The synthesis of four-bar mechanisms is a well-understood, classical design problem. The original systematic work in this field began in the late 1800s and continues to be an active area of research. Limitations to the classical theory of four-bar synthesis potentially limit its application to certain real-world problems by virtue of the small number of precision points and unspecified order. This paper presents a numerical technique for four-bar mechanism synthesis based on genetic algorithms that removes this limitation by relaxing the accuracy of the precision points.

The natural world provides numerous cases for analogy and inspiration in engineering design. During the early stages of design, particularly during concept generation when several variants are created, biological systems can be used to inspire innovative solutions to a design problem. However, identifying and presenting the valuable knowledge from the biological domain to an engineering designer during concept generation is currently a somewhat disorganized process or requires extensive knowledge of the biological system. To circumvent the knowledge requirement problem, we developed a computational approach for discovering biological inspiration during the early stages of design that integrates with established function-based design methods. This research defines and formalizes the information identification and knowledge transfer processes that enable systematic development of biologically inspired designs. The framework that supports our computational design approach is provided along with an example of a smart flooring device to demonstrate the approach. Biologically inspired conceptual designs are presented and validated through a literature search and comparison to existing products.

This paper argues from extensive research findings in design psychology and industrial design processes, as well as our own observations, that interactive generative systems can be powerful tools for human designers. Moreover, interactive generative systems can fit naturally into human design thinking and industrial design practice. This discussion is focused on aesthetic design fields like knitwear and graphic design, but is largely applicable to major branches of engineering. Human designers and generative systems have complementary abilities. Humans are extremely good at perceptual evaluation of designs, according to criteria that are extremely hard to program. As a result, they can provide fitness evaluations for evolutionary generative systems. They can also tailor the biases that generation systems use to reach useful solutions quickly. We discuss an application of these approaches: Kelly's evolutionary systems for color scheme design. Automatic design systems can work interactively with human designers by generating complete designs from partial specifications, that can then be used as starting points for designing by modification. We discuss an application of this approach: Eckert's garment shape design system.

Managing and reusing knowledge in architecture, engineering, and construction firms can lead to greater competitive advantage, improved designs, and more effective management of constructed facilities. However, reuse often fails because knowledge is not captured; it is captured out of context, rendering it not reusable; or there are no formal mechanisms for finding and retrieving reusable knowledge. This paper presents ongoing research on design knowledge reuse that introduces the notion of knowledge in context from a corporate perspective. We argue that in order for knowledge to be reusable, the user should be able to see the rich context in which this knowledge was originally created and interact with it. We call a repository of such knowledge in context the corporate memory. We describe empirical observations of designers reusing knowledge from their personal design experiences. Based on these observations, we formalize two key activities in the process of knowledge reuse from a corporate repository: finding reusable items and understanding these items in context. We formalize six degrees of exploration that lead to understanding. We describe a prototype system, CoMem, that supports these activities. CoMem is distinguished from the document-centric state of practice solutions by its approach of “overview first, zoom and filter, and then details on demand.” In order to accomplish this approach we propose three metaphors: corporate map, fisheye lens, and storyteller. These metaphors and their implementation in CoMem are illustrated using an industry scenario.

Successful realization of large-scale product development programs is challenging because of complex product and process dependencies and complicated team interactions. Proficient teamwork is underpinned by knowledge of the manner in which tasks performed by different design participants fit together to create an effective whole. Based on an extensive industrial case study with a diesel engine company, this paper first argues that the overview and experience of senior designers play an important part in supporting teamwork by coordinating activities and facilitating proactive communication across large project teams. As experts move on and novices or contractors are hired, problems are likely to occur as tacit overview knowledge is lost. If informal, overview-driven processes break down, the risk of costly oversights will increase, and greater management overhead will be required to realize successful product designs. Existing process models provide a means to express the connectivity between tasks and components thus to compensate partially for the loss of tacit overview. This paper proposes the use of design confidence, a metric that reflects the designer's belief in the maturity of a particular design parameter at a given point in the process, to address the limitations of existing models. The applicability of confidence-based design models in providing overview, as well as their shortcomings, will be demonstrated through the example of a diesel engine design process. Confidence can be used to make overview knowledge explicit and convey additional information about the design artifact, thereby informing communication and negotiation between teams.

