How designers communicate within design teams, and with users, suppliers, and customers, differs in formality both between industries and between different situations within one project. This paper identifies three layers of structure in design communication, each of which can be more or less formal: the design process, the interaction between participants, and the representations of design information that are constructed and used. These layers can be formal across a spectrum from explicit rules to habitual conventions. The paper draws on a range of contrasting case studies in mechanical engineering and knitwear design, as well as a larger corpus of cases comparing design domains more generally, to analyze how formality affects design interaction in different situations and process contexts. Mismatches in the understanding of formality can lead to misunderstandings, in particular across expertise boundaries and between designers and their clients or customers. Formality can be modulated in the mannerism of communication, the rhetoric employed, and how representations are constructed, to make communication more effective. The effort and skill put into modulating formality is greater in domains where designers work with end users, like architecture, than it is in companies where designers interact mainly with other professionals.

Engineering of complex systems often involves teamwork. The members of an engineering team must work together to identify design requirements, explore design spaces, generate design alternatives, and make both interactive and joint design decisions. Because of the latency of information and the disciplinary differences, it is often a difficult process for the members of a team to reach agreements when needed. Negotiation has been studied as a method for facilitating information exchange, mutual understanding, and joint decision making. An argumentation-based negotiation approach was previously proposed by the authors to support collaborative engineering design. In this paper, we present an experiment study that was conducted to evaluate the impact of this negotiation support approach on the process and the outcome of collaborative design. The results of the experiment show both positive effects and limitations of the approach.

Early childhood education has long-lasting influences on people, and an appropriate companion toy can play an essential role in children's brain development. This paper establishes a complete framework to guide the design of intelligent companion toys for preschool children from 2 to 6 years old, which is child-centered and environment-oriented. The design process is divided into three steps: requirement confirmation, the smart design before the sale, and the iterative update after the sale. This framework considers the characteristics of children and highlights the integration of human and artificial intelligence in design. A case study is provided to prove the superiority of the new framework. In addition to enriching the research on intelligent toy design, this paper also guides for practitioners to design smart toys and helps in children's cognitive development.

Inspirational stimuli are known to be effective in supporting ideation during early-stage design. However, prior work has predominantly constrained designers to using text-only queries when searching for stimuli, which is not consistent with real-world design behavior where fluidity across modalities (e.g., visual, semantic, etc.) is standard practice. In the current work, we introduce a multi-modal search platform that retrieves inspirational stimuli in the form of 3D-model parts using text, appearance, and function-based search inputs. Computational methods leveraging a deep-learning approach are presented for designing and supporting this platform, which relies on deep-neural networks trained on a large dataset of 3D-model parts. This work further presents the results of a cognitive study (n = 21) where the aforementioned search platform was used to find parts to inspire solutions to a design challenge. Participants engaged with three different search modalities: by keywords, 3D parts, and user-assembled 3D parts in their workspace. When searching by parts that are selected or in their workspace, participants had additional control over the similarity of appearance and function of results relative to the input. The results of this study demonstrate that the modality used impacts search behavior, such as in search frequency, how retrieved search results are engaged with, and how broadly the search space is covered. Specific results link interactions with the interface to search strategies participants may have used during the task. Findings suggest that when searching for inspirational stimuli, desired results can be achieved both by direct search inputs (e.g., by keyword) as well as by more randomly discovered examples, where a specific goal was not defined. Both search processes are found to be important to enable when designing search platforms for inspirational stimuli retrieval.

Design Expert Systems can be built using many small, cooperating, limited function expert systems called Single Function Agents (SiFAs). Using this approach we will be able to investigate and discover primitive problem-solving and interaction patterns, specific for multiagent design systems, and should gain a deeper understanding of the types of knowledge involved. This paper presents some categories of conflicts that have been studied using the SiFA approach, and makes a brief presentation of the SINE implementation of SiFAs.

