Engineering change (EC) is a source of uncertainty. While the number of changes to a design can be optimized, their existence cannot be eliminated. Each change is accompanied by intended and unintended impacts both of which might propagate and cause further knock-on changes. Such change propagation causes uncertainty in design time, cost, and quality and thus needs to be predicted and controlled. Current engineering change propagation models map the product connectivity into a single-domain network and model change propagation as spread within this network. Those models miss out most dependencies from other domains and suffer from “hidden dependencies”. This paper proposes the function-behavior-structure (FBS) linkage model, a multidomain model which combines concepts of both the function-behavior-structure model from Gero and colleagues with the change prediction method (CPM) from Clarkson and colleagues. The FBS linkage model is represented in a network and a corresponding multidomain matrix of structural, behavioral, and functional elements and their links. Change propagation is described as spread in that network using principles of graph theory. The model is applied to a diesel engine. The results show that the FBS linkage model is promising and improves current methods in several ways: The model (1) accounts explicitly for all possible dependencies between product elements, (2) allows capturing and modeling of all relevant change requests, (3) improves the understanding of why and how changes propagate, (4) is scalable to different levels of decomposition, and (5) is flexible to present the results on different levels of abstraction. All these features of the FBS linkage model can help control and counteract change propagation and reduce uncertainty and risk in design.
A Multidomain Engineering Change Propagation Model to Support Uncertainty Reduction and Risk Management in Design
Hamraz, B., Caldwell, N. H. M., and John Clarkson, P. (September 28, 2012). "A Multidomain Engineering Change Propagation Model to Support Uncertainty Reduction and Risk Management in Design." ASME. J. Mech. Des. October 2012; 134(10): 100905. https://doi.org/10.1115/1.4007397
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