This paper (Wu 2016), which was published in AI EDAM online on August 22, 2016, has been retracted by Cambridge University Press as it is very similar in content to a published ASME Conference Proceedings paper. The article in question and the ASME Conference Proceedings paper were submitted for review with AI EDAM and the ASME at similar times, but copyright was assigned to ASME before the paper was accepted in AI EDAM and therefore the article in AI EDAM is being retracted. (In recent years, industrial nations around the globe have invested heavily in new technologies, software, and services to advance digital design and manufacturing using cyber-physical systems, data analytics, and high-performance computing. Many of these initiatives, such as cloud-based design and manufacturing, fall under the umbrella of what has become known as Industry 4.0 or Industrial Internet and are often hailed as pillars of a new industrial revolution. While an increasing number of companies are developing or already offer commercial cloud-based software packages and services for digital design and manufacturing, little work has been reported on providing a review of the state of the art of these commercial software and services as well as identifying research gaps in this field. The objective of this paper is to present a state-of-the-art review of digital design and manufacturing software and services that are currently available on the cloud. The focus of this paper is on assessing to what extent engineering design, engineering analysis, manufacturing, and production across all phases of the product development lifecycles can already be performed based on the software and services accessed through the cloud. In addition, the key capabilities and benefits of these software packages and services are discussed. Based on the assessment of the core features of commercial software and services, it can be concluded that almost all phases of product realization can be conducted through digital design and manufacturing software and services on the cloud. Finally, existing research gaps and related challenges to overcome are identified. The state-of-the-art review serves to provide a technology guide for decision makers in their efforts to select suitable cloud-based software and services as alternatives to existing in-house resources as well as to recommend new research areas.)

This paper presents a possible future direction for agent-based simulation using complex agents that can learn from experience and report their individual evaluations. Adding learning to the agent model permits the simulation of potentially important agent behavior such as curiosity. The agents can then report evaluations of a design that are situated in their individual experience. The paper describes the architecture of curious agents used in the situated evaluation of designs. It then describes an example of the application of such curious agents in the evaluation of the curating of an exhibition in an art gallery.

Three-dimensional (3-D) geometry can be described in many ways, with both a varying syntax and a varying semantics. As a result, several very diverse schemas and file formats can be deployed to describe geometry, depending on the application domain in question. In a multidisciplinary domain such as the domain of architecture, engineering, and construction, this range of specialized schemas makes file format conversions inevitable. The approach adopted by current conversion tools, however, often results in a loss of information, most often due to a “mistranslation” between different syntaxes and/or semantics, leading to errors and limitations in the design conception stage and to inefficiency due to the required remodeling efforts. An approach based on semantic web technology may reduce the loss of information significantly, leading to an improved processing of 3-D information and hence to an improved design practice in the architecture, engineering, and construction domain. This paper documents our investigation of the nature of this 3-D information conversion problem and how it may be encompassed using semantic web technology. In an exploratory double test case, we show how the specific deployment of semantic rule languages and an appropriate inference engine are to be adopted to improve this 3-D information exchange. It shows how semantic web technology allows the coexistence of diverse descriptions of the same 3-D information, interlinked through explicit conversion rules. Although only a simple example is used to document the process, and a more in-depth investigation is needed, the initial results indicate the suggested approach to be a useful alternative approach to obtain an improved 3-D information exchange.

Currently design documentation rarely records the designer's decision process or the reasons behind those decisions. This paper describes an effort to improve design documentation by having the computer act as an intelligent apprentice to the designer to capture the rationale during the design process. The apprentice learns about the features that make a specific case different from the standard. Whenever the designer proposes a design action that differs from the apprentice's expectations, the interface will ask for the designer for justifications to explain the differences. Later queries for design rationale are answered using a combination of the apprentice's domain knowledge and the designer-supplied justifications. The apprentice model is being implemented in a prototype system called ADD (Augmenting Design Documentation). The initial focus of the work is on HVAC (Heating, Ventilation, and Air Conditioning) design. Our starting point for implementing the apprentice model is observing how people develop HVAC system designs and then explain those designs.

