Development and characterisation of error functions in design

Abstract

Abstract:As simulation is increasingly used in product development, there is a need to better characterise the errors inherent in simulation techniques by comparing such techniques with evidence from experiment, test and in-service. This is necessary to allow judgement of the adequacy of simulations in place of physical tests and to identify situations where further data collection and experimentation need to be expended. This paper discusses a framework for uncertainty characterisation based on the management of design knowledge leading to the development and characterisation of error functions. A classification is devised in the framework to identify the most appropriate method for the representation of error, including probability theory, interval analysis and Fuzzy set theory. The development is demonstrated with two case studies to justify rationale of the framework. Such formal knowledge management of design simulation processes can facilitate utilisation of cumulated design knowledge as companies migrate from testing to simulation-based design.

The Maximum Resource Allocation for Uncertainty Reduction in a Decision-analytic Concept Selection

“…Uncertainty has been increasingly incorporated in design engineering and concurrent engineering. To model uncertainty, past research has used fuzzy sets [1,2], possibility distributions [3,4], statistics (means, variances, and standard deviations) of distributions [5][6][7], statistical distributions [8][9][10][11], imprecise probability distributions (i.e., p-box) [12], probabilities [13][14][15], and geometric Brownian motions [16,17]. For example, Ferna´ndez et al [18] have used uniform distributions to model uncertainties in selecting rapid prototyping materials and processes.…”