A framework and taxonomy for the design and analysis of margins

• Published in: 2017 Annual Reliability and Maintainability Symposium (RAMS) pp. 1 - 6
• Main Authors: Newcomer, Justin T., Bierbaum, Rene L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2017
• Subjects: Laboratories; Margin Testing; Product Qualification; Production; Qualifications; Quantification of Margins and Uncertainties; Robustness; Taxonomy; Uncertainty
• DOI: 10.1109/RAM.2017.7889751

There are many statistical challenges in the design and analysis of margin testing for product qualification. To further complicate issues, there are multiple types of margins that can be considered and there are often competing experimental designs to evaluate the various types of margin. There are two major variants of margin that must be addressed for engineered components: performance margin and design margin. They can be differentiated by the specific regions of the requirements space that they address. Performance margin are evaluated within the region where all inputs and environments are within requirements, and it expresses the difference between actual performance and the required performance of the system or component. Design margin expresses the difference between the maximum (or minimum) inputs and environments where the component continues to operate as intended (i.e. all performance requirements are still met), and the required inputs and conditions. The model Performance = f(Inputs, Environments? + ε (1) can be used to help frame the overall set of margin questions. The interdependence of inputs, environments, and outputs should be considered during the course of development in order to identify a complete test program that addresses both performance margin and design margin questions. Statistical methods can be utilized to produce a holistic and efficient program, both for qualitative activities that are designed to reveal margin limiters and for activities where margin quantification is desired. This paper discusses a holistic framework and taxonomy for margin testing and identifies key statistical challenges that may arise in developing such a program.