The robustness of a design has a major influence on how much the product's performance will vary and is of great concern to design, quality, and production engineers. While variability is always central to the definition of robustness, the concept does contain ambiguity, and although subtle, this ambiguity can have significant influence on the strategies used to combat variability, the way it is quantified and ultimately, the quality of the final design. In this contribution, the literature for robustness metrics was systematically reviewed. From the 108 relevant publications found, 38 metrics were determined to be conceptually different from one another. The metrics were classified by their meaning and interpretation based on the types of the information necessary to calculate the metrics. Four different classes were identified: (1) sensitivity robustness metrics; (2) size of feasible design space robustness metrics; (3) functional expectancy and dispersion robustness metrics; and (4) probability of compliance robustness metrics. The goal was to give a comprehensive overview of robustness metrics and guidance to scholars and practitioners to understand the different types of robustness metrics and to remove the ambiguities of the term robustness. By applying an exemplar metric from each class to a case study, the differences between the classes were further highlighted. These classes form the basis for the definition of four specific subdefinitions of robustness, namely the “robust concept,” “robust design,” “robust function,” and “robust product.”
Robustness Metrics: Consolidating the Multiple Approaches to Quantify Robustness
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 29, 2016; final manuscript received July 7, 2016; published online September 12, 2016. Assoc. Editor: Zissimos P. Mourelatos.
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Moritz Göhler, S., Eifler, T., and Howard, T. J. (September 12, 2016). "Robustness Metrics: Consolidating the Multiple Approaches to Quantify Robustness." ASME. J. Mech. Des. November 2016; 138(11): 111407. https://doi.org/10.1115/1.4034112
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