Search

The objective of this research is to understand how different representations of requirements influence idea generation in terms of quantity, addressment, novelty, and variety of conceptual sketches. Requirements are testable statements of need, desires, and wishes of the stakeholders that are used by engineers to frame the problem. Project success is highly dependent on well-defined requirements documents. An experimental study was conducted with 52 fourth-year mechanical engineering undergraduate students. Two design problems are used with three different representations: a problem statement with embedded requirements, a problem statement and a traditional requirement list, and a problem statement with contextualized scrum stories. Each student addressed both design problems with two different representations of requirements. It was found that the use of contextualized scrum story representations significantly affected the novelty of solution fragments and addressment of requirements, while no significant change in variety and quantity was seen. Finally, it was found that quantity is not directly related to the number of requirements addressed in the sketches. [ABSTRACT FROM AUTHOR]

Sketching is an important tool for engineers during concept generation. Sketch quantity during this early stage of design has been linked with eventual design outcomes and sketch quality has been linked with design perceptions. As such, both are important metrics to track. Prior work has also found gender differences in some of these sketch attributes, and considering gender imbalances in the field, it is important to assess whether there are differences in performance by gender during concept generation and if the use of digital tools exacerbate or mitigate any of these potential differences in performance by gender. Given the increase in use of digital tablets for sketching, it is important to assess how tablet use affects early stage engineering design sketches. This is especially important as tablets can automatically smooth lines and help perfect sketch features, but these features may also take longer to use and may result in fewer sketches produced. This study investigates differences in sketch quality, quantity, and understandability (the effectiveness of the sketch as a communication tool) between sketching on a tablet and sketching with pen on paper during an engineering design concept generation exercise. Results indicate that there is no difference in sketch quantity or understandability between the two tools. However, sketch quality, smoothness, and proportion/accuracy are all higher for the pen and paper condition than for the tablet condition. Finally, no gender differences in performance for either sketch quantity or quality were found. [ABSTRACT FROM AUTHOR]

Today's Information Systems (IS) design research projects pursue digital innovation to conquer complex societal challenges. Many of these projects reach out beyond disciplinary and organisational boundaries, as evident in interdisciplinary consortia and academia‐industry collaboration. The design activities in each project differ based on contextual requirements and the team's underlying design logic. As diversity increases, shared understanding is essential for project success. Established design research methodologies need complementary tools to support design researchers in configuring their design activities and representing them faithfully, dimensions that contribute to a shared understanding. This article presents Baustein as an instance of such design tools. Baustein is tailorable to the contextual requirements of each design research project, comprising an ensemble of card‐deck, ready‐made configurations, and a manual. To ensure theoretical and practical relevance, the design of Baustein is based on primary empirical data (workshop and interviews with 16 IS design researchers) and a literature analysis of 99 published IS design research projects. We demonstrate its proof‐of‐value through three main evaluation episodes, altogether involving over 110 IS design researchers. With Baustein, design research teams can balance the trade‐off between creative messiness and standardised configurations of design activities. [ABSTRACT FROM AUTHOR]

Engineering designers are tasked with complex problems necessitating the use and development of various supports for navigating complexity. Prescriptive design process models are one such tool. However, little research has explored how engineering designers perceive these models' recommendations for engagement in design work. In this exploratory study, we analyzed data from individual semi-structured interviews with 18 mechanical engineering students to identify participant perceptions of design process models. As many design process model visualizations lack explicit attention to some social and contextual dimensions, we sought to compare perceptions among two models drawn from engineering texts and one model that was developed with the intent to emphasize social and contextual dimensions. We identified perceptions of the recommendations from the design process models related to starting and moving through a design process, gathering information, prototyping, evaluating or testing, and what they should consider. Participant perceptions across the three process models suggest different design process models make perceptions of certain recommendations more salient than others. However, participant perceptions also varied for the same process model. We suggest several implications for design education and training based on participant perceptions of the process models, particularly the importance of leveraging multiple design process models. The comprehensive descriptions of participant perceptions provide a foundation for further investigations bridging designers' perceptions to intent, behavior, and, ultimately, design outcomes. [ABSTRACT FROM AUTHOR]

As modern electronic devices are increasingly miniaturized and integrated, their performance relies more heavily on effective thermal management. In this regard, two-phase cooling methods which capitalize on thin-film evaporation atop structured porous surfaces are emerging as potential solutions. In such porous structures, the optimum heat dissipation capacity relies on two competing objectives that depend on mass and heat transfer. Optimizing these objectives for effective thermal management is challenging due to the simulation costs and the high dimensionality of the design space which is often a voxelated microstructure representation that must also be manufacturable. We address these challenges by developing a data-driven framework for designing optimal porous microstructures for cooling applications. In our framework, we leverage spectral density functions to encode the design space via a handful of interpretable variables and, in turn, efficiently search it. We develop physics-based formulas to simulate the thermofluidic properties and assess the feasibility of candidate designs based on offline image-based analyses. To decrease the reliance on expensive simulations, we generate multi-fidelity data and build emulators to find Pareto-optimal designs. We apply our approach to a canonical problem on evaporator wick design and obtain fin-like topologies in the optimal microstructures which are also characteristics often observed in industrial applications. [ABSTRACT FROM AUTHOR]

