The GREASE project is an investigation of the application of artificial intelligence to cutting fluid selection and blending for metal machining operations. The problem is to first diagnose the machining operations to determine what fluid characteristics are required, then to select a cutting fluid which satisfies the required characteristics. The problem is exacerbated by the need to select a single fluid to be used by multiple types of operations on a variety of materials. Diagnosis is relatively simple, but treatment specification is difficult due to the variety of operations to be handled.

GREASE uses heuristic search in which the evaluation function is heuristically constructed. The construction of the evaluation function begins with the determination of the characteristics of an optimal fluid based on deep knowledge of the machining operations and materials. This is then altered heuristically according to problems diagnosed with the current fluid. Once the evaluation function is complete, it is used to select an existing fluid from the product line. GREASE has been tested extensively with results which equal that of the experts and has been field tested by the Chevron Corporation.

MAPCon II is the second generation (Muralidhar and Irish, IEEE Journal on Selected Areas in Commumcctions 6(5), 869–873, 1988) of an expert system that interactively guides a user in performing off-line configuration for local area networks that use MAP, the manufacturing automation protocol. This paper describes the configuration task in general and MAPCon in particular.

Though MAPCon's purpose is off-line configuration, its problem domain requires that it accomplish other reasoning objectives in addition to those commonly associated with configuration. It is in the process of being expanded into an on-line network supervisor. We develop a taxonomy of reasoning objectives and show how MAPCon combines two different kinds of reasoning to accomplish its objectives. Our experience confirms that of other researchers, and suggests that building robust, practical systems will require us to understand more clearly the interfaces among different reasoning objectives.

The paper has four parts: 1. a definition of configuration and other reasoning objectives; 2. a summary of the problem domain in which MAPCon operates; 3. a description of MAPCon as the user sees it; 4. a look ‘under the hood’ to see how MAPCon combines different objectives.

A critical problem with existing computer-based geometric modeling is the labor intensive task involved in specifying data input for the description of three-dimensional (3-D) objects. This paper describes a new, 3-D input system aimed at alleviating this problem. It is based on the use of a three-dimensional digitizer for the direct input of spatial coordinates, and an intelligent interactive user interface. The intent of this system is to create a high level, intelligent interface between the designer and a geometric solid modeler which would lighten the designer's burden in performing arduous 3-D geometric description tasks. The user interface is developed around the Knowledge Craft expert system building tool, using the rule-based Carnegie Representation Language OPS5 (CRL-OPS). The system uses schematic networks or frames and production rules to encode knowledge about geometric primitive digitization methods, object feature operators, solid modeler requirements, and input command functions. It also employs a forward-chaining inference strategy to direct the knowledge. This ensures that only a minimal amount of valid data entry is required by the user. However, if excessive data is entered the intelligent interface has the capability to extract the required information. As a result, the solid modeler can automatically create the appropriate object “primitive” or the specific object “feature” upon recognition by the expert system. It will be demonstrated that these capabilities can simplify the 3-D model description process.

This paper describes OARPLAN, a prototype planning system that generates construction project plans from a description of the objects that comprise the completed facility. OARPLAN is based upon the notion that activities in a project plan can be viewed as intersections of their constituents: objects, actions and resources. Planning knowledge in OARPLAN is represented as constraints based on activity constituents and their interrelationships; the planner functions as a constraint satisfaction engine that attempts to satisfy these constraints. The goal of the OARPLAN project is to develop a planning shell for construction projects that (i) provides a natural and powerful constraint language for expressing knowledge about construction planning, and (ii) generates a facility construction plan by satisfying constraints expressed in this language.

To generate its construction plan, OARPLAN must be supplied with extensive knowledge about construction objects, actions and resources, and about spatial, topological, temporal and other relations that may exist between them. We suggest that much of the knowledge required to plan the construction of a given facility can be drawn directly from a three-dimensional CAD model of the facility, and from a variety of databases currently used in design and project management software. In the prototype OARPLAN system, facility data must be input directly as frames. However, we are collaborating with database researchers to develop intelligent interfaces to such sources of planning data, so that OARPLAN will eventually be able to send high level queries to an intelligent database access system without regard for the particular CAD system in which the project was designed.

We begin by explaining why classical AI planners and domain specific expert system approaches are both inadequate for the task of generating construction project plans. We describe the activity representation developed in OARPLAN and demonstrate its use in producing a plan of about 50 activities for a steel-frame building, based on spatial and topological constraints that express structural support, weather protection and safety concerns in construction planning. We conclude with a discussion of the research issues raised by our experiments with OARPLAN to date.