Conflicts are likely to arise among participants in a collaborative design process as the inevitable outgrowth of the differing perspectives and viewpoints involved. The opportunities for conflict are magnified if many perspectives are brought to bear on a common artifact early in the design process, as in concurrent engineering or integrated engineering. Design advice tools can assist in the process of resolving these conflicts by making critiques and suggestions conveniently available to design participants, and by offering a fair means of evaluating and comparing suggested alternatives for compromise solution. In previous work we introduced a protocol based on notions of economic utility by which design advice systems can recognize conflict and mediate negotiation fairly. This protocol allowed design teams to express the desire to maximize or minimize the values of design parameters over totally ordered bounded domains of values, such as real numeric intervals. In this paper we extend this approach by allowing expressed preferences of design teams to be qualitative as well as quantitative, by allowing teams to express interest in parameters before they actually come into existence, and by relaxing many other of the earlier restrictions on the ways teams may express their preferences.

Industrial designers make sketches and physical models to start and develop ideas and concept designs. Such representations have advantages that they support fast, intuitive, rich, sensory exploration of solutions. Although existing tools and techniques provide adequate support where the shape of the product is concerned, the exploration of surface qualities such as material and printed graphics is supported to a much lesser extent. Moreover, there are no tools that have the fluency of sketching that allow combined exploration of shape, material, and their interactions. This paper evaluates Skin, an augmented reality tool designed to solve these two shortcomings. By projecting computer-generated images onto the shape model Skin allows for a “sketchy” tangible interaction where designers can explore surface qualities on a three-dimensional physical shape model. The tool was evaluated in three design situations in the domain of ceramics design. In each case, we found that the joint exploration of shape and surface provided creative benefits in the form of new solutions; in addition, a gain in efficiency was found in at least one case. The results show that joint exploration of shape and surface can be effectively supported with tangible augmented reality techniques and suggest that this can be put to practical use in industry today.

This paper presents a new technique for shape and topology optimization of fluid channels using generative design synthesis methods. The proposed method uses the generative abilities of graph grammars with simulation and analysis power of conventional computational fluid dynamics methods. The graph grammar interpreter GraphSynth is used to carry out graph transformations, which define different topologies for a given multiple-inlet multiple-outlet problem. After evaluating and optimizing the generated graphs, they are first transformed into meaningful three-dimensional shapes. These solutions are then analyzed by a computational fluid dynamics solver for final evaluation of the possible solutions. The effectiveness of the proposed method is checked by solving a variety of available test problems and comparing them with those found in the literature. Furthermore, by solving very complex large-scale problems, the robustness and effectiveness of the method is tested. To extend the work, future research directions are presented.

In this paper we explore the influence of the physical and social environment (the design space) son the formation of shared understanding in multidisciplinary design teams. We concentrate on the creative design meeting as a microenvironment for studying processes of design communication. Our applied research context entails the design of mixed physical–digital interactive systems supporting design meetings. Informed by theories of embodiment that have recently gained interest in cognitive science, we focus on the role of interactive “traces,” representational artifacts both created and used by participants as scaffolds for creating shared understanding. Our research through design approach resulted in two prototypes that form two concrete proposals of how the environment may scaffold shared understanding in design meetings. In several user studies we observed users working with our systems in natural contexts. Our analysis reveals how an ensemble of ongoing social as well as physical interactions, scaffolded by the interactive environment, grounds the formation of shared understanding in teams. We discuss implications for designing collaborative tools and for design communication theory in general.

The current paper describes the Multidisciplinary Combinatorial Approach (MCA), the idea of which is to develop discrete mathematical representations, called “Combinatorial Representations” (CR) and to represent with them various engineering systems. During the research, the properties and methods embedded in each representation and the connections between them were investigated thoroughly, after which they were associated with various engineering systems to solve related engineering problems. The CR developed up until now are based on graph theory, matroid theory, and discrete linear programming, whereas the current paper employs only the first two. The approach opens up new ways of working with representations, reasoning and design, some of which are reported in the paper, as follows: 1) Integrated multidisciplinary representation—systems which contain interrelating elements from different disciplines are represented by the same CR. Consequently, a uniform analysis process is performed on the representation, and thus on the whole system, irrespective of the specific disciplines, to which the elements belong. 2) Deriving known methods and theorems—new proofs to known methods and theorems are derived in a new way, this time on the basis of the combinatorial theorems embedded in the CR. This enables development of a meta-representation for engineering as a whole, through which the engineering reasoning becomes convenient. In the current paper, this issue is illustrated on structural analysis. 3) Deriving novel connections between remote fields—new connections are derived on the basis of the relations between the different combinatorial representations. An innovative connection between mechanisms and trusses, shown in the paper, has been derived on the basis of the mutual dualism between their corresponding CR. This new connection alone has opened several new avenues of research, since knowledge and algorithms from machine theory are now available for use in structural analysis and vice versa. Furthermore, it has opened opportunities for developing new design methods, in which, for instance, structures with special properties are developed on the basis of known mechanisms with special properties, as demonstrated in this paper. Conversely, one can use these techniques to develop special mechanisms from known trusses.