This article presents a novel three-dimensional topology optimization framework developed for 3D spatial packaging of interconnected systems using a geometric projection method (GPM). The proposed gradient-based topology optimization method simultaneously optimizes the locations and orientations of system components (or devices) and lengths, diameters, and trajectories of interconnects to reduce the overall system volume within the prescribed 3D design domain. The optimization is subject to geometric and physics-based constraints dictated by various system specifications, suited for a wide range of transportation (aerospace or automotive), heating, ventilation, air-conditioning, and refrigeration, and other complex system applications. The system components and interconnects are represented using 3D parametric shapes such as cubes, cuboids, and cylinders. These objects are then projected onto a three-dimensional finite element mesh using the geometric projection method. Sensitivities are calculated for the objective function (bounding box volume) with various geometric and physics-based (thermal and hydraulic) constraints. Several case studies were performed with different component counts, interconnection topologies, and system boundary conditions and are presented to exhibit the capabilities of the proposed 3D multi-physics spatial packaging optimization framework. [ABSTRACT FROM AUTHOR]

The design representations of lattice structures are fundamental to the development of computational design approaches. Current applications of lattice structures are characterized by ever-growing demand on computational resources to solve difficult optimization problems or generate large datasets, opting for the development of efficient design representations which offer a high range of possible design variants, while at the same time generating design spaces with attributes suitable for computational methods to explore. In response, the focus of this work is to propose a parametric design representation based on crystallographic symmetries and investigate its implications for the computational design of lattice structures. The work defines design rules to support the design of functionally graded structures using crystallographic symmetries such that the connectivity between individual members in a structure with varying geometry is guaranteed and investigates how to use the parametrization in the context of optimization. The results show that the proposed parametrization achieves a compact design representation to benefit the computational design process by employing a small number of design variables to control a broad range of complex geometries. The results also show that the design spaces based on the proposed parametrization can be successfully explored using a direct search-based method. [ABSTRACT FROM AUTHOR]

Dimensional and geometric variations are significant factors of products at the manufacturing stage. Because of these variations, the physical appearance or functionality of the final product may deviate from expectations. As of the present, research on variation analysis has been conducted at the feature level. To model the information and analyze the variation transfers of products, a graphical model is constructed by using the product feature and information. Through analysis of the relationship between the product and network model, a modeling process for the feature information network is proposed. Nodes, lines, and constraints in the network are defined in detail, and the dimension unit is defined to represent the dimension information within a part. Variations caused by connections between parts are divided into two categories of influence. Combining the dimension unit and the influence between parts, a variation analysis process is devised based on the proposed network model. To verify the effectiveness and feasibility of the proposed method, a case study is performed based on the grand assembly of a hull block. The result shows that the product can be modeled and the variation can be analyzed by the proposed network model. [ABSTRACT FROM AUTHOR]

Bayesian optimization (BO) is a sequential optimization strategy that is increasingly employed in a wide range of areas such as materials design. In real-world applications, acquiring high-fidelity (HF) data through physical experiments or HF simulations is the major cost component of BO. To alleviate this bottleneck, multi-fidelity (MF) methods are used to forgo the sole reliance on the expensive HF data and reduce the sampling costs by querying inexpensive low-fidelity (LF) sources whose data are correlated with HF samples. However, existing multi-fidelity BO (MFBO) methods operate under the following two assumptions that rarely hold in practical applications: (1) LF sources provide data that are well correlated with the HF data on a global scale, and (2) a single random process can model the noise in the MF data. These assumptions dramatically reduce the performance of MFBO when LF sources are only locally correlated with the HF source or when the noise variance varies across the data sources. In this paper, we view these two limitations and uncertainty sources and address them by building an emulator that more accurately quantifies uncertainties. Specifically, our emulator (1) learns a separate noise model for each data source, and (2) leverages strictly proper scoring rules in regularizing itself. We illustrate the performance of our method through analytical examples and engineering problems in materials design. The comparative studies indicate that our MFBO method outperforms existing technologies, provides interpretable results, and can leverage LF sources which are only locally correlated with the HF source. [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]

