Analyses of styles in design have paid little attention to how people see style and how designers use perceptions of style to guide designing. Although formal and computational methods for analyzing styles and generating designs provide impressively parsimonious accounts of what some styles are, they do not address many of the factors that influence how humans understand styles. The subtlety of human style judgments raises challenges for computational approaches to style. This paper differentiates between a range of distinct meanings of “style” and explores how designers and ordinary people learn and apply perceptual similarity classes and style concepts in different situations to interpret and create designed artifacts. A range of psychological evidence indicates that style perception is dependent on knowledge and involves the interaction of perceptual recognition of style features and explanatory inference processes that create a coherent understanding of an object as an exemplar of a style. This article concludes by outlining how formal style analyses can be used in combination with psychological research to develop a fuller understanding of style perception and creative design.

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.

We present a novel and trainable gesture-based system for next-generation intelligent interfaces. The system requires a non-contact depth sensing device such as an RGB-D (color and depth) camera for user input. The camera records the user's static hand pose and palm center dynamic motion trajectory. Both static pose and dynamic trajectory are used independently to provide commands to the interface. The sketches/symbols formed by palm center trajectory is recognized by the Support Vector Machine classifier. Sketch/symbol recognition process is based on a set of geometrical and statistical features. Static hand pose recognizer is incorporated to expand the functionalities of our system. Static hand pose recognizer is used in conjunction with sketch classification algorithm to develop a robust and effective system for natural and intuitive interaction. To evaluate the performance of the system user studies were performed on multiple participants. The efficacy of the presented system is demonstrated using multiple interfaces developed for different tasks including computer-aided design modeling.

This paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences.

Digital libraries of case studies of analogical design have been popular since their advent in the early 1990s. We consider four benefits of digital libraries of case studies of analogical design in the context of biologically inspired design. First, a digital library affords documentation. The 83 case studies in our work come from 8 years of extended, collaborative design projects in an interdisciplinary class on biologically inspired design. Second, a digital library provides on-demand access to the case studies. We describe a web-based library of case studies of biologically inspired design called the Design Study Library (DSL). Third, a compilation of case studies supports analyses of broader patterns and trends. As an example, an analysis of DSL's case studies found that environmental sustainability was a major factor in about a third of the case studies and an explicit design goal in about a fourth. Fourth, a digital library of case studies can support analogical learning. Preliminary results from an exploratory study indicate that DSL may support novice learning about the processes of biologically inspired design.

Since the introduction of spatial grammars 45 years ago, numerous grammars have been developed in a variety of fields from architecture to engineering design. Their benefits for solution space exploration when computationally implemented and combined with optimization have been demonstrated. However, there has been limited adoption of spatial grammars in engineering applications for various reasons. One main reason is the missing, automated, generalized link between the designs generated by the spatial grammar and their evaluation through finite-element analysis (FEA). However, the combination of spatial grammars with optimization and simulation has the advantage over continuous structural topology optimization in that explicit constraints, for example, modeling style and fabrication processes, can be included in the spatial grammar. This paper discusses the challenges in providing a generalized approach by demonstrating the implementation of a framework that combines a three-dimensional spatial grammar interpreter with automated FEA and stochastic optimization using simulated annealing (SA). Guidelines are provided for users to design spatial grammars in conjunction with FEA and integrate automatic application of boundary conditions. A simulated annealing method for use with spatial grammars is also presented including a new method to select rules through a neighborhood definition. To demonstrate the benefits of the framework, it is applied to the automated design and optimization of spokes for inline skate wheels. This example highlights the advantage of spatial grammars for modeling style and additive manufacturing (AM) constraints within the generative system combined with FEA and optimization to carry out topology and shape optimization. The results verify that the framework can generate structurally optimized designs within the style and AM constraints defined in the spatial grammar, and produce a set of topologically diverse, yet valid design solutions.

The paper presents a knowledge-based system (KBS) for the conceptual design of grippers for handling fabrics. Its main purpose is the integration of the domain knowledge in a single system for the systematic design of this type of grippers. The knowledge presented, in terms of gripper, material and handling process, are classified. The reasoning strategy is based upon a combination of a depth-first search method and a heuristic method. The heuristic search method finds a final solution from a given set of feasible solutions and can synthesize new solutions to accomplish the required specifications. Details of the main features of the system are given, including its ability to take critical design decisions according to four criteria, weighted by the designer. The knowledge-based system was implemented in the Kappa P. C. 2.3.2 environment. Two examples are given to illustrate some critical aspects concerning the KBS development, to explain the operation of the proposed searching heuristic method, and to show its effectiveness in producing design concepts for grippers.

