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Facing the challenges posed by increasingly complex, dynamic, and unforeseen requirements, the design process is grappling with the critical issue of ensuring sustained product satisfaction amid changing demands. This paper introduces an approach for evaluating design adaptability, considering potential future requirements. Entropy serves as a crucial indicator to quantify design effort and the Markov process is employed to simulate potential requirement changes. The information contents of design requirements and design solutions are defined based on information entropy theory, and the design adaptability of a design candidate is evaluated by calculating the extra design effort for satisfying the design requirements, which is the difference in information content between the design candidate and design requirements. Moreover, a simulation method for requirement evolution is proposed, which integrates information entropy theory and the Markov process to accommodate potential future requirements. The general design adaptability of design solutions is then calculated based on conditional entropy, taking into account the evolving design requirements. Finally, the effectiveness of the proposed approach is validated through a case study involving the design and evaluation of a hybrid additive manufacturing device. [ABSTRACT FROM AUTHOR]

Multilayer perceptron (MLP) and convolutional neural network (CNN) encounter a critical scalability issue when applied to the performance evaluation task for frame structure designs. Specifically, a model of MLP or CNN is limited to structures of a particular topology type and fails immediately when applied to other topology types. In order to tackle this challenge, we propose a scalable performance evaluation method (called FrameGraph) for frame structure designs using graph neural network (GNN), offering applicability to a wide range of topology types simultaneously. FrameGraph consists of two main parts: (1) Components and their connections in a frame structure are denoted as edges and vertices in a graph, respectively. Subsequently, a graph dataset for frame structure designs with different topologies is constructed. (2) A well-defined GNN design space is established with a general GNN layer, and a controlled random search approach is employed to derive the optimal GNN model for this performance evaluation task. In numerical experiments of car door frames and car body frames, FrameGraph achieved the highest prediction precisions (96.28% and 97.87%) across all structural topologies compared to a series of classical GNN algorithms. Furthermore, the comparison with MLP and FEM highlighted FrameGraph's significant efficiency advantage. This verifies the feasibility and optimality of FrameGraph for the performance evaluation task of frame structures with different topologies. [ABSTRACT FROM AUTHOR]

The nexus of two relatively recent technologies, additive manufacturing and unmanned aircraft systems (UAS), has enabled new and unique capabilities that have only started to be realized in integrated systems. This article explores and quantifies the impact of 3D printing parts for UAS, or entire UAS systems, on an agent platform, while this agent travels to multiple locations as part of a mission objective. The fully printed or enhanced UAS can then be released at launch points farther away from the goal locations. This, in turn, can accelerate mission completion times and reduce travel costs depending upon the ratio between vehicle speed and 3D printing rate. Thousands of scenarios are optimized across the design space to minimize the travel path length for the agent platform as a result of 3D printing en route to the locations of interest. Results indicate that based on the print capability and agent travel speed, an exponential decay in the amount of travel distance of the agent platform occurs. For unity ratios of print speed and agent speed in the considered design space, a decrease of 55% in the total required distance of our agent is observed. This reduction in total travel distance can reduce time, fuel, cost, and other aspects including other environmental and social impacts. A generalized optimization formulation is also presented at the end to enable similar analyses with other en route range-extending technology such as battery charging. [ABSTRACT FROM AUTHOR]

There is a growing consensus that hydrology education should move towards student-led learning formats and simultaneously incorporate recent hydrologic technologies that reflect workforce expectations. Prior theory suggests improvements in learning outcomes from student-led learning; however, little empirical evidence has been collected to confirm this success. We measured the classroom impact of three teaching modalities: 1) instructor-led lectures, 2) student-led hydrologic modeling, and 3) student-led design evaluation studios. Educational outcomes were measured with student surveys, direct observation of class activity, and student grades. In aggregate, the student population did not express a significant preference for one modality over another, yet individual students showed dramatic preferences for each modality. This suggests best practice is to increase the variety of teaching modalities we employ as instructors to positively engage more students. The total frequency of interactions between students and the instructor was similar across all three modalities; however, the frequency of student-initiated engagements (both total and unique engagements) significantly increased in both student-led modalities. Variations in student enthusiasm did not correlate with written assessment scores. Alternating modalities improved interest in hydrologic science and perceptions of positive experiences, however, did not change the retention of hydrologic concepts. Multiple teaching modalities should be employed to engage the greatest number of students and generate enthusiasm, and multiple methods of assessing student learning should be employed. [ABSTRACT FROM AUTHOR]