Many design problems involve reasoning about points in high-dimensional space. A common strategy is to first embed these high-dimensional points into a low-dimensional latent space. We propose that a good embedding should be isometric--i.e., preserving the geodesic distance between points on the data manifold in the latent space. However, enforcing isometry is non-trivial for common neural embedding models such as autoencoders. Moreover, while theoretically appealing, it is unclear to what extent is enforcing isometry necessary for a given design analysis. This paper answers these questions by constructing an isometric embedding via an isometric autoencoder, which we employ to analyze an inverse airfoil design problem. Specifically, the paper describes how to train an isometric autoencoder and demonstrates its usefulness compared to non-isometric autoencoders on the UIUC airfoil dataset. Our ablation study illustrates that enforcing isometry is necessary for accurately discovering clusters through the latent space. We also show how isometric autoencoders can uncover pathologies in typical gradient-based shape optimization solvers through an analysis on the SU2-optimized airfoil dataset, wherein we find an over-reliance of the gradient solver on the angle of attack. Overall, this paper motivates the use of isometry constraints in neural embedding models, particularly in cases where researchers or designers intend to use distance-based analysis measures to analyze designs within the latent space. While this work focuses on airfoil design as an illustrative example, it applies to any domain where analyzing isometric design or data embeddings would be useful. [ABSTRACT FROM AUTHOR]

Bridging the gaps among various categories of stochastic microstructures remains a challenge in the design representation of microstructural materials. Each microstructure category requires certain unique mathematical and statistical methods to define the design space (design representation). The design representation methods are usually incompatible between two different categories of stochastic microstructures. The common practice of preselecting the microstructure category and the associated design representation method before conducting rigorous computational design restricts the design freedom and hinders the discovery of innovative microstructure designs. To overcome this issue, this article proposes and compares two novel methods, the deep generative modeling-based method, and the curvature functional-based method, to understand their pros and cons in designing mixed-category stochastic microstructures for desired properties. For the deep generative modeling-based method, the variational autoencoder is employed to generate an unstructured latent space as the design space. For the curvature functional-based method, the microstructure geometry is represented by curvature functionals, of which the functional parameters are employed as the microstructure design variables. Regressors of the microstructure design variables-property relationship are trained for microstructure design optimization. A comparative study is conducted to understand the relative merits of these two methods in terms of computational cost, continuous transition, design scalability, design diversity, dimensionality of the design space, interpretability of the statistical equivalency, and design performance. [ABSTRACT FROM AUTHOR]

The pace of technological advancements has been rapidly increasing in recent years, with the advent of artificial intelligence, virtual/augmented reality, and other emerging technologies fundamentally changing the way human beings work. The adoption and integration of these advanced technologies necessitate teams with diverse disciplinary expertise, to help teams remain agile in an ever-evolving technological landscape. Significant disciplinary diversity amongst teams, however, can be detrimental to team communication and performance. Additionally, accelerated by the COVID-19 pandemic, the adoption and use of technologies that enable design teams to collaborate across significant geographical distances have become the norm in today's work environments, further complicating communication and performance issues. Little is known about the way in which technology-mediated communication affects the collaborative processes of design. As a first step toward filling this gap, the current work explores the fundamental ways experts from distinct disciplinary backgrounds collaborate in virtual design environments. Specifically, we explore the conversational dynamics between experts from two complementary yet distinct fields: non-destructive evaluation (NDE) and design for additive manufacturing (DFAM). Using Markov modeling, the study identified distinct communicative patterns that emerged during collaborative design efforts. Our findings suggest that traditional assumptions regarding communication patterns and design dynamics may not be applicable to expert design teams working in virtual environments. [ABSTRACT FROM AUTHOR]

Design representations play a pivotal role in the design process. In particular, design representations enable the formation of a shared understanding between team members, enhancing team performance. This paper explores the relationship between design representation modality (low-fidelity prototypes and sketches) and shared understanding among designers during communicative acts between design dyads. A mixed-methods study with 44 participants was conducted to investigate if representation modality affects shared understanding and identifies the factors that shape shared understanding during communication. Quantitative results suggest that low-fidelity prototypes and sketches did not significantly differ in terms of the shared understanding they facilitated within dyads. Qualitative analysis identified four factors at the representation- and actor-level that influence how shared understanding is built between individuals during design communication. This research extends our understanding of the utility of design representations given the needs of communicative contexts; specifically, this work demonstrates that designers must understand the perspectives of listeners during communication to create representations that accurately represent the information that a listener seeks to gain. [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]

