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In order to avoid errors in engineering design that affect the later product life cycle, especially the manufacturing process, an analysis or evaluation has to be performed at the earliest possible stage. As this evaluation is very knowledge-intensive and often this knowledge is not directly available to the engineer, this paper presents an approach for a knowledge-based and collaborative engineering design agent. The technology based on multi-agent systems enables problem-solving support by an autonomous knowledge-based system which has its own beliefs, goals, and intentions. The presented approach is embedded in a CAD development environment and validated on an application example from engineering design. [ABSTRACT FROM AUTHOR]

While the design practice in the architecture, engineering, and construction (AEC) industry continues to be a creative activity, approaching the design problem from a perspective of the decision-making science has remarkable potentials that manifest in the delivery of high-performing sustainable structures. These possible gains can be attributed to the myriad of decision-making tools and technologies that can be implemented to assist design efforts, such as artificial intelligence (AI) that combines computational power and data wisdom. Such combination comes to extreme importance amid the mounting pressure on the AEC industry players to deliver economic, environmentally friendly, and socially considerate structures. Despite the promising potentials, the utilization of AI, particularly reinforced learning (RL), to support multidisciplinary design endeavours in the AEC industry is still in its infancy. Thus, the present research discusses developing and applying a Markov Decision Process (MDP) model, an RL application, to assist the preliminary multidisciplinary design efforts in the AEC industry. The experimental work shows that MDP models can expedite identifying viable design alternatives within the solutions space in multidisciplinary design while maximizing the likelihood of finding the optimal design. [ABSTRACT FROM AUTHOR]

The usefulness of mobile phones has expanded significantly in the latest years permitting customers to carry out extra tasks in a mobile context. User Interface and User Experience play an essential position withinside the improvement of mobile applications nowadays. Specially to design the interface for aged people's usage. It allows to optimize the design and enhance it to be user-friendly. User Interface (UI) and User Experience (UX) elaborates a set of concepts, the workflow of design thinking and usability principles. The Graphical User Interface (GUI) of mobile phones are based on a certain set of principles, evaluation and research methodology. This research paper sums up various concepts of UI/UX such as evaluation, design, usability principles and also some common usability issues in the mobile applications of certain sectors. [ABSTRACT FROM AUTHOR]

Designers from around the world have proposed numerous engineering design solutions for problems related to the COVID-19 pandemic, many of which leverage the rapid prototyping and manufacturing capabilities of additive manufacturing (AM). While some of these solutions are motivated by complex and urgent requirements (e.g., face masks), others are motivated by simpler and less urgent needs (e.g., hands-free door openers). Previous research suggests that problem definition influences the creativity of solutions generated for the problem. In this study, we investigate the relationship between the definition of problems related to the COVID-19 pandemic and the characteristics of AM solutions that were openly shared for these problems. Specifically, we analyze 26 AM solutions spanning three categories: (1) hands-free door openers (low complexity problem), (2) face shields (moderate complexity problem), and (3) face masks (high complexity problem). These designs were compared on (1) DfAM utilization, (2) manufacturability (i.e., build time, cost, and material usage), and (3) creativity. We see that the solutions designed for the high complexity problem, i.e., face masks, were least suitable for AM. Moreover, we see that solutions designed for the moderate complexity problem, i.e., face shields, had the lowest build time, build cost, and material consumption. Finally, we observe that the problem definition did not relate to the creativity of the AM solutions. In light of these findings, designers must sufficiently emphasize the AM suitability and manufacturability of their solutions when designing for urgent and complex problems in rapid response situations. [ABSTRACT FROM AUTHOR]

