Functions are important in designing. However, several issues hinder progress with the understanding and usage of functions: lack of a clear and overarching definition of function, lack of overall justifications for the inevitability of the multiple views of function, and scarcity of systematic attempts to relate these views with one another. To help resolve these, the objectives of this research are to propose a common definition of function that underlies the multiple views in literature and to identify and validate the views of function that are logically justified to be present in designing. Function is defined as a change intended by designers between two scenarios: before and after the introduction of the design. A framework is proposed that comprises the above definition of function and an empirically validated model of designing, extended generate, evaluate, modify, and select of state-change, and an action, part, phenomenon, input, organ, and effect model of causality (Known as GEMS of SAPPhIRE), comprising the views of activity, outcome, requirement–solution–information, and system–environment. The framework is used to identify the logically possible views of function in the context of designing and is validated by comparing these with the views of function in the literature. Describing the different views of function using the proposed framework should enable comparisons and determine relationships among the various views, leading to better understanding and usage of functions in designing.

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.

The paper presents aspects of designer action that stress cognitive strategies for effective design problem solving, under the headings of exploration and representation. It proposes that links among design moves and shifts between design arguments are of prime importance in exploration and the design space should accommodate and expose them. The primacy of self-generated representations in the form of free-hand sketches and the role of arbitrary visual stimuli as supporting design reasoning is introduced. The expositions lead to the conclusion that the design space should be conceived as a multilevel and multifacet construct that supports on the spot experimentation and provides essential feedback not only regarding designs, but also concerning the process of designing.

Critical chains composed of critical flows and functions have been demonstrated as an effective qualitative analogy retrieval approach based on performance metrics. In prior work, engineers used expert knowledge to transform functional models into critical chain models, which are abstractions of the functional model. Automating this transformation process is highly desirable so as to provide for a robust transformation method. Within this paper, two paradigms for functional modeling abstraction are compared. A series of pruning rules provide an automated transformation approach, and this is compared to the results generated previously through an expert knowledge approach. These two approaches are evaluated against a set of published functional models. The similarity of the resulting transformation of the functional models into critical chain models is evaluated using a functional chain similarity metric, developed in previous work. Once critical chain models are identified, additional model evaluation criteria are used to evaluate the utility of the critical chain models for design analogy identification. Since the functional vocabulary acts as a common language among designers and engineers to abstract and represent critical design artifact information, analogous matching can be made about the functional vocabulary. Thus, the transformation of functional models into critical chain models enables engineers to use functional abstraction as a mechanism to identify design analogies. The critical flow rule is the most effective first step when automatically transforming a functional model to a critical chain model. Further research into more complex critical chain model architectures and the interactions between criteria is merited.

Conceptual design produces a number of functions the designed product is to fulfill, several solution principles (means) for each function, and multiple overall principle solutions (concepts). Besides concept synthesis, it is important to determine the (few) early solution properties that are of interest at the concept stage. Further activities are assessing the consequences of the chosen means and their instantiation, the effects of changes, and how decisions affect other elements. Using a quantitative functional representation can facilitate these tasks, but a balance is needed between product-dependent tools predicting many detailed properties, and product-independent, generally applicable tools with limited prediction capabilities. A balance between a closed, general set of predefined building blocks and extensibility by modeling application-specific, individual elements is also necessary. In this paper, a generally applicable conceptual design model is presented, which has been established by theoretical reasoning applied to a number of products. These products were the subjects of previous company-ordered student projects. The resulting information model spans continuously from requirements to concepts and permits modeling desired functionality (functions), achieved functionality (means and their value choices), and explicit constraints (internal and external relations between parameters of requirements, functions and means). To indicate the suitability in principle, the model has been implemented in an interactive, incremental prototype for computer support that permits modeling, storage, and reuse in a database. It can be concluded that the model permits explicit modeling of complex relations, automatic change propagation, and handling of many concept alternatives. Integrated, bidirectional, and continuous connections from requirements to concepts facilitate conceptual design, reuse, documentation of the results, and allow changes to be made and their effects assessed easily. Incremental constraint networks are approved, for example, in configuration design or geometry modelers, and the significance of this article is to enable their use also for quantitative analysis of incomplete, evolving concepts in original design tasks allowing different principle solutions, and for various products of mechanical design.

