The field of research in design computing and cognition focuses on computational theories and systems that enact design. Design computing and cognition produces a unifying framework to model and explain design beyond the description of “design computing and cognition,” as in “design computing” and “design cognition” as two cognate disciplines. Research in design computing and cognition recognizes not only the essential relationship between human cognitive processes as models of computation but also how models of computation inspire conceptual realizations of human cognition in design. The articles in this Special Issue address the concomitant key areas of research in design computing and cognition: computational models of design, computational representations in design, computational design systems, and design cognition. The computationally inspired perspectives, metaphors, models, and theories that the papers deliver create a base for computing and cognition to (re)shape design practice and its role in design science and inquiry.

In engineering design, the end goal is the creation of an artifact, product, system, or process that fulfills some functional requirements at some desired level of performance. As such, knowledge of functionality is essential in a wide variety of tasks in engineering activities, including modeling, generation, modification, visualization, explanation, evaluation, diagnosis, and repair of these artifacts and processes. A formal representation of functionality is essential for supporting any of these activities on computers. The goal of Parts 1 and 2 of this Special Issue is to bring together the state of knowledge of representing functionality in engineering applications from both the engineering and the artificial intelligence (AI) research communities.

In engineering design, the end goal is the creation of an artifact, product, system, or process that fulfills some functional requirements at some desired level of performance. As such, knowledge of functionality is essential in a wide variety of tasks in engineering activities, including modeling, generation, modification, visualization, explanation, evaluation, diagnosis, and repair of these artifacts and processes. A formal representation of functionality is essential for supporting any of these activities on computers. The goal of Parts 1 and 2 of this Special Issue is to bring together the state of knowledge of representing functionality in engineering applications from both the engineering and the artificial intelligence (AI) research communities.

In this paper we lay the foundations for exchanging, adapting, and interoperating engineering analysis models (EAMs). Our primary foundation is based upon the concept that engineering analysis models are knowledge-based abstractions of physical systems, and therefore knowledge sharing is the key to exchanging, adapting, and interoperating EAMs within or across organizations. To enable robust knowledge sharing, we propose a formal set of ontologies for classifying analysis modeling knowledge. To this end, the fundamental concepts that form the basis of all engineering analysis models are identified, described, and typed for implementation into a computational environment. This generic engineering analysis modeling ontology is extended to include distinct analysis subclasses. We discuss extension of the generic engineering analysis modeling class for two common analysis subclasses: continuum-based finite element models and lumped parameter or discrete analysis models. To illustrate how formal ontologies of engineering analysis modeling knowledge might facilitate knowledge exchange and improve reuse, adaptability, and interoperability of analysis models, we have developed a prototype engineering analysis modeling knowledge base, called ON-TEAM, based on our proposed ontologies. An industrial application is used to instantiate the ON-TEAM knowledge base and illustrate how such a system might improve the ability of organizations to efficiently exchange, adapt, and interoperate analysis models within a computer-based engineering environment. We have chosen Java as our implementation language for ON-TEAM so that we can fully exploit object-oriented technology, such as object inspection and the use of metaclasses and metaobjects, to operate on the knowledge base to perform a variety of tasks, such as knowledge inspection, editing, maintenance, model diagnosis, customized report generation of analysis models, model selection, automated customization of the knowledge interface based on the user expertise level, and interoperability assessment of distinct analysis models.

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.

The present paper studies the process of information generation during design and focuses on the relationship between the information importance and the required effort for its generation. Multiple associative relationships among design entities (handled as design descriptors) are used to represent the design knowledge. The characteristics of the dependent and the primary descriptors are examined and their distinct roles in the design process are discussed. Term definitions concerning the information importance and the design effort are also introduced. The descriptors are used to form a matrix. A number of operations on this matrix results in its transformation, with the final matrix reflecting the quantitative relationship between the information importance and the design effort. From the aforementioned matrix, a unique sorted list for the primary design descriptors is produced. Following this list during descriptor instantiation ensures the production of design information of maximum importance with the least effort in the early design stages. The design of a belt conveyor is used as a basis for a better understanding of the theoretical analysis and for a demonstration of the use of the suggested descriptor list.

Function structures are used during conceptual engineering design to transform the customer requirements into specific functional tasks. Although they are usually constructed from a well-understood black-box description of an artifact, there is no clear approach or formal set of rules that guide the creation of function structures. To remedy the unclear formation of such structures and to provide the potential for automated reasoning of such structures, a graph grammar is developed and implemented. The grammar can be used by a designer to explore various solutions to a conceptual design problem. Furthermore, the grammar aids in disseminating engineering functional information and in teaching the function structure concept to untrained engineers. Thirty products are examined as a basis for developing the grammar rules, and the rules are implemented in an interactive user environment. Experiments with student engineers and with the automated creation of function structures validate the effectiveness of the grammar rules.