Applications for additive manufacturing (AM) continue to increase as more industries adopt the technology within their product development processes. There is a growing demand for designers to acquire and hone their design for AM (DfAM) intuition and generate innovative solutions with AM. Resources that promote DfAM intuition, however, historically default to physical or digitally non-immersive modalities. Immersive virtual reality (VR) naturally supports 3D spatial perception and reasoning, suggesting its intuitive role in evaluating geometrically complex designs and fostering DfAM intuition. However, the effects of immersion on DfAM evaluations are not well-established in the literature. This study contributes to this gap in the literature by examining DfAM evaluations for a variety of designs across modalities using varying degrees of immersion. Specifically, it observes the effects on the outcomes of the DfAM evaluation, the effort required of evaluators, and their engagement with the designs. Findings indicate that the outcomes from DfAM evaluations in immersive and non-immersive modalities are similar without statistically observable differences in the cognitive load experienced during the evaluations. Active engagement with the designs, however, is observed to be significantly different between immersive and non-immersive modalities. By contrast, passive engagement remains similar across the modalities. These findings have interesting implications on how organizations train designers in DfAM, as well as on the role of immersive modalities in design processes. Organizations can provide DfAM resources across different levels of immersion, enabling designers to customize how they acquire DfAM intuition and solve complex engineering problems. [ABSTRACT FROM AUTHOR]

As human–machine interaction (HMI) technology advances, new interaction modes such as gesture and touch interactions are increasingly integrated into manufacturing facilities. These modern methods present unique benefits and drawbacks compared to traditional button interaction. Yet, there is a notable absence of a comprehensive quantitative evaluation method for selecting device interaction modes. This paper introduces a systematic approach to address this gap. Firstly, a multi-criteria evaluation system is established based on commonly used evaluation metrics for human–machine teams. We then employ a hybrid multi-criteria decision-making (HMCDM) method that combines subjective weighting from the rank correlation analysis (G1) method with the VIKOR method. This method provides a clear decision ordering for various interaction modes. When applied to evaluate three interaction modes in an assembly facility, our results suggest that button interaction is the most effective, scoring S(0.115), R(0.046), and Q(0.000). This finding aligns with results from other HMCDM methods, validating the reliability of the proposed HMCDM approach. Our method serves as a reliable guide for operators in choosing interaction modes and offers designers a foundation for further optimization. [ABSTRACT FROM AUTHOR]

Product aesthetics play a crucial role in user satisfaction and product success. However, the feasibility and accuracy of using electromyographic (EMG) data for product aesthetic quality evaluation are unknown. Here, we analysed the correlations and associations between EMG physiological data and product aesthetic quality of 60 subjects by SPSS software and designed a product aesthetic quality evaluation model based on the multivariate logistic (M) algorithm to answer this question. It was found that the EMG data of the zygomatic and buccal muscles did not have significant correlations with product aesthetic quality, whereas five indicators in the EMG data of the frowning muscles had significant correlations relationships with product aesthetic quality. We also found that the overall accuracy of the product aesthetic quality evaluation model was 72.3%, which was able to better distinguish product aesthetic quality. This study demonstrates that product aesthetic quality can be evaluated using EMG data and that the product aesthetic quality evaluation model can provide an objective and accurate decision-making reference to help engineers and designers in product aesthetic quality evaluation and product design. This study lays the foundation for the future integrated multimodal physiology to conduct aesthetics quality evaluations with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) technology has made remarkable progress in recent years and will be widely used in the future. As a bridge for information exchanges between users and VR systems, the interaction interface is pivotal for providing users with a good experience and has emerged as a key research focus. In this review, we conducted a comprehensive search of the Web of Science and CNKI databases from 2011 to 2023 to identify articles dedicated to VR interaction interface design. Through a meticulous analysis of 438 articles, this paper offers a substantial contribution to the emerging field of VR interactive interface research, providing an in-depth review of the principal research advancements. This review revealed that the majority of studies are centered on practical case analyses within specific application scenarios, employing empirical evaluation methods to assess objective or subjective metrics. We then concentrated on elucidating the foundational principles of interface design and their evaluation methodologies, providing a reference for future research endeavors. Additionally, the limitations, challenges, and future directions in VR interaction interface design research were discussed, highlighting the need for further research in design evaluation to continuously refine the development of standards and guidelines for VR interactive interface design. According to the findings of this review, there is a necessity to enhance research on information design for multi-channel interactive interfaces. Furthermore, it is essential to focus on the diverse characteristics of users to propose more inclusive design solutions. Adopting interdisciplinary approaches could lead to breakthroughs in the creation of personalized and adaptive VR interaction interfaces. [ABSTRACT FROM AUTHOR]