Design artifacts provide a mechanism for illustrating design information and concepts, but their effectiveness relies on alignment across design agents in what these artifacts represent. This work investigates the agreement between multi-modal representations of design artifacts by humans and artificial intelligence (AI). Design artifacts are considered to constitute stimuli designers interact with to become inspired (i.e., inspirational stimuli), for which retrieval often relies on computational methods using AI. To facilitate this process for multi-modal stimuli, a better understanding of human perspectives of non-semantic representations of design information, e.g., by form or function-based features, is motivated. This work compares and evaluates human and AI-based representations of 3D-model parts by visual and functional features. Humans and AI were found to share consistent representations of visual and functional similarities, which aligned well with coarse, but not more granular, levels of similarity. Human-AI alignment was higher for identifying low compared to high similarity parts, suggesting mutual representation of features underlying more obvious than nuanced differences. Human evaluation of part relationships in terms of belonging to the same or different categories revealed that human and AI-derived relationships similarly reflect concepts of "near" and "far." However, levels of similarity corresponding to "near" and "far" differed depending on the criteria evaluated, where "far" was associated with nearer visually than functionally related stimuli. These findings contribute to a fundamental understanding of human evaluation of information conveyed by AI-represented design artifacts needed for successful human-AI collaboration in design. [ABSTRACT FROM AUTHOR]

Developing an artificial design agent that mimics human design behaviors through the integration of heuristics is pivotal for various purposes, including advancing design automation, fostering human-AI collaboration, and enhancing design education. However, this endeavor necessitates abundant behavioral data from human designers, posing a challenge due to data scarcity for many design problems. One potential solution lies in transferring learned design knowledge from one problem domain to another. This article aims to gather empirical evidence and computationally evaluate the transferability of design knowledge represented at a high level of abstraction across different design problems. Initially, a design agent grounded in reinforcement learning (RL) is developed to emulate human design behaviors. A data-driven reward mechanism, informed by the Markov chain model, is introduced to reinforce prominent sequential design patterns. Subsequently, the design agent transfers the acquired knowledge from a source task to a target task using a problem-agnostic high-level representation. Through a case study involving two solar system designs, one dataset trains the design agent to mimic human behaviors, while another evaluates the transferability of these learned behaviors to a distinct problem. Results demonstrate that the RL-based agent outperforms a baseline model utilizing the first-order Markov chain model in both the source task without knowledge transfer and the target task with knowledge transfer. However, the model's performance is comparatively lower in predicting the decisions of low-performing designers, suggesting caution in its application, as it may yield unsatisfactory results when mimicking such behaviors. [ABSTRACT FROM AUTHOR]

Power-split hybrid transmissions are the core components of power-split hybrid electric vehicles (HEVs), and the quest for a more energy-efficient and higher-performing power-split hybrid transmission has long been the focus of study. In contrast to previously published methodologies, this paper proposes a novel approach for directly synthesizing power-split hybrid transmissions that makes use of the results of previously synthesized planetary gear trains (PGTs) rather than necessitating a resynthesis of their PGTs. A new topological graph that can construct a bridge between the PGTs and power-split hybrid transmission has been developed, reducing the computational complexity in the synthesis process. The new topological graph is obtained by adding topological characteristics of the power-split hybrid transmission to the PGT graph. A standard structure matrix is proposed to further screen out all the isomorphic configurations. The present method can generate various types of multi-PGT hybrid transmissions while avoiding mechanical structural interference. The design process of configurations for power-split hybrid transmission with three-column PGTs (3-PGT) is used as an example to prove the rationality of the method. [ABSTRACT FROM AUTHOR]

Developing an artificial design agent that mimics human design behaviors through the integration of heuristics is pivotal for various purposes, including advancing design automation, fostering human-AI collaboration, and enhancing design education. However, this endeavor necessitates abundant behavioral data from human designers, posing a challenge due to data scarcity for many design problems. One potential solution lies in transferring learned design knowledge from one problem domain to another. This article aims to gather empirical evidence and computationally evaluate the transferability of design knowledge represented at a high level of abstraction across different design problems. Initially, a design agent grounded in reinforcement learning (RL) is developed to emulate human design behaviors. A data-driven reward mechanism, informed by the Markov chain model, is introduced to reinforce prominent sequential design patterns. Subsequently, the design agent transfers the acquired knowledge from a source task to a target task using a problem-agnostic high-level representation. Through a case study involving two solar system designs, one dataset trains the design agent to mimic human behaviors, while another evaluates the transferability of these learned behaviors to a distinct problem. Results demonstrate that the RL-based agent outperforms a baseline model utilizing the first-order Markov chain model in both the source task without knowledge transfer and the target task with knowledge transfer. However, the model’s performance is comparatively lower in predicting the decisions of low-performing designers, suggesting caution in its application, as it may yield unsatisfactory results when mimicking such behaviors.