Studies of creativity have tended to focus on isolated individuals, under the assumption that it can be defined as a characteristic of an extraordinary person, product, or process. Existing computational models of creative behavior have inherited this emphasis on independent generative processes. However, an increasing multidisciplinary consensus regards creativity as a systems property, and extends the focus of inquiry to include the interaction between generative individuals and evaluative social groups. To acknowledge the complementarity of evaluative processes by social groups, experts, and peers, this paper presents experimentation with a framework of design as a social activity. This model is used to inspect phenomena associated with creativity in the interaction between designers and their societies. In particular, this paper describes the strength of social ties as a mechanism of social organization, and explores its potential relation to creativity in a computational social simulation. These experiments illustrate ways in which the role of designers as change agents of their societies can be largely determined by how the evaluating group self-organizes over time. A key potential implication is that the isolated characteristics of designers may be insufficient to formulate conclusions about the nature and effects of their behavior. Instead, causality could be attributed to situational factors that define the relationship between designers and their evaluators.

This study presents an application to optimize the use of an L-cut guillotine machine. The application has two distinct parts to it; first, a number of rectangular shapes are placed on as few metal sheets as possible by using genetic algorithms. Second, the sequence for cutting these pieces has to be generated. The guillotine's numeric control then uses this sequence to make the cuts.

In intelligent computer-aided design the concept of intelligence is related to that of integration. Using feature-based computer-aided design models is thought to make a complete integration. This paper presents a feature recognition approach based on the use of a feature grammar. Given the complexity of feature recognition in interactions, the basic idea of the approach is to find the latent and logical structure of features in interaction. The approach includes five main phases. The first phase, called regioning, identifies the potential zones for the birth of features. The second phase, called virtual extension, builds links and virtual faces. The third phase, called structuring, transforms the region into a structure compatible with the structure of the features represented by the feature grammar. The fourth phase, called Identification, identifies the features in these zones. The fifth phase, called modeling, represents the model by features. The feature modeling system software is developed based on this approach.

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.

Functional reasoning (FR) enables people to derive and explain function of artifacts in a goal-oriented manner. FR has been studied and employed in various disciplines, including philosophy, biology, sociology, and engineering design, and enhanced by the techniques borrowed from computer science and artificial intelligence. The outcome of FR research has been applied to engineering design, planning, explanation, and learning. A typical FR system in engineering design usually incorporates representational mechanisms of function concept together with description mechanisms of state, structure, or behavior, and explanations and reasoning mechanisms to derive and explain functions. As for representation, philosophers have long argued whether function of an artifact is a genuine property of it. As for explanation and reasoning, they have produced theories for functional ascription by an external viewer as part of an explanation. To build an FR-based system, the theory based on which the system is built and the underlying assumptions must be explicitly identified. This point is not always clear in the engineering of FR-based systems. Understanding the underlying assumptions, logical formulation, and limitations of FR theories will help developers assessing their systems correctly. The purpose of this paper is to review various FR theories and their underlying assumptions and limitations. This later serves as a benchmark for comparing various FR techniques.

Shapes play an important role in many human activities, but are rarely seen in their natural form as raw and unanalyzed. Rather, shapes come analyzed, structured in terms of their certain parts, forming shape decompositions. Different kinds of shape decompositions are developed, the most interesting among which are the decompositions that could be used as shape approximations. Two kinds of such decompositions, discrete and bounded, are examined in greater detail. Computations with shapes conducted in the framework of shape grammars and related shape algebras have been standard for over 3 decades. Similar computations are possible with analyzed shapes or shape decompositions. Different algebras to compute with shape decompositions are developed and compared to the shape algebras. The measure of their agreement determines how well the shapes are approximated by their decompositions.

Design couples synthesis and analysis in iterative cycles, alternatively generating solutions, and evaluating their validity. The accuracy and depth of evaluation has increased markedly because of the availability of powerful simulation tools and the development of domain-specific knowledge bases. Efforts to extend the state of the art in evaluation have unfortunately been carried out in stovepipe fashion, depending on domain-specific views both of function and of what constitutes “good” design. Although synthesis as practiced by humans is an intentional process that centers on the notion of function, computational synthesis often eschews such intention for sheer permutation. Rather than combining synthesis and analysis to form an integrated design environment, current methods focus on comprehensive search for solutions within highly circumscribed subdomains of design. This paper presents an overview of the progress made in representing design function across abstraction levels proven useful to human designers. Through an example application in the domain of mechatronics, these representations are integrated across domains and throughout the design process.

The need to model and to reason about design alternatives throughout the design process demands robust representation schemes of function, behavior, and structure. Function describes the physical effect imposed on an energy or material flow by a design entity without regard for the working principles or physical solutions used to accomplish this effect. Behaviors are the physical events associated with a physical artifact (or hypothesized concept) over time (or simulated time) as perceived by an observer. Structure, the most tangible concept, partitions an artifact into meaningful constituents such as features, Wirk elements, and interfaces in addition to the widely used assemblies and components. The focus of this work is on defining a model for function-based representations that can be used across various design methodologies and for a variety of design tasks throughout all stages of the design process. In particular, the mapping between function and structure is explored and, to a lesser extent, its impact on behavior is noted. Clearly, the issues of a function-based representation's composition and mappings directly impact certain computational synthesis methods that rely on (digitally) archived product design knowledge. Moreover, functions have already been related to not only form, but also information of user actions, performance parameters in the form of equations, and failure mode data. It is essential to understand the composition and mappings of functions and their relation to design activities because this information is part of the foundation for function-based methods, and consequently dictates the performance of those methods. Toward this end, the important findings of this work include a formalism for two aspects of function-based representations (composition and mappings), the supported design activities of the model for function-based representations, and examples of how computational design methods benefit from this formalism.

