Direct planning of assembly sequences is either very complicated computationally, or requires an ‘expert’ user. This paper presents a new approach to the automatic generation of assembly plans which involves two phases. An ‘exploded’ layout of the assembled product is first found from its topological and geometric description by a graph-based procedure. Then, a knowledge-based technique is used for planning of selected assembly sequences by utilizing theoretical as well as heuristic knowledge of mechanical components and assembly processes. Besides serving the assembly planning phase, automatic generation of exploded-views could also be a most desirable feature of any computer-aided design system. An example demonstrates the application of the method to an actual product.

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.

Although current efforts have indicated the need for design verification and evaluation systems, little has been done to develop knowledge representation systems for implementing this class of expert systems. In this paper, we describe CANDLE, a knowledge representation system built to solve design verification and evaluation problems. We define the class of design verification and evaluation systems and the functionality required of knowledge representation systems used to develop these systems. We present an example design verification and evaluation problem, illustrating how this problem imposes these functionality requirements on the knowledge representation system used. In the remainder of the paper we discuss CANDLE, a knowledge representation system created to develop design verification and evaluation systems. We show that CANDLE meets the knowledge representation requirements for this class of systems. We also present some of CANDLE'S functionality that exceeds the defined knowledge representation requirements and provides a knowledge engineer with the capability to expand the functionality of a design verification and evaluation system.

A general constraint-based formulation for the analysis and partial synthesis of two-dimensional truss structures is presented. This formulation is general in that it handles statically determinate and statically indeterminate trusses with pin and roller supports, and concentrated joint loads. The formulation is constraint-based in that the physical behavior of truss components is declaratively represented using constraints.

The analysis and partial synthesis of a truss structure manifest themselves in proving the satisfiability of the constraints associated with the structural components. An artificial intelligence approach called constraint logic programming is used for representing and satisfying constraints. A constraint logic programming language, called CLP(R), is used for implementing the formulation. The implemented program consists of sixteen rules. These rules are used for both the analysis and partial synthesis of truss structures. Several truss analysis and synthesis examples using the formulation are presented.

Previous research on qualitative reasoning about shape and fit laid the foundations to determine whether two objects fit together. Continued investigation has refined the theory and has produced a functioning implementation. This paper describes extensions to the theory and the details of the implementation.

The reasoning process has been divided into five layers: grouping, topology, orientation, matching, and confirmation. The grouping layer clusters features such as cubes or cylinders into groups for each surface of an object. The topology layer recognizes patterns formed by the groups on each surface, and describes the pattern in terms of topological structures. The orientation layer selects promising surfaces from the two objects and attempts to align the two surfaces. If the orientation layer aligns the topological structures on the two surfaces, the matching layer tries to pair the features within the topological structures. The confirmation layer inspects paired features to determine whether the surfaces are compatible. If the surfaces are compatible, then the two objects qualitatively fit together.

An MPR is a deep model of a manufacturing process. It is claimed to be easier to acquire than experiential diagnostic rules, and the acquisition of an MPR can be done either by a knowledge engineer or an intelligent interrogator program. An MPR can be used for simulating processes, for centralized or distributed model-based diagnosis of problems with processes, for designing the processes themselves, for determining the need for quality control testing and sensor checks, for determining when knowledge about the process is incomplete and additional knowledge needs to be acquired, and for compiling diagnostic rules.

Recent knowledge-based expert systems for structural engineering design have focused on design-independent knowledge (abstract reasoning rules for designing), and while great strides have been made in that area, there is still a significant need to develop systems to take advantage of the wealth of knowledge contained in every substantial structural design. On the other hand, previous database-oriented design efforts have focused primarily on knowledge-poor databases of solutions, in which the traditional engineering handbook of solutions has simply been replaced by digital data. The challenge is to find a way to capture and apply the kind of case-based, design-dependent knowledge that structural engineers have traditionally used. The long-term results will be better structural designs and better structural designers. This paper discusses the character of the design-dependent knowledge in a structural engineering context, describes two initial applications of case-based reasoning to component design, and presents a general paradigm for a knowledge-based design system integrating rule-based and case-based reasoning.

Expert systems employing current methodologies suffer from two major problems: they are brittle and their development is time-consuming and tedious. Learning, the key to intelligent human behavior and expertise, has the potential of alleviating these difficulties. The paper reviews a number of machine learning techniques and provides a framework for their classification. The description of each technique is followed by an example taken from the domain of structural design. The applicability of machine learning techniques to expert systems is discussed, including some prototype applications and their shortcomings. Three promising research directions are outlined as a partial solution for the shortcomings.

In an effort to learn more about how testers test code, we observed several testers while they designed tests to check a change which had been made to code. Using a case study methodology, we gathered empirical data from the ‘real world’—professional testers, and actual software products. We found that testers do share some common work patterns. These patterns can be seen in their information gathering, their use of heuristics and their construction of mental models. This work is extremely knowledge intensive, experience appears to have a useful effect. In this paper we will discuss how we collected and analysed our data. Then we will present some of our observations about how the testers gathered information, used heuristics, formed mental models and were affected by their previous experience in the course of designing their test scenarios. Based on these observations we comment on training and tools for testers.

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.

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.

Computer programs that combine traditional numeric methods with symbolic algebra and with specific knowledge of application-based techniques can provide new levels of computational support for engineering design. We illustrate this with a computer-based ‘control engineer’s assistant’. Although this program is focussed on control system design, it demonstrates techniques that should be widely applicable across many engineering disciplines. In particular, we show how, with symbolic computing, a computer-aided design system can usefully simulate engineering models early in the design process, before all (or any) system parameters have been specified numerically. Our system employs a flexible, extensible, object-oriented representation for control systems, which admits multiple mathematical models of designs and provides a framework for integrating tools that operate on diverse representations.