The use of generative design grammars for computational design synthesis has been shown to be successful in many application areas. The development of advanced search and optimization strategies to guide the computational synthesis process is an active research area with great improvements in the last decades. The development of the grammar rules, however, often resembles an art rather than a science. Poor grammars drive the need for problem specific and sophisticated search and optimization algorithms that guide the synthesis process toward valid and optimized designs in a reasonable amount of time. Instead of tuning search algorithms for inferior grammars, this research focuses on designing better grammars to not unnecessarily burden the search process. It presents a grammar rule analysis method to provide a more systematic development process for grammar rules. The goal of the grammar rule analysis method is to improve the quality of the rules and in turn have a major impact on the quality of the designs generated. Four different grammars for automated gearbox synthesis are used as a case study to validate the developed method and show its potential.

Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimization, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods.

Some designs are sufficiently creative that they are considered to be inventions. The invention process is typically characterized by a singular moment when the prevailing thinking concerning a long-standing problem is, in a “flash of genius,” overthrown and replaced by a new approach that could not have been logically deduced from what was previously known. This paper discusses such logical discontinuities using an example based on the history of one of the most important inventions of the 20th century in electrical engineering, namely, the invention of negative feedback by AT&T's Harold S. Black. This 1927 invention overthrew the then prevailing idiom of positive feedback championed by Westinghouse's Edwin Howard Armstrong. The paper then shows how this historically important discovery can be readily replicated by an automated design and invention technique patterned after the evolutionary process in nature, namely, genetic programming. Genetic programming employs Darwinian natural selection along with analogs of recombination (crossover), mutation, gene duplication, gene deletion, and mechanisms of developmental biology to breed an ever improving population of structures. Genetic programming rediscovers negative feedback by conducting an evolutionary search for a structure that satisfies Black's stated high-level goal (i.e., reduction of distortion in amplifiers). Like evolution in nature, genetic programming conducts its search probabilistically without resort to logic using a process that is replete with logical discontinuities. The paper then shows that genetic programming can routinely produce many additional inventive and creative results. In this regard, the paper discusses the automated rediscovery of numerous 20th-century patented inventions involving analog electrical circuits and controllers, the Sallen–Key filter, and six 21st-century patented inventions. In addition, two patentable new inventions (controllers) have been created in the same automated way by means of genetic programming. The paper discusses the promising future of automated invention by means of genetic programming in light of the fact that, to date, increased computer power has yielded progressively more substantial results, including numerous human-competitive results, in synchrony with Moore's law. The paper argues that evolutionary search by means of genetic programming is a promising approach for achieving creative, human-competitive, automated design because illogic and creativity are inherent in the evolutionary process.

Embodiment design (ED) is an early phase of product development. ED problems consist of finding solution principles that satisfy product requirements such as physics behaviors and interactions between components. Constraint satisfaction techniques are useful to solve constraint-based models that are often partial, heterogeneous, and uncertain in ED. This paper proposes new constraint satisfaction techniques to tackle piecewise-defined physics phenomena or skill-based rules and multiple categories of variables arising in design applications. New search heuristics and a global piecewise constraint are introduced in the branch and prune framework. The capabilities of these techniques are illustrated with both academic and real-world problems. Complete models of the latter are presented.

We present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. Two evolutionary algorithms were used in the development process: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. Our design was approved for flight, and three copies of it were successfully flown on NASA's Space Technology 5 mission between March 22 and June 30, 2006. These evolved antennas represent the first evolved hardware in space and the first evolved antennas to be deployed.

In this paper, the design of geometrical shapes of function carriers and their layout in given space is called configuration design. The constraint satisfaction problem in configuration design may be difficult to solve due to the lack of tight constraints and the countless combinations of the layout; a diversity of solutions that satisfy the constraints should be allowed. Therefore, to allow such diversity, we directed our attention to developmental processes in biology and proposed an adaptive-growth-type 3D representation based on evolutionary algorithms. Here, the adaptive-growth-type means the shape expressed in the process, which develops through interaction with an outside environment, like shape generation of a living organism in the natural world. The usefulness of the representation was verified by applying it to the component layout problem in the early stage of satellite design.

Since the operation of technical systems can be explained using physical laws, why then might we not use them explicitly in designing these systems? The characteristic initial binding variable, with which appropriate physical laws are sought, first needs to be extracted from the function of the future technical system. If there are several appropriate physical laws (i.e., operators), we evaluate them using the Analytic Hierarchy Process (AHP) method. The most suitable is then selected with regard to the chosen criteria based on design requirements. When one physical law is not sufficient for the design of a technical system, several laws are linked together using binding variables to form a conceptual chain (i.e., macro-operator). Such a chain does not only contain supporting physical laws; physical laws indirectly introduce basic models of shape, their basic topology, geometry, and basic material properties into the chain. A prototype computer-aided design system is based on the prescriptive conceptual design model presented below.