Customers can directly express their preferences on many options when ordering products today. Mass customization manufacturing thus has emerged as a new trend for its aiming to satisfy the needs of individual customers. This process of offering a wide product variety often induces an exponential growth in the volume of information and redundancy for data storage. Thus, a technique for managing product configuration is necessary, on the one hand, to provide customers faster configured and lower priced products, and on the other hand, to translate customers' needs into the product information needed for tendering and manufacturing. This paper presents a decision-making scheme through constructing a product family model (PFM) first, in which the relationship between product, modules, and components are defined. The PFM is then transformed into a product configuration network. A product configuration problem assuming that customers would like to have a minimum-cost and customized product can be easily solved by finding the shortest path in the corresponding product configuration network. Genetic algorithms (GAs), mathematical programming, and tree-searching methods such as uniform-cost search and iterative deepening A* are applied to obtain solutions to this problem. An empirical case is studied in this work as an example. Computational results show that the solution quality of GAs retains 93.89% for a complicated configuration problem. However, the running time of GAs outperforms the running time of other methods with a minimum speed factor of 25. This feature is very useful for a real-time system.

Neuroevolution, or evolving neural networks with evolution algorithms such as genetic algorithms, is becoming one of the hottest areas in hybrid systems research. One of the areas that become under research using neuroevolutions is the controllers. In this paper, we shall present two engineering controllers based on neuroevolutions techniques. One of the controllers is used to monitor the temperature and humidity in an industry. This controller is having a linear behavior. The second controller is concerned with scheduling parts in queues in an industry. The scheduling controller is having a nonlinear behavior. The results obtained by the proposed controllers based on neuroevolution are compared with results obtained by traditional methods such as neural networks with backpropagation and ordinary simulation for the controller. The results show that the neuroevolution approaches outperform the results obtained by other methods.

Inspiration is useful for exploration and discovery of new solution spaces. Systems in natural and artificial worlds and their functionality are seen as rich sources of inspiration for idea generation. However, unlike in the artificial domain where existing systems are often used for inspiration, those from the natural domain are rarely used in a systematic way for this purpose. Analogy is long regarded as a powerful means for inspiring novel idea generation. One aim of the work reported here is to initiate similar work in the area of systematic biomimetics for product development, so that inspiration from both natural and artificial worlds can be used systematically to help develop novel, analogical ideas for solving design problems. A generic model for representing causality of natural and artificial systems has been developed, and used to structure information in a database of systems from both the domains. These are implemented in a piece of software for automated analogical search of relevant ideas from the databases to solve a given problem. Preliminary experiments at validating the software indicate substantial potential for the approach.

In computer-aided architectural design, words are an underemployed source of information. Through a series of case studies, we deduced a design annotation data model. All entities in this model can be captured from the design draft, except one: the word relation. Therefore, a system was developed that generates word graphs using single words from the draft as input. The system searches for semantic relations between words and for new intermediate words that can connect two existing words. The system has filters that select only those graphs that are considered interesting by the designers. The envisioned applications of word graphs in the context of computer-aided architectural design are to contribute to the architect's design and to enhance the fluency of the design. These expectations are met, but must be considered in relation to the architect's drafting behavior.

Conceptual design produces a number of functions the designed product is to fulfill, several solution principles (means) for each function, and multiple overall principle solutions (concepts). Besides concept synthesis, it is important to determine the (few) early solution properties that are of interest at the concept stage. Further activities are assessing the consequences of the chosen means and their instantiation, the effects of changes, and how decisions affect other elements. Using a quantitative functional representation can facilitate these tasks, but a balance is needed between product-dependent tools predicting many detailed properties, and product-independent, generally applicable tools with limited prediction capabilities. A balance between a closed, general set of predefined building blocks and extensibility by modeling application-specific, individual elements is also necessary. In this paper, a generally applicable conceptual design model is presented, which has been established by theoretical reasoning applied to a number of products. These products were the subjects of previous company-ordered student projects. The resulting information model spans continuously from requirements to concepts and permits modeling desired functionality (functions), achieved functionality (means and their value choices), and explicit constraints (internal and external relations between parameters of requirements, functions and means). To indicate the suitability in principle, the model has been implemented in an interactive, incremental prototype for computer support that permits modeling, storage, and reuse in a database. It can be concluded that the model permits explicit modeling of complex relations, automatic change propagation, and handling of many concept alternatives. Integrated, bidirectional, and continuous connections from requirements to concepts facilitate conceptual design, reuse, documentation of the results, and allow changes to be made and their effects assessed easily. Incremental constraint networks are approved, for example, in configuration design or geometry modelers, and the significance of this article is to enable their use also for quantitative analysis of incomplete, evolving concepts in original design tasks allowing different principle solutions, and for various products of mechanical design.