Systematic enumeration and identification of unique 3D spatial topologies (STs) of complex engineering systems (such as automotive cooling systems, electric power trains, satellites, and aero-engines) are essential to navigation of these expansive design spaces with the goal of identifying new spatial configurations that can satisfy challenging system requirements. However, efficient navigation through discrete 3D ST options is a very challenging problem due to its combinatorial nature and can quickly exceed human cognitive abilities at even moderate complexity levels. This article presents a new, efficient, and scalable design framework that leverages mathematical spatial graph theory to represent, enumerate, and identify distinctive 3D topological classes for a generic 3D engineering system, given its system architecture (SA)--its components and their interconnections. First, spatial graph diagrams (SGDs) are generated for a given SA from zero to a specified maximum number of interconnect crossings. Then, corresponding Yamada polynomials for all the planar SGDs are generated. SGDs are categorized into topological classes, each of which shares a unique Yamada polynomial. Finally, within each topological class, 3D geometric models are generated using the SGDs having different numbers of interconnect crossings. Selected case studies are presented to illustrate the different features of our proposed framework, including an industrial engineering design application: ST enumeration of a 3D automotive fuel cell cooling system (AFCS). Design guidelines are also provided for practicing engineers to aid the application of this framework to different types of real-world problems such as configuration design and spatial packaging optimization. [ABSTRACT FROM AUTHOR]

Prior work has demonstrated that design tasks can be cognitively demanding, due to the inherent ambiguity and complexity of design problems. Few studies, however, have examined the evolution of cognitive load during the engineering design process and the linkages between subdimensions of cognitive load and design task outcomes. To address this gap, the current work investigates the evolution of cognitive load across two distinct design tasks: ideation and prototyping, and the relationship between cognitive load and design task outcomes. Results suggest that there is a significant difference in cognitive load experienced by the designer during ideation and prototyping. Additionally, findings suggest that cognitive load during ideation is positively correlated with the uniqueness, usefulness, and elegance of ideas. [ABSTRACT FROM AUTHOR]

This study investigates how artificial intelligence (AI) can be integrated into first-year design education. The goal of the study is to examine how students combine orthographic projections with AI-generated images. To answer this question, a design representation course called Visual Communication I (VC-I), taught at Istanbul Technical University's Faculty of Architecture, was selected. A three-step assignment called "ISO-meets-AI" was planned and implemented in the VC-I. The steps of the assignment were as follows: (i) producing orthographic projections of created physical compositions; (ii) generating image output from text input using an AI program (Midjourney); and (iii) combining the AI-generated images with the orthographic projections. The assignment was completed by 50 students from the departments of architecture, interior architecture, and industrial design. Tutors defined the evaluation categories and sub-criteria based on an initial analysis. While tutors evaluated every submission based on the established categories and sub-criteria, students only assessed their own work through a survey. Lastly, a second survey was given to the students in order to better understand their willingness to employ AI in their future studies. The first survey's comparative evaluation results of the tutors and students, as well as the results of the second survey, are presented. [ABSTRACT FROM AUTHOR]

Understanding how engineers formulate and evolve mental models holds the potential to inform the development of materials that benefit systems thinking. A first step toward realizing this benefit is measuring and assessing change in mental models following educational interventions. In this work, engineering students' mental models are elicited from common household products before and after learning functional modeling and are compared to the mental models of students who do not learn functional modeling. Results show statistically significant improvements in mental model representations on two of the three given systems after the functional modeling intervention, whereas no significant differences were found for students who did not learn function. Furthermore, results show statistical improvements in the identification of system components common to three systems and higher mental model scores for participants with prior experience disassembling the product. Taken together, these results suggest that functional modeling likely supports the ability to communicate knowledge, retrieve knowledge, and/or interpret existing mental models of engineered systems providing a foundation for systems understanding and communication. As we improve our understanding of how students form, change, and communicate their mental models of engineered systems, educators can shape curricula to facilitate the skills necessary for the comprehensive systems understanding that is important for professional engineers and designers. [ABSTRACT FROM AUTHOR]

Wave energy converter (WEC) mechanisms have been increasingly attracting the interest of institutions and companies, because of the energetic crisis and the energy demanded by small off-grid off-shore systems. Examples of WEC systems design date back to 1892 and, since then, different mechanisms have been proposed, based on different working principles. In this work, an attempt has been made to build an atlas of WEC devices, collecting them from the scientific and technical literature, as broadly as possible to the best of the authors' abilities. The working principle, the wave propagation direction, and the coast proximity have been used to classify the mechanisms of the atlas that have been illustrated by means of standardized esthetics. The topological nature of each device has been also extracted by applying both the polynomial representation of its kinematic chain (KC) together with a planar representation of the corresponding graph. These representations gave rise to a further and more detailed classification of the collected WEC devices that have been gathered together in peculiar topological families. Finally, system power has been also included to complete the information related to the devices illustrated in the atlas. [ABSTRACT FROM AUTHOR]