In engineering design, navigating complex decision-making landscapes demands a thorough exploration of the design, performance, and constraint spaces, often impeded by resource-intensive simulations. Data-driven methods can mitigate this challenge by harnessing historical data to delineate feasible domains, accelerate optimization, or evaluate designs. However, the implementation of these methods usually demands machine learning expertise and multiple trials to choose the right method and hyperparameters. This makes them less accessible for numerous engineering situations. Additionally, there is an inherent trade-off between training speed and accuracy, with faster methods sometimes compromising precision. In our paper, we demonstrate that a recently released general-purpose transformer-based classification model, TabPFN, is both fast and accurate. Notably, it requires no dataset-specific training to assess new tabular data. TabPFN is a prior-data fitted network, which undergoes a one-time offline training across a broad spectrum of synthetic datasets and performs in-context learning. We evaluated TabPFN's efficacy across eight engineering design classification problems, contrasting it with seven other algorithms, including a state-of-the-art automated machine learning (AutoML) method. For these classification challenges, TabPFN consistently outperforms in speed and accuracy. It is also the most data-efficient and provides the added advantage of being differentiable and giving uncertainty estimates. Our findings advocate for the potential of pre-trained models that learn from synthetic data and require no domain-specific tuning to make data-driven engineering design accessible to a broader community and open ways to efficient general-purpose models valid across applications. Furthermore, we share a benchmark problem set for evaluating new classification algorithms in engineering design. [ABSTRACT FROM AUTHOR]

Product shape evaluation is an important part of new product development. In the shape design stage, design schemes are often presented through visual images. The presentation of visual images causes evaluators to form different cognitive experiences and evaluation results. In recent years, virtual reality (VR) technology has been widely used in the field of industrial design, enriching the presentation forms of design scheme images. Although VR technology has shown the potential to improve evaluators’ perception and cognitive experiences in product shape design, research comparing it with traditional methods remains relatively scattered. This study used two-wheel electric vehicles as an example to examine the difference in evaluators’ cognition of product shape in VR and a real setting (RS). First, we established a semantic scale comprising seven pairs of opposite adjectives to evaluate the shape scheme. Second, we built VR and RS evaluation environments using head-mounted displays and paper renderings, respectively. The participants evaluated the vehicle shape design in alternating viewing and underwent semi-structured interviews on cognitive experience. We analyzed the experimental and interview results based on three aspects of product shape cognition. The results demonstrated that volume cognition was significantly more accurate in VR environments. Furthermore, graphic cognition, particularly regarding shape details, differed partially between environments. VR provided a better sense of immersion and more variable viewing angles than RS. Treatment cognition did not exhibit significant differences between environments, as it depended on human experience rather than visualization. These findings suggest that VR tools are more suited for shaping design evaluations early. Selecting suitable visual presentation tools based on evaluators’ cognitive characteristics at different evaluation nodes to display design schemes is a practical, economical, and efficient strategy. [ABSTRACT FROM AUTHOR]

Engineers often do not select the best designs available to them. This research investigates whether specific design actions impact performance in a design exploration task and whether the reasoning factors underpinning these actions can be inferred directly. This study uses objective performance metrics to quantitatively evaluate multiple dimensions of design behavior and cognition within concept selection. Fifty-six participants were tasked with identifying an optimal design for the gripping contact of a dishwashing robot. Results identified that specific design actions correlated with improved design performance, including exploring fewer design alternatives and isolating parameters. We found that reasoning factors stated by participants did not accurately map onto their observed actions and did not correlate with task performance. Implications related to future computational design support tools are discussed. [ABSTRACT FROM AUTHOR]

The evaluation of design results plays a crucial role in the development of design. This study presents a design work evaluation system for design education that assists design instructors in conducting objective evaluations. An automatic design evaluation model based on convolutional neural networks has been established, which enables intelligent evaluation of student design works. During the evaluation process, the CAM is obtained. Simultaneously, an eye-tracking experiment was designed to collect gaze data and generate eye-tracking heat maps. By comparing the heat maps with CAM, an attempt was made to explore the correlation between the focus of the evaluation’s attention on human design evaluation and the CNN intelligent evaluation. The experimental results indicate that there is some certain correlation between humans and CNN in terms of the key points they focus on when conducting an evaluation. However, there are significant differences in background observation. The research results demonstrate that the intelligent evaluation model of CNN can automatically evaluate product design works and effectively classify and predict design product images. The comparison shows a correlation between artificial intelligence and the subjective evaluation of human eyes in evaluation strategy. Introducing artificial intelligence into the field of design evaluation for education has a strong potential to promote the development of design education.