Function drives many early design considerations in product development, highlighting the importance of finding functionally similar examples if searching for sources of inspiration or evaluating designs against existing technology. However, it is difficult to capture what people consider is functionally similar and therefore, if measures that quantify and compare function using the products themselves are meaningful. In this work, human evaluations of similarity are compared to computationally determined values, shedding light on how quantitative measures align with human perceptions of functional similarity. Human perception of functional similarity is considered at two levels of abstraction: (1) the high-level purpose of a product and (2) how the product works. These human similarity evaluations are quantified by crowdsourcing 1360 triplet ratings at each functional abstraction and creating low-dimensional embeddings from the triplets. The triplets and embeddings are then compared to similarities that are computed between functional models using six representative measures, including both matching measures (e.g., cosine similarity) and network-based measures (e.g., spectral distance). The outcomes demonstrate how levels of abstraction and the fuzzy line between "highly similar" and "somewhat similar" products may impact human functional similarity representations and their subsequent alignment with computed similarity. The results inform how functional similarity can be leveraged by designers, with applications in creativity support tools, such as those used for design-by-analogy, or other computational methods in design that incorporate product function. [ABSTRACT FROM AUTHOR]

Designers routinely create informal "thinking" sketches to explore a design space, "talking" sketches to communicate design ideas during the early phases of the design process, and "learning" prototypes to test potential concepts. This study presents two new tools to assess novice designers' sketch attributes and prototyping reflections in the context of an introductory design course. First, it proposes a rubric for assessing the quality of early-stage design sketches including line smoothness, proportion, and understandability. Of particular note is the contribution of assessing understandability as a metric for sketches as communication tools. This study also presents a tool to capture designer reflections after each iteration of a prototype. Not only does this record what is learned about a design but also designers' personal and emotional reactions to the process. Sketching-related results show a positive correlation between sketch quality and understandability, indicating the importance of sketch quality especially when designers use sketches to communicate. Results also indicate that early-stage sketch quantity, but not quality, is linked with design outcomes. This study also finds a link between the frequency of sketching and higher maximum sketch quality scores (i.e., at least one highly rated sketch) as well as a correlation between individuals' maximum sketch quality scores and overall design outcomes. Preliminary results around prototyping indicate that reflection on both the technical and emotional aspects of prototyping may be a worthwhile area of further study. Finally, several results point to novice designers' lack of consistent focus on users in their prototyping reflections and presentations. [ABSTRACT FROM AUTHOR]

A picture is worth a thousand words, and in design metric estimation, a word may be worth a thousand features. Pictures are awarded this worth because they can encode a plethora of information. When evaluating designs, we aim to capture a range of information as well, including usefulness, uniqueness, and novelty of a design. The subjective nature of these concepts makes their evaluation difficult. Still, many attempts have been made and metrics developed to do so, because design evaluation is integral to the creation of novel solutions. The most common metrics used are the consensual assessment technique (CAT) and the Shah, Vargas-Hernandez, and Smith (SVS) method. While CAT is accurate and often regarded as the "gold standard," it relies on using expert ratings, making CAT expensive and time-consuming. Comparatively, SVS is less resource-demanding, but often criticized as lacking sensitivity and accuracy. We utilize the complementary strengths of both methods through machine learning. This study investigates the possibility of using machine learning to predict expert creativity assessments from more accessible nonexpert survey results. The SVS method results in a text-rich dataset about a design. We utilize these textual design representations and the deep semantic relationships that words and sentences encode to predict more desirable design metrics, including CAT metrics. We demonstrate the ability of machine learning models to predict design metrics from the design itself and SVS survey information. We show that incorporating natural language processing (NLP) improves prediction results across design metrics, and that clear distinctions in the predictability of certain metrics exist. Our code and additional information about our work are available on the MIT DeCoDE Lab website. [ABSTRACT FROM AUTHOR]

