Robustness Metrics: Consolidating the multiple approaches to quantify Robustness

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.”</jats:p

Toward Meaningful Manufacturing Variation Data in Design - Feature Based Description of Variation in Manufacturing Processes

AbstractThe need to mitigate the effects of manufacturing variation already in design is nowadays commonly acknowledged and has led to a wide use of predictive modeling techniques, tolerancing approaches, etc. in industry. The trustworthiness of corresponding variation analyses is, however, not ensured by the availability of sophisticated methods and tools alone, but does evidently also depend on the accuracy of the input information used. As existing approaches for the description of manufacturing variation focus however, almost exclusively, on monitoring and controlling production processes, there is frequently a lack of objective variation data in design. As a result, variation analyses and tolerancing activities rely on numerous assumptions made to fill the gaps of missing or incomplete data. To overcome this hidden subjectivity, a schema for a consistent and standardised description of manufacturing variation is suggested. It extends existing ISO GPS annotation by information about influences on the manufacturability of a chosen design solution and in this way enables the systematic acquisition of variation data meaningful for design practice