The selection of floors and furniture in offices can impact the interior environment and potentially influence employees' emotions and health. This study aims to investigate the effects of floor material, furniture color, and age on design evaluation, fixation duration, and time to first fixation. Twenty-four younger adults (M = 21.9, SD = 1.75) and twenty-four older adults (M = 48.1, SD = 6.85) participated in the experiment, where they viewed four different designs: wood floor—brown furniture (WF-BF), wood floor—dark furniture (WF-DF), tile floor—brown furniture (TF-BF), and tile floor—dark furniture (TF-DF). Participants also evaluated the designs and their emotions. Results showed the main effects and interactions of floor material, furniture color, and age. WF-BF performed the best in design evaluation, fixation duration, and time to first fixation, and also scored highest in joy, admiration, fascination, and desire. Older adults favored the texture of wood, while younger adults showed better acceptance of TF-DF. This study provides insights for interior design in office environments. [ABSTRACT FROM AUTHOR]

With increasing competition from economic globalisation, many manufacturing companies strive to maintain their competitive advantage by meeting users’ personalised requirements. Hence, numerous enterprises provided personalised products by inviting users to complete product configurations online. Most users have not received systematic training, so their configured solutions may not comply with the essential aesthetics principle. Meanwhile, the design teams must ensure that the configured products conform to essential aesthetics to maintain the brand reputation. However, most existing studies were proposed for the single-product design solution, which cannot assist designers in evaluating and optimising mass user-configured solutions efficiently and automatically. It still lacks a new method to evaluate and optimise user-configured product solutions automatically from the essential aesthetic perspective. To fill this gap, a novel data-driven method is proposed to select the proper product observing perspective, conduct the comprehensive aesthetic evaluation based on the general traditional aesthetic principles, and propose optimisation suggestions based on the knowledge graph. To validate the feasibility of the proposed method, the bicycle is taken as the case in this paper. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (AI) models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial. To make generative models performance-aware and enable them to create high-performing designs, surrogate modeling of these metrics is necessary. However, the currently used representations of 3D shapes either require extensive computational resources to learn or suffer from significant information loss, which impairs their effectiveness in surrogate modeling. To address this issue, we propose a new 2D representation of 3D shapes. We develop a surrogate drag model based on this representation to verify its effectiveness in predicting 3D car drag. We construct a diverse dataset of 4535 high-quality 3D car meshes labeled by drag coefficients computed from computational fluid dynamics simulations to train our model. Our experiments demonstrate that our model can accurately and efficiently evaluate drag coefficients with an R2 value above 0.84 for various car categories. Our model is implemented using deep neural networks, making it compatible with recent AI image generation tools (such as stable diffusion) and a significant step toward the automatic generation of drag-optimized car designs. Moreover, we demonstrate a case study using the proposed surrogate model to guide a diffusion-based deep generative model for drag-optimized car body synthesis. [ABSTRACT FROM AUTHOR]

In many design automation applications, accurate segmentation and classification of 3D surfaces and extraction of geometric insight from 3D models can be pivotal. This paper primarily introduces a machine earning-based scheme that leverages graph neural networks for handling 3D geometries, specifically for surface classification. Our model demonstrates superior performance against two state-of-the-art models, PointNet + + and PointMLP, in terms of surface classification accuracy, beating both models. Central to our contribution is the novel incorporation of conformal predictions, a method that offers robust uncertainty quantification and handling with marginal statistical guarantees. Unlike traditional approaches, conformal predictions enable our model to ensure precision, especially in challenging scenarios where mistakes can be highly costly. This robustness proves invaluable in design applications, and as a case in point, we showcase its utility in automating the computational fluid dynamics meshing process for aircraft models based on expert guidance. Our results reveal that our automatically generated mesh, guided by the proposed rules by experts enabled through the segmentation model, is not only efficient but matches the quality of expert-generated meshes, leading to accurate simulations. [ABSTRACT FROM AUTHOR]

