Shape interpretation with design computing

How information is interpreted has significant impact on how it can be used. This is particularly important in design where information from a wide variety of sources is used in a wide variety of contexts and in a wide variety of ways. This paper is concerned with the information that is created, modified and analysed during design processes, specifically with the information that is represented in shapes. It investigates how design computing seeks to support these processes, and the difficulties that arise when it is necessary to consider alternative interpretations of shape. The aim is to establish the problem of shape interpretation as a general challenge for research in design computing, rather than a difficulty that is to be overcome within specific processes. Shape interpretations are common characteristics of several areas of enquiry in design computing. This paper reviews these, brings an integrated perspective and draws conclusions about how this underlying process can be supported

Relating geometry descriptions to its derivatives on the web

Sharing building information over the Web is becoming more popular, leading to advances in describing building models in a Semantic Web context. However, those descriptions lack unified approaches for linking geometry descriptions to building elements, derived properties and derived other geometry descriptions. To bridge this gap, we analyse the basic characteristics of geometric dependencies and propose the Ontology for Managing Geometry (OMG) based on this analysis. In this paper, we present our results and show how the OMG provides means to link geometric and non-geometric data in meaningful ways. Thus, exchanging building data, including geometry, on the Web becomes more efficient

A framework of web-based conceptual design

A web-based conceptual design prototype system is presented. The system consists of four parts which interpret on-line sketches as 2D and 3D geometry, extract 3D hierarchical configurations, allow editing of component behaviours, and produce VRML-based behavioural simulations for design verification and web-based application. In the first part, on-line freehand sketched input is interpreted as 2D and 3D geometry, which geometrically represents conceptual design. The system then infers 3D configuration by analysing 3D modelling history. The configuration is described by a parent–child hierarchical relationship and relative positions between two geometric components. The positioning information is computed with respect to the VRML97 specification. In order to verify the conceptual design of a product, the behaviours can be specified interactively on different components. Finally, the system creates VRML97 formatted files for behavioural simulation and collaborative design application over the Internet. The paper gives examples of web-based applications. This work forms a part of a research project into the design and establishing of modular machines for automation manufacture. A consortium of leading automotive companies is collaborating on the research project

SPIDA: Abstracting and generalizing layout design cases

Abstraction and generalization of layout design cases generate new knowledge that is more widely applicable to use than specific design cases. The abstraction and generalization of design cases into hierarchical levels of abstractions provide the designer with the flexibility to apply any level of abstract and generalized knowledge for a new layout design problem. Existing case-based layout learning (CBLL) systems abstract and generalize cases into single levels of abstractions, but not into a hierarchy. In this paper, we propose a new approach, termed customized viewpoint - spatial (CV-S), which supports the generalization and abstraction of spatial layouts into hierarchies along with a supporting system, SPIDA (SPatial Intelligent Design Assistant)

Engineering design in a different way: cognitive perspective on the contact and channel model approach

Engineering design often involves the integration of new design ideas into existing products, requiring designers to think simultaneously about abstract properties and functions as well as concrete solution constraints. Often designers struggle to reason with functional descriptions, while not fixating on existing solutions. This paper introduces the Contact & Channel Model (C&CM) approach, which combines abstract functional models of technical systems with the concrete geometric descriptions that many designers are familiar with. By locating functions at working surface pairs, they receive a concrete location in mental models. The C&CM approach can be applied to analyze existing product descriptions and synthesize creative new solutions for parts of the system or for entire new systems. At the moment the approach is being developed into an complete modeling and problem solving approach. C&CM has been used for several years in undergraduate engineering teaching at the University of Karlsruhe (TH) and is increasingly being introduced into industry by its use in research and development projects, by its students and its alumni

Design knowledge capture for the space station

The benefits of design knowledge availability are identifiable and pervasive. The implementation of design knowledge capture and storage using current technology increases the probability for success, while providing for a degree of access compatibility with future applications. The space station design definition should be expanded to include design knowledge. Design knowledge should be captured. A critical timing relationship exists between the space station development program, and the implementation of this project

An approach to design knowledge capture for the space station

The design of NASA's space station has begun. During the design cycle, and after activation of the space station, the reoccurring need will exist to access not only designs, but also deeper knowledge about the designs, which is only hinted in the design definition. Areas benefiting from this knowledge include training, fault management, and onboard automation. NASA's Artificial Intelligence Office at Johnson Space Center and The MITRE Corporation have conceptualized an approach for capture and storage of design knowledge