DOE++: Software Tool for Experiment Design and Analysis (DOE)
DOE software designed with reliability in mindTM
ReliaSoft’s
DOE++ software tool facilitates traditional Design of Experiments (
DOE ) techniques for studying the factors that may affect a product
or process in order to identify significant factors and optimize designs.
The software also expands upon standard methods to provide the proper
analysis treatment for interval and right censored data — offering a
major breakthrough for reliability-related analyses!
Software Features
DOE++ guides you through the designs and analyses necessary for all phases of the experiment design and analysis strategy (DOE strategy), from screening for significant factors, through in-depth analysis of the targeted factors and factorial interactions, to selecting input levels for optimal performance. The software supports a variety of experiment design types, including factorial designs, fractional factorial designs, Taguchi robust designs, response surface method designs and reliability DOE.
DOE++ Features Summary
Capability Highlights
Available Training
ReliaSoft offers a comprehensive three-day training course that begins with the fundamentals of experiment design and analysis and continues with advanced DOE concepts such as factorial design, fractional factorial design, robust design, response surface methodology and mixture design. The course also provides hands-on application examples using the DOE++ software tool.
RS 588 - DOE: Experiment Design and Analysis
Step-by-Step Training Guide (*.pdf)
Applications and Benefits
The DOE++ software provides an extensive array of tools to help you design experiments that are effective for studying the factors that may affect a product or process and analyze the results of such experiments. Some of the many useful applications include the ability to:
- Identify the significant factors that affect a product or process.
- Evaluate ways to improve and optimize the design.
- Go beyond traditional Design of Experiment ( DOE ) techniques in order to apply the proper analysis treatment for product lifetime data — the response information that is often of interest to reliability engineers.