This paper presents results from a design neurocognition study on the effect of gender on EEG frequency band power when performing constrained and open design. We used electroencephalography to measure the brain activity of 84 professional designers. We investigated differences in frequency power associated with gender of 38 female and 46 male designers, while performing two prototypical design tasks. The aim of the study was to explore whether gender moderates brain activity while performing a constrained versus an open design task. Neurophysiological results for aggregate activations across genders and between tasks indicate a main effect of gender for theta, alpha 2, and beta 1 frequency bands. Females show higher theta, alpha 2, and beta 1, namely in the right dorsolateral prefrontal cortex, right occipitotemporal cortex, secondary visual cortex, and prefrontal cortex in both tasks. Females show higher beta bands than males, in areas of the left prefrontal cortex, in the constrained design. While in the open design, females showed higher theta, alpha, and beta 2 in the left prefrontal cortex and secondary visual cortex for all frequency bands. Results within gender between tasks indicate higher theta and alpha in the prefrontal cortex in the constrained design for both genders. Whilst for open design, results indicate higher theta and alpha 1 in the right hemisphere and higher alpha 2 and beta bands across hemispheres for both genders. Results within gender reveal common brain areas and frequency bands in distinguishing constrained from open design.

Shape grammars are rule-based formalisms for the specification of shape languages. Most of the existing shape grammars are developed on paper and have not been implemented computationally thus far. Nevertheless, the computer implementation of shape grammar is an important research question, not only to automate design analysis and generation, but also to extend the impact of shape grammars toward design practice and computer-aided design tools. In this paper, we investigate the implementation of shape grammars on a computer system, using a graph-theoretic representation. In particular, we describe and evaluate the implementation of the existing Rabo-de-Bacalhau transformation grammar. A practical step-by-step approach is presented, together with a discussion of important findings noticed during the implementation and evaluation. The proposed approach is shown to be both feasible and valuable in several aspects: we show how the attempt to implement a grammar on a computer system leads to a deeper understanding of that grammar, and might result in the further development of the grammar; we show how the proposed approach is embedded within a commercial computer-aided design environment to make the shape grammar formalism more accessible to students and practitioners, thereby increasing the impact of grammars on design practice; and the proposed step-by-step implementation approach has shown to be feasible for the implementation of the Rabo-de-Bacalhau transformation grammar, but can also be generalized using different ontologies for the implementation.

Computer-aided design (CAD) plays an essential role in creative idea generation on 2D screens during the design process. In most CAD scenarios, virtual object translation is an essential operation, and it is commonly used when designers simulate their innovative solutions. The degrees of freedom (DoF) of virtual object translation modes have been found to directly impact users’ task performance and psychological aspects in simulated environments. Little is known in the existing literature about the sense of agency (SoA), which is a critical psychological aspect emphasizing the feeling of control, in translation modes on 2D screens during the design process. Hence, this study aims to assess users’ SoA in virtual object translation modes on mouse-based, touch-based, and handheld augmented reality (AR) interfaces through subjective and objective measures, such as self-report, task performance, and electroencephalogram (EEG) data. Based on our findings in this study, users perceived a greater feeling of control in 1DoF translation mode, which may help them come up with more creative ideas, than in 3DoF translation mode in the design process; additionally, the handheld AR interface offers less control feel, which may have a negative impact on design quality and creativity, as compared with mouse- and touch-based interfaces. This research contributes to the current literature by analyzing the association between virtual object translation modes and SoA, as well as the relationship between different 2D interfaces and SoA in CAD. As a result of these findings, we propose several design considerations for virtual object translation on 2D screens, which may enable designers to perceive a desirable feeling of control during the design process.

The two critical issues related to product design exploration are addressed: the balance between stylistic consistency and innovation, and the control of design process under a great diversity of requirements. To address these two issues, the view of understanding product design exploration is first sought. In this view, the exploration of designs is not only categorized as a problem-solving activity but also as a problem-finding activity. A computational framework is developed based on this view, and it encompasses the belief that these two activities go hand in hand to accomplish the design tasks in an interactive design environment. The framework adopts an integration approach of two key computational techniques, shape grammars and evolutionary computing, for addressing the above two critical issues. For the issues of stylistic consistency, this paper focuses on the computational techniques in balancing the conflicts of stylistic consistency and innovation with shape grammars. For the issues of controlling design process, the practical concerns of monitoring the design process through various activities starting from the preparation works to the implementation of shape grammars have been emphasized in the development of this framework. To evaluate the effectiveness of the framework, the experiments have been set up to reflect the practical situations with which the designers have to deal. The system generates a number of models from scratch with numerical analysis that can be evaluated effectively by the designers. This reduces the designers' time and allows the designers to concentrate their efforts on performing higher level of design activities such as evaluation of designs and making design decisions.

