In this paper we describe a conceptual framework and implementation of a tool that supports task-directed, distributed routine design (RD) augmented with simulation-based design testing. In our research, we leverage the modular distributed modeling (MDM) methodology to simulate the interaction of design components in an assembly. The major improvement we have made in the RD methodology is to extend it with the capabilities of incorporating remotely represented off-the-shelf components in design and simulation-based testing of a distributed assembly. The deliverable of our research is the RD-MDM platform, which is capable of automatically selecting intellectually protected off the shelf design components over the Internet, integrating these components in an assembly, running simulations for design testing, and publishing the approved design without disclosing the proprietary information.

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.

Design by analogy is a powerful part of the design process across the wide variety of modalities used by designers such as linguistic descriptions, sketches, and diagrams. We need tools to support people's ability to find and use analogies. A deeper understanding of the cognitive mechanisms underlying design and analogy is a crucial step in developing these tools. This paper presents an experiment that explores the effects of representation within the modality of sketching, the effects of functional models, and the retrieval and use of analogies. We find that the level of abstraction for the representation of prior knowledge and the representation of a current design problem both affect people's ability to retrieve and use analogous solutions. A general semantic description in memory facilitates retrieval of that prior knowledge. The ability to find and use an analogy is also facilitated by having an appropriate functional model of the problem. These studies result in a number of important implications for the development of tools to support design by analogy. Foremost among these implications is the ability to provide multiple representations of design problems by which designers may reason across, where the verb construct in the English language is a preferred mode for these representations.

Genetic programming is a systematic method for getting computers to automatically solve problems. Genetic programming uses the Darwinian principle of natural selection and analogs of recombination (crossover), mutation, gene duplication, gene deletion, and certain mechanisms of developmental biology to progressively breed, over a series of many generations, an improved population of candidate solutions to a problem. This paper makes the points that genetic programming now routinely delivers human-competitive machine intelligence for problems of automated design and can serve as an automated invention machine.

Rationale research in software development is a challenging area because although there is no shortage of advocates for its value, there is also no shortage of reasons for why rationale is unlikely to be captured in practice. Despite more than 30 years of research there still remains much uncertainty: how useful are the potential benefits and how insurmountable are the barriers? Will the value of the rationale (design and otherwise) justify the cost of collecting it? Although there have been numerous rationale research projects, many, if not most, received little or no empirical evaluation. There also have not been many studies examining what the needs are of the practitioners who would be supported by the rationale. This article discusses the “doom and gloom” predictions of rationale's failure, provides a survey of evaluations of rationale systems, and discusses what we hope is a brighter outlook for rationale research in the future. There are development standards and synergistic research areas that may help with rationale research and its acceptance in the software community with which we should be working. This article also presents the results of a pilot survey of software developers who were asked how they would envision using rationale and what they believe the most important barriers are. Although some results were as expected, there were also some surprises. Research on technology transfer indicates that, among other things, to transition successfully from research into practice we need to understand the need that is being met and demonstrate the value of our approach. Until we have determined how our work is needed by the people we are trying to help we will remain researching under uncertainty.

A fillet curve is provided at the root of the spur gear tooth, as stresses are high in this portion. The fillet curve may be a trochoid or an arc of suitable size as specified by designer. The fillet stress is influenced by the fillet geometry as well as the number of teeth, modules, and the pressure angle of the gear. Because the relationship is nonlinear and complex, an artificial neural network and a backpropagation algorithm are used in the present work to predict the fillet stresses. Training data are obtained from finite element simulations that are greatly reduced using Taguchi's design of experiments. Each simulation takes around 30 min. The 4-5-1 network and a sigmoid activation function are chosen. TRAINLM function is used for training the network with a learning rate parameter of 0.01 and a momentum constant of 0.8. The neural network is able to predict the fillet stresses in 0.03 s with reasonable accuracy for spur gears having 25–125 teeth, a 1–5 mm module, a 0.05–0.45 mm fillet radius, and a 15°–25° pressure angle.

The Designers' Workbench is a system developed by the Advanced Knowledge Technologies Consortium to support designers in large organizations, such as Rolls-Royce, to ensure that the design is consistent with the specification for the particular design as well as with the company's design rule book(s). In the principal application discussed here, the evolving design is described using a jet engine ontology. Design rules are expressed as constraints over the domain ontology. Currently, to capture the constraint information, a domain expert (design engineer) has to work with a knowledge engineer to identify the constraints, and it is then the task of the knowledge engineer to encode these into the Workbench's knowledge base. This is an error-prone and time-consuming task. It is highly desirable to relieve the knowledge engineer of this task, so we have developed a system, ConEditor+, that enables domain experts themselves to capture and maintain these constraints. Further, we hypothesize that to appropriately apply, maintain, and reuse constraints, it is necessary to understand the underlying assumptions and context in which each constraint is applicable. We refer to them as “application conditions,” and these form a part of the rationale associated with the constraint. We propose a methodology to capture the application conditions associated with a constraint and demonstrate that an explicit representation (machine interpretable format) of application conditions (rationales) together with the corresponding constraints and the domain ontology can be used by a machine to support maintenance of constraints. Support for the maintenance of constraints includes detecting inconsistencies, subsumption, redundancy, fusion between constraints, and suggesting appropriate refinements. The proposed methodology provides immediate benefits to the designers, and hence, should encourage them to input the application conditions (rationales).