Manufacturing process planning is a difficult problem with a prohibitively large search space. It is normally tackled by decomposing goal objects into features, and then sequencing features to obtain a plan. This paper investigates an alternative approach. The capabilities of a manufacturing process are represented by a formal language of shape, in which sentences correspond to manufacturable objects. The language is interpreted to describe process plans corresponding to the shape generation, complete with cost estimates. A macro layer that describes single operations of the machine is implemented on top of the formal language. The space it describes is searched by the generative simulated annealing algorithm, a stochastic search technique based on simulated annealing. Plans that are close to the optimum are generated in reasonable time.

The geometric dimensioning and tolerancing (GD&T) specifications of a design are directly associated with its performance and functional requirements. They also govern the manufacturing and quality control processes needed to achieve those requirements. This paper reviews recent work in geometric tolerance representation and reasoning and presents a generic and uniform graph-based representation scheme, called the Tolerance Network, to represent GD&T specifications across a part or assembly. The network can accommodate GD&T specifications related to the function, behavior, manufacturing, and inspection requirements embedded in design specifications and supports the use of different types of tolerances. The network also accommodates common design practices such as the specification of overconstrained features and parts. The necessary properties of such a network are discussed that allow under- and overconstrained design specifications to be detected and analyzed.

As more and more people are increasingly turning to nature for design inspiration, tools and methodologies are developed to support the systematic bioideation process. State-of-the-art approaches struggle with expanding their knowledge bases because of interactive work required by humans per biological strategy. As an answer to this persistent challenge, a scalable search for systematic biologically inspired design (SEABIRD) system is proposed. This system leverages experience from the product aspects in design by analogy tool that identifies candidate products for between-domain design by analogy. SEABIRD is based on two conceptual representations, product and organism aspects, extracted from, respectively, a patent and a biological database, that enable leveraging the ever growing body of natural-language biological texts in the systematic bioinspired design process by eliminating interactive work by humans during corpus expansion. SEABIRD's search is illustrated and validated with three well-known biologically inspired design cases.

Function modeling is considered potentially useful in various fields of engineering, including engineering design. However, a close look at practices reveals that practitioners do not use formal function modeling so much, while the concept of “function” frequently appears in many practical methods without a vigorous definition. This paper tries to understand why formal function modeling is not practically utilized in industry by analyzing usage cases of function. By observing product development activities in industry, the paper identifies three problems that prevent formal function modeling from wider applications in practices, namely, practitioners' neglect of function modeling, the lack of practically useful function reasoning, and the complexity of the methods and tools of formal function modeling that make them impractical. Finally, the paper proposes strategies to tackle these problems and illustrates some research efforts in this regard.

Functional modeling is an analytical approach to design problems that is widely taught in certain academic communities but not often used by practitioners. This approach can be applied in multiple ways to formalize the understanding of the systems, to support the synthesis of the design in the development of a new product, or to support the analysis and improvement of existing systems incrementally. The type of usage depends on the objectives that are targeted. The objectives can be categorized into two key groups: discovering a totally new solution, or improving an existing one. This article proposes to use the functional modeling approach to achieve three goals: to support the representation of physics-based reasoning, to use this physics-based reasoning to assess design options, and finally to support innovative ideation. The exemplification of the function-based approach is presented via a case study of a glue gun proposed for this Special Issue. A reverse engineering approach is applied, and the authors seek an incremental improvement of the solution. As the physics-based reasoning model presented in this article is heavily dependent on the quality of the functional model, the authors propose a general approach to limit the interpretability of the functional representations by mapping the functional vocabulary with elementary structural blocks derived from bond graph theory. The physics-based reasoning approach is supported by a mathematical framework that is summarized in the article. The physics-based reasoning model is used for discovering the limitations of solutions in the form of internal contradictions and guiding the design ideation effort.