In this paper the need for Intelligent Computer Aided Design (Int.CAD) to jointly support design and learning assistance is introduced. The paper focuses on presenting and exploring the possibility of realizing “learning” assistance in Int.CAD by introducing a new concept called Shared Learning. Shared Learning is proposed to empower CAD tools with more useful learning capabilities than that currently available and thereby provide a stronger interaction of learning between a designer and a computer. “Controlled” computational learning is proposed as a means whereby the Shared Learning concept can be realized. The viability of this new concept is explored by using a system called PERSPECT. PERSPECT is a preliminary numerical design tool aimed at supporting the effective utilization of numerical experiential knowledge in design. After a detailed discussion of PERSPECT's numerical design support, the paper presents the results of an evaluation that focuses on PERSPECT's implementation of “controlled” computational learning and ability to support a designer's need to learn. The paper then discusses PERSPECT's potential as a tool for supporting the Shared Learning concept by explaining how a designer and PERSPECT can jointly learn. There is still much work to be done before the full potential of Shared Learning can be realized. However, the authors do believe that the concept of Shared Learning may hold the key to truly empowering learning in Int.CAD.

A fixed set of components with a fixed set of properties is often regarded as a defining characteristic of configuration design problems. In configuration design for larger and more complex systems (e.g., street cars), however, this often is not really true. The reason is that sometimes the component library offers no component that meets the actual requirements, so that a new component has to be added to the library. In general, configuration design for larger products must be interactive: interactive in the sense that the user controls the configuration process and in the sense that the phases of knowledge acquisition and configuration are partly mixed. This paper describes a method and a corresponding software tool (SyDeR, System Design for Reusability) that support the interactive configuration design of complex products, especially in the tendering phase. It combines three different technologies:

[bull ] structural modeling of technical systems;

[bull ] a library for technical solutions, which is based upon taxonomies and allows the reuse of the technical solutions;

[bull ] constraint techniques to propagate design decisions and check designs for consistency to support interactive configuration design.

This paper gives an overview of the functionality of the SyDeR tool and describes the main ideas behind the modeling language, which is especially oriented towards the requirements of system design problems. It also explains how we integrated structural modeling, taxonomies, and advanced constraint reasoning techniques into real-world applications.

In this paper, a methodology for inducing trends in a first principle reasoning system for design innovation is presented. Dimensional Variable Expansion is used in 1stPRINCE (FIRST PRINciple Computational Evaluator) to create additional design variables and introduce new prototypes. Trends are observed at each generation of the prototype and induction is used to predict optimal constraint activity at the limit of the iterative procedure. The inductive mechanism is applied to a constant-radius beam under flexural load and a tapered beam of varying radius and superior performance is derived. A circular wheel is created from a primitive-prototype consisting of a rectangular, spinning block that is optimized for minimum resistance to spinning. Although presented as a technique to perform innovative design, the inductive methodology can also be utilized as an AI approach to shape optimization.

Conceptual innovation in mechanical engineering design has been extremely challenging compared to the wide applications of automated design systems in digital circuits. This paper presents an automated methodology for open-ended synthesis of mechanical vibration absorbers based on genetic programming and bond graphs. It is shown that our automated design system can automatically evolve passive vibration absorbers that have performance equal to or better than the standard passive vibration absorbers invented in 1911. A variety of other vibration absorbers with competitive performance are also evolved automatically using a desktop PC in less than 10 h.

