Serving as a guest editor of this special journal issue, I review in this introductory paper some of the important issues concerning applying case-based reasoning (CBR) techniques to the design domain. Through discussions of these issues, I attempt to give the current status of the case-based design field, with surveys of existing case-based design systems and a summary from the first workshop held on this subject. For those who are less familiar with CBR, this paper also contains a section on what CBR method is and how it is contrasted with other reasoning approaches. The last section of this article introduces the papers included in this issue.

This paper presents the use of first-order logic to improve upon currently employed engineering design knowledge management techniques. Specifically, this work uses description logic in unison with Horn logic, to not only guide the knowledge acquisition process but also to offer much needed support in decision making during the engineering design process in a distributed environment. The knowledge management methods introduced are highlighted by the ability to identify modeling knowledge inconsistencies through the recognition of model characteristic limitations, such as those imposed by model idealizations. The adopted implementation languages include the Semantic Web Rule Language, which enables Horn-like rules to be applied to an ontological knowledge base and the Semantic Web's native Web Ontology Language. As part of this work, an ontological tool, OPTEAM, was developed to capture key aspects of the design process through a set of design-related ontologies and to serve as an application platform for facilitating the engineering design process. The design, analysis, and optimization of a classical I-beam problem are presented as a test-bed case study to illustrate the capabilities of these ontologies in OPTEAM. A second, more extensive test-bed example based on an industry-supplied medical device design problem is also introduced. Results indicate that well-defined, networked relationships within an ontological knowledge base can ultimately lead to a refined design process, with guidance provided by the identification of infeasible solutions and the introduction of “best-case” alternatives. These case studies also show how the application of first-order logic to engineering design improves the knowledge acquisition, knowledge management, and knowledge validation processes.

Traceability of information provides the basis for assessing the credibility of engineering information, better understanding it, and making judgments about the appropriateness of its use for a particular design task. The presented research attempts to answer how the proposed traceability methodology and framework could help designers to improve communication, eventually create new channels of communication, and contribute to the creation of shared understanding in collaborative design processes. The discussion of these issues is based on a literature review, empirical research, observations of industrial practice, and feedback from initial implementation. The research is focused on information objects (IOs), archetypically represented in the engineering domain as technical documents that are often complex structures constituted of textual, numerical, and graphical fragments. The presented approach is based on an abstraction of IOs' relationships organized around specific contexts that are defined by a subset of product development ontology. Each IO could be repeatedly represented in various contexts that may contain different subsets of objects and their relationships. Such a representation also acts as a container in which the ontology concept instances are associated with IOs being developed and traced during the design episode. The usage of the proposed traceability methodology is discussed with examples of implementation and possible utilization situations. The paper is focused on explaining how the developed functionalities could help to resolve manifestations of inadequate information flow, which cause communication barriers in engineering companies. In addition, the proposed traceability methodology offers the possibility to record the detailed history of actions and events associated with IOs in the usage process of the product life cycle management systems. Based on the research findings, this paper argues that such a network of the traceability links and relationships may be viewed as a novel design communication channel.

Creative conceptual design requires significant previous design knowledge. Case-based reasoning enables learning from previous design experience and has a great potential in supporting creative conceptual design by means of seeking to retrieve, reuse, and revise most appropriate cases to generate inspired solutions. However, traditional case-based reasoning based creative conceptual design models focus on design strategies research, pay little attention to defining a consistent knowledge representation model, and neglect the research to make various types of knowledge retrieval tractable. Faced with such drawbacks, the expected design knowledge cannot be retrieved properly, especially in cases where multidisciplinary knowledge is concerned or exact query terms are absent. In order to solve these issues, this paper presents a combined approach to support creative conceptual design process. First, function–behavior–structure knowledge cell is introduced as a unified consistent design knowledge representation model. Second, a hybrid similarity measure is proposed to increase the overall possibility of obtaining useful design knowledge by considering semantic understanding ability. Third, an intelligent creative conceptual design system has been developed with a case study of a novel insulin pump design to demonstrate its usage, and two experiments are conducted to evaluate the performance of the proposed approach. The results show that the proposed approach outperforms other case-based reasoning based creative conceptual design models.

