Computational support for design-by-analogy (DbA) is a growing field, as it aids the process for designers looking to draw inspiration from external sources by harnessing the power of data mining and data visualization. This study presents a unique exploration-based approach for the analogical retrieval process using a computational tool called VISION (Visual Interaction tool for Seeking Inspiration based On Nonnegative Matrix Factorization). Leveraging the U.S. patent database as a source of inspiration, VISION enables designers to visualize a patent repository and explore for analogical inspiration in a user-driven manner. To achieve this, we perform hierarchical Nonnegative Matrix Factorization to generate a clustered structure of patent data and employ D3.js to visualize the patent structure in a node-link network, in which user interaction capabilities are enabled for data exploration. In this study, we also analyze the effect of data size (ranging from 100 to 3000 patents) on two performance aspects of VISION – the clustering quality of topic modeling results and the frame rate of interactive data visualization. The findings show that the tool exhibits more randomized and inconsistent topic modeling results when the database size is too small. But, increasing the database size lowers the frame rate to the point that it could diminish designers’ ability to retrieve and recall information. The scope of the work here is to present the creation of the DbA visualization tool called VISION and to evaluate its data scale limitations in order to provide a basis for developing a visual interaction tool for the analogical retrieval process during DbA.

At its heart, design is a decision-making process. These decisions, and the reasons for making them, comprise the design rationale (DR) for the designed artifact. If available, DR provides a comprehensive record of the reasoning behind the decisions made during the design. Unfortunately, while this information is potentially quite valuable, it is usually not explicitly captured. Instead, it is often buried in other design and development artifacts. In this paper, we study how to identify rationale from text documents, specifically software bug reports and design discussion transcripts. The method we examined is statistical text mining where a model is built to use document features to classify sentences. Choosing which features are most likely to be good predictors is important. We studied two evolutionary algorithms to optimize feature selection – ant colony optimization and genetic algorithms. We found that for many types of rationale, models built with an optimized feature set outperformed those built using all the document features.

The term “creative space” describes a relatively recent phenomenon of innovative workplace design. Such creative workspaces are becoming popular in industry and academia. However, the impact of specific spatial design decisions on creativity and innovation is not yet fully understood. This paper provides an overview of state-of-the-art research on creative work and learning environments. We conducted a systematic literature search within the Scopus database and identified a total of 73 relevant sources discussing creative spaces within academic, practice, and other innovation environments. Among the included sources are 51 academic publications and 22 sources from company research and illustrative coffee-table books. We analyzed the sources using three lenses of interest: (1) the types of theoretical and practical contributions that are provided, (2) the spatial characteristics that are suggested to be beneficial for creativity and innovation, and (3) the discussed potential of new technologies for designing or researching creative spaces. The results provide in-depth insight into the current state of research on the topic of creative spaces. Practitioners, educators, and researchers can use the presented overview to investigate the possible impact of creative workspace design and identify research gaps that can be filled by conducting further research in the field.

The sustainability concept is becoming ingrained in the international engineering community. The next generation of engineers has to be trained to appreciate economic, environmental, and societal impacts of its decisions, with an international perspective and at a local and global scale. The aim of this paper is to contribute to identifying the strengths and limits of current sustainability education practices to will be able to improve future decision-makers' and product designers' training. The first step of our research concerns the identification of knowledge, skills, attitudes, and values associated with sustainability and ecodesign to highlight what kind of engineers' competencies for sustainability have to be trained. Based on this identification, we examine the French education system from secondary school to university to study sustainability and ecodesign integration in different technological French curricula and practices. The objective is to estimate the effective place of sustainable development and ecodesign in the technological training courses. We analyze programs and complete our approach by observing teachers in their classrooms or by conducting interviews. Even if concepts, knowledge and skills related to ecodesign appear in many different programs, our study emphasizes that there is not a continuum between pre-secondary school, high school, and university. As a consequence, teachers have difficulties to understand the aims and the coherence of the program, and students are not really comfortable with the issues of sustainability and ecodesign. We propose to improve programs and practices by developing multidisciplinary curricula covering most of the sustainable issues and integrating best practices of international universities.

