The conventional engineering design process consisting of three phases, synthesis, analysis and evaluation can be extended for solving rehabilitation problems by including a diagnosis phase during which abnormalities and malfunctions are identified and characterized. After diagnosis, the design objectives are clearly specified and the conventional engineering design process can begin. Different problem solving strategies, information representation, and processing are useful for the different phases of rehabilitation design. This paper describes the RETAIN knowledge-based rehabilitation design system which integrates a relational database, production rules and algorithmic functions. RETAIN diagnoses retaining wall failures and synthesizes preliminary rehabilitation designs and cost estimates. Its framework and methodology may be applied to other infrastructure components such as pavements, water and sewage networks, bridge piers and marine structures. RETAIN decomposes rehabilitation problems into influencing failure modes then identifies a set of rehabilitation strategies such that each strategy remedies one or more failure modes. Complete rehabilitation solutions are formed by searching and combining strategy components. Conceptual knowledge for design synthesis is organized in relational database tables. The paper includes an example of design synthesis with relational operations for selecting rehabilitation strategies and forming complete rehabilitation solutions.

Artificial intelligence (AI) applications to design have tended to focus on modeling and automating aspects of single discipline design tasks. Relatively little attention has thus far been devoted to representing the kinds of design ‘metaknowledge’ needed to manage the important interface issues that arise in concurrent design, that is, multidisciplinary design decision-making. This paper provides a view of the process and management of concurrent design and evaluates the potential of two AI approaches—blackboard architectures and co-operative distributed problem-solving (CDPS)—to model and support the concurrent design of complex artifacts. A discussion of the process of multidisciplinary design highlights elements of both sequential and concurrent design decision-making. We identify several kinds of design metaknowledge used by expert managers to: partition the design task for efficient execution by specialists; set appropriate levels of design conservatism for key subsystem specifications; evaluate, limit and selectively communicate design changes across discipline boundaries; and control the sequence and timing of the key (highly constrained and constraining) design decisions for a given type of artifact. We explore the extent to which blackboard and CDPS architectures can provide valid models of and potential decision support for concurrent design by (1) representing design management metaknowledge, and (2) using it to enhance both horizontal (interdisciplinary) and vertical (project life cycle) integration among product design, manufacturing and operations specialists.

In order to enhance the knowledge acquisition capability of the expert system DOMES (Design Of Mechanisms by an Expert System), which is developed at the University of Rhode Island for creative type synthesis of mechanisms, methodologies have been developed to build a knowledge engineer module based on natural language understanding. Specifically, artificial intelligence concepts and Lisp programming techniques have been incorporated in this module to implement the following: (1) analysing and understanding design criteria supplied by human designers in the form of English sentences; and (2) transforming these design criteria into Lisp code and storing them in the knowledge base of DOMES. This knowledge engineer module enhances the capability and improves the performance of the expert system DOMES by providing an effective means for knowledge acquisition based on natural language understanding. The concepts and implementation techniques in developing this module are general and can also be utilized for other knowledge-based systems.

The function of a process planning system is to determine the methods by which a product is to be manufactured economically and competitively. In a modern manufacturing environment, a process planning system consists of highly trained people and complex software. The plans prepared by a process planning system are not always executed as planned. It is useful if the system can discover why plans fail, when they do fail. In order to learn why plans fail, the system must analyse a number of plans, both successful and unsuccessful, to find patterns in the failures of plans. This type of analysis is difficult for people, who are much better at analysing single events than multiple events.

The aim of the project described here is to design and implement a computer program which will help human planners in a process planning system to understand why plans fail. To achieve this aim, a program called IMAFO (Intelligent MAnufacturing FOreman) has been developed. IMAFO uses decision tree induction to analyse examples of both successful and unsuccessful plans.

The difficulties presented by this application are discussed and solutions are presented. Problems addressed include finding an appropriate set of attributes for describing the plans, using data efficiently, consolidating input from distinct sources, and presenting decision trees in an understandable form. Potential applications and directions for future research are considered.

Protective packaging buffer design is a complex process which involves manipulating both analytical and graphical data, and expert knowledge. The design activities of this process can be represented as a series of procedural tasks, and it is therefore suitable for design automation efforts. Currently, the design activities of the protective packaging buffers are predominantly manual and done on a trial and error basis. In this paper, PROPACK, a knowledge-based CAD system which has been developed to automate this design process will be discussed. A description of how the knowledge and information in PROPACK are structured and stored in the database will also be made. Preliminary tests have found that PROPACK is able to generate possible design solutions that either require external or simple internal cushioning features used for protection. These results have found to compare quite favourably with the BASF worked examples.