A generalized approach to fast interpretation of objects and their features has so far eluded researchers. In manufacturing, this interpretation can be approached from the vision point of view or from the CAD data perspective. Presently, CAD systems are widely used in several aspects of manufacturing production. It is therefore more efficient to use CAD data for object reasoning in manufacturing, especially when systems will eventually be data driven. Components can be modelled on a CAD system using various modelling techniques and the representation of their geometric information is still CAD system dependent. However, the advent of the Initial Graphics Exchange Specification (IGES) now makes it possible to represent CAD data in a neutral and standard manner.

This paper describes a scheme for recognizing and representing features for CAD data extracted using the IGES interface. The concepts developed are based on graph-based feature representation, where features are represented by a set of faces as well as their topological adjacency.

Strategies for classifying features and methods of decomposing a complicated feature into several simpler features for recognition purposes are discussed.

Though simulation models are extensively used for detailed design analysis, they find limited role in preliminary design decisions. We have developed a machine learning based approach to enable detailed simulation models to be harvested for supporting early-stage design of engineering systems.

I discuss how the capabilities of the Edinburgh Designer System can be extended and used to support symbolic computation for robotics. I conclude that the Algebra Engine requires to handle temporal constructs, groups and tolerances, that the taxonomy can support activity modules and that automatic plan formation would require the creation of a specialist.

Studies on the application of AI techniques to engineering design, analysis, and manufacturing (AI EDAM) problems have been expanding steadily over the past decade. These studies have led to the founding of many new journals and the initiation of series of conferences. If all these research efforts were successful, some of us might have been unemployed but living happily off the royalties from our successful research products that were deployed in practice.

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).

Design rationale is a topic that implies different things to different people. To some it implies argumentation and frameworks for argumentation. To others it implies the documentation of design, like that required for many types of industrial or government work. Still others describe design rationale as the capture and potential reuse of normal communication about design. These perspectives of design rationale use different representations, which influence their ability to capture and to retrieve and use information. We propose an integrated approach to design rationale where design communication is captured and, over time, incrementally structured into argumentation and other formalisms to enable improved retrieval and use of this information. Two systems, PHIDIAS and the Hyper-Object Substrate, are used to demonstrate: (1) how to capture and integrate a variety of design information, (2) how to support the structuring of unstructured information, and (3) how to use design information to actively support design.

This paper describes an intelligent alarm processing expert system which is integrated in a large Supervisory Control and Data Acquisition system for power distribution networks. The expert system works as an operator support tool by diagnosing network disturbances and device malfunctions. The expert system is based on a hierarchic, multi-level problem-solving architecture, integrating both model-based and heuristic techniques acting upon an object-oriented data structure. Several enhancements have been designed and implemented to enable the system to perform its task online and real-time. The expert system covers online processing of real-time data and intelligent alarm processing, as well as the automatic creation and update of the knowledge base. It consists of approximately 25000 objects (units) and 190 rules. The system uses the expert system tool KEE, runs on SUN workstations, and is integrated in the Supervisory Control and Data Acquisition system via LAN. The expert system was implemented for the Public Utilities Board Singapore controlling its 22 kV distribution network and has been online since November 1990.

This paper addresses manufacturing research involving contributions in computer science, control theory and material processing science. The research objective has been to develop intelligent, †self-directed‡ control systems which enable in situ control path generation based on †product-processing‡ feedback. A †product-directed‡ control philosophy which emphasizes product quality is described together with a generic architecture for representing process knowledge. Specific details are presented involving the development and application of a self-directed material processing system for autoclave curing of composites together with recent research results, production challenges and future research in †self-improving‡ material processing systems.

This paper proposes and evaluates an artificial neural network based method of modeling the dynamic behavior of spatially distributed continuous engineering processes. The technique is applicable to situations where the differential equations governing the behavior of a system are nonlinear and poorly understood, such as is the case for frost-heave and thaw-settlement processes in soils. A description is first provided of a means of modeling the unknown component of governing differential equations. A range of levels of understanding of these equations is considered. A method of discretizing the resultant neural models of these equations is then illustrated, and the way in which these can be used to simulate the behavior of a process is described. The performance of the proposed neural network approach is then assessed in a series of experiments simulating the nonlinear thermal behavior of translucent solid materials. The system is proven capable of providing highly accurate simulations of system behavior sustained over many thousands of simulation time steps. The paper concludes with an identification of several ongoing areas of further development and application of the proposed tool.

