Product/service systems (PSSs) are increasingly found in markets, and more resources are being invested in PSS design. Despite the substantial research into PSS design, the current literature exhibits an incomplete understanding of it as a cognitive activity. This article demonstrates that the methods used to analyze product designers’ cognitive behavior can be used to produce comparable and commensurable results when analyzing PSS designers. It also generates empirical grounding for the development of hypotheses based on a cognitive study of a PSS design session in a laboratory environment using protocol analysis. This study is a part of a larger project comparing PSS design with product design. The results, which are based on the function–behavior–structure coding scheme, show that PSS design, when coded using this scheme, can be quantitatively compared with product design. Five hypotheses were developed based on the results of the study of this design session concerning where and how designers expend their cognitive design effort. These hypotheses can be used to design experiments that test them and provide the grounding for a fuller understanding of PSS design.

An academic makerspace, home to tools and people dedicated to facilitating and inspiring a making culture, is characterized by openness, creativity, learning, design, and community. This nontraditional learning environment has found an immense increase in popularity and investment in the last decade. Further, makerspaces have been shown to be highly gendered, privileging men's and masculine understandings of making. The spike in popularity warrants deeper analysis, examining the value of these spaces for women and if learning is occurring in these spaces, specifically at higher education institutions. We implemented a phenomenologically based interviewing process to capture the making experiences of 20 women students, recruited through purposive and snowball sampling. By eliciting the narratives of women students, we captured how making, designing, and creating evolved through gendered experiences in the university makerspace. Each interview was transcribed and resulted in around 868 pages of single-spaced text transcriptions. The data were analyzed through multiple cycles of open and axial coding for common themes and patterns, where makerspaces create a culture of learning, facilitate students’ design journey, and form a laboratory for creativity. These themes forwarded the creation of a learning model that showcases how design and learning interact in the makerspace. This work demonstrates that women students are engaging learning and inspiration; developing confidence and resilience; and learning how to work with others and collaborate.

This paper presents in detail a parametric shape grammar for the generation of ancient Greek and Roman library plans. Ancient Libraries showcase a significant variability of scale, monumentality, and design with the use of different architectural components, and researchers cannot identify them as easily as other building types. The grammar is in 2D and captures all the variability of libraries from the simpler forms of the Hellenistic period to the more monumental of the Roman period, creating a vast range of designs. The paper presents the grammar-generated plans of the known libraries based on the evidence in the archaeological record. A frequency analysis of the occurrence of the rules in the derivations ranks the different characteristics of libraries and the architectural forms with which they occur in the corpus, to determine the probability of specific rules and to interpret them as mandatory or optional. The contribution of this work is that the shape grammar summarizes the design principles of ancient libraries visually and helps establish the building type of the ancient library. The frequency analysis assists tremendously in the reconstruction of fragmentarily preserved ancient libraries by providing a system in the reconstruction of the missing parts, when ambiguity is involved.

The personalization of products and services has become an inevitable trend in the manufacturing and service industry, but it is very difficult to identify users' personalized requirements accurately. This paper solves this problem by constructing an identifying model for personalized requirement based on user profiling. Firstly, the framework of the proposed model and the process of identifying the user's personalized requirements with this model are introduced, and then an experimental scheme for obtaining users' profiling data is designed. On this basis, an experiment is performed by investigating users' requirements for the computer to obtain the data, and the data are used for the analysis based on the proposed model. The analysis result shows that the model can reveal the difference among heterogeneous users well, find out the implicit requirements of users, and identify the gap between existing products and users' personalized requirements, which provides support to the subsequent improvement of product design.

Collision avoidance for robots and vehicles in unpredictable environments is a challenging task. Various control strategies have been developed for the agent (i.e., robots or vehicles) to sense the environment, assess the situation, and select the optimal actions to avoid collision and accomplish its mission. In our research on autonomous ships, we take a machine learning approach to collision avoidance. The lack of available ship steering data of human ship masters has made it necessary to acquire collision avoidance knowledge through reinforcement learning (RL). Given that the learned neural network tends to be a black box, it is desirable that a method is available which can be used to design an agent's behavior so that the desired knowledge can be captured. Furthermore, RL with complex tasks can be either time consuming or unfeasible. A multi-stage learning method is needed in which agents can learn from simple tasks and then transfer their learned knowledge to closely related but more complex tasks. In this paper, we explore the ways of designing agent behaviors through tuning reward functions and devise a transfer RL method for multi-stage knowledge acquisition. The computer simulation-based agent training results have shown that it is important to understand the roles of each component in a reward function and the various design parameters in transfer RL. The settings of these parameters are all dependent on the complexity of the tasks and the similarities between them.