A difficult task in concurrent engineering tasks is to provide appropriate manufacturability feedback to the designer in a timely manner. Many tools provide designers with cost and manufacturability evaluations, but they do not necessarily help the designer to identify what aspects of the design to change in order to improve it from a manufacturing perspective. A computer-assisted concurrent-engineering technology is described which identifies cost-critical tolerances in the design and generates cost-reducing design suggestions. The purpose of the system is to help focus the designer’s attention on the specific aspects of the design that influence manufacturing cost. This can aid the designer in optimizing the manufacturing costs of prismatic, one-off parts created on a CNC machining center. This work uses a program called the Manufacturing Evaluation Agent to produce cost-reducing design suggestions. The Manufacturing Evaluation is made up of two portions: a manufacturing planner and a suggestion generator. The manufacturing planner, P3, takes a design and generates a manufacturing constraint net that represents all manufacturing steps and their sequencing. Each constraint in the network has a label indicating the portions of the design (or manufacturing environment) that gave rise to that constraint. The Suggestion Generator analyzes the manufacturing constraint net to find the cost-critical areas, and uses the labels to find the portions of the design responsible for these cost-critical parts of the manufacturing plan. By pinpointing the cost-critical areas of the design and suggesting alternatives, the Manufacturing Evaluation Agent can help the de-signer to more quickly develop a superior design. This results in more rapid turnaround time, lower cost designs, and fewer engineering change orders once the design is sent to the factory floor.

This paper presents a framework and a prototype for designing Integrated Construction Management (ICM) software applications using reusable components. The framework supports the collaborative development of ICM software applications by a group of ICM application developers from a library of software components. The framework focuses on the use of an explicit software development process to capture and disseminate specialized knowledge that augments the description of the ICM software application components in a library. The importance of preserving and using this knowledge has become apparent with the recent trend of combining the software development process with the software application code. There are three main components in the framework: design patterns, design rationale model, and intelligent search algorithms. Design patterns have been chosen to represent, record, and reuse the recurring design structures and associated design experience in object-oriented software development. The Design Recommendation and Intent Model (DRIM) was extended in the current research effort to capture the specific implementation of reusable software components. DRIM provides a method by which design rationale from multiple ICM application designers can be partially generated, stored, and later retrieved by a computer system. To address the issues of retrieval, the paper presents a unique representation of a software component, and a search mechanism based on Reggia's setcover algorithm to retrieve a set of components that can be combined to get the required functionality is presented. This paper also details an initial, proof-of-concept prototype based on the framework. By supporting nonobtrusive capture as well as effective access of vital design rationale information regarding the ICM application development process, the framework described in this paper is expected to provide a strong information base for designing ICM software.

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.

The main hypothesis investigated in this paper is based upon the suggestion that the discursive reasoning in architecture supported by an explicit knowledge of spatial configurations can enhance both design productivity and the intelligibility of design solutions. The study consists of an examination of an architect's performance while solving intuitively a well-defined problem followed by an analysis of the spatial structure of their design solutions. One group of architects will attempt to solve the design problem logically, rationalizing their design decisions by implementing their explicit knowledge of spatial configurations. The other group will use an implicit form of such knowledge arising from their architectural education to reason about their design acts. An integrated model of protocol analysis combining linkography and macroscopic coding is used to analyze the design processes. The resulting design outcomes will be evaluated quantitatively in terms of their spatial configurations. The analysis appears to show that an explicit knowledge of the rules of spatial configurations, as possessed by the first group of architects can partially enhance their function-driven judgment producing permeable and well-structured spaces. These findings are particularly significant as they imply that an explicit rather than an implicit knowledge of the fundamental rules that make a layout possible can lead to a considerable improvement in both the design process and product. This suggests that by externalizing the design knowledge and restructuring it in a design model, creative thought can efficiently be evolved and stimulated.

Support vector machine (SVM) methods are widely used for classification and regression analysis. In many engineering applications, only one class of data is available, and then one-class SVM methods are employed. In reliability applications, the one-class data may be failure data since the data are recorded during reliability experiments when only failures occur. Different from the problems handled by existing one-class SVM methods, there is a bias constraint in the SVM model in this work and the constraint comes from the probability of failure estimated from the failure data. In this study, a new one-class SVM regression method is proposed to accommodate the bias constraint. The one class of failure data is maximally separated from a hypersphere whose radius is determined by the known probability of failure. The proposed SVM method generates regression models that directly link the states of failure modes with design variables, and this makes it possible to obtain the joint probability density of all the component states of an engineering system, resulting in a more accurate prediction of system reliability during the design stage. Three examples are given to demonstrate the effectiveness of the new one-class SVM method.