Evolutionary and Adaptive strategies (ES & AS) for diverse multilevel search across a preliminary, whole-system design hierarchy defined by discrete and continuous variable parameters are described. Such strategies provide high-level decision support when integrated with preliminary design software describing the major elements of an engineering system. Initial work involving a Structured Genetic Algorithm (stGA) with appropriate mutation regimes to encourage search diversity is described and preliminary results are presented. The shortcomings of the stGA approach are identified and alternative strategies are introduced. A dual agent strategy (GAANT) involving elements of an ant colony search and an evolutionary search concurrently manipulating the discrete and continuous variable parameter sets is presented. Appropriate communication between the two search agents results in a more efficient search across the hierarchy than that achieved by the stGA, while also simplifying the chromosomal representation. This simplification allows the further development of the preliminary design hierarchy in terms of complexity. The technique therefore represents a significant contribution to configuration design where multilevel, mixed discrete/continuous parameter design problems can be prevalent.

This paper presents our research on developing an ontology-based framework that can represent morphological characteristics related to assembly joints. Joints within the physical structure of an assembly are inevitable because of the limitations of component geometries and the associated, required engineering properties. Consequently, a framework is needed that can capture and propagate assembly design and joint information in a robust assembly model throughout the entire product development processes. The framework and model are based on an understanding of the morphological characteristics of an assembly and its different physical effects. The morphological characteristics are consequences of the principal physical processes and of the design intentions. Therefore, the morphological characteristics should be carefully represented while considering the geometry and topology of assembly joints. In this research, assembly joint topology is defined by a mereotopology, which is a region-based theory for the parts and associated concepts. This formal ontology can differentiate often ambiguous assembly and joining relations. Furthermore, the mereotopological definitions for assembly joints are implemented in Semantic Web Rule Language (SWRL) rules and Web Ontology Language triples. This process provides universality to the mereotopological definitions. Two geometrically and topologically similar joint pairs are presented to describe how the assembly joints can be defined in mereotopology and be transformed into SWRL rules. Web3D is also employed to support network-enabled sharing of assembly geometry. Finally, the proposed modeling framework is demonstrated using a real fixture assembly. This case study demonstrates the usability of the proposed framework for network-based design collaboration.

Design rationale (DR) explains why an artifact is designed the way it is. An explicit representation of DR is helpful to designers, allowing them to understand, improve, and reuse previous designs. The argumentation-based representation is the mainstream approach to DR representation. It has a semiformal graphical format to depict the structure of arguments for solving a design problem. This paper argues that because the design is not just a problem-solving process but also a cognitive activity that is continuously iterative and evolving, the conventional argumentation-based representation of DR has some inherent limitations. An improved, intent-driven representation model is proposed to capture and formalize the DR and its evolving history to support DR reuse. The model's knowledge structure, consisting of DR elements and their relationships, is detailed. A preliminary knowledge representation of the model based on Web Ontology Language is introduced. Furthermore, the context of DR is defined to document the complete DR and support effective traceability of design thinking. A graphical DR modeling system is developed, and an example is demonstrated to verify the system's application and the effectiveness of the proposed representation model. The paper provides an effective method to retain and manage a designer's implicit design knowledge, which has the potential to significantly improve the integrated management of product development knowledge.

Function is an ambiguous concept, whereas having explicit and precise concepts is critical for building a systematic science of engineering design. Based on Bunge's scientific ontology, this paper is devoted to developing an explicit and precise concept of function for design science. First, we attempt to clarify the concept of behavior, which is closely related to function and is also shown as an ambiguous concept in engineering. Second, the concept of action is imported from scientific ontology into design science. Third, a scientific ontology based concept of function is proposed, together with an ontology-based functional taxonomy. A case of a function definition of a civil aircraft type demonstrates that the proposed concept of function is more explicit and precise than previous ones, and it can lead to better functional design results.

Aiming to help researchers capture output from the early stages of engineering design projects, this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when, and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyze the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research.

Artificial intelligence (AI) applications to design have tended to focus on modeling and automating aspects of single discipline design tasks. Relatively little attention has thus far been devoted to representing the kinds of design ‘metaknowledge’ needed to manage the important interface issues that arise in concurrent design, that is, multidisciplinary design decision-making. This paper provides a view of the process and management of concurrent design and evaluates the potential of two AI approaches—blackboard architectures and co-operative distributed problem-solving (CDPS)—to model and support the concurrent design of complex artifacts. A discussion of the process of multidisciplinary design highlights elements of both sequential and concurrent design decision-making. We identify several kinds of design metaknowledge used by expert managers to: partition the design task for efficient execution by specialists; set appropriate levels of design conservatism for key subsystem specifications; evaluate, limit and selectively communicate design changes across discipline boundaries; and control the sequence and timing of the key (highly constrained and constraining) design decisions for a given type of artifact. We explore the extent to which blackboard and CDPS architectures can provide valid models of and potential decision support for concurrent design by (1) representing design management metaknowledge, and (2) using it to enhance both horizontal (interdisciplinary) and vertical (project life cycle) integration among product design, manufacturing and operations specialists.