There has been a plethora of design theory and methodology (DTM) research conducted to answer important questions centered around how ideas are developed and translated into successful products. Understanding this is vital because of the role creativity and innovation have in long-term economic success. However, most of these researches focused on U.S. samples, leaving to question if differences exist across cultural borders. Answering this question is key to support a successful global economy. The current work provides a first step at answering this question by examining similarities and differences in generating concepts and screening practices between students in an emerging market, Morocco, and those in a more established market, the U.S., during a design thinking workshop. Our results show that while students in the U.S. sample produced more ideas than the Moroccan sample, there was no difference in the perceived quality of ideas generated (idea goodness). In addition, while U.S. women were found to produce more ideas than U.S. men, there were no gender effects for students in the Moroccan sample. Finally, the results show that ideas with low goodness had a higher probability of passing concept screening if it was evaluated by its owner regardless of the population studied--identifying the potential impact of ownership bias across cultures. As a whole, these results suggest that key aspects of design theory and methodology research may in fact translate across cultures but also identified key areas for further investigation. [ABSTRACT FROM AUTHOR]

Multiobjective design optimization studies typically derive Pareto sets or use a scalar substitute function to capture design trade-offs, leaving it up to the designer's intuition to use this information for design refinements and decision-making. Understanding the causality of trade-offs more deeply, beyond simple postoptimality parametric studies, would be particularly valuable in configuration design problems to guide configuration redesign. This article presents the method of multiobjective monotonicity analysis to identify root causes for the existence of trade-offs and the particular shape of Pareto sets. This analysis process involves reducing optimization models through constraint activity identification to a point where dependencies specific to the Pareto set and the constraints that cause them are revealed. The insights gained can then be used to target configuration design changes. We demonstrate the proposed approach in the preliminary design of a medical device for oral drug delivery. [ABSTRACT FROM AUTHOR]

Designers are challenged to create sustainable products that resonate with customers, often focusing on engineered sustainability while neglecting perceived sustainability. We previously proposed a method for extracting perceived sustainable features from online reviews using annotations and natural language processing (NLP), testing our method with French press coffee carafes. We identified that perceived sustainability may not always align with engineered sustainability. We now investigate how designers can validate perceived features extracted from online reviews using a relatively new design method of collage placement where participants drag and drop products on a two-by-two axis collage and select features from a dropdown menu. We created collage activities for participants to evaluate French press products on the three aspects of sustainability: social, environmental, and economic, and on how much they like the products. During the activity, participants placed products along the two axes of the collage, sustainability and likeability, and labeled products with descriptive features. We found that participants more often selected our previously extracted features when placing products higher on the sustainability axis, validating that the perceived sustainable features resonate with users. We also measured a low correlation between the two axes of the collage activity, indicating that perceived sustainability and likeability can be measured separately. In addition, we found that product perceptions across sustainability aspects may differ between demographics. Based on these results, we confirm that the collage is an effective tool for validating sustainability perceptions and that features perceived as sustainable from online reviews resonate with customers when thinking of various sustainability aspects. [ABSTRACT FROM AUTHOR]

Evaluating design ideas is an integral part of designing built environments. It involves multiple stakeholders with diverse backgrounds reviewing design solutions by studying their form and performance data. Although there are computational systems for supporting evaluation tasks, they are either highly specialised for designers or configured for a particular workflow with limited functions. We developed a Design Analytics method aiming at a collaborative and data-driven evaluation of alternatives in the design-evaluate-feedback cycle. Adopting this approach, we introduce D-ART as a prototype system composed of customisable Web interfaces for presenting design alternatives, enabling stakeholders to participate in data-informed discourse on alternatives and providing feedback to the design team. Its system design considers requirements gathered through literature review, critical analysis of the existing systems and collaboration with our industry partners. Finally, we assessed D-ART's design through an expert review evaluation, which generally reported positive results on the system's goals. [ABSTRACT FROM AUTHOR]

