Easy-to-Use Platform for Traditional DOE Analyses
Intuitive, Flexible and Highly Integrated Work Environment
The DOE++ interface is a powerful and flexible work center that provides all of the tools you need for creating experiment designs, entering response data and then analyzing and reporting the results. The Project Explorer's hierarchical tree structure makes it easy to view and manage multiple design folios, plot sheets, reports and related analyses all together in a single project file. The file can also contain analyses performed with ReliaSoft's Weibull++ and ALTA software (version 7.5 or later) and you can instantly switch among the three applications with the click of a button.
To help you become productive quickly after installing the software, the intuitive, user-friendly interface has been designed to have a familiar "look and feel" consistent with other software that you may already know how to use. In addition, DOE++ comes with useful online tips and help, along with an extensive collection of annotated sample files to demonstrate available techniques and applications.
Support for a Variety of Traditional DOE Design Types
DOE++ allows you to work with a wide selection of traditional DOE design types and provides an intuitive wizard that guides you through the process of configuring the design for your particular application. Supported design types include:
One Factor Designs, also known as one-way ANOVAs, are used to determine whether a particular factor has an effect on a specified output or response. [View Example]
Factorial Designs help you to determine which factors have a significant effect on the output or response and allow you to identify interactions between factors.
- Full Factorial Designs test all possible combinations of factors at each level. Available full factorial designs include:
- Fractional Factorial Designs test a subset of the possible combinations of factors at the levels in question. Available fractional factorial designs include:
Response Surface Method (RSM) Designs allow you to study the quadratic effects of the factors, making this design type well-suited to predictive modeling and optimization. Available RSM designs include Central Composite [View Example] and Box-Behnken [View Example].
Taguchi Robust Designs aim to minimize the variability of the response in spite of noise factors by combining an inner array of control factors with an outer array of noise factors. [View Example]