The study explored ways in which briefs can be structured to support creative design outcomes. The procedure involved a controlled, yet analytical experiment carried out in a university setting to test the use of analogical reasoning under different conditions to enhance the creativity of design solutions. The first goal was to verify whether visual and text analogies contribute to enhancing design creativity, measured by novel and useful solutions. The second goal was to explore the use of visual and text analogies in creative designs when instructions to formulate negative statements about the problem were provided. A final goal was to test whether correlations exist between the students’ self-reported creativity and their design outcomes scores. 179 first-year undergraduate students participated in this study. The results showed that design briefs with specific instructions to use text or visual analogies yielded highly novel outcomes. However, when text or visual analogies were triggered by statements concerning negative issues of the design problem, more useful outcomes were produced. Moreover, significant relationships were found between self-perceived creativity and the novelty of the outcomes generated in the visual and text analogy briefs. It is suggested that both types of analogies should be employed as effective design studio pedagogical tools to enhance creativity. Negative statements should be considered when the design goal is to improve existing features of current solutions. [ABSTRACT FROM AUTHOR]

Novel design creations often exist in the form of patents. It is well acknowledged that patents are a great source of design inspiration, therefore designers are encouraged to engage patents early in the design process. Studies of patent analysis have been carried out to benefit engineering design activities such as patent classification, technology forecasting, idea generation and emerging design-prior art comparison. However, the design intent behind inventions has received little attention in patent analysis. Designers can gain better design insight by looking at a patent from a systematic perspective starting from design intent to principal solutions. In this paper, an approach is proposed to extract and link the knowledge conveyed within patent descriptive sections to typical early engineering design stages, namely Motivation, Specification and Structure. This knowledge is then conceptualised into TRIZ engineering parameters and reconciled functional basis to enable cross-patent analysis. When compared to expert analysis, the proposed approach achieves an average of 63% accuracy with respect to Motivation, 56% with respect to Specification function and 44% with respect to Specification flow. Potential applications of such linking for early design use are then demonstrated through a prototype knowledge network. [ABSTRACT FROM AUTHOR]

Knowledge collection, extraction, and organization are critical activities in all aspects of the engineering design process. However, it remains challenging to surface and organize design knowledge, which often contains implicit or tacit dimensions that are difficult to capture in a scalable and accessible manner. Knowledge graphs (KGs) have been explored to address this issue, but have been primarily semantic in nature in engineering design contexts, typically focusing on sharing explicit knowledge. Our work seeks to understand knowledge organization during an experiential activity and how it can be transformed into a scalable representation. To explore this, we examine 23 professional designers' knowledge organization practices as they virtually engage with data collected during a teardown of a consumer product. Using this data, we develop a searchable knowledge graph as a mechanism for representing the experiential knowledge and afford its use in complex queries. We demonstrate the knowledge graph with two extended examples to reveal insights and patterns from design knowledge. These findings provide insight into professional designers' knowledge organization practices and represent a preliminary step toward design knowledge bases that more accurately reflect designer behavior, ultimately enabling more effective data-driven support tools for design. [ABSTRACT FROM AUTHOR]

Using prototypes to engage stakeholders during front-end design activities is crucial for successful design outcomes. Compared to prototyping that is used for iterative refinement during back-end engineering design activities, prototyping that informs problem definition, requirements and specifications development, concept generation, and other front-end design activities is understudied. To identify patterns in prototyping strategies for engaging stakeholders during the design front end, we conducted semi-structured interviews with 26 design practitioners across three product design domains: automotive, consumer products, and medical devices. Seventeen strategies evident across the collection of practitioners were used in generally consistent ways, with some variation based on context, e.g., project scope, stakeholders engaged, and the stakeholder interaction situation. Twelve of those 17 strategies were used by industry practitioners across the three domains, and five of those 17 strategies were used by practitioners from the medical device domain and either the automotive or consumer products domain. The descriptions and in-context examples of prototyping strategies used to engage stakeholders during front-end design can guide the design strategies of both experienced and novice designers. [ABSTRACT FROM AUTHOR]

Product evaluation throughout the design process is a fundamental task for product success, which also helps to reduce design-related costs. Physical prototyping is a common method to assess design alternatives, but often requires significant amounts of time and money. Extended reality (XR) technologies are changing how products are presented to the user, making virtual prototyping an effective tool for product evaluation. However, it is generally assumed that our perceptual and emotional responses to a product viewed in an XR modality are comparable to those elicited by the physical product. This paper reports the results of a study where a group of participants evaluated three designs of a product (i.e., umbrella stands) when viewed in a real setting, virtual reality (VR), and VR with passive haptics. Our goal was to observe the influence of visual media in product perception, and how the use of a complementary item (i.e., a physical umbrella) for interaction as well as user design expertise influence product assessment. Results show that the Jordan's psycho-pleasure category of assessment was the most affected by the presentation medium, whereas the ideo-pleasure category was the only category not influenced by the medium. We also highlight that the use of VR with passive haptics could be an effective tool for product evaluation, as illustrated by the study of umbrella stands and young consumers. Our study also shows that the user's background does not influence the level of confidence in their responses, but it can influence the assessment of certain product features. Finally, the use of a complementary item for interaction may have a significant effect on product perception. [ABSTRACT FROM AUTHOR]