Significant research has been undertaken focusing on the application of evolutionary algorithms for design exploration at conceptual design stages. However, standard evolutionary algorithms are typically not well-suited to supporting such optimization-based design exploration due to the lack of design diversity in the optimization result and the poor search efficiency in discovering high-performing design solutions. In order to address the two weaknesses, this paper proposes a hybrid evolutionary algorithm, called steady-stage island evolutionary algorithm (SSIEA). The implementation of SSIEA integrates an island model approach and a steady-state replacement strategy with an evolutionary algorithm. The combination aims to produce optimization results with rich design diversity while achieving significant fitness progress in a reasonable amount of time. Moreover, the use of the island model approach allows for an implicit clustering of the design population during the optimization process, which helps architects explore different alternative design directions. The performance of SSIEA is compared against other optimization algorithms using two case studies. The result shows that, in contrast to the other algorithms, SSIEA is capable of achieving a good compromise between design diversity and search efficiency. The case studies also demonstrate how SSIEA can support conceptual design exploration. For architects, the optimization results with diverse and high-performing solutions stimulate richer reflection and ideation, rendering SSIEA a helpful tool for conceptual design exploration.

This paper deals with design characteristics-oriented approach to architectural design based on the combination of three methods – recognition, generation, and evaluation. Design characteristics are understood as a set of specific features which constitute a discriminant of a class of architectural forms. The Biederman recognition-by-components theory is used to recognize the design structure. An evolutionary algorithm, which serves as a generative tool, is driven by the fuzzy evaluation based on Birkhoff's aesthetic measure. Phenotypes of architectural objects are seen as configurations of Biederman's basic components essential for visual perception. Genotypes of these objects are represented by graphs with bonds, where nodes represent object components, node bonds represent component surfaces, while graph edges represent relations between surfaces. Graph evolutionary operators, that is, crossover and mutation, are defined in such a way that they preserve characteristic features seen as design requirements specified for designed objects. The fitness function is determined by the fuzzy evaluation of designs based on Birkhoff's aesthetic measure for polygons adapted for three-dimensional solids. The approach is illustrated by examples of designing objects with the use of a fuzzy evaluation mechanism, which takes into account both aesthetic criteria and the degree to which design requirements corresponding to object characteristic features are satisfied.

Today, in the field of architecture, bio-inspired algorithms can be used to design and seek solutions to design problems. Two of the most popular algorithms are the genetic algorithm (GA) and swarm intelligence algorithm. However, no study has examined the simultaneous use of these two bio-inspired algorithms in the field of architecture. Therefore, this study aims to test whether these two bio-inspired algorithms can work together. To this end, GA is used in this study to optimize the rule-based swarm algorithm for the conceptual design process. In this optimization test, the objective was to increase the surface area, and the constraints are parcel boundary and building height. Further, the forms associated with swarm agents were determined as variables. Following the case studies, the study concludes that the two bio-inspired algorithms can effectively work together.

In an increasingly competitive economic environment, innovation has become an invaluable asset to the organization and for effective knowledge management (KM). Nowadays, organizations are knowledge based and their success and survival depend on creativity, diversity, and innovation. A knowledge map is a vital tool for better KM and innovation. To this effect, the innovation processes on KMin education system through knowledge transfer activities will facilitate the shift from teaching as knowledge transmission to teaching as learning facilitation. In this context, we present a new approach based on, the one hand, the critical knowledge mapping being based on an extraction work of the expert knowledge and on the other hand, the description of the conceptual framework design which allows one to exploit and integrate knowledge capitalized and external knowledge by the open innovation process. In addition, this practice makes it possible to examine how KM, in particular, the knowledge mapping, can be used to establish the flow of the internal and external information in order to increase the efficiency of creativity and invention.

This paper sheds light on the new possibilities for user research activities facilitated by access to makerspaces. We present four case studies of user research conducted in two university-based makerspaces as examples of makerspace-driven user research. Further, by comparing the cases to three classical user research activities, namely observation, prototyping, and user journey mapping, we highlight the main aspects of this new context of user research. We find that accessibility to makerspaces enables user researchers to build low-fidelity yet high-functionality prototypes for exploring users’ preferences and motivations in controlled and repeatable ways. These prototypes fall into the category of experience prototypes, but they have greater functionality than the prototypes previously used in this field. Thus, a user researcher can explore a topic more systematically and in a more hypothesis-driven manner. In summary, this study encourages stakeholders in the early stages of product development to consider a makerspace as a resource for user-related requirement elicitation rather than for only specific product iteration.