In this paper, a model of collective learning in design is developed in the context of team design. It explains that a team design activity uses input knowledge, environmental information, and design goals to produce output knowledge. A collective learning activity uses input knowledge from different agents and produces learned knowledge with the process of knowledge acquisition and transformation between different agents, which may be triggered by learning goals and rationale triggers. Different forms of collective learning were observed with respect to agent interactions, goal(s) of learning, and involvement of an agent. Three types of links between team design and collective learning were identified, namely teleological, rationale, and epistemic. Hypotheses of collective learning are made based upon existing theories and models in design and learning, which were tested using a protocol analysis approach. The model of collective learning in design is derived from the test results. The proposed model can be used as a basis to develop agent-based learning systems in design. In the future, collective learning between design teams, the links between collective learning and creativity, and computational support for collective learning can be investigated.

The feasibility and relative merits of integrating knowledge-based systems (KBSs) and artificial neural networks (ANNs) for application to engineering problems are presented and evaluated. The strength of KBSs lies in their ability to represent human judgment and solve problems by providing explanations from and reasoning with heuristic knowledge. ANNs demonstrate problem solving characteristics not inherent in KBSs, including an ability to learn from example, develop a generalized solution applicable to a range of examples of the problem, and process information extremely rapidly. In this respect, KBSs and ANNs are complementary, rather than alternatives, and may be integrated into a system that exploits the advantages of both technologies. The scope of application and quality of solutions produced by such a hybrid extend beyond the boundaries of the individual technologies. This paper identifies and describes how KBSs and ANNs can be integrated, and provides an evaluation of the advantages that will accrue in engineering applications.

In configuration design parts are selected and connected to meet customer specifications and engineering and physical constraints. Specifications include preferences (e.g., “prefer lower cost to higher performance, all things being equal”), bounds on various resources (e.g., “the computer should have four PCI slots”), and other information to customize a configuration. Constraints typically arise from exogenous concerns, such as the available parts, the way parts can interact, and the manufacturing plant.

The architecture and implementation of a mechanical designer's assistant shell called DEJAVU is presented. The architecture is based on an integration of design and CAD with some of the more well known concepts in case-based reasoning (CBR). DEJAVU provides a flexible and cognitively intuitive approach for acquiring and utilizing design knowledge. It is a domain independent mechanical design shell that can incrementally acquire design knowledge in the domain of the user. DEJAVU provides a design environment that can learn from the designer(s) until it can begin to perform design tasks autonomously or semi-autonomously. The main components of DEJAVU are a knowledge base of design plans, an evaluation module in the form of a design plan system, and a blackboard-based adaptation module. The existance of these components are derived from the utilization of a CBR architecture. DEJAVU is the first step in developing a robust designer's assistant shell for mechanical design problems. One of the major contributions of DEJAVU is the development of a clean architecture for the utilization of case-based reasoning in a mechanical designer's assistant shell. In addition, the components of the architecture have been developed, tailored or modified from a general CBR context into a more synergistic relationship with mechanical design.

A new methodology for solving resource leveling problems is introduced using Artificial Neural Networks (ANN). This paper describes a new efficient and robust approach which is unique to those utilized by traditional heuristic and optimization resource leveling techniques. The Resource Leveling Artificial Neural Network (RLANN) exploits advantages of both Hopfield networks and competition-based artificial neural networks. The universal scheme of the RLANN is applicable to construction project networks produced with Critical Path Method (CPM), in forms of either arrow or precedence diagrams. The scheme is comprised of two layers, an input and a competition layer, of artificial node matrices fully connected by links. Solving mechanisms inside the RLANN are based on an equation of motion and a competition strategy that control the level of daily resource usage. While the equation of motion governs activities to be shifted within schedule constraints, the competition process finds the best positions for the activities to achieve optimum results. The approach is simple and can be implemented on either a personal computer or a parallel processing device. The solutions produced are comparable to, or better than, those generated by other heuristic or optimization techniques. This paper describes the development of the RLANN, its solving mechanisms, and its uses in construction resource leveling problems. The comparison of the result of the RLANN to those of other traditional techniques is also included. The conclusions highlight the applicability of this model to other civil engineering problems.

The expertise of designers consists, primarily, of information about the relationship between goals or performance criteria and the attributes of the desired artifact that will result in performances that will satisfy these criteria. The designer like experts in other fields is typically better at applying the knowledge that constitutes his expertise than he is at articulating this knowledge. Generation and simulation models are discussed as a means of generating a set of designs for which the set of attributes defining these designs and the performance of these designs in terms of the criteria considered are explicitly defined. Pareto optimization is discussed as a means of structuring these designs on the basis of their performance. The induction algorithm ID3 is used as a means of inferring general statements about the nature of solutions which exhibit Pareto optimal performance in terms of a set of performance criteria. The rules inferred in building design domain are compared with those extracted using a heuristic based learning system.