This paper presents a new concept, a ‘human-computer co-operative system’, as the next-generation knowledge-based system for application to project risk management. It first discusses the characteristics of project risks followed by the development of a common expert system for managing such risks. Then, system limitations are identified in terms of knowledge association, and a ‘human–computer co-operative system’ is proposed to overcome these limitations by explicitly incorporating human intuitive ability into the computer system. Finally, evaluations of the human–computer co-operative system are also described.

The design of electronic power transformers is an activity that requires the application of well-established algorithms from electromagnetic theory and heuristic, judgmental techniques derived from experience in the design and manufacturing of these devices. This paper describes an object-oriented knowledge-based system, Encore, that was developed at Houston Downhole Sensors (a division of Schlumberger Well Services). Encore combines object-oriented, rule-based, and procedural programming techniques to design 60 Hz power transformers. The system uses a heuristic search strategy to generate design alternatives, and then selects the “best” design based on size and efficiency considerations The heuristics are represented and applied as rules; the electromagnetic components are modelled as frame-like objects. The object-oriented nature of this system facilitated enhancements; by specializing some of the objects comprising a power transformer, a power inductor model was quickly developed. Encore reduces design time from a couple of days to less than an hour; it is being used to design the transformers and inductors of power supplies in new Schlumberger well-logging tools. The system was implemented on a Xerox interlisp Workstation using an object-oriented environment called STROBE.

This paper presents an expert system (called ADES, i.e. ATP Design Expert System) for the automatic design of Automatic Train Protection systems (ATP). An ATP system is a railway signalling system constituted by a set of logic circuits that control the safe movement of trains within a railway station.

AI techniques proved feasible to address the particular design problem discussed: ADES is able to rapidly design good control circuits to meet operational requirements by using a well-structured, explicitly represented, in depth knowledge of Automatic Train Protection. The use of AI techniques facilitates the maintenance and extension of ADES to face new or unplanned requirements.

Implementing both the expert system and its environment tools in the PROLOG language, by using meta-interpretation techniques, has led to the rapid prototyping of the overall system. Optimization techniques have also been developed to allow ADES to be efficiently executed.

Methodologies have been developed and implemented in LISP and OPS-S languages which address type synthesis of mechanisms. Graph theory and separation of structure from function concepts have been integrated into an expert system called DOMES (Design Of Mechanism by an Expert System) to effectively implement the following three activities: 1. enumeration of all non-isomorphic labelled graphs; 2. identification of those graphs which satisfy structural constraints; 3. sketching of mechanisms corresponding to a given graph.

Developed theories and algorithms are applied to a robot gripper design and a variable-stroke piston engine design. The results from these two applications indicate that the automated techniques effectively identify all previously obtained solutions via manual techniques. Additional solutions are also identified and several errors of the manual process are detected. The developed methodologies and software appear to perform a complete and unbiased search of all possible candidate designs and are not prone to the errors of the manual process. Other important features of DOMES are: 1. it can learn and reason, by analogy, about a new design problem based on its experience of the problems previously solved by the system: 2. it has the capability to incrementally expand its knowledge base of rejection criteria by converting into LISP code information obtained through a query-based interactive session with a human designer; 3. it can select the set of rejection criteria relevant to a design problem from its knowledge base of rejection criteria. These procedures could become a powerful tool for design engineers, especially at the conceptual stage of design.

Case-based design promises important advantages over rule-based design systems. However, the actual implementation of the paradigm poses many problems which put the advantages into question. In our work on CADRE, a case-based building design system, we have encountered seven fundamental problems which we think are common to most case-based design systems. We describe the problems and the ways we either solved or worked around them in the CADRE system. This leads us to conclusions about the general applicability of case-based reasoning to building design.

The specific semantics of CAD objects are analysed concerning their nature their dynamicity and their consistency during the design process. The nature of CAD objects is concerned with their structure and relationships. The dynamicity is concerned with the evolving nature of the objects, i.e. their behavior. The consistency is concerned with their completeness and relationships with integrity constraints.

A new methodology for semantic constraints management and control is defined. It relies extensively on database and expert system technologies for the implementation of new concepts, e.g. logical prototypes of objects and object equivalence class.

It provides a sound and unified basis for modelling the dynamic nature of complex objects, concerning both the management of their structure and the certification of the update operations, i.e. the control of their correctness.

The functionalities of CADB, a prototype expert database system that supports these features, are detailed. CADB is currently implemented in Prolog on VAX™ 11/785 and APOLLO™ workstations.