Extending product life is one of the hopeful approaches to reduce the environmental issue, which is one of the most critical issues of today. However, many products are thrown away because of obsolescence of functions and their performance. Therefore, we should design products to be functionally upgradable. Moreover, such upgradable products may create business chances at later stages of product life cycles. The objective of this research is to propose a design methodology for upgradability. This methodology employs a functional modeling scheme, FBS modeling, because upgrade design is a distinctive application of functional design that aims at maximizing functional flexibility with minimal structural changes after the product is manufactured. Here, the functional flexibility refers to an ability of a product to adapt its functions to changes of user needs. This paper proposes and models design processes and design operations in the upgrade design. Especially, the methodology supports finding out candidates of modifications of the function structure and configuration of a platform, which is common structure of a product among several generations, and upgrade modules. One of its central issues of upgrade design is treatment of future uncertainty. For this purpose, we propose two design strategies: delayed selection of components, and expanding and shrinking platform. A prototype system and a case study of upgrade design for a vacuum cleaner are also illustrated. The case study indicates that the system succeeded in systematically supporting a designer to execute the design methodology. Regarding the functional design, as an extension of FBS modeling, this paper proposes a method to relate abstract entity concepts in FBS modeling to concrete components through a quantitative behavior model and range calculation, in addition to deployment of FBS modeling for the design methodology.

Fixation prevents the associations that are bridges to new designs. The inability to see alternative solutions, or even to see how to map known solutions onto current problems, is a particularly acute problem in the design of software-intensive systems. Here, we explored two related ways of liberating fixated thinking: abstracting and rerepresenting. Although both techniques helped designers generate original ideas, not all the added ideas fit the problem constraints. We discuss ways the results might be used to generate reflective design aids that help designers to first generate original ideas and later prune them.

Shape annealing, a computational design method applied to structural design, has been extended to the design of traditional and innovative three-dimensional domes that incorporate the design goals of efficiency, economy, utility, and elegance. In contrast to deterministic structural optimization methods, shape annealing, a stochastic method, uses lateral exploration to generate multiple designs of similar quality that form a structural language of solutions. Structural languages can serve to enhance designer creativity by presenting multiple, spatially innovative, yet functional design solutions while also providing insight into the interaction between structural form and the trade-offs involved in multi-objective design. The style of the structures within a language is a product of the shape grammar that defines the allowable structural forms and the optimization model that provides a functional measure of the generated forms to determine the desirable designs. This paper presents an application of geodesic dome patterns that have been embodied in a shape grammar to define a structural language of domes. Within this language of domes, different dome styles are generated by changing the optimization model for dome design to include the design goals of maximum enclosure space, minimum surface area, minimum number of distinct cross-sectional areas, and visual uniformity. The strengths of the method that will be shown are 1) the generation of both conventional domes similar to shape optimization results and spatially innovative domes, 2) the generation of design alternatives within a defined design style, and 3) the generation of different design styles by modifying the language semantics provided by the optimization model.

Today's economy exhibits a growing trend toward highly specialized solution providers cooperatively offering configurable products and services to their customers. This paradigm shift requires the extension of current standalone configuration technology with capabilities of knowledge sharing and distributed problem solving. In this context a standardized configuration knowledge representation language with formal semantics is needed in order to support knowledge interchange between different configuration environments. Languages such as Ontology Inference Layer (OIL) and DARPA Agent Markup Language (DAML+OIL) are based on such formal semantics (description logic) and are very popular for knowledge representation in the Semantic Web. In this paper we analyze the applicability of those languages with respect to configuration knowledge representation and discuss additional demands on expressivity. For joint configuration problem solving it is necessary to agree on a common problem definition. Therefore, we give a description logic based definition of a configuration problem and show its equivalence with existing consistency-based definitions, thus joining the two major streams in knowledge-based configuration (description logics and predicate logic/constraint based configuration).

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.