Despite alefforts for a sustainable production system, many companies are still struggling to implement environmental aspects in their daily product development processes. Among the evaluation and improvement methods, life cycle assessment (LCA) is one of the most popular tools to achieve this goal. Up to date, LCA has been applied to many products, services, and industrial systems to evaluate their environmental impact aspects. However, there is a wide range of indicators available to be applied for LCA, and choosing an inappropriate indicator may lead the product designer to achieve wrong and weak results. Therefore, this paper proposes to overcome this difficulty by developing a method that can be used as a knowledge transfer to product designers and LCA practitioners in order to help them to make the most appropriate choice of LCA indicators. This method should have some characteristics, such as (a) to be adaptable to a given context and (b) to be dynamic, scalable, and easy to learn. The purpose of this paper is to present the Evaluation Method for Choosing Indicator (EMCI) developed to facilitate the learning process of LCA methods and to quickly select their most appropriate indicators. To validate the EMCI method, a case study on a French textile industry has been implemented. The focus was to evaluate how LCA indicators and methods were chosen to be integrated into the suitable eco-design LCA tool.

There is an increasing demand for humanitarian aid around the world. At the same time, the number of makerspaces has been growing exponentially. Recently, the humanitarian sector has become interested in how these new design spaces can help crisis-affected populations. Despite the emergence of humanitarian makerspaces, there is little research to date that documents their outcomes and impacts. A multi-case study approach is taken to analyze three makerspaces that support migrants, refugees, and asylum seekers in Greece. A maturity grid tool is used to show that humanitarian makerspaces are driving impact in six key areas. The study underlines how these makerspaces support different design activities and have different outcomes. It also considers the challenges which are preventing humanitarian makerspaces from achieving their ultimate goals, drawing attention to the need for an enabling ecosystem in both the local and humanitarian context. This research brings clarity to the poorly understood phenomenon of humanitarian makerspaces and highlights the important role of design in humanitarian interventions. It also reveals practical insights for humanitarian organizations who are considering setting up makerspaces in crisis-affected communities.

This paper presents a new VR interaction environment for the evaluation of digital prototypes, specifically in designer–client review sessions, and documents its implementation via experience mapping. Usability of VR controllers and basic manipulation remains a barrier for lay users, and a range of typical implementations are reviewed, highlighting the need for an easily accessible interface for this setting. The resulting interface configuration – the Control Carousel – demonstrates how the appropriate use of familiar mechanisms can increase VR accessibility. Three case studies using the Carousel in commercial design projects are described, and the subsequent interface refinements outlined. Finally, the development of an experience map describing the logistical, interactive, and emotive factors affecting the Carousel's implementation is documented. This provides insights on how experience mapping can be used as part of a human-centred design process to ensure VR environments are attuned to the requirements of users, in this instance delivering improved collaborative reviews.

Metaphors are powerful tools for design, enabling designers to encapsulate sets of properties and relations as short verbal descriptions. This paper aims to clarify how simple spatial configurations may emerge from concise metaphoric descriptions at the conceptual design phase. To this aim, we propose a framework for a metaphor-based design process. As a basis for the framework, we introduce the concept of “complementary visual potential” – a property which ties the spatial configuration of the objects in the composition with their metaphoric roles. The framework is developed by studying the practice of metaphor-based spatial configuration design in Japanese rock gardens. Accordingly, it is implemented and tested in this context by attempting to generate alternative designs for an existing rock composition in the famous garden of Ryōan-ji. This is followed by a discussion of its possible implications and potential for generalization to other areas of design.

In the traditional Axiomatic Design (AD) theory, the mapping from the functional domain to the physical domain is based on the designers’ own knowledge and experience, and there is no systematical approach including the design resources provided outside the designers themselves’ access. Thus, the raw materials for the design process are largely limited, which means they can hardly support the designers’ increasingly creative and innovative conceptions. To help AD theory better support the design process, this paper proposes a computer-aided approach for the mapping from the functional domain to the physical domain within a distributed design resource environment, which consists of numerous design resources offered on the Internet by the providers widely distributed in different locations, institutes, and disciplines. To prove the feasibility of this proposed approach, a software prototype is established, and a natural leisure hotel is designed as an implementation case.