Design concept evaluation is a key process in the new product development process with a significant impact on the product's success and total cost over its life cycle. This paper is motivated by two limitations of the state-of-the-art in concept evaluation: (1) the amount and diversity of user feedback and insights utilized by existing concept evaluation methods such as quality function deployment are limited. (2) Subjective concept evaluation methods require significant manual effort which in turn may limit the number of concepts considered for evaluation. A deep multimodal design evaluation (DMDE) model is proposed in this paper to bridge these gaps by providing designers with an accurate and scalable prediction of new concepts' overall and attribute-level desirability based on large-scale user reviews on existing designs. The attribute-level sentiment intensities of users are first extracted and aggregated from online reviews. A multimodal deep regression model is then developed to predict the overall and attribute-level sentiment values based on the features extracted from orthographic product images via a fine-tuned ResNet-50 model and from product descriptions via a fine-tuned bidirectional encoder representations from transformer model and aggregated using a novel self-attention-based fusion model. The DMDE model adds a data-driven, user-centered loop within the concept development process to better inform the concept evaluation process. Numerical experiments on a large dataset from an online footwear store indicate a promising performance by the DMDE model with 0.001 MSE loss and over 99.1% accuracy. [ABSTRACT FROM AUTHOR]

Background: The interface design of in-vehicle head-up display (HUD) is an enlarging research area with interface usability as its core; usability reflects all perspectives of human—machine interaction and thus the evaluation and optimization of usability have multiple objectives. The evaluation and optimization of interface quality involved in usability are subjective and subconscious. Nevertheless, very little attention has been paid to these issues in optimizing usability across multiple objectives. Methods: In this paper, a hybrid scheme evaluation and optimization method based on entropy weight and VIKOR is proposed. First, according to the content of PSSUQ (Post Study System Usability Question), we have established a new usability evaluation system based on the characteristics of HUD. The entropy weight method was used to reduce the subjective factors of the decision-makers and to achieve the objective weight of each indicator. The VIKOR method was used for obtaining the order of alternate schemes and then the optimal interface design scheme was selected. Results: A case study was carried out to illustrate the applicability of the developed model in the usability evaluation of the HUD interface design. The results showed that scheme 1 was the optimized scheme, with minimal value of Si (0.141), Ri (0.119) and Qi (0.000) among the three schemes. When other decision-making methods were applied, the results showed that the optimized scheme was scheme 1, respectively, which verified the feasibility of the proposed method. The entropy—VIKOR model can be used to evaluate and optimize the HUD interface design effectively, which may serve as a reference for designers to achieve insights during the design process and scheme decision-making.

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., Competing Interests: The author(s) declare(s) that the contents of the article are in agreement with the ethics described in http://biblio.unibe.ch/portale/elibrary/BOP/jemr/ethics.html and that there is no conflict of interest regarding the publication of this paper., (Copyright (©) 2024 Jia Jiayi, Zhao Tianjiao, Yang Junyu, Wang Qian.)

Current legged landers are typical truss structures acting as one type of fundamental equipment for the close-range extraterrestrial exploration missions. Unluckily, the development process applying the current design framework always consumes a long time span searching for the final design, accompanying masses of trial and error with inefficiency and diseconomy. Its kernel reason is that the stages from concept to scheme employ the paradigm of structural-analysis-oriented redesign and untimely embed physical prototype experiments in masses of iterative design cycles. Furthermore, the current framework cannot support the creative development of future legged landers with novel functions and mechanisms. Herein, we present a complete computable design framework for speeding up the development of both current and future legged landers, highlighted by new mathematical models and new principles of forward-design paradigm and multi-mode synergistic design paradigm. It applies the numerical prototype simulation instead of the physical prototype experiment in most iterative processes. This work will facilitate the extraterrestrial exploration missions using the current legged landers (truss-based) and the future legged lander (robot-based). [ABSTRACT FROM AUTHOR]

