The ability to understand the implications of the geometry of solid objects is an important aspect of intelligent behavior. This paper presents work designed to enable reasoning with relatively loose conceptualizations of geometry. The method operates by comparing target geometry to known geometry, and involves the manipulation of models based upon prototypes. In particular, three techniques of simplification, approximation, and transformation are discussed. Finally, an application of the method to the domain of stress concentration prediction is presented.

Although the case-based reasoning (CBR) process is domain dependent, certain aspects of it can readily be captured into a generic framework which in turn can be applied to various engineering domains. One such exercise that has been carried out is described here. In this paper, we present the notion that CBR can be formalized and applied in a specialized framework in an integrated knowledge-based environment. We first analyze the CBR process to abstract the steps involved in the development of a CBR system. We then propose a framework in which most of these steps are formalized so that they can be applied in a domain-independent manner. The salient features of this framework, called CASETOOL (CASE-based reasoning TOOL-kit), are then described. The highlight of this approach is the use of a concept called design criticism in the CBR process. The versatility of the tool is demonstrated through an application from the bridge engineering domain.

Research in computer-aided design/engineering (CAD/E) has focused on enhancing the capability of computer systems in a design environment, and this work has continued in this trend by illustrating the use of the Dempster-Shafer theory to expand the computer’s role in a CAD/E environment. An expert system was created using Dempster-Shafer methods that effectively modeled the professional judgment of a skilled tribologist in the selection of rolling element bearings. A qualitative and symbolic approach was used, but access to simple quantitative models was provided to the expert system shell. Although there has been significant discussion in the literature regarding modification/improvement of the Dempster-Shafer theory, Shafer’s theories were found adequate in all respects for replicating the expert’s judgment. However, an understanding of the basic theory is required for interpreting the results.

Most design tasks involve the management of conflict. Conflict arises when contradictory requirements are imposed upon characteristics of artifacts, upon the process of their creation and/or upon their intended use. Even individual design requires trade-offs because of competing design criteria, such as functionality, safety, cost, and social acceptance. The ability of designers to avoid or minimize conflict through judicious tradeoffs, careful negotiations and other methods become their most valuable skills.

The feasibility and relative merits of integrating knowledge-based systems (KBSs) and artificial neural networks (ANNs) for application to engineering problems are presented and evaluated. The strength of KBSs lies in their ability to represent human judgment and solve problems by providing explanations from and reasoning with heuristic knowledge. ANNs demonstrate problem solving characteristics not inherent in KBSs, including an ability to learn from example, develop a generalized solution applicable to a range of examples of the problem, and process information extremely rapidly. In this respect, KBSs and ANNs are complementary, rather than alternatives, and may be integrated into a system that exploits the advantages of both technologies. The scope of application and quality of solutions produced by such a hybrid extend beyond the boundaries of the individual technologies. This paper identifies and describes how KBSs and ANNs can be integrated, and provides an evaluation of the advantages that will accrue in engineering applications.

Computational simulation of physical systems generally requires human experts to set up a simulation, run it, evaluate the quality of the simulation output, and repeatedly invoke the simulator with modified input until a satisfactory output quality is achieved. This reliance on human experts makes use of simulators by other programs difficult and unreliable, though invocation of simulators by other programs is critical for important tasks such as automated engineering design optimization. Presented is a framework for constructing intelligent controllers for computational simulators that can automatically detect a wide variety of problems that lead to low-quality simulation output, using a set of evaluation methods based on knowledge of physics and numerical analysis stored in a data/knowledgebase of models and simulations. An experimental implementation of this framework in an intelligent automated controller for a widely used computational fluid dynamics simulator is described.

Sometime in the mid-1980s, in an urban restaurant some-where in the United States, I was sitting next to a Japanese engineer who was talking to a U.S. engineer. The following is the record of their conversation that I made with my Sony Walkman.