Language plays at least two roles in design. First, language serves as representations of ideas and concepts through linguistic behaviors that represent the structure of thought during the design process. Second, language also performs actions and creates states of affairs. Based on these two perspectives on language use in design, we apply the computational linguistics tools of latent semantic analysis and lexical chain analysis to characterize how design teams engage in concept formation as the accumulation of knowledge represented by lexicalized concepts. The accumulation is described in a data structure comprised by a set of links between elemental lexicalized concepts. The folding together of these two perspectives on language use in design with the information processing theories of the mind afforded by the computational linguistics tools applied creates a new means to evaluate concept formation in design teams. The method suggests that analysis at a linguistic level can characterize concept formation even where process-oriented critiques were limited in their ability to uncover a formal design method that could explain the phenomenon.

This paper proposes an aesthetic-driven evolutionary algorithm for user-centered design. The evolutionary algorithm is based on a genetic algorithm (GA). It is developed to work as an art form generator that enhances user's productivity and creativity through reproduction, evaluation, and selection. Users can input their preferences and guide the generating direction to the system. A two-step fitness function is developed to evaluate morphology and aesthetics of the generated art forms. Fractals created by an iterated function system are used for representing art forms in our process. Algorithmic aesthetics are developed based on the aesthetic measure theory, surveys of human preferences, and popular long-lasting symbols. The algorithmic aesthetics is used for evaluating aesthetics of art forms together with subjective nonquantifiable aspects, and placed in the fitness function. The GA basically creates two-dimensional art forms. However, any two-dimensional image can be included through the property of a condensation set of fractals. The proposed GA can increase design productivity by about 80%. Examples of jewelry designs and physical prototypes created by the proposed system are included.

Architects use sketching and diagramming in their design process to perform functional reasoning, formal arrangements, analogy transfer, structure mapping, and knowledge acquisition. This paper describes a research framework of the author's efforts in the studies of design drawings and the building of computational sketching tools to support the early conceptual design process in architecture. The first part of the paper discusses empirical studies conducted to determine or guess a designer's thought process from sketches and thus identifies domain-specific graphical symbols. It proposes a reasoning process framework of drawing marks, acts, and reacts. The second part of the paper illustrates how design support tools could be developed based on these concepts and describes the various applications of the study, such as indexing and retrieving of design drawings or images based on the recognition of geometric shapes and the spatial relationships among them.

Analogical reasoning plays an important role in design. In particular, cross-domain analogies appear to be important in innovative and creative design. However, making cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions useful in adapting past designs. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from design experiences in familiar domains by abstraction over patterns of regularity. Three important issues in abstraction from experiences are what to abstract from an experience, how far to abstract, and what methods to use. In this short paper, we describe how structure-behavior-function models of designs in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we describe the model-based learning method with a scenario of learning feedback mechanism.

This paper presents an approach to optimal design of elastic flywheels using an Injection Island Genetic Algorithm (iiGA), summarizing a sequence of results reported in earlier publications. An iiGA in combination with a structural finite element code is used to search for shape variations and material placement to optimize the Specific Energy Density (SED, rotational energy per unit weight) of elastic flywheels while controlling the failure angular velocity. iiGAs seek solutions simultaneously at different levels of refinement of the problem representation (and correspondingly different definitions of the fitness function) in separate subpopulations (islands). Solutions are sought first at low levels of refinement with an axi-symmetric plane stress finite element code for high-speed exploration of the coarse design space. Next, individuals are injected into populations with a higher level of resolution that use an axi-symmetric three-dimensional finite element code to “fine-tune” the structures. A greatly simplified design space (containing two million possible solutions) was enumerated for comparison with various approaches that include: simple GAs, threshold accepting (TA), iiGAs and hybrid iiGAs. For all approaches compared for this simplified problem, all variations of the iiGA were found to be the most efficient. This paper will summarize results obtained studying a constrained optimization problem with a huge design space approached with parallel GAs that had various topological structures and several different types of iiGA, to compare efficiency. For this problem, all variations of the iiGA were found to be extremely efficient in terms of computational time required to final solution of similar fitness when compared to the parallel GAs.