Linkage-type mechanisms have numerous applications in industry especially for automation Unfortunately, they are less popular due to lack of proper design tools. This paper describes our efforts to remove the technological barrier in mechanisms design automation. Although the ideas presented apply to automation of mechanisms design in general, the paper discusses the development of an expert system for a particular sub-set of mechanisms called Dwell mechanisms.

Many essential and desirable motion characteristics of mechanisms are so implicit that they are difficult to control by analytical methods. By systematically and extensively studying the entire motion characteristics of hundreds of linkages, a comprehensive classification system and heuristics were developed. This qualitative classification scheme led to a finite set of linkage models that cover the entire design space in the sense that any possible design falls under one or more of the models. Our system, called Dwell-Expert, incorporates this design expertise to select the best linkage model for a given set of design specifications and to compare that model against alternatives. The new design methodology and its implementation in AGNESS (A Generalized Network-based Expert System Shell) are explained. A design example is also presented. Our system can reduce even an experienced designer’s initial-design time from a day or more to a minute or less, assuming specifications have already been formulated. Such results motivate extension of this design methodology to other areas of mechanical design and engineering design in general.

A spatial reasoning system must be able to represent and manipulate the location of objects in its world model. There are two general schemes for doing this—absolute symbolic schemes based on an independent coordinate system, and relative schemes which reference objects in relation to other objects of known location. This paper is concerned strictly with the latter.

Given a relative scheme, there are several possible strategies for segmenting space. Two such strategies are identified and discussed. The first, called the situation-specific strategy is the one currently being explored and employed in most spatial reasoning systems. The second, here referred to as the general-purpose or cognitive strategy is the one used by the human cognitive system. It is suggested that while both strategies have outstanding strengths and weakness, the latter holds greater potential for achieving maximal coverage with minimal resources. The paper then proceeds to describe the structure of cognitive models of locative space and to specify how such models can be built from 3-D geometric models.

This description is based on a cognitively motivated implementation called SEE-TELL. SEE-TELL takes as its input a 3-D geometric model and outputs a proposition of the form locative (referent, relatum). The function which maps the input to the output is a two part heuristic procedure. The first part determines the referent and relatum and the second part assigns the locative predicate. The system can assign the predicates on, right-of/left-of, and front-of/back-of. On and right-of/left-of are, respectively, illustrative of invariant and variant locatives. Front-of/back-of allows for a conflict between an ego/observer and a canonical object. These three situations are thought to cover the different classes of problems that can arise in assigning any locative.

The paper concludes by summarizing the findings, identifying the shortcomings and limitations, and making suggestions for future work.

Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.

This paper describes the task/episode accumulation model (TEA model) of non-routine mechanical design, which was developed after detailed analysis of the audio and video protocols of five mechanical designers. The model is able to explain the behavior of designers at a much finer level of detail than previous models. The key features of the model are (a) the design is constructed by incrementally refining and patching an initial conceptual design, (b) design alternatives are not considered outside the boundaries of design episodes (which are short stretches of problem solving aimed at specific goals), (c) the design process is controlled locally, primarily at the level of individual episodes. Among the implications of the model are the following: (a) CAD tools should be extended to represent the state of the design at more abstract levels, (b) CAD tools should help the designer manage constraints, and (c) CAD tools should be designed to give cognitive support to the designer.

In this report the architecture of an intelligent design machine capable of performing routine design in different design domains is described. This machine is crafted to operate as a part of a larger system driven by a human designer. Notable features are its use of best-first search strategies for problem-solving control and its ability to adjust problem-solving control strategies, perform automated redesign following specification changes, and resolve constraint violations using domain knowledge. The claims made for this machine are analyzed and it is argued that these claims are founded on established principles of design for intelligent systems. An implementation of this architecture in a rule-based system named Proteus is discussed and its operation is examined using as an example the domain of register-transfer-level computer design.

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 develops a general framework for knowledge-based computer tools that promote simultaneous engineering in mechanical design. Design compatibility analysis (DCA) serves as the underlying concept for these knowledge-based systems. DCA focuses on the compatibility between the design requirements (specification) and the proposed design, evaluates the design based on the compatibility knowledge of experts, gives justifications for the evaluation, and suggest improvements. DCA accommodates a product's various life-cycle issues (e.g. functionality, manufacturability, reliability) with a unified focus, i.e. compatibility, and thus helps designers to incorporate these issues at the early stages of design (simultaneous engineering). The resulting framework not only serve as the basis for various design expert systems but will also enhance our understanding of the life-cycle design issues. We illustrate the proposed method with two examples: system design of power generation plants and design for assembly (DFA) of mechanical products.