To complement studies on design sketching within particular phases of design processes in specific design domains, this paper analyzes descriptions of design processes given by designers from a wide variety of fields. This research forms part of a wider project on comparisons across design domains and draws attention to the many types and properties of sketches in different contexts. Further, it focuses on the multiple roles that sketching can take in idea generation, as well as in reasoning and communicating design ideas. In particular this paper examines how the different types and roles of sketches can be more or less formal and how this can lead to misunderstandings.

Design problems are often presented as structured briefs with detailed constraints and requirements, suggesting a fixed definition. However, past studies have identified the importance of exploring design problems for creative design outcomes. Previous protocol studies of designers has shown that problems can “co-evolve” with the development of solutions during the design process. But to date, little evidence has been provided about how designers systematically explore presented problems to create better solutions. In this study, we conducted a qualitative analysis of 252 design problems collected from publically available sources, including award-winning product designs and open-source design competitions. This database offers an independent sample of presented problems, designers’ alternative problem descriptions, and innovative solutions. We report the results of this large-scale qualitative analysis aimed at characterizing changes to problems during the design process. Inductive coding was used to identify content patterns in “discovered” problem descriptions, with qualitative codes reliably scored by two independent coders. A total of 32 distinct patterns of problem exploration were identified across designers and presented problems. Each pattern is described in the form of a generalized strategy to guide designers as they explore problem spaces. The exploration patterns identified in this study are the first empirical evidence of problem exploration in independent design problems. Further, the presence of exploration patterns in discovered problems is associated with the selection of the corresponding solution as a challenge finalist. These empirically identified strategies for problem exploration may be useful for computational tools supporting designers.

In this paper we describe a method for improving genetic-algorithm-based optimization using search control. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed method can significantly improve the efficiency and reliability of the GA optimizer.

This paper provides a brief introduction to the field of creativity studies. After more than 60 years of scientific study, there is much that has been learned about how to teach and facilitate creative thinking. We review some of the well-known methods for fostering creativity on demand, as well as the research that has explored the efficacy of these approaches.

Configuration problems often involve large product catalogs, and the given user requests can be met by many different kinds of parts from this catalog. Hence, configuration problems are often weakly constrained and have many solutions. However, many of those solutions may be discarded by the user as long as more interesting solutions are possible. The user often prefers certain choices to others (e.g., a red color for a car to a blue color) or prefers solutions that minimize or maximize certain criteria such as price and quality. In order to provide satisfactory solutions, a configurator needs to address user preferences and user wishes. Another important problem is to provide high-level features to control different reasoning tasks such as solution search, explanation, consistency checking, and reconfiguration. We address those problems by introducing a preference programming system that provides a new paradigm for expressing user preferences and user wishes and provides search strategies in a declarative and unified way, such that they can be embedded in a constraint and rule language. The preference programming approach is completely open and dynamic. In fact, preferences can be assembled from different sources such as business rules, databases, annotations of the object model, or user input. An advanced topic is to elicit preferences from user interactions, especially from explanations of why a user rejects proposed choices. Our preference programming system has successfully been used in different configuration domains such as loan configuration, service configuration, and other problems.

We describe a method for constructing a structural model of an unlabeled target two-dimensional line drawing by analogy to a known source model of a drawing with similar structure. The source case is represented as a schema that contains its line drawing and its structural model represented at multiple levels of abstraction: the lines and intersections in the drawing, the shapes, the structural components, and connections of the device are depicted in the drawing. Given a target drawing and a relevant source case, our method of compositional analogy first constructs a representation of the lines and the intersections in the target drawing, then uses the mappings at the level of line intersections to transfer the shape representations from the source case to the target; next, it uses the mappings at the level of shapes to transfer the full structural model of the depicted system from the source to the target.

This article presents Kuaba, a new design rationale representation approach that enables employing design rationale to support reuse of model-based designs, particularly, software design. It is shown that this can be achieved through the adoption of an appropriate vocabulary that allows design rationale representations to be computationally processed. The architecture and implementation of an integrated design environment to support recording design rationale using Kuaba is also shown. The Kuaba approach integrates the design rationale representation model with the formal semantics provided by the metamodel of the design method or modeling language used for describing the artifact being designed. This integration makes the design rationale representations more specific according to the design methods and enables a type of software design reuse at the highest abstraction level, where rationales can be integrated and reemployed in designing a new artifact.