An Artificial Neural Network (ANN) model has been developed for analyzing traffic in an inland waterway network. The main purpose of this paper is to determine how well such a relatively fast model for analyzing a queuing network could substitute for far more expensive simulation. Its substitutability for simulation is judged by relative discrepancies in predicting tow delays between the ANN and simulation models. This model is developed by integrating five distinct ANN submodels that predict tow headway variances at (1) merge points, (2) branching (i.e., diverging) points, (3) lock exits, and (4) link outflow points (e.g., at ports, junctions, or lock entrances), plus (5) tow queuing delays at locks. Preliminary results are shown for those five submodels and for the integrated network analysis model. Eventually, such a network analyzer should be useful for designing, selecting, sequencing, and scheduling lock improvement projects, for controlling lock operations, for system maintenance planning, and for other applications where many combinations of network characteristics must be evaluated. More generally, this method of decomposing complex queuing networks into elements that can be analyzed with ANNs and then recombined provides a promising approach for analyzing other queuing networks (e.g., in transportation, communication, computing, and production systems).

This paper proposes interval constraint networks and interval propagation techniques for automatic tolerance design. To develop an intelligent automatic tolerance design mechanism, the following interrelated problems are addressed: (1) representing the relationships between the tolerances of the entities, attributes, and the functional requirements of a design component by a constraint network, (2) developing tolerance propagation techniques in the network that will satisfy the constraints between the entities, attributes and functional requirement. For the purpose of tolerance design, we propose new definitions for consistency of constraints and consistency of a constraint network. New forward and backward propagation techniques achieve worst-case tolerance analysis and synthesis, respectively. A tank design problem illustrates the use of the procedures for tolerance analysis and synthesis. The discussion and examples compare the new constraint network and propagation techniques with previous works.

Design support systems need to be developed on the basis of an understanding of the human design process to be useful during design. The explicit representation of design history and rationale are of particular importance for explanation and reuse. Within the DESIRE framework for compositional modelling, a generic task model of design has been developed that clearly specifies the role of design history and design rationale within the design process. The model provides a structure to distinguish different types of design rationale, according to the functional role they play in the design process. It has been used to structure the modelling process of an example aircraft design task, which illustrates the various instances of design rationale that can be generated.

To be useful for architects and related designers searching for creative, expressive forms, performance-based digital tools must generate a diverse range of design solutions. This gives the designer flexibility to choose from a number of high-performing designs based on aesthetic preferences or other priorities. However, there is no single established method for measuring diversity in the context of computational design, especially in the field of architecture. This paper explores different metrics for quantifying diversity in parametric design, which is an increasingly common digital approach to early-stage exploration, and tests how human users perceive these diversity measurements. It first provides a review of existing methodologies for measuring diversity and describes how they can be adapted for parametrically formulated design spaces. This paper then tests how these different metrics align with human perception of design diversity through an online visual survey. Finally, it offers a quantitative comparison between the different methods and a discussion of their attributes and potential applications. In general, the comparison indicates that at the level of diversity difference that becomes visually meaningful to humans, the measurable difference between metrics is small. This paper informs future researchers, developers, and designers about the measurement of diversity in parametric design, and can stimulate further studies into the perception of diversity within sets of design options, as well as new design methodologies that combine architectural novelty and performance.

Computational abstraction of engineering design leads to an elegant theory defining (1) the process of design as an abstract model of computability, the Turing machine; (2) the artifacts of design as enumerated strings from a (possibly multidimensional) grammar; and (3) design specifications or constraints as formal state changes that govern string enumeration. Using this theory, it is shown that engineering design is a computable function. A computational methodology based on the theory is then developed that can be described as a form follows function design paradigm.

A user interface (UI) design that meets the preferences, differences, and needs of the group of users can potentially increase the usability of a system. Users, in general, feel more familiar with the context that reflects their cultural values and practices. The Arabic culture plays a significant role in how Arab users interact and communicate with technologies. The customs, artifacts, and traditions of the Arab world are different in nature from the Western cultures. Thus, it is essential to consider these differences when designing the UI prototype. This study investigated the role of certain cultural preferences in the design of UI for Arab users. A think-aloud approach and Hofstede's cultural dimensions were used on 23 Arab users to generate the necessary design guidelines for the UI of mobile health application. Then, 78 participants were recruited to evaluate the proposed UI design. The usability results showed high satisfaction among Arab users about the role of culture in the design of the UI. Findings from this study can be used by designers and developers to aid their design of UI for group-specific cultural preferences and values.