The goal of this research is to develop a computer-aided visual analogy support (CAVAS) framework to augment designers' visual analogical thinking by stimulating them by providing relevant visual cues from a variety of categories. Two steps are taken to reach this goal: developing a flexible computational framework to explore various visual cues, i.e., shapes or sketches, based on the relevant datasets and conducting human-based behavioral studies to validate such visual cue exploration tools. This article presents the results and insights obtained from the first step by addressing two research questions: How can the computational framework CAVAS be developed to provide designers in sketching with certain visual cues for stimulating their visual thinking process? How can a computation tool learn a latent space, which can capture the shape patterns of sketches? A visual cue exploration framework and a deep clustering model CAVAS-DL are proposed to learn a latent space of sketches that reveal shape patterns for multiple sketch categories and simultaneously cluster the sketches to preserve and provide category information as part of visual cues. The distance-and overlap-based similarities are introduced and analyzed to identify long- and short-distance analogies. Performance evaluations of our proposed methods are carried out with different configurations, and the visual presentations of the potential analogical cues are explored. The results have demonstrated the applicability of the CAVAS-DL model as the basis for the human-based validation studies in the next step. [ABSTRACT FROM AUTHOR]

Three-dimensional spatial packaging of interconnected systems with physical interactions (SPI2) design plays a vital role in the functionality, operation, energy usage, and life cycle of practically all engineered systems, from chips to ships. SPI2 design problems are highly nonlinear, involving tightly constrained component placement, governed by coupled physical phenomena (thermal, hydraulic, electromagnetic, etc.), and involve energy and material transfer through intricate geometric interconnects. While many aspects of engineering system design have advanced rapidly in the last few decades through breakthroughs in computational support, SPI2 design has largely resisted automation and in practice requires at least some human-executed design steps. SPI2 system reasoning and design decisions can quickly exceed human cognitive abilities at even moderate complexity levels, thwarting efforts to accelerate design cycles and tackle increasingly complex systems. Existing design methods treat pieces of the SPI2 problem separately without a fundamental systems approach, are sometimes inefficient to evaluate various possible designs, and present barriers to effective adoption in practice. This article explores a vision of a holistic SPI2 design approach needed to develop next-generation automated design methods capable of rapidly producing viable SPI2 design candidates. We review several technical domains related to holistic SPI2 design, discuss existing knowledge gaps and practical challenges, examine exciting opportunities at the intersection of multiple domains that can enable comprehensive exploration of SPI2 design spaces, and present one viable two-stage SPI2 design automation framework. Holistic SPI2 design opens up a new direction of high industrial and societal relevance for the design research community. [ABSTRACT FROM AUTHOR]

3D modelling and rapid prototyping skills have become an essential technique in design education and crossdisciplinary educational settings provide opportunities for students to develop design expertise on 3D modelling. In the present study, we focused on 3D modelling in a product design context, and we analysed what kind of design concepts emerge in students' work when they are taught 3D modelling in a cross-disciplinary setting. This study aims to provide design educators with new insights into novices' design practices in cross-discipline design education. The research employed a qualitative case-study approach to analyse textual and visual representations (i.e., concept maps, sketches, 3D models, and reflective reports) to explore the students' design concepts. The cross-disciplinary setting consisted of graphic design, interior design, and product design students who were given a product form design task. All students succeeded in creating description-based concepts that met the problem requirements. However, principle-based concepts emerged only in product design students' processes. We argue that these kinds of principle-based concepts are related to the product's structure and functionality, when engaged students can apply product design domain-specific knowledge. In conclusion, this kind of design task has benefits for individual learning and developing usable and aesthetically appealing products. However, there is a clear need for studies that develop workshops and tasks where students can participate in collaborative multi-disciplinary projects. [ABSTRACT FROM AUTHOR]

This paper examines the potential of immersive virtual reality technology for design education. A quasi-experimental study has been conducted with 40 students of different expertise levels. The students analysed a design representation using one of the two visualisation technologies: immersive virtual reality (IVR) and non-immersive virtual reality (nIVR). The results show that the expertise in the used technology and the expertise in the design domain significantly affect design understanding. On the other hand, the effect of contextual expertise was not found significant. Spatial ability affected design understanding in nIVR but not in the IVR. Visualisation technology did not have an overall effect on understanding, but IVR helped students with lower expertise to understand specific aspects of a design better (e.g. rotation-based mechanisms). The study suggests that researchers and educators control the students' expertise when assessing the effect of technology on design education. Overall, the results support the constructivist learning theory, as IVR can support context-dependent and context-independent understanding. [ABSTRACT FROM AUTHOR]