System reconfiguration is essential in complex systems management, as it is an enabler of system adaptability with regard to system evolutions. System evolutions have to be managed to ensure system effectiveness and efficiency through its whole life cycle, particularly when it comes to complex systems that take years of development and dozens of years of usage. In this context, system reconfiguration ensures system operation and maintains system "ilities" (e.g., reliability, availability, maintainability, testability, and safety). This research has been conducted in the context of a large international aerospace, space, ground transportation, defense, and security company. This research aims at supporting system reconfiguration during operations. Within current industrial practices, the development of reconfiguration support is challenging as it requires integrating data related to observations (from operations) and system design (from engineering). More specifically, there is a need to integrate and link relevant reconfiguration data concerning the system objectives, operational context, and level of functioning. This paper proposes integrating and linking this fundamental data within a reconfiguration method. MBSysRec is a multidisciplinary method that involves configuration generation and a multicriteria decision-making method for configuration evaluation and selection to support system reconfiguration during operations. The method has been implemented on two projects based on historical data. Resulting configurations have been discussed and assessed by system reconfiguration experts. A Search and Rescue (SAR) case study is used to demonstrate the method. The method is proven effective for finding relevant system configurations for reconfiguring the already deployed system to achieve search and rescue missions. [ABSTRACT FROM AUTHOR]

Euro7 and California HD-OBD present a shift of approach in emissions control. Legislative bodies concentrate on individual vehicle conformity to standards during its lifetime on top of type approval processes in test environment. The main change is NOx trackers in software and sensors in the exhaust pipes of all vehicles. As a consequence of constant supervision not only single point faults are taken into account in the analysis, but also cumulative parameter drift of components due to aging. To achieve normative requirements and prevent emission standards violation during exploitation, methodologies known from automotive functional safety domain and SOTIF are used to evaluate and modify a propulsion system design. An illustrative example of analysis is presented in the paper. [ABSTRACT FROM AUTHOR]

The engineering design process is a comprehensive process in which many parameters such as functionality, manufacturability, aesthetics, and cost are considered and optimized. Systematic design methods can be used to take into account these parameters and not to overlook the details. However, the shortening of the product lifetimes has made it necessary to shorten the design and development processes. Artificial intelligence technologies can help designers speed up selection and evaluation processes in the early design phase. This study, it is aimed to evaluate the design options by including the fuzzy logic method in the systematic design approach of Pahl and Beitz. The design is partially evaluated in previous studies using fuzzy logic method in design. In this study, the design is examined in all its aspects. The validity and effectiveness of the method were demonstrated by a wheelchair conceptual design. In addition, the presented method has been compared with the traditional conceptual design method in terms of its results. In both methods, the same design option was determined as the ideal design at the end of the evaluation steps. With the fuzzy logic method, the evaluation process was completed in a shorter time than the classical conceptual design process. In addition, the rating scale between 0-100 increases the sensitivity of the results. The use of the fuzzy logic method in conceptual design provides fast, sensitive, and comprehensive evaluation besides being simple and easy to understand. [ABSTRACT FROM AUTHOR]

Evaluation epitomizes a significant part of design processing in practice and education. It represents a major tool for judging outcomes of learning in academia. It is also essential for selecting proposals and making decisions in practice. Addressing this vital part of design processing, this study analyzes the design evaluation construct, variables, and criteria. Within the scope of architectural design pedagogy, the study develops a new model for design evaluation. Based on its configuration and components, the proposed model is named the 5CC framework. The study consists of two main parts. The first is explorative and reviews and analyzes the literature in the assessment and evaluation fields. It is knowledge based and heuristically driven. The second is derivative and used to develop the model of design evaluation. It is heuristically augmented, knowledge based, and mission driven. The model combines design criteria and detailed components that entail typical design process phases and product elements. The main objective of the model is to help learners, educators, and other design-related stakeholders judge design proposals and products succinctly, explicitly, comprehensively, systematically, and graphically. [ABSTRACT FROM AUTHOR]

One of the great challenges of artificial spinal disc (ASD) design lies in the reproduction of the complex mechanics of an intervertebral disc (IVD) that is characterized by a viscoelastic, nonlinear, and anisotropic behavior. Although the development of multi-material additive manufacturing (AM) combined with biomimetic design provide new opportunities for the realization of ASDs with complex behavior, the influence of different biomimetic designs on the kinematics of ASD in conjunction with AM is not yet explored. Therefore, this study proposes and fabricates four types of biomimetic, multi-material ASD designs based on mimicking either the material stiffness gradient or the structure found in a natural IVD. The results show that all the designs exhibit a desired viscoelastic behavior, while the ASD design based on a chainmail-like structure exhibits a nature-mimicking nonlinear rotational load response. In terms of restoring the natural trend of an IVD’s anisotropic behavior, the ASD design that mimics the structure found in an IVD outperforms the design that solely mimics an IVD’s material stiffness gradient. Additionally, all the designs proposed in this study show comparable instant helical axis (IHA) and instant center of rotation (ICOR) to an IVD's regarding their location and moving direction.