Pollination is a crucial ecological process with far-reaching impacts on natural and agricultural systems. Approximately 85% of flowering plants depend on animal pollinators for successful reproduction. Over 75% of global food crops rely on pollinators, making them indispensable for sustaining human populations. Wind, water, insects, birds, bats, mammals, amphibians, and mollusks accomplish the pollination process. The design features of flowers and pollinators in angiosperms make the pollination process functionally effective and efficient. In this paper, we analyze the design aspects of the honeybee-enabled flower pollination process using the axiomatic design methodology. We tabulate functional requirements (FRs) of flower and honeybee components and map them onto nature-chosen design parameters (DPs). We apply the "independence axiom" of the axiomatic design methodology to identify couplings and to evaluate if the features of a flower and a honeybee form a good design (i.e., uncoupled design) or an underperforming design (i.e., coupled design). We also apply the axiomatic design methodology's "information axiom" to assess the pollination process's robustness and reliability. Through this exploration, we observed that the pollination process is not only a good design but also a robust design. This approach to assessing whether nature's processes are good or bad designs can be valuable for biomimicry studies. This approach can also inform design considerations for bio-inspired innovations such as microrobots. [ABSTRACT FROM AUTHOR]

Continuous constraint satisfaction is prevalent in many science and engineering fields. When solving continuous constraint satisfaction problems, it is more advantageous for practitioners to derive all feasible regions (i.e., the solution space) rather than a limited number of solution points, since these feasible regions facilitate design concept generation and design tradeoff evaluation. Several central processing unit (CPU)-based branch-and-prune methods and geometric approximation methods have been proposed in prior research to derive feasible regions for continuous constraint satisfaction problems. However, these methods have not been extensively adopted in practice, mainly because of their high computational expense. To overcome the computational bottleneck of extant CPU-based methods, this paper introduces a GPU-based parallel region classification method to derive feasible regions for continuous constraint satisfaction problems in a reasonable computational time. Using interval arithmetic, coupled with the computational power of GPU, this method iteratively partitions the design space into many subregions and classifies these subregions as feasible, infeasible, and indeterminate regions. To visualize these classified regions in the design space, a planar visualization approach that projects all classified regions into one figure is also proposed. The GPU-based parallel region classification method and the planar visualization approach are validated through two case studies about the bird function and the welded beam design. These case studies show that the method and the approach can solve the continuous constraint satisfaction problems and visualize the results effectively and efficiently. A four-step procedure for implementing the method and the approach in practice is also outlined. [ABSTRACT FROM AUTHOR]

Digital tools for sketching, such as tablets, have become popular for streamlining design work and keeping a large quantity of sketches in one place. However, their impact on design creativity, novelty, and concept evolution is not yet well understood. Here, we present a controlled human subjects study that assesses the influence of tablets (iPads) on concept novelty and evolution in the context of an engineering design concept generation exercise. We expect that iPad use will not influence concept novelty due to its similar speed of use as pen and paper sketching. We expect to see different patterns in concept evolution between the two types of tools, namely, that iPad users will demonstrate more iteration on a concept (concept evolution) than pen and paper users due to the fact that iPad features make it easy to copy and paste previous sketches and then modify them. We find that the tool used is not correlated with concept novelty. Additionally, we find no strong differences in overall concept evolution quantities between the two tools, though we see that iPad sketches exhibited more cases of consecutive concept evolution than nonconsecutive whereas paper and pen sketches showed an equal amount of both consecutive and nonconsecutive concept evolution. Results indicate that overall, iPads may not significantly inhibit designers' creative skills and thus could be a reasonable replacement for pen and paper sketching, which has implications for both design education and practice. [ABSTRACT FROM AUTHOR]

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A large amount of research has been undertaken on tubular bolted end-plate connections loaded in axial tension, and considerably less on such connections in flexure. This paper studies the combined effects of these two loads and presents the first laboratory tests on these connections under combined tension and flexure. Using validated finite element models, a parametric study with 360 numerical models is performed on these connections under axial and eccentric tension loads. After screening this database of experimental tests and numerical results to isolate only connection failure modes, current design methods for predicting the connection capacity under eccentric tension are evaluated and revised. In addition, based on interaction curves, a novel approach is proposed to design these splices under combined loads. For all methods, the use of the plate ultimate and yield stress was compared for the end-plate material strength parameter. Lastly, a reliability analysis is performed for the application of the recommended design methods to Canadian practice. [ABSTRACT FROM AUTHOR]

