Computer-aided engineering (CAE) software tools for design representation, analysis and optimization play a central role in aerospace engineering. Increasingly these tools are being integrated to automate data transfer, thereby reducing design cycle times and increasing the power of design search and optimization techniques. The majority of work concerning process integration and design automation has focused on the technical issues involved in improving the capability and interoperability of design tools and the means of incorporating them into automated workflows. Of lesser concern has been assessing the performance of these workflows prior to implementation. This paper reports on research at Rolls-Royce that used process mapping and discrete event simulation to help design and predict the performance of an automated design system that is being developed for the early stages of civil aero engine design. The findings of this research are that process simulation can add value to mapping the design process by quantifying the expected outcomes of different implementation scenarios and, thereby, indicating opportunities for further process improvements.@en

Task-network modelling approaches are widely used to understand and improve product development (PD) processes. The best-practice application of these tools is often presented prescriptively. This paper proposes that best-practice, and academic understanding, of PD process modelling can be further developed through an empirical approach, in which modelling tools are taken as given and practice is analysed in terms of how industry stakeholders use these tools. We argue this inductive approach can result in process modelling guidelines which explicitly recognise the challenges of modelling in industry. We thus analyse four cases of process modelling at Rolls-Royce plc and find that: 1) modelling is a social process of knowledge-creation; 2) effective modelling requires selection of the appropriate methods and tools at the right time; and 3) understanding the purpose of each interaction between participants in the modelling process helps to choose the best approach. To conclude we present guidelines to suggest which modelling approach should be used in which context, where context is described in terms of the purpose of a given modelling interaction.@en

This paper addresses the general issue of software tool support for designers, helping them to structure, to communicate and to document activities of generation, evaluation and decision. Here the focus is on detailed consideration of desired and undesired behavioural relationships among elements of complex design artefacts and with end users. This is an area that has recently been under discussion by proponents and critics of Affordance Based Design methods. Our solution approach is to extend an existing graph based software tool for design rationale capture that has been in widespread use in an international aerospace company for several years. We are integrating its Issue Based Information System (IBIS) based design argumentation with hierarchical Functional Analysis Diagrams (FAD), a form of Concept Map. The resulting software is being tested by practical application on pilot projects in the company, and initial experiences have been very favourable. The new graph element types, bidirectional relationship types between graphs, and supporting navigational facilities are described. Their use is illustrated by example of an integrated hierarchical FAD and assembly geometry model of a gas turbine engine.@en

This paper discusses a case study in which process simulation was applied to support the integration of life-cycle engineering activities into the existing design process at a major UK manufacturer of capital equipment. we demonstrate the practical value of process simulation as a tool to support the specification of changes to a complex, concurrent engineering design process. The paper also liilustrates how development of a design process simulation model can provide significant benefit to companies, not just in terms of the numerical results of simulation analysis, but through the understanding of process behaviour which is gained through validating the behaviour of the model in different iteration scenarios.

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