Artifacts, such as software systems, pervade organizations and society. In the field of information systems (IS) they form the core of research. The evaluation of IS artifacts thus represents a major issue. Although IS research paradigms are increasingly intertwined, building and evaluating artifacts has traditionally been the purview of design science research (DSR). DSR in IS has not reached maturity yet. This is particularly true of artifact evaluation. This paper investigates the “what” and the “how” of IS artifact evaluation: what are the objects and criteria of evaluation, the methods for evaluating the criteria, and the relationships between the “what” and the “how” of evaluation? To answer these questions, we develop a taxonomy of evaluation methods for IS artifacts. With this taxonomy, we analyze IS artifact evaluation practice, as reflected by ten years of DSR publications in the basket of journals of the Association for Information Systems (AIS). This research brings to light important relationships between the dimensions of IS artifact evaluation, and identifies seven typical evaluation patterns: demonstration; simulation- and metric-based benchmarking of artifacts; practice-based evaluation of effectiveness; simulation- and metric-based absolute evaluation of artifacts; practice-based evaluation of usefulness or ease of use; laboratory, student-based evaluation of usefulness; and algorithmic complexity analysis. This study also reveals a focus of artifact evaluation practice on a few criteria. Beyond immediate usefulness, IS researchers are urged to investigate ways of evaluating the long-term organizational impact and the societal impact of artifacts. [ABSTRACT FROM PUBLISHER]

As augmented reality (AR) and gamification design artifacts for education proliferate in the mobile and wearable device market, multiple frameworks have been developed to implement AR and gamification. However, there is currently no explicit guidance on designing and conducting a human-centered evaluation activity beyond suggesting possible methods that could be used for evaluation. This study focuses on human-centered design evaluation pattern for gamified AR using Design Science Research Methodology (DSRM) to support educators and developers in constructing immersive AR games. Specifically, we present an evaluation pattern for a location-based educational indigenous experience that can be used as a case study to support the design of augmented (or mixed) reality interfaces, gamification implementations, and location-based services. This is achieved through the evaluation of three design iterations obtained in the development cycle of the solution. The holistic analysis of all iterations showed that the evaluation process could be reused, evolved, and its complexity reduced. Furthermore, the pattern is compatible with formative and summative evaluation and the technical or human-oriented types of evaluation. This approach provides a method to inform the evaluation of gamified AR apps. At the same time, it will enable a more approachable evaluation process to support educators, designers, and developers.

In this paper, we deal with the issue of implicit monitoring of perceptual responses to product design through electroencephalography (EEG) and eye tracking. Four evaluation factors, namely, preference, luxury, complexity, and harmony are considered to investigate how people perceive the car design. In particular, the quantified perceptual responses are predicted based on EEG and gaze data. Average root-mean-square errors of 0.210 and 1.215 are obtained from subject-dependent and subject-independent regressions on a 7-point score scale, respectively, which demonstrates that perception of car design can be predicted via implicit monitoring. [ABSTRACT FROM AUTHOR]