Product/service systems (PSSs) are increasingly found in markets, and more resources are being invested in PSS design. Despite the substantial research into PSS design, the current literature exhibits an incomplete understanding of it as a cognitive activity. This article demonstrates that the methods used to analyze product designers’ cognitive behavior can be used to produce comparable and commensurable results when analyzing PSS designers. It also generates empirical grounding for the development of hypotheses based on a cognitive study of a PSS design session in a laboratory environment using protocol analysis. This study is a part of a larger project comparing PSS design with product design. The results, which are based on the function–behavior–structure coding scheme, show that PSS design, when coded using this scheme, can be quantitatively compared with product design. Five hypotheses were developed based on the results of the study of this design session concerning where and how designers expend their cognitive design effort. These hypotheses can be used to design experiments that test them and provide the grounding for a fuller understanding of PSS design.

KANT is a knowledge-based system designed to diagnose Olivetti personal computers connected as remote terminals to a host computer through a SNA link. KANT is a collection of a few knowledge-based units: some of them operate in the field, and some operate back at the home office. They are configured in two blackboard systems which exchange data via the SNA link. The first blackboard runs on a cheap personal computer, only employs shallow knowledge, and performs the diagnoses that can be achieved in the field. The second blackboard runs on corporate mainframes, employs deep knowledge, and supports the more sophisticated analysis that is required from the project team for fixing complex problems.

Intelligent agents must update their knowledge base as they acquire new information about their environment. The modal logic S5n has been designed for representing knowledge bases in societies of agents. Halpern and Vardi have proposed the notion of refinement of S5n Kripke models in order to solve multi-agent problems in which knowledge evolves. We argue that there are some problems with their proposal and attempt to solve them by moving from Kripke models to their corresponding trees. We define refinement of a tree with a formula, show some properties of the notion, and illustrate with the muddy children puzzle. We show how some diagnosis problems in engineering can be modelled as knowledge-based multi-agent systems, and hence how our approach can address them.

This article focuses on a shape grammar, the rabo-de-bacalhau housing style, that was developed to enable the adaptation of existing houses to new requirements and most particular on the process of inference of the grammar. In the article we describe the process undertaken to develop the grammar and what the achievements of the transformation grammar are regarding the possibilities of a mass customization of a dwelling's rehabilitation work. The goal of this article is to describe and discuss how the designer's knowledge was encoded into shape rules. The process used to extract the architect's knowledge and to incorporate it into the transformation grammar enable us to abstract the designer's actions and to define a sequence of actions that can define a possible strategy of design. The proposed design methodology generates dwelling layouts that are legal because they follow the grammar language and adequate because they meet the a priori user and design requirements.

A crucial early stage in the engineering design process is the conceptual design phase, during which an initial solution design is generated. The quality of this initial design has a great bearing on the quality and success of the produced artefact. Typically, the knowledge required to perform this task is only acquired through many years of experience, and so is often at a premium. This has led to a number of attempts to automate this phase using intelligent computer systems. However, the knowledge of how to generate designs has proved difficult to acquire directly from human experts, and as a result, is often unsatisfactory in these systems. The application of inductive machine learning techniques to the acquisition of this sort of knowledge has been advocated as one approach to overcoming the difficulties surrounding its capture. Rather than acquiring the knowledge from human experts, the knowledge would be inferred automatically from a set of examples of the design process. This paper describes the authors' investigations into the general viability of this approach in the context of one particular conceptual design task, that of the design of fluid power circuits. The analysis of a series of experiments highlights a number of issues that would seem to arise regardless of the working domain or particular machine learning algorithm used. These issues, presented and discussed here, cast serious doubts upon the practicality of such an approach to knowledge acquisition, given the current state of the art.

In many system designs, it is a challenging task for system designers to predict the system reliability due to limited information about component designs, which is often proprietary to component suppliers. This research addresses this issue by considering the following situation: all the components share the same system load, and system designers know component reliabilities with respect to the component load, but do not know other information, such as component limit-state functions. The strategy is to reconstruct the equivalent component limit-state functions during the system design stage such that they can accurately reproduce component reliabilities. Because the system load is a common factor shared by all the reconstructed component limit-state functions, the component dependence can be captured implicitly. As a result, more accurate system reliability can be produced compared with traditional methods. An engineering example demonstrates the feasibility of the new system reliability method.