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The purpose of this paper is to formalize the Early Design Reliability Prediction Method (EDRPM) into a comprehensive framework, then to provide a case study using an Electrical Power System (EPS) which shows the usefulness of the methods. EDRPM has been developed to facilitate decision making in early design using quantitative reliability results [1]. Candidate components and design alternative are eliminated using justification provided by EDRPM. The output of this method is a set of design alternative that have a reliability values at or greater than a preset reliability goal. At the completion of applying EDRPM, additional metrics can be used to determine a final design. This research addresses the need for reliability methods to be moved earlier in the design process. Current methods are applicable after components have been selected. EDRPM is used during functional design, and when concepts are generated. This method also calculates functional failure rates which are applied to generate the function and component distributions. The results of the case study shows that several candidate components and design alternatives can be eliminated using EDRPM. It is demonstrated that only a subset of designs that meet the failure rate piece of the reliability goal should not be eliminated. The reliability goal is the combination of two parts; the failure rate and the probability of not exceeding the failure rate. Several of these design still have a probability of exceeding the second piece of the reliability goal given that they meet the first.Copyright © 2012 by ASME

In this paper, we introduce a new methodology for reasoning about the functional failures during early design of complex systems. The proposed approach is based on the notion that a failure happens when a functional element in the system does not perform its intended task. Accordingly, a functional criticality is defined depending on the role of functionality in accomplishing designed tasks. A simulation-based failure analysis tool is then used to analyze functional failures and reason about their impact on overall system functionality. The analysis results are then integrated into an early stage system architecture analysis framework that analyzes the impact of functional failures and their propagation to guide system-level architectural design decisions. With this method, a multitude of failure scenarios can be quickly analyzed to determine the effects of architectural design decisions on overall system functionality. Using this framework, design teams can systematically explore risks and vulnerabilities during the early (functional design) stage of system development prior to the selection of specific components. Application of the presented method to the design of a representative aerospace electrical power system (EPS) testbed demonstrates these capabilities.

This article presents a method for the integration of multiple computing processes used in design and design assessment for a parametric design problem. The method is based on a representation in which the processes are regarded as performing transformations between model states representing attributes of the designed artefact. A classification of attributes and of transformations is presented. The sequences of transformations and model states are modelled using Petri nets, and these models are used as a basis for the development of a computer-based controller that manages the interprocess interaction. The controller directs the concurrent and sequential operation of a number of computing processes operating on different workstations in a network. These processes include geometric modelling and a variety of design analysis actions that exchange data with each other in the optimum design of parametric parts. The example of an automotive crankshaft design is given.

Design representation of parts during conceptual design stage has been a challenging task because of the incompleteness of the information available. Traditional geometric design requires too much information on the geometric attributes, and does not consider the functionality of the part any more than that provided by individual features. Functional design in the form of 〈verb, noun〉 representation does not have adequate correlation with the geometric design. In this article, we propose a new form of the design representation of parts during conceptual design. The representation is calledsketching abstraction. In this representation, the discretionary geometry of the part that has functional relevance is captured using functional features, while the non-discretionary geometry is represented using a linkage mechanism. The functional features are related to the part function using data structures calledfunction-form matrices. The sketching abstraction is annotated with a set of primitives, and a grammar has been developed that parses the sketch to extract a set of canonical relationships between the functional features. These relationships can be used to extract part designs that are functionally similar but geometrically dissimilar. The sketching abstraction has a relationship with the solid model of the part as well. Thus we attempt to bridge the gap between function and form representations and provide the designer with a tool that can be used for generating design alternatives. We illustrate the theory developed in the domain of stamped metal parts.

One of the fundamental axioms of concurrent engineering is that undertaking functional design without foreseeing the manufacturing process leads to production delays and increased costs. This widely accepted concurrent engineering principle is given a formal basis by development of a mathematical model for the conversion of a feature-based design representation to a manufacturing representation. Within the domain of thin-walled components, it is shown that the conversion to tooling cost representations can result in a discontinuous function when the sets of design and manufacturing representations have been formulated as topological spaces. This discontinuity formally reflects the folklore that a small design change can significantly increase product cost. The mathematical sophistication required within this model is suggestive of why manufacturability evaluations can be quite difficult.