Design of Experiments Training (A-Z in 3 days, 2.2 ASQ RUs), software based training

Experts consider Design of Experiments (DOE) the most powerful statistical tool available to help scientists and engineers invent the best products and processes possible.

Many traditional DOE training programs require 3 days of training to cover the DOE basics and another 3 days of training for the advanced DOE material. Thus this course is more comprehensive and cost effective.

Learning Outcomes:

Students will get hands-on experience working several DOE exercises.

-          Learn DOE basics (2-level DOEs):

- Understand important considerations when selecting responses (outputs), factors (inputs), and factor levels to study.

- Know which standard experiment is appropriate.

- Know how to block effects from lurking variables.

- Understand how to interpret experimental charts, graphs, plots, and ANOVA Table.

- Be able to identify statistically significant factors and interactions.

- Be able to build a 1^st order experimental model for the response (Y) and verify that it is adequate.

- Understand how to use the model to optimize the response.

-          Learn advanced DOE techniques (Robust Designs and More):

Dual Responses

- Know how to create a model for the Standard Deviation (s) and use it to reduce the standard deviation of the response.

- Understand how to use the s-model to estimate product or process capability (C_p, C_pk).

- Know how to use both the Y-model and the s-model to locate the response on target while minimizing variation. This technique will be performed using both overlaid contour plots and a numeric search routine to find the optimum.

Response Surface Methodology (RSM)

- Know how to use multilevel DOEs such as Central Composite Designs and Box-Behnken Designs. These experimental designs are needed when there is a curvilinear relationship between one or more of the factors and the response. These efficient experimental designs are used to build a 2^nd order experimental model.

- Be able to use contour plots to find the best targets for the factors in order to optimize the response.

- Understand how to use contour plots to perform sensitivity analysis and tolerance design.

- Understand how to move toward a new optimal experimental region when the current results aren’t satisfactory. A technique called the Path-of-Steepest-Ascent will be demonstrated in class.

- Know how to make the response insensitive to noise factors.

Multiple Response Optimization

- Be able to simultaneously optimize multiple responses using numeric search routines.

Who Should Attend:

This course is intended for Quality Managers, Quality Engineers, R&D personnel, Product Engineers, Process Engineers, and Designers.

Location:

All training is in-house at your location. This is the most cost effective approach for training. Also some time can be used to discuss a DOE opportunity at your business.

Course Materials:

Participants will receive a course manual and a demo CD of the software used during training, which is active for 30 days after it is loaded.

Six Sigma Hands-on Training

Six Sigma methodology is a management strategy to use statistical tools and project work to achieve breakthrough gains in profitability and quality by reducing variation in product and process performance. A Six Sigma Program can be sensibly implemented one project at a time in both R&D and Manufacturing Departments. Advanced Response Engineering’s approach is to first conduct DOE Training with your appropriate personnel. Next select a project with significant payback to optimize and form a small cross-functional core team of your personnel that will be led by an ARE facilitator. Each of the Six Sigma DMAIC process steps and tools used will be discussed as the project progresses from one phase to the next until completed.

Generally, a Six Sigma project requires cross-functional and cross-departmental support to be successful. Management’s support is necessary to make sure that appropriate personnel and resources are available as needed. Thus Upper Management’s support is essential to create a successful Six Sigma Program.