An expert system approach for designing air-conditioning systems for cars and trucks is presented. A brief introduction to the automotive application of the vapor-compression refrigeration cycle is provided as general background. The method presented uses an integrated approach combining the power of conventional analysis programs, databases and model-based expert system technology. Some sample rules from the knowledge base have been included in the paper to illustrate the application of the domain knowledge and its interaction with algorithmic programs. The system architecture is very open and modular, and it lends itself to easy modifications and future expansions. Possibilities for system enhancements are also outlined in the paper. The approach presented in this paper provides substantial benefits to the automotive air-conditioning design engineer particularly in the early stages of new vehicle platform planning and development. A pilot system has been successfully tested at General Motors for preliminary design of automotive air-conditioning systems.

The application of truth maintenance techniques to the component selection phase of the engineering design process is described. The aims of the research include the selection of components that most closely match a set of requirements which include fluid characteristics, economic constraints, and company policies or preferences. In addition, we consider the storage and retrieval of requirement sets and corresponding component selections. The benefits of maintaining temporal knowledge are twofold: to enhance performance through reuse when new requirement sets are similar to previous sets, and to re-assess selections in the light of changes over time, for example, to the set of available components, to company policy, or, perhaps, to national safety regulations.

We propose a temporal truth maintenance system (TTMS) to support this management of selection knowledge over time.

Spreadsheets are difficult to use in applications, where only incomplete or inexact data (e.g., intervals) are available-a typical situation in various design and planning tasks. It can be argued that this is due to two fundamental shortcomings of the computational paradigm underlying spreadsheets. First, the distinction between input and output cells has to be fixed before computations. Second, cells may have only exact values. As a result, spread-sheets support the user only with primitive iterative problem solving schemes based on trial-and-error methods. A constraint-based computational paradigm for next generation interval spreadsheets is presented. The scheme makes it possible to exploit incomplete/inexact data (intervals), and it can support problem solving in a top-down fashion. Current spreadsheets constitute a special case of the more general interval constraint spreadsheets proposed.

Computational abstraction of engineering design leads to an elegant theory defining (1) the process of design as an abstract model of computability, the Turing machine; (2) the artifacts of design as enumerated strings from a (possibly multidimensional) grammar; and (3) design specifications or constraints as formal state changes that govern string enumeration. Using this theory, it is shown that engineering design is a computable function. A computational methodology based on the theory is then developed that can be described as a form follows function design paradigm.

An approach to providing computational support for concurrent design is discussed in the context of an industrial cable harness design problem. Key issues include the development of an architecture that supports collaboration among specialists, the development of hierarchical representations that capture different characteristics of the design, and the decomposition of tasks to achieve a trade-off between efficiency and robustness. An architecture is presented in which the main design tasks are supported by agents – asynchronous and semiautonomous modules that automate routine design tasks and provide specialized interfaces for working on particular aspects of the design. The agent communication and coordination mechanisms permit members of an engineering team to work concurrently, at different levels of detail and on different versions of the design. The design is represented hierarchically, with detailed models maintained by the participating agents. Abstractions of the detailed models, called “agent model images,” are shared with other agents. In conjunction with the architecture and design representations, issues pertaining to the exchange of information among different views of the design, management of dependencies and constraints, and propagation of design changes are discussed.

Development of expert systems involves knowledge acquisition that can be supported by applying machine learning techniques. The basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM) is presented. How decision-tree induction is used to build and refine the knowledge base of the process is also discussed.

The idea of developing an intelligent supervisory system with a learning component [Intelligent MAnufacturing FOreman (IMAFO)] that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data from the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information, and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledge base of an expert system) are generated from the rules induced by IMAFO. The procedure to refine these types of rules is also explained.