The goal of this paper is to reexamine assumptions about sketch-based interfaces for modeling in the context of designers' needs and practices. Research questions examine (a) the type of sketch support and (b) the timing of support. Both concepts try to determine when, what, why and how to augment design processes in a way that is useful to designers. Two experiments (one in architecture and one in product design) based on ergonomics theory are conducted and intend to question some of these assumptions. The Port Zeeland experiment examines how 20 novices perceive and copy a blurred architectural sketch, which provides clues for a sketch interpretation system. The “Tragere” experiment studies how 12 professional product designers, some of whom are “idea generators” and others “idea pursuers,” perceive, recognize, and handle a design sketch. The results take a designer's point of view in assessing the timing and value of sketch assistance in product design. The quantitative data analysis provides rich clues about when, why and how product sketches should be supported. The paper explores the strategies developed by designers to perceive and recognize graphical content and discusses the generation of three-dimensional volumes, the univocity state between sketches and three-dimensional models, and the treatment of features in freehand sketches. The paper concludes with observations on the timing and value of support, as first integrated in NEMo, a tool for early stage architectural design, and then in PEPS3, an early stage framework for product design.

The aim of this paper is to investigate the challenges associated with the industrial implementation of generative design systems. Though many studies have been aimed at validating either the technical feasibility or the usefulness of generative design systems, there is, however, a lack of research on the practical implementation and adaptation in industry. To that end, this paper presents two case studies conducted while developing design systems for industrial uses. The first case study focuses on an engineering design application and the other on an industrial design application. In both cases, the focus is on detail-oriented performance-driven generative design systems based on currently available computer-assisted design tools. The development time and communications with the companies were analyzed to identify challenges in the two projects. Overall, the results show that the challenges are not related to whether the design tools are intended for artistic or technical problems, but rather in how to make the design process systematic. The challenges include aspects such as how to fully utilize the potential of generative design tools in a traditional product development process, how to enable designers not familiar with programming to provide design generation logic, and what should be automated and what is better left as a manual task. The paper suggests several strategies for dealing with the identified challenges.

Engineering risk methods and tools account for and make decisions about risk using an expected-value approach. Psychological research has shown that stakeholders and decision makers hold domain-specific risk attitudes that often vary between individuals and between enterprises. Moreover, certain companies and industries (e.g., the nuclear power industry and aerospace corporations) are very risk-averse whereas other organizations and industrial sectors (e.g., IDEO, located in the innovation and design sector) are risk tolerant and actually thrive by making risky decisions. Engineering risk methods such as failure modes and effects analysis, fault tree analysis, and others are not equipped to help stakeholders make decisions under risk-tolerant or risk-averse decision-making conditions. This article presents a novel method for translating engineering risk data from the expected-value domain into a risk appetite corrected domain using utility functions derived from the psychometric Engineering Domain-Specific Risk-Taking test results under a single-criterion decision-based design approach. The method is aspirational rather than predictive in nature through the use of a psychometric test rather than lottery methods to generate utility functions. Using this method, decisions can be made based upon risk appetite corrected risk data. We discuss development and application of the method based upon a simplified space mission design in a collaborative design-center environment. The method is shown to change risk-based decisions in certain situations where a risk-averse or risk-tolerant decision maker would likely choose differently than the expected-value approach dictates.

One mode of creative design is for designers to draw analogies that connect the design domain (e.g., a mechanical device) to some other domain from which inspiration is drawn (e.g., a biological system). The identification and application of analogies can be supported by software tools that store, structure, present, or propose source domain stimuli from which such analogies might be constructed. For these tools to be effective and not impact the design process in negative ways, they must fit well with the information and interaction needs of their users. However, the user requirements for these tools are seldom explicitly discussed. Furthermore, the literature that supports the identification of such requirements is distributed across a number of different domains, including those that address analogical design (especially biomimetics), creativity support tools, and human–computer interaction. The requirements that these literatures propose can be divided into those that relate to the information content that the tools provide (e.g., level of abstraction or mode of representation) and those that relate to the interaction qualities that the tools support (e.g., accessibility or shareability). Examining the relationships between these requirements suggests that tool developers should focus on satisfying the key requirements of open-endedness and accessibility while managing the conflicts between the other requirements. Attention to these requirements and the relationships between them promises to yield analogical design support tools that better permit designers to identify and apply source information in their creative work.

The design of even the simplest product requires thousands of decisions. Yet few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions do not need support or is it because techniques have yet to be developed that are usable on a wide basis? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams – some are about the product and others about the processes that support the product – some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. To address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed. This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.