In mass customization, software configurators enable novice end-users to design customized products and services according to their needs and preferences. However, traditional configurators hardly provide an engaging experience while avoiding the burden of choice. We propose a Design Participation Model to facilitate navigating the design space, based on two modules. Modeler enables designers to create customizable designs as parametric models, and Navigator subsequently permits novice end-users to explore these designs. While most parametric designs support direct manipulation of low-level features, we propose interpolation features to give customers more flexibility. In this paper, we focus on the implementation of such interpolation features into Navigator and its user interface. To assess our approach, we designed and performed user experiments to test and compare Modeler and Navigator, thus providing insights for further developments of our approach. Our results suggest that barycentric interpolation between qualitative parameters provides a more easily understandable interface that empowers novice customers to explore the design space expeditiously.

With the rise of the “Maker Movement” and the entrepreneurial university, academic makerspaces became widespread. These facilities provide tools and machines that enable making and tinkering; and while the offerings, organizational and operational models, and outreach of the academic makerspaces can vary widely across institutions, their common value proposition is enabling innovation, entrepreneurship, and hands-on project-based learning and these studies are largely qualitative and exploratory by nature. Through a case study, this paper presents an in-depth analysis and insights on the users and usage of an academic makerspace. Using the data generated by and collected from the users of an academic makerspace, we evaluate the effects of having access to the makerspace on users' teaching and learning experiences, and their satisfaction with the offerings. Our results show that attracting courses and educators to the facilities played a strong role in growing the user base, courses and teaching activities introduced new teaching and learning activities to adopt the offerings, group and project work is positively impacted, and the users are very satisfied with the facilities and having the access to its offerings. The analysis also showed that the demand for the offerings can be challenging to manage during certain periods, most of the users come from three departments (mechanical, electrical, civil engineering), and the diversity of the users could improve with the introduction of new offerings, such as a wet lab for bio/chemistry experiments and a food lab to tinker with food processing and preparation.

A data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM.

Design is a decision-making process for which knowledge is a prerequisite. Most decisions are taken at the conceptual stage and have pronounced influence on the final design. The literature, therefore, recommends the incorporation of sustainability criteria, such as environment, at this stage. Difficulty in performing life cycle assessment (LCA) due to low availability of information at the conceptual stage for evaluation and highly abstract nature of solutions, inadequate incorporation of DfE (Design for Environment) guidelines and LCA reports into the design process, and a lack of effective communication of the same to the designers for prompt decision-making are major motivations for the development of a support. This paper discusses a “conceptual Tool for environmentally benign design” – concepTe – that supports designers in decision-making during the conceptual design stage, by offering environmental impact (EI) estimates of abstract solutions with associated uncertainty, for evaluation and selection of the most environmentally benign solution as concept. The EI estimates are calculated by a module in the tool based on a proposed EI estimation method, which requires the support of a knowledge base to fetch appropriate LCA information corresponding to the design element being conceptualized. This knowledge base is grounded in the domain-agnostic SAPPhIRE model ontology, allows semantic operability of the knowledge, and offers the results to the designers in a familiar domain language to aid decision-making. A “proof of concept” of the tool is developed for application in design of building in the AEC (Architectural design, Engineering, and Construction) domain. Further, empirical studies are conducted to evaluate the effectiveness of the “proof of concept” to support decision-making and results are found favorable. The paper also discusses the future scope for further development of the tool into a holistic design decision-making platform.

In today's aggressive global market, innovation is key for success and design solutions require not only to achieve competitive edge, but also to address the growing environmental, social, and economic needs of the community at large. Consideration of these three pillars of sustainability makes a design inclusive, and life cycle thinking is found to be a promising approach across the literature. However, most supports for design address certain facets or aid singular tasks, and the use of design methods and tools, which have the potential to significantly improve the design process, is low due to inappropriate use and selection of these methods. InDeaTe (Innovation Design database and Template) is a holistic, knowledge-driven, computer-based tool for design of sustainable systems, such as products, manufacturing systems andservice systems and has been developed to address and integrate the aspects of sustainability on a singular design platform. It comprises of the generic design process Template that imbibes life cycle thinking into the process by incorporating consideration of every life cycle phase in each design stage, where design activities are performed iteratively. It further supports the design process by aiding the use and selection of appropriate design methods and tools in concurrence with the primary motivation of improving sustainability of the system with the aid of the InDeaTe Design Database. This paper discusses the ontological underpinnings behind the conceptualization of the InDeaTe methodology and the development of the supporting tool. The paper further reports empirical findings from six different case studies conducted for evaluating the effectiveness of InDeaTe tool in supporting design for sustainability (DfS). The results show that InDeaTe tool has potential in supporting DfS.

