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One Factor Reliability Designs

One Factor Reliability Designs: Example

The data set used in this example is available in the example database installed with the software (called "doe9_examples.rsr9"). To access this database file, choose File > Help, click Open Examples Folder, then browse for the file in the DOE sub-folder.

The name of the example project is "Reliability DOE - One Factor Reliability DOE."

In this example, one factor reliability DOE is used to determine if there is a difference in three different materials that can be used in a product and, if there is, to determine which material is the best in terms of the product life. 10 units were tested for each material, and the test stopped at 500 hours. The product's life is assumed to follow the Weibull distribution.

Designing the Experiment

The design matrix and the response data are given in the "3 Levels One Factor" folio. The following steps describe how to create this folio on your own.

Analysis and Results

The data set for this example is given in the "3 Levels One factor" folio of the example project. After you enter the data from the example folio, you can perform the analysis by doing the following:

Note: To minimize the effect of unknown nuisance factors, the run order is randomly generated when you create the design in DOE++. Therefore, if you followed these steps to create your own folio, the order of runs on the Data tab may be different from that of the folio in the example file. This can lead to different results. To ensure that you get the very same results described next, show the Standard Order column in your folio, then click a cell in that column and choose Sheet > Sheet Actions > Sort > Sort Ascending. This will make the order of runs in your folio the same as that of the example file. Then copy the response data from the example file and paste it into the Data tab of your folio.

The compared factor levels are listed in the Contrast column (e.g., "Type A - Type B" is the comparison of those two materials). When a p value is red, the compared levels are significantly different. For example, there is a significant difference between the Type A and Type B materials, but there is no significant difference between Type B and Type C.

Then view the life characteristic plot.

This plot shows the calculated life characteristic for each type of material. The tick marks at the top and bottom of the vertical lines mark the two-sided confidence bounds on the life characteristic. Since the Weibull distribution was used to calculate the data, the life characteristic is eta (i.e., the time at which unreliability = 63.2%).

Conclusions

The analysis showed that the type of material used does affect the product life. From the life characteristic plot, you can see that the Type C material has the largest eta value and therefore the longest expected life.

 

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