Issue 86, April 2008

Hot Topics

Degradation Analysis in Destructive Testing - Part II
Degradation analysis is an important data analysis technique for projecting failure data from the degradation history of a quality or performance characteristic that is associated with the reliability of a product. This approach typically requires that the degradation be measured for multiple units over time. However, in some cases, destructive testing is necessary to obtain degradation measurements and taking multiple measurements over the life of the same unit is therefore not feasible. The previous month's issue of the Reliability HotWire presented an approach for handling such a data analysis problem and predicting a failure distribution model using ALTA 7. In this article an alternative method is presented using Weibull++ 7.

To read more, see http://www.weibull.com/hotwire/issue86/hottopics86.htm 

Discussion of the Residual Sum of Squares in DOE
Variation occurs in nature, be it the tensile strength of a particular grade of steel, the caffeine content in your energy drink or the distance traveled by your vehicle in a day. Variations are also seen in the observations recorded during multiple executions of a process, even when conditions are kept as homogeneous as possible. The natural variations that occur in a process, even when all factors are maintained at the same level, are often termed noise. In Design of Experiments (DOE) analysis, a key goal is to study the effect(s) of one or more factors on measurements of interest (or responses) for a product or process. It thus becomes extremely important to distinguish the changes in the response caused by a factor from those caused by noise. A number of statistical methods, generally referred to as hypothesis testing, are available to achieve this. These methods normally involve quantifying the variability due to noise or error in conjunction with the variability due to different factors and their interactions. It is then possible to determine whether the changes observed in the response(s) due to changes in the factor(s) are significant. In this article, we will discuss different components of error and give an example of how the error and each of its components are defined and used in the context of hypothesis testing.

To read more, see http://www.weibull.com/hotwire/issue86/relbasics86.htm 

• Can I edit the optimal solutions in DOE++s Optimization tab?
• What functionality is available in the Tasks area of my XFRACAS Portal page?

To read more, see http://www.weibull.com/hotwire/issue86/tooltips86.htm 

Contributions Welcomed
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