Interval arithmetic has been extensively applied to systems of linear equations by the interval matrix arithmetic community. This paper demonstrates through simple examples that some of this work can be viewed as particular instantiations of an abstract “design operation,” the RANGE operation of the Labeled Interval Calculus formalism for inference about sets of possibilities in design. These particular operations promise to solve a variety of design problems that lay beyond the reach of the original Labeled Interval Calculus. However, the abstract view also leads to a new operation, apparently overlooked by the matrix mathematics community, that should also be useful in design; the paper provides an algorithm for computing it.

It is now becoming increasingly common for products to be designed via an interdisciplinary approach, particularly when an engineering enterprise adopts the concurrent engineering approach. Essentially, this implies that several heterogeneous sources of knowledge are simultaneously involved during the design phase of the product. The benefits often cited as a result of such an approach are increased quality and innovation, decreased time-to-market, and lower manufacturing costs. However, the approach also accentuates the problem of how to handle and manage conflicts, which frequently arise due to several factors as discussed in this paper. This paper also describes a system called Schemebuilder, which is an integrated design workbench aimed at supporting the conceptual and embodiment phases of interdisciplinary systems design, and its mechanisms for handling and resolving conflicts.

Collaborative design conflicts are an important type of process “exception,” that is, a real-life contingency such as a process change, execution error, or missed opportunity that leads to suboptimal performance of a collaborative process. This paper presents an integrated computational approach to collaborative process exception handling that avoids important weaknesses in current conflict management methods through the synergistic integration of conflict, workflow, and rationale technology. The approach is based upon an inclusive dependency language plus coordination services for dependency capture, process enactment, and exception handling. An initial implementation of this method called “iDCSS” is presented and challenges for future evolution of this technology are identified.

Qualitative physics, a subfield of artificial intelligence, adapts intuitive and non-numerical reasoning for descriptive analysis of physical systems. The application of a set-based qualitative algebra to matrix analysis (QMA) allows for the development of a qualitative matrix stiffness methodology for linear elastic structural analysis. The unavoidable introduction of arithmetic ambiguity requires the reinforcement of physical constraints complementary to standard matrix operations. The overall analysis technique incorporates such constraints within the set-based framework with logic programming. Truss, beam, and frame structures demonstrate constraint relationships, which prune spurious solutions resulting from qualitative arithmetic relations. Though QMA is not a panacea for all structural applications, it provides greater insight into new notions of physical analysis.

This paper defines, develops algorithms for, and illustrates the utility in design of a class of mathematical operations. These accept as inputs a system of linear constraint equations, Ax = b, an interval matrix of values for the coefficients, A, and an interval vector of values for either x or b. They return a set of values for the “domain” of the other vector, in the sense that all combinations of the output vector values set and values for A, when inserted into the constraint equation, correspond to values for the input vector that lie within the input interval. These operations have been mostly overlooked by the interval matrix arithmetic community, but are mathematically interesting and useful in the design, for example, of structures.

The assessment of damage of structural concrete elements relies on the engineering judgment of the person in charge of the inspection and evaluation. This paper describes a rule-based prototype expert system developed to assist an engineer engaged in the task of assessing postearthquake damage to structural concrete elements in the task of providing guidance for inspection as well as criteria for evaluation and courses of action to take afterwards. The expert system, called DASE, identifies the most likely failure modes that may be developed by columns or beams of a damaged building, determines the severity of damage and, suggests immediate actions to take afterwards, such as rehabilitation procedures or tests, to maintain an acceptable local safety level. Floor damage classification and restoration guidelines can also be provided, assuming that all of the structural components in a building's floor have been inspected. The Analytic Hierarchy Process has been implemented as the overall framework to determine the severity of damage, since it allows judgments and personal values to be represented in a systematic and rational manner. DASE provides graphics that customize the user interface and an explanation facility. The knowledge acquisition process consisted primarily of the analysis of documented knowledge found through literature research.

