On June 20th, 1992, a workshop entitled the “Preliminary Stages of Engineering Analysis and Modeling” was held at Carnegie Mellon University, Pittsburgh, U.S.A. The workshop was organized in conjunction with the 2nd International Conference on Artificial Intelligence in Design (AID ’92), and aimed to provide a forum for engineers and computer scientists to examine how AI techniques could be exploited effectively in assisting engineers with the preliminary stages of engineering analysis and modeling. This report has two objectives; firstly it serves as a summary of the workshop and secondly it introduces four of the workshop papers that have been rewritten and extended for this issue.

A main theme of concurrent engineering is the effective communication between relevant disciplines. Any computer tools for concurrent engineering must provide sufficient constructs and strategies for this purpose. This paper describes the AGENTS system, a domain-independent general-purpose Object-Oriented Prolog language for cooperating expert systems in concurrent engineering design. Emphasis is placed on demonstrating the use of the AGENTS constructs for distributed knowledge representation and the cooperation strategies for communication, collaboration, conflict resolution, and control. A simple case study is presented to illustrate the balance between simplicity and flexibility.

Design has been extensively studied by artificial intelligence researchers for many years. These studies have resulted in a large number of design tools that perform interesting tasks. Understanding the capabilities of these tools is, however, very difficult, which seriously impedes progress in the field. A better understanding of different tools can be achieved by analyzing the knowledge use of existing tools. Such an analysis of six configuration design tools is presented. This results in a model of configuration design that shows significant similarity in the tasks performed by these tools.

Serving as a guest editor of this special journal issue, I review in this introductory paper some of the important issues concerning applying case-based reasoning (CBR) techniques to the design domain. Through discussions of these issues, I attempt to give the current status of the case-based design field, with surveys of existing case-based design systems and a summary from the first workshop held on this subject. For those who are less familiar with CBR, this paper also contains a section on what CBR method is and how it is contrasted with other reasoning approaches. The last section of this article introduces the papers included in this issue.

While previous work in automated process planning established plan ordering on an empirical basis alone, we derive our process plans based on the Holding-Under-Uncertainty Principle. We will introduce the principle, and we will describe the operational requirements needed to make this principle implementable in practice. The principle takes into account the form and geometric tolerances needed to locate features in deriving plan steps. Rather than just focusing on technological features, our planning strategy is controlled by the geometric relationships among features. By implementing a constraint propagation paradigm, we ensure that the tolerances accumulated in generating the part geometry remain within the tolerances specified by the design.

A system that integrates design and planning for mechanical assemblies is presented. The system integrates neural network computing that captures designer's design concept and rule-based system to generate a task-level assembly plan automatically. The design concept is expressed by a standard pattern format representing qualitative assembly information. A neural network model together with feature-based model translates the input pattern into a preliminary boundary representation (B-rep). Based on a refinement B-rep assembly representation, assembly plans are generated for practical use in a single-robot assembly workcell. A feasible assembly plan that minimizes tool changes and subassembly reorientations is generated from the system. A robust part collision detection algorithm to generate the precedence relationships among the assembly's components is included in the system. By contrast with many assembly planning systems that used a prolonged question-and-answering session or required knowledge beyond what is typically available in the design database, an automated assembly planning system presented here draws input relationships directly from the conceptual design and the geometry of the assembly. The system developed under this study extracts all reasoning information from the product model and permits the components to be assembled in a multitude of directions. Several experiments illustrate the effectiveness of the designed assembly planning system.

This paper discusses a work in progress in the development of computer tools for qualitative modeling analysis and evaluation of conceptual structural designs. In the conceptual design stage the description of a structure is incomplete and imprecise, and does not permit the use of traditional numerical analysis tools. We describe a prototype system, QLRS, for qualitative evaluation of lateral load resistance in frames. The primary goal of the evaluation of structural response is to identify undesirable structural behavior. In QLRS, the evaluation process consists of three basic tasks. (1) identification of the story and structure models comprising the lateral load resisting system. We term this task structural system interpretation. (2) Qualitative analysis of the story and structure models, and approximate evaluation of the story drifts. We term this task structural behavior interpretation. (3) Assessment of the performance of the lateral load resisting system, in which the results of the structural system interpretation and the structural behavior interpretation are compared against the requirements for complete load path and relative story drift. Currently, QLRS is able to reason about load path discontinuities and soft-story behavior problems in 2-D moment resisting frames.

We believe that case-based reasoning (CBR) will prove applicable to design, at least in part, as we have seen designers making extensive use of past cases. Construction of useful systems, however, requires the resolution of many open issues. In this paper we consider three issues in particular: (1) What sort of content should be captured in a design case? (2) How should the content of a complex case be segmented into chunks for use? (3) How should the resulting chunks be indexed for retrieval? These are among the issues we are seeking to address through construction of Archie-II, a case-based aid for conceptual design in architecture. In addition to our approaches to these issues, this paper also discusses our strategic choice to build a design aiding system as opposed to a system that generates designs on its own.