Representing objects and their interactions can be quite challenging when an application requires many complicated, interconnected objects that are restricted in how they can be instantiated. In this paper, we present our approach to conceptual modelling. We have used this approach with success in a number of applications, the largest of which is the PROSE family of configurators. PROSE was first deployed in 1990 and has been used to configure over 4 billion dollars worth of AT&T and Lucent telecommunications equipment. We will discuss our approach to conceptual modelling, which is based on knowledge representation, show how it meets our representation and reasoning needs, and then discuss the relative merits of the approach.2

This paper describes the technical principles and representation behind the constraint-based, automated configurator COCOS. Traditionally, representation methods for technical configuration have focused either on reasoning about structure of systems or quantity of components, which is not satisfactory in many target areas that need both. Starting from general requirements on configuration systems, we have developed an extension of the standard CSP model. The constraint-based approach allows a simple system architecture, and a declarative description of the different types of configuration knowledge. Knowledge bases are described in terms of a component-centered knowledge base written in an object-oriented representation language with semantics directly based on the underlying constraint model. The approach combines a simple, declarative representation with the ability to configure large-scale systems and is in use for actual production applications.

This paper is intended to serve as part of the context in which the other papers in this special issue should be read. Its main goal is to revisit the basic definition of the configuration task, on which many people depend, to show some of its flaws, and to point out how it shapes thinking about the problem. We are concerned about characterizing the reasoning processes used to produce a configuration.

In the push for new vehicle designs, the distinctions between vehicle classes are quickly becoming blurred. We use shape grammars to quantify the differences between vehicle classes through the application of class-specific rules and the constraint of rule applications to within parametric ranges determined for each class. This allows for the development of new vehicle forms that clearly fall within a class or purposefully cross over the boundaries of classes and mix rules and ranges to create unique and interesting cross-over vehicles.

Analogy is a core cognition process used to produce inferences as well as new ideas using previous knowledge and experience. Ontology is a formal representation of a set of domain concepts and their relationships. The use of analogy and ontology in design activities to support design creativity have previously been explored. This paper explores an approach to construct ontologies with sufficient richness and coverage to support reasoning over real-world datasets for prompting creative idea generation. This approach has been implemented into a computational tool for assisting designers in generating creative ideas during the early stages of design. The tool, called “the Retriever”, has been developed based on ontology by embracing the aspects of analogical reasoning. A case study has indicated that the tool can be effective and useful for idea generation. The results have indicated that the tool, in its current formulation, can significantly improve the fluency and flexibility of idea generation and the usefulness of ideas, as well as slightly increase the originality of ideas, for the case study concerned.

The goal of this paper is to raise awareness and generate discussion about research methodology in engineering design. Design researchers are viewed as a single communicating community searching for scientific theories of engineering design; that is, theories that can be tested by formal methods of hypothesis testing. In the paper, the scientific method for validating theories is reviewed, and the need for operational definitions and for experiments to identify variables and meaningful abstractions is stressed. The development of a design problem taxonomy is advocated. Generating theories is viewed as guided search. Three types of design theories are described: prescriptive, cognitive descriptive, and computational. It is argued that to seek prescriptions is premature and that, unless the human and institutional variables are reduced to knowledge and control, cognitive descriptive theories will be impossibly complex. A case is made for a computational approach, though it also shown that computational and cognitive research approaches can be mutually supportive.

In equipment monitoring and diagnostics, it is very important to distinguish between a sensor failure and a system failure. In this paper, we develop a comprehensive methodology based on a hybrid system of AI and statistical techniques. The methodology is designed for monitoring complex equipment systems, which validates the sensor data, associates a degree of validity with each measurement, isolates faulty sensors, estimates the actual values despite faulty measurements, and detects incipient sensor failures. The methodology consists of four steps: redundancy creation, state prediction, sensor measurement validation and fusion, and fault detection through residue change detection. Through these four steps we use the information that can be obtained by looking at: information from a sensor individually, information from the sensor as part of a group of sensors, and the immediate history of the process that is being monitored. The advantage of this methodology is that it can detect multiple sensor failures, both abrupt as well as incipient. It can also detect subtle sensor failures such as drift in calibration and degradation of the sensor. The four-step methodology is applied to data from a gas turbine power plant.