This paper discusses the application of genetic programming to the synthesis of compound two-dimensional kinematic mechanisms, and benchmarks the results against one of the classical kinematic challenges of 19th century mechanical design. Considerations for selecting a representation for mechanism design are presented, and a number of human-competitive inventions are shown.

Creativity of designers can be enhanced by the application of appropriate triggers. The presence of triggers helps designers to search solution spaces. The searching of a solution space increases the possibility of finding creative solutions. Both representation and content of the triggers or stimulus to which the designers are exposed are believed to play a vital role in the representation and content of the outcome of the designers during problem solving. We studied the effect of representation of triggers on ideas generated by six design engineers while trying to solve a given problem. A variety of representations (video/animation and audio, text, explanation, and others) that are potentially useful to designers for five prespecified triggers were administered to each designer, who generated ideas in response to each trigger–representation combination individually. The effect of representations of these triggers on the content and representation of the solutions generated by the design engineers was studied. The results showed significant influence of the representation of the triggers on the representations, number, and quality of the resulting ideas that were generated.

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.

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.

Documented engineering design rationale has the potential to become a key source of information about past designs. Ease of comprehension of design rationale might play a crucial role in ensuring that the full potential of documented information is realized and that the effort and time necessary to capture design rationale pay off. This research proposes an empirical methodology for evaluating how structuring design rationale and supplying it with visual nontextual cues influences reading and interpretation. The study compares reading and interpretation of technical documentation presented in different formats to engineering graduate trainees in the aerospace industry.

Scientific data is often analyzed in the context of domain-specific problems, for example, failure diagnostics, predictive analysis, and computational estimation. These problems can be solved using approaches such as mathematical models or heuristic methods. In this paper we compare a heuristic approach based on mining stored data with a mathematical approach based on applying state-of-the-art formulae to solve an estimation problem. The goal is to estimate results of scientific experiments given their input conditions. We present a comparative study based on sample space, time complexity, and data storage with respect to a real application in materials science. Performance evaluation with real materials science data is also presented, taking into account accuracy and efficiency. We find that both approaches have their pros and cons in computational estimation. Similar arguments can be applied to other scientific problems such as failure diagnostics and predictive analysis. In the estimation problem in this paper, heuristic methods outperform mathematical models.

This paper deals with the system of agents treated as a concurrent modular system, which is able to support the designer in solving complex design tasks. The behavior of design agents is modeled by sets of grammar rules. Each agent manages a graph grammar and a database of facts concerning the subtask for which it is responsible. The course of designing is determined by the interaction between cooperating specialized agents. The design context is expressed by the environment in which agents act and predicates describing design criteria. The organization, design methodology, and a semantic model of a grammar-based multiagent design system are presented. The notions of a valid design solution and a design solution consistent with the design criteria are also introduced. The proposed approach is illustrated by the example of designing a house estate.

Design rationale should be regarded both as a tool for the practice of design, and as a method to enable the science of design. Design rationale answers questions about why a given design takes the form that it does. Answers to these why questions represent a significant portion of the knowledge generated from design research. This knowledge, along with that from empirical studies of designs in use, contributes to what Simon called the sciences of the artificial. Most research on the nature and use of design rationale has been analytic or theoretical. In this article, we describe an empirical study of the roles that design rationale can play in the conduct of design research. We report results from an interview study with 16 design researchers investigating how they construe and carry out design as research. The results include an integrated framework of the affordances design rationale can contribute to design research. The framework and supporting qualitative data provide insight into how design rationale might be more effectively leveraged as a first-class methodology for research into the creation and use of artifacts.

To support the concurrent design processes of mechatronic subsystems, unified mechatronics modeling and cooperative body–brain coevolutionary synthesis are developed. In this paper, both body-passive physical systems and brain-active control systems can be represented using the bond graph paradigm. Bond graphs are combined with genetic programming to evolve low-level building blocks into systems with high-level functionalities including both topological configurations and parameter settings. Design spaces of coadapted mechatronic subsystems are automatically explored in parallel for overall design optimality. A quarter-car suspension system case study is provided. Compared with conventional design methods, semiactive suspension designs with more creativity and flexibility are achieved through this approach.

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.