variation
Affect
average and variation
Have
no effect
Example
- "Hitting the Target"
Example
- "Reducing Variation"
DOE
Software
DOE
Wisdom
Minitab
No
Risk Money Back Guarantee!
If you are not completely satisfied with our courses (within
15 days of purchase), simply cancel and receive a 100% refund!
No questions asked! All courses are totally risk free!