Affective engineering is being increasingly used to describe a systematic approach to the analysis of consumer reactions to candidate designs. It has evolved from Kansei engineering, which has reported improvements in products such as cars, electronics, and food. The method includes a semantic differential experiment rating candidate designs against bipolar adjectives (e.g., attractive–not attractive, traditional–not traditional). The results are statistically analyzed to identify correlations between design features and consumer reactions to inform future product developments. A number of key challenges emerge from this process. Clearly, suitable designs must be available to cover all design possibilities. However, it is also paramount that the best adjectives are used to reflect the judgments that participants might want to make. The current adjective selection process is unsystematic, and could potentially miss key concepts. Poor adjective choices can result in problems such as misinterpretation of an experimental question, clustering of results around a particular response, and participants' confusion from unfamiliar adjectives that can be difficult to consider in the required context (e.g., is this wristwatch “oppressive”?). This paper describes an artificial intelligence supported process that ensures adjectives with appropriate levels of precision and recall are developed and presented to participants (and thus addressing problems above) in an affective engineering study in the context of branded consumer goods. We illustrate our description of the entire concept expansion and reduction process by means of an industrial case study in which participants were asked to evaluate different designs of packaging for a laundry product. The paper concludes by describing the important advantages that can be gained by the new approach in comparison with previous approaches to the selection of consumer focused adjectives.

Ti–6Al–4V alloy has superior material properties such as high strength-to-weight ratio, good corrosion resistance, and excellent fracture toughness. Therefore, it is widely used in aerospace, medical, and automotive industries where machining is an essential process for these industries. However, machining of Ti–6Al–4V is a material with extremely low machinability characteristics; thus, conventional machining methods are not appropriate to machine such materials. Ultrasonic-assisted machining (UAM) is a novel hybrid machining method which has numerous advantages over conventional machining processes. In addition, minimum quantity lubrication (MQL) is an alternative type of metal cutting fluid application that is being used instead of conventional lubrication in machining. One of the parameters which could be used to measure the performance of the machining process is the amount of cutting force. Nevertheless, there is a number of limited studies to compare the changes in cutting forces by using UAM and MQL together which are time-consuming and not cost-effective. Artificial neural network (ANN) is an alternative method that may eliminate the limitations mentioned above by estimating the outputs with the limited number of data. In this study, a model was developed and coded in Python programming environment in order to predict cutting forces using ANN. The results showed that experimental cutting forces were estimated with a successful prediction rate of 0.99 with mean absolute percentage error and mean squared error of 1.85% and 13.1, respectively. Moreover, considering too limited experimental data, ANN provided acceptable results in a cost- and time-effective way.

This study adopted a Kansei engineering methodology to examine users’ Kansei values toward automotive interior designs by conducting experiments under two models: sensory experience and perceived value evaluation models. In the sensory experience model, 60 participants were recruited to experience using 27 vehicle samples and to assign scores to eight Kansei keywords. Hayashi's quantification theory type 1 was used to calculate the weight of 12 interior space items and 40 interior design categories. In the perceived value evaluation model, 60 participants completed a questionnaire on the influence levels of the Kansei keywords on their Kansei values from the perspectives of four consumer perceived value dimensions. The questionnaire scores were then evaluated using factor analysis to extract nine primary factors affecting participants’ perceptions. This study determined that the interesting and dynamic perceptions corresponded to five design items under the Excitement Quality factors, vigorous and recreational perceptions corresponded to four items under the Relaxation and Comfort factor, and technological and luxurious perceptions corresponded to six items under the Luxury and Splendor factor and the Esteemed Image factor. These findings can help designers and researchers in the relevant sectors create automotive interior designs that fulfill the expectations of users.

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.

A genetic algorithm is a metaheuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper provides one definition of periodic vehicle routing problem for single and multidepots conforming to a wide range of real-world problems and also develops a novel hybrid genetic algorithm to solve it. The proposed hybrid genetic algorithm applies a modified approach to generate a population of initial chromosomes and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. Moreover, during the implementation a hybrid algorithm, cyclic transfers, an effective class of neighborhood search is applied. The author uses three genetic operators to produce good new offspring. The objective function consists of two terms: total traveled distance at each depot and total waiting time of all customers to take service. Distances are assumed Euclidean or straight line. These conditions are exactly consistent with the real-world situations and have received little attention in the literature. Finally, the experimental results have revealed that the proposed hybrid method can be competitive with the best existing methods as asynchronous parallel heuristic and variable neighborhood search in terms of solution quality to solve the vehicle routing problem.