Precision machines are necessary to manufacture parts and subassemblies that require tight tolerances. During the design of precision machines, like any design, it is critical that the best concept is chosen in the early stages of the design process because 80% of the final cost and quality of a product are designed in at this phase. In addition, changes and optimization late in the design process have limited impact on cost and quality. Typically, during the design of precision machines, engineers and skilled machinists develop several machine concepts and down select based on heuristics and past design experience rather than quantitative measures. This paper describes a computation tool, Precision Machine Design Assistant (PMDA), which automates basic machine error simulation and concept evaluation. The tool uses a combination of machine error motion modelling and constraint-based design methods. By combining these methods in a computational environment, multiple machine concepts may be rapidly modeled, analyzed, and compared. The goal of the program is to assist the designer in the selection of a superior concept for detail design. The PMDA methods and implementation are demonstrated in an example.

Product families help companies reach customers in several different markets, lessen the time needed to develop new products, and reduce costs by sharing common components among many products. The product platform can be considered as a set of technologies, components, or functions, and their arrangements, that are utilized for more than one product. Configuration design focuses on the components in a product and their connections and relationships. Discrete, combinatorial design spaces are used to model design requirements regarding physical connections, module partitions, and assembly sequences for the product family. To ensure that products satisfy all design requirements, it is necessary to combine these design spaces into a common configuration space into which all requirements can be mapped. This paper presents computational methods for modeling and combining design spaces so those configurations can be identified that satisfy all constraints. A new representation of assembly sequences facilitates the development of an assembly design space, elements of which can be enumerated readily. Because the size of the combinatorial design spaces can become quite large, computational efficiency is an important consideration. A new designer guided method, called the partitioning method, is presented for decomposing configuration design problems in a hierarchical manner that enables significant reductions in design space sizes. An example of a family of automotive underbodies illustrates the application of the discrete design space approach to develop a common platform.

In this paper we intend to motivate various research areas in configuration, based on our experience in developing commercial configuration solutions. Informal definitions are given for the configuration task and for configuration specification and description languages. We also offer an abstract characterization of the issue addressed in configuration models, most often represented as constraints or rules. With these definitions and examples of configuration problems from various domains, we motivate research in (1) a common ontology for configuration, (2) function representation and functional reasoning in configuration, and (3) scaling configuration to large problems.

This paper describes the findings of an experiment on how different engineers understand notions of function and functional breakdown in the context of design by modification. The experiment was conducted with a homogenous group of 20 design engineers, who had all received the same education. The subjects were asked to analyze how a hydraulic pump works and summarize their understanding in a function tree. The subjects were given either the hydraulic pump itself (with part of its casing removed) or a maintenance drawing that showed a section cut of the pump. This paper shows typical outputs of the designers. It discusses the different notions of function that the subjects had and the differences in the function trees they generated. The paper focuses an eight detailed analyses to show the range of approaches the subjects took.

Triggered by expert systems technology, artificial intelligence (AI) was a silver bullet in the early 1980s. AI seemed to be able to perfectly solve various problems that involved any intellectual activities. For instance, MYCIN (Shortliffe, 1976), developed at Stanford, gave a strong impression that medical doctors could have been soon supported by a clever consultation system, resulting in more accurate diagnoses. Inspired by these, also in engineering fields, a variety of experimental systems for fault diagnosis, planning, selection, and design were developed, which demonstrated promising possibilities of their applications.

The mechanism of design reasoning from function to form is suggested to consist of a two-step inference of the innovative abduction type. First is an inference from a desired functional aspect to an idea, concept, or solution principle to satisfy the function. This is followed by a second innovative abduction, from the latest concept to form, structure, or mechanism. The intermediate entity in the logical reasoning, the concept, is thus made explicit, which is significant in following and understanding a specific design process, for educating designers, and to build a logic-based computational model of design. The idea of a two-step abductive reasoning process is developed from the critical examination of several propositions made by others. We use the notion of innovative abduction in design, as opposed to such abduction where the question is about selecting among known alternatives, and we adopt a previously proposed two-step process of abductive reasoning. However, our model is different in that the two abductions used follow the syllogistic pattern of innovative abduction. In addition to using a schematic example from the literature to demonstrate our derivation, we apply the model to an existing, empirically derived method of conceptual design called “parameter analysis” and use two examples of real design processes. The two synthetic steps of the method are shown to follow the proposed double innovative abduction scheme, and the design processes are presented as sequences of double abductions from function to concept and from concept to form, with a subsequent deductive evaluation step.