Contributions of general design theory (GDT) proposed by Yoshikawa for the development of advanced CAD (computer-aided design) and for innovative design from the research results of a group at the University of Tokyo are illustrated. First, the GDT that formalizes design knowledge based on axiomatic set theory is reviewed. Second, this theoretical result is tested against experimental work on design processes. Although in principle the theoretical results agree with the experimental findings, some problems can be pointed out. From these problems a new design process model, called the refinement model, is established, which has better agreement with the experimental findings. This model implies three guiding principles in developing a future CAD system. One is that future CAD requires a mechanism for physics-centered modeling and multiple model management. Second, a mechanism for function modeling is also required, and the FBS (function-behavior-state) modeling is proposed. Third, intention modeling is also proposed for recording decision-making processes in design. These advanced modeling techniques enable creative, innovative designs. As an example, the design of self-maintenance machines is illustrated. This design example utilizes design knowledge intensively on a knowledge-intensive CAD. This is a new way of engineering and can be called knowledge-intensive engineering. The design of self-maintenance machines is, therefore, an example of knowledge-intensive design of knowledge-intensive products, which demonstrates the power of the design methodology derived from the GDT.

This paper is an informal description of some recent insights about what a device function is, how it arises in response to needs, and how function arises from the structure of a device and the functions of its components. These results formalize and clarify a set of contending intuitions about function that researchers have had. The paper relates the approaches, results, and goals of this stream of research, called functional representation (FR), with the functional modeling (FM) stream in engineering. Despite the occurrence of the term function in the two streams, often the results and techniques in the two streams appear not to have much to do with each other. I argue that, in fact, the two streams are performing research that is mutually complementary. FR research provides the basic layer for device ontology in a formal framework that helps to clarify the meanings of terms such as function and structure, and also to support representation of device knowledge for automated reasoning. FM research provides another layer in device ontology, by attempting to identify behavior primitives that are applicable to subsets of devices, with the hope that functions can be described in those domains with an economy of terms. This can lead to useful catalogs of functions and devices in specific areas of engineering. With increased attention to formalization, the work in FM can provide domain-specific terms for FR research in knowledge representation and automated reasoning.

This paper examines the use of language, specifically verbs, as stimuli for concept generation. Because language has been shown to be important to the reasoning process in general as well as to specific reasoning processes that are central to the design process, we are investigating the relationship between language and conceptual design. The use of language to facilitate different stages of the design process has been investigated in the past. Our previous work, and the work of others, showed that ideas produced can be expressed through related hierarchical lexical relationships, so we investigated the use of verbs within these hierarchical relationships as stimuli for ideas. Participants were provided with four problems and related verb stimuli, and asked to develop concepts using the stimuli provided. The stimuli sets were generated by exploring verb hierarchies based on functional words from the problem statements. We found that participants were most successful when using lower level (more specific) verbs as stimuli, and often higher level general verbs were only used successfully in conjunction with lower level verbs. We also observed that intransitive verbs (verbs that cannot take a direct object) were less likely to be used successfully in the development of concepts. Overall, we found that the verb chosen as stimulus by the participant directly affects the success and the type of concept developed.

Selective disassembly is an important issue in industrial and mechanical engineering for environmentally conscious manufacturing. This paper presents an intelligent selective disassembly approach based on ant colony algorithms, which take inspiration from the behavior of real ant colonies and are used to solve combinatorial optimization problems. For diverse assemblies, the algorithm generates different amounts of ants cooperating to find disassembly sequences for selected components, minimizing the reorientation of assemblies and removal of components. A candidate list that is composed of feasible disassembly operations, which are derived from a disassembly matrix of products, guides sequence construction in the implicit solution space and ensures the geometric feasibility of sequences. Preliminary implementation results show the effectiveness of the proposed method.

