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.

This paper describes our preliminary results in applying constraint satisfaction techniques in a system we call TRANS-FORM for designing automatic automobile power transmissions. The work is being conducted in collaboration with the Ford Motor Company Advanced Transmission Design Department in Livonia, Michigan. Our current focus is on the design of the mechanical subsystem, but we anticipate extending this later to the electrical and hydraulic subsystems also. For simplicity, in the initial work reported here we restrict ourselves to the relatively well-explored class of transmissions having four forward speeds and one reverse speed, built from two planetary gearsets, cross-connected by two permanent links. Moreover, we pursue design of such transmissions only at the ‘kinematic level’. These two restrictions correspond to limiting respectively the breadth (generality) and the depth (detail or granularity) of the search space employed. We find that, at least for the restricted version of the problem pursued here, transmission design is an application very naturally formulated as a constraint satisfaction problem. Our present problem requires only 10 variables, with an average of about seven values each, and 43 constraints—making it similar in difficulty to about the 10-queens problem. So far, two of the classic transmissions, known as Axod and HydraMatic, have been rediscovered (at the kinematic level) by our program. Preliminary results also indicate that the constraint satisfaction framework will continue to remain adequate and natural even when the search space is allowed to be much broader and deeper. We expect that searches of such expanded spaces will soon lead to the discovery of totally new transmissions.

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.

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.

The complexity of mechanical design is reflected in the complexity of the design constraints which relate functional requirements to design parameters. Reformulations of the design constraints can significantly reduce this complexity. This is accomplished by a transformation to alternative design parameters, such as a critical ratio, a non-dimensional parameter, or a simple difference; e.g. the ratio of surface area to volume for heat transfer loss, the Reynold's number in fluid mechanics, or the velocity difference across a fluid coupling. We have developed a method by which the alternative parameters are chosen for physical significance and for the ability to create a more direct correspondence to functional behavior as determined by measures of serial and block decomposability of the constraints. Rules have been developed for the creation of physically significant new parameters from the algebraic combination of the original parameters. The rules are based on engineering principles and rely on knowledge about what a parameter physically represents rather than other qualities such as dimensions. A computer based system, called EUDOXUS, has been developed to automate this procedure. The system operates on a set of design constraints to produce sets of transformed constraints in terms of alternative design parameters. The method and its implementation have demonstrated successful results for many highly nonlinear and highly coupled parameterized designs from many mechanical engineering domains.

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 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.

In this paper we present an application of AI search techniques to a class of problems that arise in transportation systems analysis. Rather than adapting the time-space network formulation typically used in Operations Research, we propose a discrete dynamic network to represent a scheduled service network. In a discrete dynamic network, there are finite, discrete, predetermined possibilities for moving from one vertex to another. Visiting a vertex has a cost (possibly zero), which may depend both on how the vertex was reached and how it will be left.

We describe the DYNET search algorithm for finding optimal paths in discrete dynamic networks. DYNET has been implemented in a working system (TRAINS) which searches the entire Dutch railway services network. An optimal path in a discrete dynamic network makes us arrive at our destination as early as possible (given our planned earliest departure time), and given this earliest arrival time (eat), will allow us to leave as late as possible, thereby guaranteeing a shortest path relative to the eat. DYNET first conducts a forward search to find the earliest possible arrival time, then a backward search which uses results of the forward search, to find the latest departure to arrive at that eat. Various AI techniques (symmetries, abstraction spaces, distance estimates, etc.) improve the performance of DYNET.

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.

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.