Design space explorers are computer programs that play on an exploration metaphor to support design. They assist designers in creating alternative designs by structuring the process of design creation in a space of alternatives. Subsidiary metaphors relevant to design space explorers are generation, navigation, and reuse. This paper introduces, in two sketches, typed feature structures as a formal system in which a design space explorer and its knowledge level might be implemented. First, informal and abstract properties of typed feature structures suffice to build a sketch of the behavior of a design space explorer. Second, using an example based on single-fronted cottages (a common Australian housing type), we outline the typed feature structure machinery most relevant to design space exploration.

Production systems have traditionally been used in engineering to aid the designer in generating designs in domains not significantly influenced by geometry. However, in geometric-based domains such as mechanical engineering, their popularity has been limited. This work proposes the use of shape grammars as the framework for engineering expert systems. Shape grammars are shown to have all the generation and analysis capabilities of traditional production systems, while being able to represent knowledge about both the functionality and the form of a product. Additionally, the parametric nature of shape grammars, their ability to deal with physical form rather than abstract elements, and their ability to recognize emergent shapes give them significant advantages over traditional production systems for geometry-based engineering design.

In decision-based design, the principal role of a designer is to make decisions. Decision support is crucial to augment this role. In this paper, we present an ontology that provides decision support from both the “construct” and the “information” perspectives that address the gap that existing research focus on these two perspectives separately and cannot provide effective decision support. The decision support construct in the ontology is the compromise decision support problem (cDSP) that is used to make multiobjective design decisions. The information for decision making is archived as cDSP templates and represented using frame-based ontology for facilitating reuse, consistency maintaining, and rapid execution. In order to facilitate designers’ effective reuse of the populated cDSP templates ontology instances, we identified three types of modification that can be made when design consideration evolves. In our earlier work, part of the utilization (consistency checking) of the ontology has been demonstrated through a thin-walled pressure vessel redesign example. In this paper, we comprehensively present the ontology utilization including consistency checking, trade-off analysis, and design space visualization based on the pressure vessel example.

Design research informs and supports practice by developing knowledge to improve the chances of producing successful products. Training in design research has been poorly supported. Design research uses human and natural/technical sciences, embracing all facets of design; its methods and tools are adapted from both these traditions. However, design researchers are rarely trained in methods from both the traditions. Research in traditional sciences focuses primarily on understanding phenomena related to human, natural, or technical systems. Design research focuses on supporting improvement of such systems, using understanding as a necessary but not sufficient step, and it must embrace methods for both understanding reality and developing support for its improvement. A one-semester, postgraduate-level, credited course that has been offered since 2002, entitled Methodology for Design Research, is described that teaches a methodology for carrying out research into design. Its steps are to clarify research success; to understand relevant phenomena of design and how these influence success; to use this to envision design improvement and develop proposals for supporting improvement; to evaluate support for its influence on success; and, if unacceptable, to modify, support, or improve the understanding of success and its links to the phenomena of design. This paper highlights some major issues about the status of design research and describes how design research methodology addresses these. The teaching material, model of delivery, and evaluation of the course on methodology for design research are discussed.

Many real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives (energy efficiency, robustness, and makespan) and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exists a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.

Current limit state surrogate modeling methods for system reliability analysis usually build surrogate models for failure modes individually or build composite limit states. In practical engineering applications, multiple system responses may be obtained from a single setting of inputs. In such cases, building surrogate models individually will ignore the correlation between different system responses and building composite limit states may be computationally expensive because the nonlinearity of composite limit state is usually higher than individual limit states. This paper proposes a new efficient Kriging surrogate modeling approach for system reliability analysis by constructing composite Kriging surrogates through selection of Kriging surrogates constructed individually and Kriging surrogates built based on singular value decomposition. The resulting composite surrogate model will combine the advantages of both types of Kriging surrogate models and thus reduce the number of required training points. A new stopping criterion and a new surrogate model refinement strategy are proposed to further improve the efficiency of this approach. The surrogate models are refined adaptively with high accuracy near the active failure boundary until the proposed new stopping criterion is satisfied. Three numerical examples including a series, a parallel, and a combined system are used to demonstrate the effectiveness of the proposed method.

This paper introduces the special issue of AI EDAM on the role of gesture in designing. It starts with the context of the papers submitted and a summary of the papers accepted. We then introduce gesture studies, one of the two main domains with which this Special Issue is concerned. We do not introduce design research: we suppose the readers of AI EDAM are familiar with this domain. After this general introduction to the domain of gesture studies, we provide an overview of gestures in design, that is, the research environment of the papers in this Special Issue. We then discuss some dimensions on which these papers differ, as well as some on which they are related.