Flexure-based grippers offer an attractive alternative to conventional grippers used in robotics and automation. However, most existing designs appear to suffer from insufficient range of motion, loadability, and support stiffness. This article presents an approach to obtain well-performing flexure hinges for compact anthropomorphic grippers made via metal additive manufacturing. We propose a flexure hinge architecture that achieves a high range of motion despite the challenging combination of a small design space, high Young's modulus, and limited minimum feature size. Furthermore, we present an optimization procedure to generate suitable tendon-driven designs with high loadability. Using this framework, a flexure hinge with an outer diameter of 21.5 mm and range of motion of ±30 deg is synthesized. For the range of 0-30 deg, simulations show a lateral loadability of 52.5-18.6 N and lateral support stiffness of 12,309-11,130 N/m, determined at a gripper interface located 41.2 mm from the hinge pivot axis. Experiments confirm a loadability of at least 15.4 N and determined a stiffness of 8982 to 9727 N/m for same conditions. The results show that the flexure hinge architecture has large potential for a wide range of applications, while in combination with the optimization procedure, superior designs for tendon-driven grippers can be obtained. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is a rapidly growing technology within the industry and education sectors. Despite this, there lacks a comprehensive tool to guide AM novices in evaluating the suitability of a given design for fabrication by the range of AM processes. Existing design for additive manufacturing (DfAM) evaluation tools tend to focus on only certain key process-dependent DfAM considerations. By contrast, the purpose of this research is to propose a tool that guides a user to comprehensively evaluate their chosen design and educates the user on an appropriate DfAM strategy. The tool incorporates both opportunistic and restrictive elements, integrates the seven major AM processes, outputs an evaluative score, and recommends processes and improvements for the input design. This paper presents a thorough framework for this evaluation tool and details the inclusion of features such as dual-DfAM consideration, process recommendations, and a weighting system for restrictive DfAM. The result is a detailed recommendation output that helps users to determine not only "Can you print your design?" but also "Should you print your design?" by combining several key research studies to build a comprehensive user design tool. This research also demonstrates the potential of the framework through a series of user-based studies, in which the opportunistic side of the tool was found to have significantly improved novice designers' ability to evaluate designs. The preliminary framework presented in this paper establishes a foundation for future studies to refine the tool's accuracy using more data and expert analysis. [ABSTRACT FROM AUTHOR]

In recent years, there has been a trend of caregiving facilities utilizing toys known as "Activity Toys" for conducting rehabilitation activities aimed at enhancing the mental and physical well-being of the elderly. These toys are not exclusively designed for the elderly; rather, they are products meticulously selected from among children's toys by the Elderly Activity Development Center. Some of these toys, introduced since 2006, have already been discontinued. Nevertheless, due to the anticipated ongoing aging of the population, it is believed that there is a need to quantitatively analyze the rehabilitative effects of these toys and provide explicit guidelines for their effective utilization. Therefore, a survey was conducted involving 42 participants to extract ten representative Activity Toys and elucidate the rehabilitative effects. Furthermore, a statistical analysis was performed to explore the effects from the perspectives of both rehabilitative benefits and the allure of ownership. The results yielded guidelines for the future on-site utilization of each toy. [ABSTRACT FROM AUTHOR]

Ignoring a large number of occupants and their behavior in the Healthcare Environment Design Evaluations (HEDE) may lead to biased evaluation results and inefficiencies in the collaborative design process. This study aims to propose a VR-based approach integrating dynamic virtual crowds and human-environment interactions to improve participants’ spatial perception thereby increasing the effectiveness and efficiency of HEDE. This study followed Design Science Research (DSR) to develop an interactive VR-based workflow which was tested in a controlled experiment on the renovation of a real trauma intensive care unit and evaluated by 14 stakeholders including staff and architects. Results of the evaluation showed that dynamic virtual crowds and human-environment interactions helped participants improve spatial perception and were considered crucial for HEDE. The interactive VR-based approach met the criteria of “satisfaction” in terms of accuracy and efficiency. This study underscores the synergy between advanced technology and human-centered design, contributing to social sustainability.