Planning the shortest collision-free path among scattered obstacles is an NP-complete problem. As reviewed in this paper, a variety of deterministic as well as heuristic methods have been developed to address different instances of the problem. The focus of the deterministic methods is primarily on the optimality of the final solution and has been applied exclusively to regular shapes such as spheres or cubes. Nevertheless, due to the problem's intrinsic complexities (especially in 3D), researchers mainly resort to heuristics that offer acceptable (yet possibly suboptimal) solutions with reasonable resources. Therefore, less attention has been given to further the state-of-the-art in deterministic methods, which for 3D problems primarily focuses on approximating the solution. However, with the advancements in high-performance computing, we believe it is time to focus on solution quality. As such, this study aims to further the efforts in deterministic optimization methods for 3D path planning by overcoming some challenges of the existing methods and improving the optimality of the solution. The proposed approach is rooted in visibility-based planning methods where the obstacle-free space is modeled as a connectivity graph to be searched for the shortest path. The advantage of the proposed method in constructing the representative graph is that it does not make approximations to identify the graph nodes, unlike the existing methods. Nor does it limit the objects' geometries to specific shapes such as blocks or spheres. The capability of the method in finding the shortest collision-free paths in environments cluttered with convex polyhedra is demonstrated using sample test problems. [ABSTRACT FROM AUTHOR]

Designers should adequately develop user considerations such as usability, safety, and comfort during the design process of new systems. Nevertheless, incorporating human factors engineering principles during early design phases is not simple. The objective of this work is to assist designers in implementing human factors engineering principles during early design phases using a functional model framework. This effort expands our previous work on automating the function-human error design method (FHEDM) implementation. In this work, we use data mining techniques in a design repository to explore the construction of association rules between components, functions, flows, and user interactions. Such association rules can support designers assessing user-system interactions during the early design stages. To validate this approach, we compare the associations generated by expert designers using the FHEDM while designing a new product to those generated by an algorithm using the repository data. The results show notable similarities between the associations extracted by the algorithm and the associations identified by designers. Thus, the overall results show that association rules extracted from a rich dataset can be used to distinguish user-product interactions, demonstrating the potential of automating the identification of user-product interactions from a functional model. [ABSTRACT FROM AUTHOR]

Engineering simulations for analysis of structural and fluid systems require information of contacts between various 3D surfaces of the geometry to accurately model the physics between them. In machine learning applications, 3D surfaces are most suitably represented with point clouds or meshes and learning representations of interacting geometries form point-based representations is challenging. The objective of this study is to introduce a machine learning algorithm, ActivationNet, that can learn from point clouds or meshes of interacting 3D surfaces and predict the quality of contact between these surfaces. The ActivationNet generates activation states from point-based representation of surfaces using a multidimensional binning approach. The activation states are further used to contact quality between surfaces using deep neural networks. The performance of our model is demonstrated using several experiments, and we show that the contact quality predictions of ActivationNet agree well with the expectations. [ABSTRACT FROM AUTHOR]

Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative machine learning to design engineering may enable such automated design synthesis and is a research subject of great importance. We present a review and analysis of deep generative machine learning models in engineering design. Deep generative models (DGMs) typically leverage deep networks to learn from an input dataset and synthesize new designs. Recently, DGMs such as feedforward neural networks (NNs), generative adversarial networks (GANs), variational autoencoders (VAEs), and certain deep reinforcement learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in engineering design has skyrocketed since 2016. Anticipating the continued growth, we conduct a review of recent advances to benefit researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported the development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion, we identify possible solution pathways as key areas on which to target the future work. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic has accelerated the acceptance of virtual team collaboration as a replacement for face-to-face collaboration. Unlike face-to-face collaboration, virtual collaboration is influenced by unique factors, such as technology mediation. However, there is a lack of rigorous research that assesses the impact of virtual collaboration on the engineering design process. Therefore, the current study investigates the effect of virtual team collaboration on design outcomes by means of the model of influence, learning, and norms in organizations (MILANO) framework. To tailor MILANO for virtual collaboration, this paper first presents an empirical study of human design teams, which shows how model parameter values for face-to-face collaboration (like self-efficacy, perceived influencers, perceived degree of influence, trust and familiarity) differ from appropriate parameter values for face-to-face collaboration. The simulation results for both virtual and face-to-face collaboration show how design outcomes differ with collaboration mode. Unlike teams with a few well-defined influential individuals, the mode of collaboration does not have a significant impact on teams where all individuals are equally influential. Virtual collaboration also results in lower exploration and variety than face-to-face collaboration. [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]