Application Examples for DOE++
Latest Release
1.0.7 ♦ 8-Feb-2011
ReliaSoft's DOE++ software 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 with the introduction of Reliability DOE folios, which offer a major breakthrough for reliability-related analyses by providing the proper analysis treatment for interval and right censored data! 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 DOE techniques in order to apply the proper analysis treatment for the response information that is often of interest to reliability engineers — product lifetime data.
We have compiled some case studies to demonstrate the types of analyses that you will be able to perform with ReliaSoft's DOE++. For the actual case studies, the data and/or exact test method that were used may have been modified in cases where the actual information was deemed to be confidential.
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. Further experiments will then be conducted to optimize the process.
Example 3:
Two Level Fractional Factorial Design
A manufacturing process for an integrated circuit is examined. Five factors may affect the process. The objective is to improve the process yield in 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. The objective of the test is to identify the important factors that affect the life and to improve the product 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 theoretically fills each bottle to the correct target height, but in practice, there is variation around this target, and the bottler would like to understand better the sources of this variability and eventually reduce it. The bottler examines three factors 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 is interested in determining the operating conditions that maximize the yield of a process. Two controllable variables influence process yield: reaction time and reaction temperature.
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 UV-light system will affect the inactivation results. The goal is to determine the optimal settings of the factors that will give the largest reduction of fungal spores.
Example 9:
Taguchi Robust Design
An experiment 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
An experiment studies 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 time.