The design evaluation method of furniture products was explored for modular children’s wooden storage cabinets. An evaluation model consisting of the Kano model, hierarchical analysis, and grey relational analysis is proposed. A 25-member panel of experts was involved in the development of the evaluation guidelines and the scoring of the program. The expert group used the KJ method to obtain evaluation guidelines for modular children’s wooden furniture that met the requirements of the hierarchical analysis method and screened the functional indicators through 103 validated questionnaires for the Kano model. The evaluation guidelines of modularized children’s wooden storage cabinets were thus obtained, which are both comprehensive and targeted. Then, hierarchical analysis and grey relational analysis were combined to select the solution with the highest grey weighted correlation result as the best solution. The feasibility of the grey comprehensive evaluation method, which combines subjective preference and objective empowerment, in the field of furniture was confirmed through the practice of selecting the best of the three modular children's wooden storage cabinet design solutions, and the evaluation preference of the modular children’s wooden storage cabinet was uncovered. The design model provides an innovative evaluation tool for children's furniture manufacturers to optimize their sales strategies.

Virtual reality (VR) technology has made remarkable progress in recent years and will be widely used in the future. As a bridge for information exchanges between users and VR systems, the interaction interface is pivotal for providing users with a good experience and has emerged as a key research focus. In this review, we conducted a comprehensive search of the Web of Science and CNKI databases from 2011 to 2023 to identify articles dedicated to VR interaction interface design. Through a meticulous analysis of 438 articles, this paper offers a substantial contribution to the emerging field of VR interactive interface research, providing an in-depth review of the principal research advancements. This review revealed that the majority of studies are centered on practical case analyses within specific application scenarios, employing empirical evaluation methods to assess objective or subjective metrics. We then concentrated on elucidating the foundational principles of interface design and their evaluation methodologies, providing a reference for future research endeavors. Additionally, the limitations, challenges, and future directions in VR interaction interface design research were discussed, highlighting the need for further research in design evaluation to continuously refine the development of standards and guidelines for VR interactive interface design. According to the findings of this review, there is a necessity to enhance research on information design for multi-channel interactive interfaces. Furthermore, it is essential to focus on the diverse characteristics of users to propose more inclusive design solutions. Adopting interdisciplinary approaches could lead to breakthroughs in the creation of personalized and adaptive VR interaction interfaces.

Non-verbal communication is important in HRI, particularly when humans and robots do not need to actively engage in a task together, but rather they co-exist in a shared space. Robots might still need to communicate states such as urgency or availability, and where they intend to go, to avoid collisions and disruptions. Sounds could be used to communicate such states and intentions in an intuitive and non-disruptive way. Here, we propose a multi-layer classification system for displaying various robot information simultaneously via sound. We first conceptualise which robot features could be displayed (robot size, speed, availability for interaction, urgency, and directionality); we then map them to a set of audio parameters. The designed sounds were then evaluated in five online studies, where people listened to the sounds and were asked to identify the associated robot features. The sounds were generally understood as intended by participants, especially when they were evaluated one feature at a time, and partially when they were evaluated two features simultaneously. The results of these evaluations suggest that sounds can be successfully used to communicate robot states and intended actions implicitly and intuitively. [ABSTRACT FROM AUTHOR]

Transitioning to and through University is a delicate period for students' well-being. Moreover, the recent COVID-19 pandemic added a further toll through the various challenges related to studying, socializing, community-building, and safety. These challenges inspired the design of a mobile application, called Tecnico GO!, to support university students' well-being and academic performance. This paper presents the design rationale and evaluation of the app conducted during the academic year 2021-2022. Findings cluster around three themes: i) students studying needs; ii) building a sense of community; iii) gamification strategies. The discussion elaborates on the student's perceptions of well-being during pandemics. Students' perception of the app is positive, appreciative of the crowdsensing features, supporting learning goals, community building, and safety. On the other hand, the gamification features, as currently deployed, do not achieve the expected goals. [ABSTRACT FROM AUTHOR]