In solving a new design problem, the case-based reasoning paradigm provides a process model where previous experience in the form of multiple, individual design situations can be used in a new design context. Design synthesis presents challenges to current methodologies of CBR in the application of the various approaches to case memory organization, indexing, selection and transformation. The focus of this paper is on the transformation process. Multiple types of design knowledge are essential to derive a new design solution. A hybrid case-based design process model, CADSYN, is proposed to integrate specific design situations and generalized domain knowledge, where specific cases are represented as attribute-value pairs and domain knowledge is represented by generalized design concepts and constraints. Case transformation is treated as a constraint satisfaction problem, where a specific design case provides a starting point for a new design problem and constraints are used to revise the case for consistency with the new context.

In this paper, we show how a temporal constraint propagation technique can be embedded in the machine sequencing approach for solving the job shop scheduling problem. The temporal constraint propagation algorithm propagates the precedence constraints and machine interference constraints to reduce the search space generated by the machine sequencing approach. Further, by making use of the temporal nature of the job shop scheduling, efficient algorithms to propagate precedence constraints and machine interference constraints are developed. Experimental results reveal that embedding constraint propagation in the machine sequencing approach significantly reduces the computation time more than by just using the machine sequencing approach alone. Further, the proposed temporal constraint propagation algorithms provide an order of magnitude improvement on the computation time over the conventional constraint propagation algorithm.

A number of automated reasoning systems find their basis in process control engineering. These programs are often model-based and use individual frames to represent component functionality. This representation scheme allows the process system to be dynamically monitored and controlled as the reasoning system need only simulate the behavior of the modeled system while comparing its behavior to real-time data. The knowledge acquisition task required for the construction of knowledge bases for these systems is formidable because of the necessity of accurately modeling hundreds of physical devices. We discuss a novel approach to the capture of this component knowledge entitled automated knowledge generation (AKG) that utilizes constraint mechanisms predicated on physical behavior of devices for the propagation of truth through the component model base. A basic objective has been to construct a complete knowledge base for a model-based reasoning system from information that resides in computer-aided design (CAD) databases. If CAD has been used in the design of a process control system, then structural information relating the components will be available and can be utilized for the knowledge acquisition function. Relaxation labeling is the constraint-satisfaction method used to resolve the functionality of the network of components. It is shown that the relaxation algorithm used is superior to simple translation schemes.

Arguing that design is a social process, we expand the meaning of modeling and analysis to include all activities facilitating continual refinement and criticism of the design requirements, process and solutions. We do not assume any a priori methods for modeling or analysis; rather, we provide a framework and an approach to study designers and give them whatever modeling and analysis capabilities they choose. Our approach is the basis for a support tool, n–dim, currently under development.

We describe ACDS, an automatic diagnostic system. ACDS is capable of diagnosing faults on analog circuits in dynamic conditions. The circuit's dynamic behavior is studied by means of a series of intrastate simulations during which the qualitative state of the circuit does not change. An acquistion board collects the value of a set of quantities corresponding to accessible test points. These measurements are converted into qualitative values and are used for two purposes: first, to determine the state of the circuit components; second, to trigger the diagnostic procedure whenever a discrepancy between observed and predicted behavior is found. The main difficulty in this phase of measurement interpretation is in obtaining meaningful numerical-qualitative data conversion for values of quantities approaching a boundary between two different qualitative intervals. System performance has been verified through a number of simulations, which have shown the proposed approach to be efficient both in terms of localized faults and of flexibility in adapting to different circuits.

The architecture and implementation of a mechanical designer's assistant shell called DEJAVU is presented. The architecture is based on an integration of design and CAD with some of the more well known concepts in case-based reasoning (CBR). DEJAVU provides a flexible and cognitively intuitive approach for acquiring and utilizing design knowledge. It is a domain independent mechanical design shell that can incrementally acquire design knowledge in the domain of the user. DEJAVU provides a design environment that can learn from the designer(s) until it can begin to perform design tasks autonomously or semi-autonomously. The main components of DEJAVU are a knowledge base of design plans, an evaluation module in the form of a design plan system, and a blackboard-based adaptation module. The existance of these components are derived from the utilization of a CBR architecture. DEJAVU is the first step in developing a robust designer's assistant shell for mechanical design problems. One of the major contributions of DEJAVU is the development of a clean architecture for the utilization of case-based reasoning in a mechanical designer's assistant shell. In addition, the components of the architecture have been developed, tailored or modified from a general CBR context into a more synergistic relationship with mechanical design.

