Comprehensive Selection of DOE Design Types

Enhanced in Version 10!

Traditional DOE Design Types

DOE++ makes it easy to build, modify and evaluate any of the following design types. In Version 10, we've added the ability ignore specific results for the analysis without removing them from the data sheet and now you can also enter up to 50 measurements for every test run!

One Factor Designs (also known as one-way ANOVAs) for determining whether a particular factor has an effect on a specified output or response.

Factorial Designs for determining which factors have a significant effect on the output of the response and identifying interactions between factors.

Response Surface Method Designs for studying the quadratic effects of the factors, which makes these designs well-suited to predictive modeling and optimization. Available RSM designs include Central Composite and Box-Behnken. For central composite designs in Version 10, you can now specify a range and alpha value to ensure that the factor always stays within specified limits.

Robust Parameter Designs that 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.

New in Version 10!

Mixture Designs Folio

New in Version 10, the Mixture Designs folio is used when the factors in the experiment are proportions of components in a mixture (e.g., the proportions of iron, copper, nickel and chromium in a stainless steel alloy). Mixture designs can also be used to investigate the effect of process factors (e.g., the temperature, pressure and curing time). This allows you to optimize both the proportions of components and the way the mixture is processed.

Enhanced in Version 10!

Reliability DOE Provides the Proper Treatment for Life Data — Only in DOE++!

Available only in DOE++, reliability DOE (R-DOE) is a special category of DOE in which traditional experiment design types are combined with reliability analysis methods to investigate the effects of different factors on the life of a product.

Instead of using the F ratio (as in traditional DOE techniques), R-DOE uses the likelihood ratio, which provides the appropriate treatment for interval and right censored data and supports analysis with skewed distributions (e.g., Weibull, lognormal and exponential) — no adjustments or transformations required!

In the Synthesis version:

R-DOE
Weibull++ Reliability Life Data Analysis ALTA Accelerated Life Testing Data Analysis DOE++ Experiment Design and Analysis RGA Reliability Growth and Repairable System Analysis BlockSim System Reliability and Maintainability Analysis RENO for Risk Analysis via Discrete Event Simulation Lambda Predict Reliability Prediction Xfmea FMEA and FMECA RCM++ Reliability Centered Maintenance MPC MSG-3 Maintenance Program Creation XFRACAS Web-based FRACAS Orion eAPI Web-based Asset Management ALTA Accelerated Life Testing Data Analysis BlockSim System Reliability and Maintainability Analysis DOE++ Experiment Design and Analysis MPC MSG-3 Maintenance Program Creation Lambda Predict Reliability Prediction RCM++ Reliability Centered Maintenance RENO for Risk Analysis via Discrete Event Simulation RGA Reliability Growth and Repairable System Analysis Weibull++ Reliability Life Data Analysis Xfmea FMEA and FMECA XFRACAS Web-based FRACAS Orion eAPI Web-based Asset Management ALTA Accelerated Life Testing Data Analysis BlockSim System Reliability and Maintainability Analysis DOE++ Experiment Design and Analysis MPC MSG-3 Maintenance Program Creation Lambda Predict Reliability Prediction RCM++ Reliability Centered Maintenance RENO for Risk Analysis via Discrete Event Simulation RGA Reliability Growth and Repairable System Analysis Weibull++ Reliability Life Data Analysis Xfmea FMEA and FMECA XFRACAS Web-based FRACAS Orion eAPI Web-based Asset Management    ReliaSoft.com Footer

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