Application Examples for DOE++

DOE++ for experiment design and analysis (DOE)
Update

Latest Release
10.1.4 ♦ 31-Aug-2016

 

Purchase Options

Single-user and floating licenses. Multi-product suites and token-based licenses are also available.           [Learn More...]

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:

These examples demonstrate some of the types of analyses you can perform with this application. For additional product documentation, including the Quick Start Guide, visit the Synthesis eDocs & ePubs Library.

Example 1:

One Factor Design

Four shift operators at a pulp mill each make five pulp handsheets from unbleached pulp. Reflectance is read for each of the handsheets using a brightness tester. The goal of the experiment is to determine whether there are differences between the operators in making the handsheets and reading their brightness.

Example 2:

Two Level Full Factorial Design

Four factors may affect the growth of an epitaxial layer on polished silicon wafers. The current factor settings caused variations that exceeded the specification. The experimenters first need to determine which of the factors significantly affect the process. Then further analysis will be performed to optimize the process.

Example 3:

Two Level Fractional Factorial Design

In this example, the manufacturing process for an integrated circuit is examined. Five factors may affect the process. The objective is to determine how to improve the process yield using fewer runs than a full factorial design would require.

Example 4:

Plackett-Burman Design

A life test is performed on weld-repaired castings. There are seven factors that may affect the life of the product. The objective of the test is to identify the significant factors and then to improve the life.

Example 5:

General Full Factorial Design

A soft drink bottler is interested in obtaining more uniform fill heights in the bottles. The filling machine is set to fill each bottle to the correct target height, but in practice there is variation around this target, and the bottler would like to better understand the sources of this variability and eventually reduce it. Three factors are examined that may affect the fill heights.

Example 6:

Taguchi Orthogonal Array Design

An experiment studies the effect of four three-level factors on a fine gold wire bonding process in an IC chip-package. Taguchi OA L27 (3^13) is applied to identify the critical parameters in the wire bonding process.

Example 7:

Central Composite Design

A chemical engineer determines the operating conditions that maximize the yield of a process. Two controllable variables influence process yield: reaction time and reaction temperature. A central composite design is used to study the quadratic effects of these variables.

Example 8:

Box-Behnken Design

A UV light system is used to inactivate fungal spores of Aspergillus niger in corn meal. Three process parameters in the system may affect the response (i.e., the reduction in spores). The goal is to determine the optimal settings of the factors that will maximize spore reduction.

Example 9:

Taguchi Robust Design

An experimenter seeks to determine a method to assemble an elastomeric connector to a nylon tube while delivering the requisite pull-off performance suitable for an automotive engineering application. The primary design objective is to maximize the pull-off force, while secondary considerations are to minimize assembly effort and reduce the cost of the connector and assembly.

Example 10:

One Factor Reliability Design

There are three different materials that can be used in a product. The engineer wants to know if there is a difference between these three choices and, if there is a difference, which material is the best choice in terms of the product life.

Example 11:

Two Level Fractional Factorial Reliability Design

A two level fractional factorial reliability design is used to study the reliability of fluorescent lights. Five two-level factors may affect the product life. The purpose is to find the best factor settings to improve the life.

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

Copyright © 1992 - ReliaSoft Corporation. All Rights Reserved.
Privacy Statement | Terms of Use | Site Map | Contact | About Us

Like ReliaSoft on Facebook  Follow ReliaSoft on Twitter  Connect with ReliaSoft on LinkedIn  Follow ReliaSoft on Google+  Watch ReliaSoft videos on YouTube