Architects and designers communicate their ideas within a range of representational methods. No single instance of these methods, either in the form of orthographic projections or perspectival representation, can address all questions regarding the design, but as a whole, they demonstrate a comprehensive range of information about the building or object they intend to represent. This explicates an inevitable degree of deficiency in representation, regardless of its type. In addition, perspective-based optical illusions manipulate our spatial perception by deliberately misrepresenting the reality. In this regard, they are not new concepts to architectural representation. As a consequence, Optically Illusive Architecture (OIA) is proposed, not as a solution to fill the gap between the representing and represented spaces, but as a design paradigm whose concept derives from and accounts for this gap. By OIA we aim to cast light to an undeniable role of viewpoints in designing architectural spaces. The idea is to establish a methodology in a way that the deficiency of current representational techniques—manifested as specific thread of optical illusions—flourishes into thoughtful results embodied as actual architectural spaces. Within our design paradigm, we define a framework to be able to effectively analyze its precedents, generate new space, and evaluate their efficiencies. Moreover, the framework raises a hierarchical set of questions to differentiate OIA from a visual gimmick. Furthermore, we study two OIA-driven environments, by conducting empirical studies using Virtual Reality (VR). These studies bear essential information, in terms of design performance, and the public's ability to engage and interact with an OIA space, prior to the actual fabrication of the structures. [ABSTRACT FROM AUTHOR]

Design variety metrics measure how much a design space is explored. This article proposes that a generalized class of entropy metrics based on Sharma-Mittal entropy offers advantages over existing methods to measure design variety. We show that an exemplar metric from Sharma-Mittal entropy, namely, the Herfindahl-Hirschman index for design (HHID) has the following desirable advantages over existing metrics: (a) more accuracy: it better aligns with human ratings compared to existing and commonly used tree-based metrics for two new datasets; (b) higher sensitivity: it has higher sensitivity compared to existing methods when distinguishing between the variety of sets; (c) allows efficient optimization: it is a submodular function, which enables one to optimize design variety using a polynomial time greedy algorithm; and (d) generalizes to multiple metrics: many existing metrics can be derived by changing the parameters of this metric, which allows a researcher to fit the metric to better represent variety for new domains. This article also contributes a procedure for comparing metrics used to measure variety via constructing ground truth datasets from pairwise comparisons. Overall, our results shed light on some qualities that good design variety metrics should possess and the nontrivial challenges associated with collecting the data needed to measure those qualities. [ABSTRACT FROM AUTHOR]

Alternative reality (XR) tools are becoming more commonplace in the realm of architecture, engineering, and construction (AEC); however, these digitally immersive technologies vary greatly in their degree of virtuality and individual capabilities. While many studies detail the performance of one specific XR technology for a particular use case, little work exists comparing numerous modern XR technologies in a side-by-side manner for a single use case. In this work, we construct four equal-fidelity, alternative reality environments for the application of spacecraft habitat design evaluation, starting with a baseline habitat mockup constructed in physical reality (PR). Three digital environments—augmented reality, hybrid reality (HR), and virtual reality—were modeled after the PR environment and developed in parallel. The implementation of each environment was carefully documented, along with relative strengths and weaknesses associated with both development and use. Additionally, we have developed a novel HR implementation that includes realistic and intuitive haptics, hand tracking, a fully virtual audiovisual scene, and responsive habitat elements, all wirelessly synched with a game engine and spatially synched with the PR environment. In sum, this work serves as a resource for those considering XR technologies for any variety of applications, particularly in AEC disciplines. [ABSTRACT FROM AUTHOR]

Socialized product design (SPD) mode, enabled by advanced internet technologies and sharing economic trends, has the capability of utilizing the design resources from large numbers of socialized designers (SDs) to carry out the design tasks that used to be participated by only the internal R&D staffs of companies. During SPD projects, different kinds of mechanisms can be applied to organize the SDs. Some of them tend to be centralized control (e.g. crowdsourcing design) and some are more of distributed autonomy (e.g. opensourcing design). Both centralized control and distributed autonomy have their strengths and limitations for SPD. Centralized control enables more organized, focused, and efficient project execution, but it limits the emergence of collective intelligence among the SDs. Distributed autonomy helps to explore the innovation potential of SDs by granting them the freedom of communication and mutual inspiration, but it may cause the problem of unreliable and unpredictable design process. To complement the advantages of centralized control and distributed autonomy in SPD, an integrated model of Blackboard and Bayesian network is established in this paper. The Blackboard, whose Control modules are specially customized for human Knowledge sources, is for guaranteeing overall control of the distributed design process and at the same time permitting certain level of autonomy to the SDs. The Bayesian network, built with an improved Bayesian causal map method, is an embedded Control module of the Blackboard which evaluates design solutions according to the incomplete collective judgments from SDs. The operability of the integrated model has been verified through a case study of 3D printer conceptual design project. [ABSTRACT FROM AUTHOR]