A difficult task in concurrent engineering tasks is to provide appropriate manufacturability feedback to the designer in a timely manner. Many tools provide designers with cost and manufacturability evaluations, but they do not necessarily help the designer to identify what aspects of the design to change in order to improve it from a manufacturing perspective. A computer-assisted concurrent-engineering technology is described which identifies cost-critical tolerances in the design and generates cost-reducing design suggestions. The purpose of the system is to help focus the designer’s attention on the specific aspects of the design that influence manufacturing cost. This can aid the designer in optimizing the manufacturing costs of prismatic, one-off parts created on a CNC machining center. This work uses a program called the Manufacturing Evaluation Agent to produce cost-reducing design suggestions. The Manufacturing Evaluation is made up of two portions: a manufacturing planner and a suggestion generator. The manufacturing planner, P3, takes a design and generates a manufacturing constraint net that represents all manufacturing steps and their sequencing. Each constraint in the network has a label indicating the portions of the design (or manufacturing environment) that gave rise to that constraint. The Suggestion Generator analyzes the manufacturing constraint net to find the cost-critical areas, and uses the labels to find the portions of the design responsible for these cost-critical parts of the manufacturing plan. By pinpointing the cost-critical areas of the design and suggesting alternatives, the Manufacturing Evaluation Agent can help the de-signer to more quickly develop a superior design. This results in more rapid turnaround time, lower cost designs, and fewer engineering change orders once the design is sent to the factory floor.

This paper defines, develops algorithms for, and illustrates the design use of a class of mathematical operations. These operations accept as inputs a system of linear constraint equations, Ax = b, an interval matrix of values for the coefficients A, and an interval vector of values for either x or b. They return a set of values for the other variable that is “sufficient” in this sense. Suppose that ◯ is an interval of input vectors, and  an interval matrix. Then, one Sufficient-Points operation returns a set of vectors ~ such that for each b in ~, the set of x values that can be produced by inserting all the values of  into Ax = b is a superset of the input vector x. These operations have been partly overlooked by the interval matrix mathematics community, but are mathematically interesting and useful in the design, for example, of circuits.

Pareto optimality is a domain-independent property that can be used to coordinate distributed engineering agents. Within a model of design called Redux, some aspects of dependency-directed backtracking can be interpreted as tracking Pareto optimality. These concepts are implemented in a framework, called Next-Link, that coordinates legacy engineering systems. This framework allows existing software tools to communicate with each other and a Redux agent over the Internet. The functionality is illustrated with examples from the domain of electrical cable harness design.

This paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

Concurrent engineering is often viewed either from a technical point of view—that is, as a problem that can be solved by creating and integrating computer-based tools—or from an organizational point of view—that is, as a problem that can be solved by creating and reorganizing teams of designers. In this paper we argue that concurrent engineering requires both technical and organizational solutions, and we call the result concurrent design. We believe that the essence of concurrent design is the myriad of interactions that occur at the interfaces among all of the members of a design team and all their tools. Solving either the technical or organizational problems by assuming away the interactions will not solve the problems of concurrent design.

In this paper we present two case studies of concurrent design in practice that have changed our assumptions about design and which have changed our research agenda. We also present the evolution of concurrent design research at the Carnegie Mellon Engineering Design Research Center. In our research, we have designed, manufactured, and used our own tools as well as observed their use by others—where the tools include mobile computers, design analysis programs, and information organization tools. Through this process, we have learned about design education and design practice, and we have uncovered new issues for design research. We see the interactions among design research, practice, and education as essential to understanding concurrent design.

The paper describes a novel means of performing calculus operations on poorly understood engineering functions using networks of radial Gaussian neurons. The network architecture and training algorithm used for this purpose is described briefly. Once trained, a network can be converted into a form that provides the differential or integral of the learned function, by a simple substitution of the type of activation function used at the hidden neurons. A range of substitute activation functions, for conversion to first- and second-order partial differential and integral forms of the network, are derived. Following this, the technique is tested on a selection of calculus operations for two example civil engineering problems: assessing motion in tall structures; and modeling moisture penetration in porous materials. The converted networks produced in these experiments provide accurate models of the actual differentials and integrals of the function the original networks had been taught. A method of improving the accuracy of results by training the original network beyond the region in which the converted networks operate, is described. The paper concludes by identifying some areas for further development of the technique.