The ability of a CAD system to perceive a three-dimensional model depicted in a single freehand sketch presents the practical possibility of bringing numerous established analysis tools into the early stages of design to institute conceptual analysis. In this article we hypothesize that the key to enabling systems to reason and communicate about conceptual design is the language of sketching. We explore this approach, outline the basic algorithms required, and provide several examples of an implemented system.

When engineering content is created and applied during the product life cycle, it is often stored and forgotten. Current information retrieval approaches based on statistical methods and keyword matching are not effective in understanding the context of engineering content. They are not designed to be directly applicable to the engineering domain. Therefore, engineers have very limited means to harness and reuse past designs. The overall objective of our research is to develop an engineering ontology (EO)-based computational framework to structure unstructured engineering documents and achieve more effective information retrieval. This paper focuses on the method and process to acquire and validate the EO. The main contributions include a new, systematic, and more structured ontology development method assisted by a semiautomatic acquisition tool. This tool is integrated with Protégé ontology editing environment; an engineering lexicon (EL) that represents the associated lexical knowledge of the EO to bridge the gap between the concept space of the ontology and the word space of engineering documents and queries; the first large-scale EO and EL acquired from established knowledge resources for engineering information retrieval; and a comprehensive validation strategy and its implementations to justify the quality of the acquired EO. A search system based on the EO and EL has been developed and tested. The retrieval performance test further justifies the effectiveness of the EO and EL as well as the ontology development method.

In this paper we present a formal characterization of the engineering concepts of behavior and function of technical artifacts. We capture the meanings that engineers attach to these concepts by formalizing, within the formal ontology DOLCE, the five meanings of artifact behavior and the two meanings of function that Chandrasekaran and Josephson identified in 2000 within the functional representation approach. We begin our formalization by reserving the term “behavior” of a technical artifact as “the specific way in which the artifact occurs in an event.” This general notion is characterized formally, and used to provide definitions of actual behaviors of artifacts, and the physically possible and physically impossible behaviors that rational agents believe that artifacts have. We also define several other notions, for example, input and output behaviors of artifacts, and then show that these ontologically characterized concepts give a general framework in which Chandrasekaran and Josephson's meanings of behavior can be explicitly formalized. Finally we show how Chandrasekaran and Josephson's two meanings of artifact functions, namely, device-centric and environment-centric functions, can be captured in DOLCE via the concepts of behavioral constraint and mode of deployment of an artifact. A more general goal of this work is to show that foundational ontologies are suited to the engineering domain: they can facilitate information sharing and exchange in the various engineering domains by providing concept structures and clarifications that make explicit and precise important engineering notions. The meanings of the terms “behavior” and “function” in domains like designing, redesigning, reverse engineering, product architecture, and engineering knowledge bases are often ambiguous or overloaded. Our results show that foundational ontologies can accommodate the variety of denotations these terms have and can explain their relationships.

Research on design and analysis of complex systems has led to many functional representations with several meanings of function. This work on conceptual design uses a family of representations called structure–behavior–function (SBF) models. The SBF family ranges from behavior–function models of abstract design patterns to drawing–shape–SBF models that couple SBF models with visuospatial knowledge of technological systems. Development of SBF modeling is an instance of cognitively oriented artificial intelligence research that seeks to understand human cognition and build intelligent agents for addressing complex tasks such as design. This paper first traces the development of SBF modeling as our perspective on design evolved from that of problem solving to that of memory and learning. Next, the development of SBF modeling as a case study is used to abstract some of the core principles of an artificial intelligence methodology for functional modeling. Finally, some implications of the artificial intelligence methodology for different meanings of function are examined.