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Title: Design of Experiments
Course Type: Self Paced
Estimated time to complete: 14 Hours
Price: $495.00

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 tword 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.

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!

This course contains seven modules. An abbreviated course outline is below. For a more detailed outline, please contact us.

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!

Below are a few examples of how Designed Experiments can benefit various industries

Injection Molding
Design of Experiments is a very popular tool for molders.  It is useful for new tool trials, establishment of process windows, and fundamental to a well-run process validation study.  Almost always, molders are required to co-optimize several dimensional and visual customer requirements.  For complex parts this can be a major challenge.  Using a well-designed experiment, molders can quickly determine the correct machine settings to get excellent results for multiple customer requirements.

Biotechnology
Scientists and engineers commonly are faced with the issue of quickly characterizing a complex new technology.  Multiple input variables, multiple customer requirements, and highly-nonlinear responses are common in this industry.  First principles models are typically only roughly approximate in this industry at best.  Screening design of experiments (determination of the vital few inputs) and modeling design of experiments (characterization studies) have been used with great success in this industry.

Electro-mechanical systems
Design of experiments has been widely used as a tool to optimize design parameters and enhance design robustness in these industries.  Frequently design of experiments is used as a troubleshooting tool as well in manufacturing areas.

Semiconductor
Design of experiments has been a premier tool for process characterization and optimization since the early 1990’s. 

Agriculture
Design of experiments was originally developed as a tool in this industry.  Many of the classical design of experiments case studies originated in agriculture and have since spread to a number of other industries.

Aerospace
Numerous applications have emanated from this industry.  Characterization of new technologies as well as process optimization are common areas of application.

There are many other examples from different industries. If you have any questions about how this course can help you, please email us here.

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!

Why take our courses?

• Our courses are fully interactive, loaded with real world examples and take full advantage of state of the art adult learning techniques.

• Take one course. If you like it and sign up for another one of our e-learning courses, you will receive a 10% discount towards that course.