Design is a complicated and sophisticated process with numerous existing theories trying to describe it. To verify theories and quantitatively describe the design process, design experiment, and data analysis are crucial and inevitable. However, applying data analysis in the design experiment is tricky and design data is not fully utilized in many aspects. To explore the potential of design experiment data, this paper introduces data-driven research based on an interior design experiment, aiming to reveal the category and process of design by conducting data analysis, visualization, and recommendation. We introduce an interactive evolutionary computation (IEC) design experiment that deals with a simplified interior design task and has already been tested on 230 subjects. Using the data gathered during the experiment, we conduct data analysis and visualization involving methods including Holistic color interval and K-means clustering to show categories and processes in design. Additionally, we train a content-based recommendation system with experiment data to capture user preference and make the IEC system more efficient and intelligent. The analysis and visualization show clear design categories and capture an evident trend towards the final design outcome. The application of the recommendation system brings a prominent improvement to the IEC system. This research shows the great potential of the various data-driven methods in design research.

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.

This paper presents a fleet model explained through a complex configuration of load sharing that considers overcapacity and is based on a life cycle cost (LCC) approach for cost-related decision-making. By analyzing the variables needed to optimize the fleet size, which must be evaluated in combination with the event space method (ESM), the solution to this problem would normally require high computing performance and long computing times. Considering this, the combined use of an integer genetic algorithm (GA) and the ant colony optimization (ACO) method was proposed in order to determine the optimal solution. In order to analyze and highlight the added value of this proposal, several empirical simulations were performed. The results showed the potential strengths of the proposal related to its flexibility and capacity in solving large problems with a near optimal solution for large fleet size and potential real-world applications. Even larger problems can be solved this way than by using the complete enumeration approach and a non-family fleet approach. Thus, this allows for a more real solution to fleet design that also considers overcapacity, availability, and an LCC approach. The simulations showed that the model can be solved in much less time compared with the base model and allows for the resolution of a fleet of at least 64 trucks using GA and 130 using ACO, respectively. Thus, the proposed framework can solve real-world problems, such as the fleet design of mining companies, by offering a more realistic approach.

Design is inherently affected by human-related factors and it is of no surprise that the fine-tuning of instruments capable of measuring aspects of human behavior has attracted interest in the design field. The recalled instruments include a variety of devices that capture and quantitatively assess people's unintentional and unconscious reactions and that are generally referred as neurophysiological or biometric. The number of experimental applications of these instruments in design was extremely limited as of 2016, when Lohmeyer and Meboldt published a first report on relevant measures and their interpretation in design. In the last few years, the number of relevant publications has increased dramatically and this determines the opportunity to carry out a comprehensive review in the field. The reviewed contributions are analyzed and classified according to, among others, instruments used, the kind of stakeholders involved and the supported design research activities. The role of biometric measures with respect to traditional research methods is emphasized too. The discussed instruments can represent supports or substitutes for traditional approaches, as well as they are capable of exploring phenomena that could not be addressed hitherto. The intensity of research concerning experiments with biometric measurements is discussed too; a particular focus of the final discussion is the individuation of obstacles that prevent them from becoming commonplace in design research.

This paper proposes an intelligent model-based optimization methodology for optimizing the production cost and material removal rate subjected to surface quality constraint in turning operation of hardened AISI D2. Unlike traditional approaches, this paper deals with finding optimum cutting parameters considering the real condition of the cutting tool. Tool flank wear is predicted by the model obtained using genetic programming. On the basis of the predicted flank wear value, the surface roughness of work piece is estimated by neural networks. Applying the particle swarm optimization algorithm, the optimum machining parameters are determined. The simulation and experimental results show that machining with proposed intelligent optimization methodology has higher efficiency than conventional techniques with constant optimized cutting parameters.

This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests, in situ field CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

The original ideas on design abduction, inspired by treatments in philosophy of science, had a narrow conception on how novelty emerges in design, when looked at in terms of logic. The authors have previously presented a re-proposed notion of abduction in design, taking the differences between science and design into account. Now, in this article, the invention of the airplane by the Wright brothers is analyzed as a retrospective case study. Key parts of the re-proposed notion of design abduction are demonstrated, and two new types of design abduction are identified, namely strategic abduction and dynamic abduction. Perhaps even more importantly, a new hypothesis on the cognitive basis of design abduction is reached. While the importance of model-based abduction (and reasoning) is confirmed, the case also pinpoints the central role of verbalization and discussion in supporting design reasoning in general and especially abduction. All in all, it seems that an improved understanding of design abduction and its cognitive basis would be instrumental in promoting more effective and efficient designing.