DOE Techniques

How to get the most relevant qualitative information from a database of experiments.

• which are the most important design variables?
• Can we reduce the variables space?
• What is a reasonable number of objectives and/or constraints for my problem?

Desing of Experiments (DOE) is a methodology that maximizes the knowledge gained from experimental data. It provides a strong tool to design and analyze experiments, it eliminates redundant observations and reduces the time and resources to make experiments. Hence, DOE techiniques allow the user to try to extract as much information as possible from a limited number of test runs.

Design of Experiments (DOE) is generally used in two ways. First of all, the use of DOE is extremely important in experimental settings to identify which input variables most affect the experiment being run. Since it is frequently not feasible in a multi-variable problem to test all combinations of input parameters, DOE techniques allow the user to try to extract as much information as possible from a limited number of test runs. However, if the engineer's aim is to optimize his design, he will need to provide the optimization algorithm with an initial population of designs from which the algorithm can "learn". In this setting, the DOE is used to provide the initial data points.

DOE Algorithms

modeFRONTIER provides the user with a wide selection of DOE algorithms for sampling the design space:

Exploration DOEs are useful for getting information about the problem and about the design space. They can serve as the starting point for a subsequent optimization process, or as a database for response surface training, or for checking the response sensitivity of a candidate solution. More...

Factorial and reduced factorial DOEs are a large family of techniques indispensable in order to perform a good statistical analysis of the problem. More...

Orthogonal DOEs are well balanced, since the generated designs are orthogonal in the matricial sense. More...

Special purposes DOEs are suited for particular tasks to be achieved in design planning. More...

Benefits of modeFRONTIER's Design of Experiments

• smart exploration of the design space
• save time and money for experiments
• check for robust solution
• identify sources of variation
• provide models of the problem
• better decisions