An ongoing research effort is consolidating material and process knowledge in a critiquing system dealing with fuzzy criteria to aid designers in evaluating the incorporation of composite materials into their design. The extent of knowledge required to perform the task of evaluating composite processes and materials is often beyond the expertise of many design engineers as they lack understanding of the nature of composite material manufacturing. The system under development is known as the Composites Design and Manufacturing Critiquing System (CDMCS). The CDMCS critiques a submitted design through interaction with the user. An account of the strengths and weaknesses of the design is supplied to the user through the facilities. The current focus of the system is on process selection, but the system is generic so that other aspects of composite material manufacturing may be included. The system is implemented in Macintosh™ Common LISP. This article describes the features of the system that have been implemented. The system is currently being extended to cover more than the primary process component of the domain.

Conceptual design is an early stage in the design process that involves the generation of solution concepts to satisfy the functional requirements of a design problem. Usually, there are many solutions to a design problem; therefore, there is scope for producing improved designs if one could explore a solution space larger than is presently possible. An approach would be to use the computer to synthesize a wide variety of concepts for a given problem, and allow designers to explore these before developing the most promising ones. Adopting a research approach based on developing basic representations, knowledge base, and reasoning procedures adequate for synthesizing concepts of existing devices and mechanisms, a computer program for synthesis of solutions to a class of mechanical design problems has been developed. For a given design problem, the program can produce an exhaustive set of solution concepts, in terms of their topological and spatial configurations, which can then be explored by designers. The program has been tested in two ways: (1) by comparing the candidate solutions produced by the program with those produced by designers in two real design case studies, and (2) by using three experienced designers to evaluate the solutions, generated by the program, for their novelty and usefulness. This paper presents the theoretical basis, research method, the theory and implementation of the synthesis approach. Also, the results of the above case studies and evaluations, and a discussion of further issues highlighted by the evaluations are presented.

Learning engineering is a new subarea of knowledge engineering dealing with the methodological aspects of using learning systems in knowledge acquisition. In this paper, the justification for the development of Learning Engineering is provided, and its major subdomains and research directions are briefly discussed.

Analogical reasoning plays an important role in design. In particular, cross-domain analogies appear to be important in innovative and creative design. However, making cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions useful in adapting past designs. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from design experiences in familiar domains by abstraction over patterns of regularity. Three important issues in abstraction from experiences are what to abstract from an experience, how far to abstract, and what methods to use. In this short paper, we describe how structure-behavior-function models of designs in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we describe the model-based learning method with a scenario of learning feedback mechanism.

With increased pressures coming from global competition and requirements for greater innovation in product development, designers are hard pressed to deliver designs of higher quality and variety using a repertoire of technological options from different disciplines. This interdisciplinary product development approach has not only removed many of the traditional constraints to design but has now given designers a much wider freedom of choice as to the best solution to a design problem. The focus of this paper is a knowledge-based design environment called Schemebuilder, which is a comprehensive and integrated suite of software tools aimed at supporting the designer in the rapid development of product design models in the conceptual, through embodiment stages of design. Illustrated is the use of the software tools in the qualitative generation of alternative schemes, by application of stored working and decomposition principles in the development of a function-means tree-like information structure. With mechatronic product development as the main theme, this paper describes a closely integrated methodology that incorporates a bond graph approach to continuous-time energetic systems and high-level Petri nets for the rigorous description of discrete-time information systems. Additionally, a technique is suggested for the decomposition of free format statements of need into the rigorously defined design context and required functions, which form the starting point of the function-means development process.

The research on new types of design knowledge bases and conceptual solution catalogues is an advanced subject of design methodology. Using the separate function as an example, this paper establishes design catalogues for principle conceptual design to realize the function, and proposes a general computer-oriented action principle design process. Upon studying the relationship of knowledge in the process, this paper represents functions of action principles in function feature models and provides computer-oriented feature models for action principle design.

We are interested in investigating the different types of knowledge and mechanisms that cause design problem-solving to become routine. We use the model of Routine Parametric Design incorporated in the language DSPL (Brown & Chandrasekaran, 1989), where both the parameters to be given values and the design knowledge needed are known in advance.

We analyze the diagnosis task in the context of adaptive design and redesign of physical devices. We identify three types of diagnosis tasks that differ in the types of information they take as input: the design does not achieve a desired function of the device, the design results in an undesirable behavior, and a specific structural element in the design misbehaves. We describe a model-based approach for solving the diagnosis task in the context of adaptive design and redesign. This approach uses functional models that explicitly represent the device functions and use them to organize teleological and causal knowledge about the device. In particular, we describe a specific kind of functional model called structure—behavior—function (SBF) models in which the causal behaviors of the device are specified in terms of flow of substances through components. We illustrate the use of SBF models with three examples from Kritik2, a knowledge system that designs new devices by retrieving, diagnosing, and adapting old device designs.

Over the past decade “design assistance,” that is, where the computer is viewed as an Intelligent Design Assistant (IDA) (MacCallum et al., 1987), has emerged in knowledge based design support and has formed the basic research strategy for the CAD Centre, University of Strathclyde, since the mid-1980s. Within this philosophy, an IDA would act as a colleague to a designer, providing guidance, learning from past design experiences, carrying out semi- and fully-automated tasks, explaining its reasoning and in essence complementing the designer's own natural skills, and thus leaving the ultimate decision-making, control, and responsibility with the designer (Fig. 1).

Science has developed detailed and well-founded theories for analyzing the behavior of artifacts. For example, Boeing was able to correctly verify an entirely new airplane, the Boeing 777, before any prototype was even built. However, there are few theories, and no computer systems, that would allow us to design structures with a similar degree of automation.