Complex systems may operate in scenarios where the current requirements were "unknown" at the time of their original design. Such "unknown" requirements might be outside the probability distribution expected during the design phase or, more drastically, might not have been predicted. Yet, not meeting these "unknown" requirements can significantly reduce system value. Engineering design researchers have begun addressing this challenge by exploring how incorporating margins when the system is being designed--a form of strategic inefficiency--might increase a system's total lifetime value by reducing sensitivity to requirement changes and truncating change propagation. Quantitatively studying excess margin beyond what is required for known uncertainties has been particularly challenging as information is needed about how requirements change, how system performance is impacted by requirement changes, and how components are priced. A quantitative study around excess using 20 years of data for desktop computers, video game consoles, and video games is developed. Evidence is provided that excess can improve end-user system value when future requirements are unknown. This paper also advances the notion of strategic excess (excess incorporated in a single component), showing as one example that excess RAM would have improved system performance by 14% (on average) for 7% of total system cost. In demonstrating the value of excess, we strengthen the argument that engineers (and end-users) should embrace strategic inefficiencies--even though they might never be used--and further study the implications of system architecture and module interfaces decisions. [ABSTRACT FROM AUTHOR]

Packing and routing separately are each challenging NP-hard problems. Therefore, solving the coupled packing and routing problem simultaneously will require disruptive methods to better address pressing-related challenges, such as system volume reduction, interconnect length reduction, ensuring non-intersection, and physics (thermal, hydraulic, or electromagnetic) considerations. Here we present a novel two-stage sequential design framework to perform simultaneous physics-based packing and routing optimization. Stage 1 generates interference-free initial layouts that are fed to stage 2 as starting points to perform continuous physics-based optimization. Three distinct strategies for stage 1 have been introduced recently, (1) the force-directed layout method (FDLM), (2) an extension of the shortest path algorithms (SPAs), and (3) a unique geometric topology (UGT) generation algorithm. In stage 2, a gradient-based topology optimization method is used to simultaneously optimize both component locations and interconnect routing paths. In addition to geometric considerations, this method supports optimization based on system behavior by including physics-based objectives and constraints. The proposed framework is demonstrated using three case studies. First, the layout generation methods developed for stage 1 are compared with respect to system performance metrics obtained from stage 2. Second, a multi-objective optimization problem using the epsilon-constraint method is solved to obtain Pareto optimal solutions. Third, an extension to multi-loop systems is demonstrated. In summary, the design automation framework integrates several elements together as a step toward a more comprehensive solution of 3D packing and routing problems with both geometric and physics considerations. [ABSTRACT FROM AUTHOR]

This study aimed to investigate how width affects the design evaluation of sliding boards with a new shape. Ten caregivers at a senior facility evaluated five types of boards every two weeks. The new shape board received high ratings as the width increased, with a SUS (System Usability Scale) score of 68.5 points (95% CI 60.6-76.4). Compared to the traditional rectangular board, which had a width of 250 mm and a thickness of 5 mm, the new shape board (with a central width of 163 mm and a thickness of 8 mm) received higher ratings by 0.68 points (95% CI 0.31-1.05)using a paired comparison method. The results of a quantitative study on the usability of sliding boards from the perspective of caregivers indicated that for similar board shapes with a length of 650 mm and a thickness of 8 mm, the evaluation increases as the width increases in the range of 130 mm to 163 mm at the center.