This paper describes work in progress aimed at developing an interactive modeling tool that assists engineers with the task of physical modeling in finite element analysis. Physical modeling precedes the numerical simulation phase of finite element analysis and involves applying modeling idealizations to real world physical systems so that complex engineering problems are more amenable to numerical computation. In the paper, the nature of physical modeling is explored, a cognitive model of how engineers are thought to model complex problems is described and based on this model a knowledge-based modeling assistant is proposed. The AI approach taken is based on Chandrasekaran's propose-critique-modify design model adapted for the task of physical modeling. Within this framework, the AI paradigms of case-based reasoning, derivational analogy and model-based reasoning are exploited. By representing fundamental thermal modeling scenarios as cases, complex physical systems can be modeled in a piecewise fashion. Derivational analogy permits generative adaptation of retrieved cases by using model-based engineering traces thereby providing a basis for critiquing case solutions. An initial prototype is described which has been implemented for the domain of convection heat transfer analysis.

In preliminary design, the details of a structure are insufficient to warrant the use of numeric tools traditionally used in structural analysis. However, an accurate prediction of the behavior of a structure and its components in the preliminary design phase can have a significant effect on the final design process in reducing the number of alternative solutions, avoiding the costly design revisions, and improving the quality of design. Presently, there are few tools available for preliminary analysis of structures. This study represents an initial effort towards the development of a tool that can be used in the conceptual design stage to qualitatively evaluate the behavior of a structure.

This paper describes a prototype system, QStruc, for qualitative structural analysis, which combines first principles in structural engineering and experiential knowledge of structural behavior. The purposes of QStruc are: (1) to generate qualitative models from the schematics of a structure; and (2) to infer the qualitative response of the structure in terms of deflected shape, moments, and reactions. The qualitative analysis strategy employs: (1) a greedy depth-first approach that tries to expand the derived response as much as possible from known parameter values; (2) a causal ordering mechanism, which enables the system to identify the solution path for the qualitative analysis; (3) qualitative calculus, which enables the qualitative evaluation of the physical quantities of the causal model that describes the behavior of the structure; and (4) Quantity Lattice (Simmons, 1986) which enables the system to reason about partial ordering among physical quantities and to reduce some of the ambiguous conclusions caused by the impreciseness of the information. Examples are provided to illustrate the effectiveness and limitations of the prototype system.

PANDA, the Pumper Apparatus Novice Design Assistant, is a case-based design system developed to assist firefighters who wish to design their pumper engines. In contrast to other such systems, PANDA addresses the unique needs of a novice, non-specialist who performs design in a highly specialized domain, where the design is decomposable into elements which each fulfill their own identifiable function. In PANDA we study how to create an interactive case-based design assistant that can understand the needs and desires of a non-specialist designer and can translate them into formal specifications; that can provide assistance by using case-based design methodologies; that can deal with non-functional design criteria such as aesthetics and traditional practices; and that can guide the novice designer by discussing alternatives, tradeoffs, and adaptations. Our prototypical system verifies our methodological approach to supporting novice design activities.

The task of modeling, i.e., of creating a set of equations that can be used to predict the behavior of a physical object, is a key step in engineering analysis. This paper describes a computer system, MSG, for generating mathematical models to analyze physical systems involving heat transfer behavior. MSG is motivated by the need for modeling in an automated design process. The models are sets of equations which may include algebraic equations, ordinary differential equations and partial differential equations. MSG uses the strong domain theory to guide model construction in three sequential tasks: identify regions of interests on an object, determine relevant heat transfer and energy storage processes, and transform these processes into equations. The decisions in these tasks are guided by estimates of variation in temperature and material property, and the relative strengths of heat transfer processes.

When model-based planning systems are scaled up to deal with full-sized industrial projects, the resulting complexity in the project-specific model and production plan can create serious problems, not only in dealing with such complexity computationally, but also in user-acceptance. In the model-based planning system described in this paper, activities are dynamically generated, inherently at the detailed level of individual physical components. However, it is possible to intelligently group together collections of components which would be common to realistic work packages, and hence schedule on the basis of virtual components existing within an abstraction hierarchy. This paper describes a technique of project planning within an integrated design/planning system, which exploits fundamental knowledge of engineered systems and provides powerful and flexible planning functionality.

This paper describes an intelligent alarm processing expert system which is integrated in a large Supervisory Control and Data Acquisition system for power distribution networks. The expert system works as an operator support tool by diagnosing network disturbances and device malfunctions. The expert system is based on a hierarchic, multi-level problem-solving architecture, integrating both model-based and heuristic techniques acting upon an object-oriented data structure. Several enhancements have been designed and implemented to enable the system to perform its task online and real-time. The expert system covers online processing of real-time data and intelligent alarm processing, as well as the automatic creation and update of the knowledge base. It consists of approximately 25000 objects (units) and 190 rules. The system uses the expert system tool KEE, runs on SUN workstations, and is integrated in the Supervisory Control and Data Acquisition system via LAN. The expert system was implemented for the Public Utilities Board Singapore controlling its 22 kV distribution network and has been online since November 1990.