To enhance profitability of production cycle, any manufacturer needs effective product design and evaluation procedures. This study proposed a novel approach combining fuzzy analytic hierarchy process (FAHP) and multi-layer fuzzy inference system (MFIS). It is based on consumer online comments to improve product design. This method possesses several advantages over traditional design evaluation methods. It can quickly acquire consumer preferences, effectively handle multi-criteria decision problems and integrate uncertain and fuzzy information. The Fuzzy Analytic Hierarchy Process–Multi-layer Fuzzy Inference System (FAHP-MFIS) involves the following steps: screening of factors, hierarchical modeling, quantification of qualitative factors, and conversion of these factors into quantitative values. It is a knowledge-based system that uses logical rules. The quantity and levels of input variables directly correlates with the quantity of logical rules. However, with multi-factor and multi-level inference, the establishment of a rule base becomes impractical due to the overwhelming number of rules. To address this issue, the Taguchi orthogonal table is applied to reduce the number of logical rules. Taking a household oxygen generator for medical devices as an example, the proposed model is applied in real-time. In the first stage, web crawlers are used to collect user reviews of the household oxygen generators on large e-commerce platforms. Latent Dirichlet Allocation (LDA) models are used to screen for principal and sub-factors in the second stage. Then, sub-factors of the FAHP screening are used as inputs, and the principal factors are used as outputs. In the third stage, priority indicators are established based on principal factors such as Appearance, Basic Function, and Advanced Function. Established evaluation models are then used to rank the selected designs. The results show that the higher the priority index value of the product design scheme, the better the scheme, and vice versa. This study holds significant reference value in aiding enterprises to enhance the efficiency of their manufacturing cycle and determining the direction of product design and innovation with improved pace and accuracy. Moreover, it can be applied to other fields such as supply chain management, risk assessment, and investment decisions. [ABSTRACT FROM AUTHOR]

The selection of floors and furniture in offices can impact the interior environment and potentially influence employees’ emotions and health. This study aims to investigate the effects of floor material, furniture color, and age on design evaluation, fixation duration, and time to first fixation. Twenty-four younger adults (M = 21.9, SD = 1.75) and twenty-four older adults (M = 48.1, SD = 6.85) participated in the experiment, where they viewed four different designs: wood floor—brown furniture (WF-BF), wood floor—dark furniture (WF-DF), tile floor—brown furniture (TF-BF), and tile floor—dark furniture (TF-DF). Participants also evaluated the designs and their emotions. Results showed the main effects and interactions of floor material, furniture color, and age. WF-BF performed the best in design evaluation, fixation duration, and time to first fixation, and also scored highest in joy, admiration, fascination, and desire. Older adults favored the texture of wood, while younger adults showed better acceptance of TF-DF. This study provides insights for interior design in office environments.

Pollination is a crucial ecological process with far-reaching impacts on natural and agricultural systems. Approximately 85% of flowering plants depend on animal pollinators for successful reproduction. Over 75% of global food crops rely on pollinators, making them indispensable for sustaining human populations. Wind, water, insects, birds, bats, mammals, amphibians, and mollusks accomplish the pollination process. The design features of flowers and pollinators in angiosperms make the pollination process functionally effective and efficient. In this paper, we analyze the design aspects of the honeybee-enabled flower pollination process using the axiomatic design methodology. We tabulate functional requirements (FRs) of flower and honeybee components and map them onto nature-chosen design parameters (DPs). We apply the “independence axiom” of the axiomatic design methodology to identify couplings and to evaluate if the features of a flower and a honeybee form a good design (i.e., uncoupled design) or an underperforming design (i.e., coupled design). We also apply the axiomatic design methodology’s “information axiom” to assess the pollination process’s robustness and reliability. Through this exploration, we observed that the pollination process is not only a good design but also a robust design. This approach to assessing whether nature’s processes are good or bad designs can be valuable for biomimicry studies. This approach can also inform design considerations for bio-inspired innovations such as microrobots.

Researchers and practitioners alike agree that for companies to survive and thrive they must develop and support radical innovation. However, these ideas are complex and risky, and not all succeed. Because of this, decision makers are often left to make hard decisions in terms of which ideas can move on and which are abandoned. The goal of this article was to provide evidence on the impact of individuals' preferences for creativity on the effectiveness of their decision making for radical ideas using principles from signal detection theory (SDT). To do this, we used data from a previous study of 2252 idea evaluations by engineering students and classified these decisions based on SDT to see if we could predict the likelihood of occurrence of hit (correct identification), miss (type 1 error), false alarm (type II error), and correct rejection. The results showed that lower levels of risk tolerance resulted in an increased likelihood that a hit occurred. On the other hand, higher levels of motivation resulted in an increased likelihood of a type I error occurring, or that an individual would more likely neglect a good idea that had a high chance of future success. Finally, increased risk tolerance resulted in an increased likelihood that type II error occurred, or that an individual would expend resources on an idea with limited likelihood of success. The results serve as empirical evidence on decision making in radically innovative tasks and provide a methodology for studying decision making in innovative design. [ABSTRACT FROM AUTHOR]