Artificial Intelligence has been very active in developing high-level symbolic reasoning paradigms that have resulted in practical expert systems. However, with a few exceptions, it has paid little attention to the automation of spatial reasoning. On the other hand, spatial reasoning has attracted the interest of several researchers in Robotics. One of the important problems that have been investigated is motion planning, and very significant results have been obtained. This paper describes an implemented system for designing pipe layouts automatically using motion planning techniques. It introduces a new approach to pipe layout design automation in which pipe routes are treated as trajectories left behind by rigid objects (‘robots’). We have implemented this approach in a basic Pipe Router that is described in detail in this paper. We have extended this router in order to make it capable of treating a variety of other constraints which are typical of practical pipe layout design problems. These constraints relate to the process carried out in the pipes, to the design of their mechanical support, and to the constructability and the ease of operation and maintenance of the designed pipe systems.

A designer often has to deal with complex and ill-structured situations during specification synthesis and preliminary engineering design. To assist in the development of computer-aided design systems, it is desirable to capture the designers decision-making process during these design states. The research presented in this paper is towards this direction. Based on the conceptual understanding of the process, three postulates are presented. The following two postulates; (1) the decisions are neither optimum nor just satisfying but retain certain characteristics of both, (2) the design is driven by the important objective(s) among all the specified objectives, at the preliminary design, although the remaining objectives do have a weak influence on the preliminary design; are used to develop a compensatory and a non-compensatory model of the decision-making. These models are formulated with the help of fuzzy set theory and they implicitly or explicitly follow the two postulates. These models are suitable for discrete decision situations where the above mentioned postulates apply. Examples of material selection during a preliminary structural design are used to illustrate the effectiveness of these models.

Design is not done in a vacuum. Engineers often rely on prior designs to make new design decisions instead of solving every new problem from scratch. Prior designs that represent good solutions to the tightly coupled nature of mechanical devices are used as guides. Moreover, prior failures are used to avoid repeating old mistakes. In this paper we present a computer-based approach to exploiting the knowledge embodied in prior designs. Reasoning from design cases requires the ability to use cases, or pieces of cases that realize subfunctions of the device being designed. It is, however, difficult to recognize and retrieve relevant cases or case pieces using a given design specification. Because there is no one-to-one correspondence between the desired behavior of a device and the individual component behaviors, it is often not possible to find relevant design cases by using the given overall behavioral specification as an index into case memory. We approach this problem by elaborating the given behavior specification into a description that gives rise to indices with which relevant components can be retrieved. The elaborations are carried out in a behavior-preserving manner using two transformation operators that (a) rely on physical laws if it is known which ones are relevant, or (b) hypothesize behaviors and then search the case memory for ways in which the required behaviors may be achieved. These two approaches are used opportunistically in CADET, a case-based mechanical design system.

In this paper, a methodology for inducing trends in a first principle reasoning system for design innovation is presented. Dimensional Variable Expansion is used in 1stPRINCE (FIRST PRINciple Computational Evaluator) to create additional design variables and introduce new prototypes. Trends are observed at each generation of the prototype and induction is used to predict optimal constraint activity at the limit of the iterative procedure. The inductive mechanism is applied to a constant-radius beam under flexural load and a tapered beam of varying radius and superior performance is derived. A circular wheel is created from a primitive-prototype consisting of a rectangular, spinning block that is optimized for minimum resistance to spinning. Although presented as a technique to perform innovative design, the inductive methodology can also be utilized as an AI approach to shape optimization.