With growing city density and mass gatherings held all over the world in urban spaces, crowd disasters have been happening each year. In considering the avoidance of crowd disasters and the reduction of fatalities, it is important to analyze the efficient spatial layout of the public space in situations of high crowd density. Compared with traditional empirical design methods, computational approaches have better abilities for quantitative analysis and are gradually being adopted in the planning and management of the urban public space. In this paper, we investigated the official documents, publicly available videos, and materials of the Shanghai waterfront crowd disaster which happened on December 31, 2014. Based on the investigation, a detailed site survey was conducted and pedestrian flow data were acquired. To test the influence of different spatial layouts, an agent-based simulator is built, following the ASPFver4.0 (Agent Simulator of Pedestrian Flow) pedestrian walking rules. With the surveyed pedestrian flow data, the original spatial layout of the Shanghai Bund waterfront together with five other comparison scenarios are tested, including both space design and crowd management improvements. In the simulation results, the efficiencies of different space design and crowd management solutions are compared. The results show that even simple crowd control measures such as capacity reserve and more proper route planning will allow for a positive improvement in crowd safety. The results also compare the efficiency of different spatial operations and give general suggestions to the problems urban public space designers should consider in high-density environments.

The user's gaze can provide important information for human–machine interaction, but the analysis of manual gaze data is extremely time-consuming, inhibiting wide adoption in usability studies. Existing methods for automated areas of interest (AOI) analysis cannot be applied to tangible products with a screen-based user interface (UI), which have become ubiquitous in everyday life. The objective of this paper is to present and evaluate a method to automatically map the user's gaze to dynamic AOIs on tangible screen-based UIs based on computer vision and deep learning. This paper presents an algorithm for automated Dynamic AOI Mapping (aDAM), which allows the automated mapping of gaze data recorded with mobile eye tracking to the predefined AOIs on tangible screen-based UIs. The evaluation of the algorithm is performed using two medical devices, which represent two extreme examples of tangible screen-based UIs. The different elements of aDAM are examined for accuracy and robustness, as well as the time saved compared to manual mapping. The break-even point for an analyst's effort for aDAM compared to manual analysis is found to be 8.9 min gaze data time. The accuracy and robustness of both the automated gaze mapping and the screen matching indicate that aDAM can be applied to a wide range of products. aDAM allows, for the first time, automated AOI analysis of tangible screen-based UIs with AOIs that dynamically change over time. The algorithm requires some additional initial input for the setup and training, but analyzed gaze data duration and effort is only determined by computation time and does not require any additional manual work thereafter. The efficiency of the approach has the potential for a broader adoption of mobile eye tracking in usability testing for the development of new products and may contribute to a more data-driven usability engineering process in the future.

Design effort is a key resource for product design projects. Environments where design effort is scarce, and therefore valuable, include hackathons and other time-limited design challenges. Predicting design effort needs is key to successful project planning; therefore, understanding design effort-influencing factors (objective considerations that are universally accepted to exert influence on a subject, that is, types of phenomena, constraints, characteristics, or stimulus) will aid in planning success, offering an improved organizational understanding of product design, characterizing the design space and providing a perspective to assess project briefs from the outset. This paper presents the Collaborative Factor Identification for Design Effort (CoFIDE) Method based on Hird's (2012) method for developing resource forecasting tools for new product development teams. CoFIDE enables the collection of novel data of, and insight into, the collaborative understanding and perceptions of the most influential factors of design effort levels in design projects and how their behavior changes over the course of design projects. CoFIDE also enables design teams, hackathon teams, and makerspace collaborators to characterize their creative spaces, to quickly enable mutual understanding, without the need for complex software and large bodies of past project data. This insight offers design teams, hackathon teams, and makerspace collaborators opportunities to capitalize on positive influences while minimizing negative influences. This paper demonstrates the use of CoFIDE through a case study with a UK-based product design agency, which enabled the design team to identify and model the behavior of four influential factors.