Volume 3B: 48th Design Automation Conference (DAC)最新文献

{"title":"On Spiral Folding of Planar Membranes With Finite Thickness and Curved Creases","authors":"V. Parque, T. Miyashita","doi":"10.1115/detc2022-90145","DOIUrl":"https://doi.org/10.1115/detc2022-90145","url":null,"abstract":"\u0000 Spiral folding of flat and planar membranes with finite thickness is of relevant interest to develop the spin-type deployable membrane structures for space environments and for consumer applications. Examples involve the design and development of origami-based structures, airbags, antenna design, wrapping of food by thin membranes, wheel design, and membrane deployment for medical applications. In this paper, we propose the governing equations to fold planar membranes with finite thickness by using curved creases, whose governing equations render fold patterns whose radius of curvature tends to increase linearly by accommodating membrane thickness. The consideration of curvature along in the crease patterns is relevant and potential to balance the tension of outer layers with the compression of inner layers, and to distribute the out-of plane and localized bending near the creases and vertices. We present the mathematical formulations that consider the curved creases and describe folding examples of a planar membrane with a defined thickness. Our computational experiments have shown (1) the versatility to model a plural number of curvature profiles, and (2) the feasibility of global deployment by using the compliant and explicit numerical simulations. From viewpoints of configuration and deployment performance, the curved crease patterns are potential to extend the versatility and smoothness of spiral folding mechanisms.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115280447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Need for Desalination in Humanitarian Crises","authors":"Jonathan T Bessette, A. Winter","doi":"10.1115/detc2022-89713","DOIUrl":"https://doi.org/10.1115/detc2022-89713","url":null,"abstract":"\u0000 Humanitarian crises ranging from political unrest to natural disasters are becoming increasingly prevalent with global climate change. Correspondingly, there are an increasing number of regions that consist both of high crises risk and saline water contamination. Such regions include the Middle East, Subsaharan Africa (particularly along the Great Rift Valley), Southeast Asia (including the Mekong Delta and Pacific Islands), and coastal regions. However, there is a lack of robust, deployable desalination technologies for humanitarian crises. This is mainly attributed to the highly-constrained environment which necessitate: minimization of consumables, rapid speed of deployment and simplification of operation and maintenance. Such constraints are often secondary thoughts, are difficult to traditionally quantify, and differ from stable commercial situations where operations are supported by an accessible supply chain and network of technicians. These barriers have particularly hindered the adoption of membrane technology and thus, high volume desalination and chemical contaminant removal. This work justifies the need for desalination technology in humanitarian crises via geospatial analysis of saline water databases and exploration of regional case studies, formulates design requirements for an emergency-use desalination system based on needs extracted from open-interviews of stakeholders and literature review, evaluates some of the gaps within currently employed deployable desalination systems and explores the potential opportunities of other desalination technology.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132114778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing Privacy Risk Frameworks for Evolving Cyber-Physical Social Systems: Knowledge Gaps Illuminated by the Case of Autonomous Vehicles and Bystander Privacy","authors":"V. Rao, Ankita Joshi, Soo Min Kang, Susan Lin, (Erin) Junghyun Song, Drew Miller, K. Goucher-Lambert, A. Agogino","doi":"10.1115/detc2022-90958","DOIUrl":"https://doi.org/10.1115/detc2022-90958","url":null,"abstract":"\u0000 Designers and engineers increasingly engage with and must design for sociotechnical systems, also described as cyber-physical-social systems (CPSS). Leading frameworks like System-Theoretic Process Analysis and Value-Sensitive Design intend to help designers consider the consequences and impacts of their work with CPSS. However, such frameworks may not sufficiently account for human-centered scenarios. This complicates designers’ efforts to balance user needs with traditional forms of risk assessment. In this work, we explore foundations for the design of human-centered risk frameworks and examine a case study of autonomous vehicles and bystanders’ privacy as an example CPSS to address this gap. We develop an exploratory scenario-based risk framework and conduct expert interviews with experienced professionals (N = 7) working in the fields of autonomous vehicle design, development, policy and security to understand their perspectives on risk assessment and gather feedback on our framework. Reconciling interview findings with existing knowledge of evolving CPSS, we identify three broad knowledge gaps that could motivate future research in this space. First, we argue that there is a knowledge gap in developing human-centered frameworks and best practices to consider all stakeholders during the design of evolving CPSS. Second, we argue that a knowledge gap exists in acknowledging, reconciling, and proactively managing disciplinary discontinuities in vocabularies and mental models in evolving CPSS. Lastly, we argue that a critical knowledge gap exists around how to adapt scenario-based frameworks to accommodate the shifting challenges of designing evolving CPSS. We conclude with a discussion of preliminary implications for designing human-centered frameworks for autonomous vehicles and CPSS more generally.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128797717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the Understanding of Nudging Strategies in Cyber-Physical-Social System In Manufacturing Environments","authors":"Xiaoou Yang, Ahreum Lim, Aliki Nicolaides, Beshoy Morkos","doi":"10.1115/detc2022-90863","DOIUrl":"https://doi.org/10.1115/detc2022-90863","url":null,"abstract":"\u0000 The involvement of artificial intelligence in manufacturing settings has revolutionized the relationship between humans, computers, and workforce environments. The prior cyber-physical systems are developing rapidly due to advances in technology. However, human consideration has not advanced at the same rate. Only highlighting technology advancement is not sufficient, given the high interconnectivity between human-technology in Industry 4.0. Integrating the human factor in design and implementation of cyber-physical technology leads to the holistic development of cyber-physical-social systems (CPSS). Nonetheless, little is known regarding workers’ behavior and ethics that mainly pertain to the human factors of CPSSs. Also, little effort is given to seek a methodologically rigorous way to investigate human factors in CPSS. To fill the gap, this paper proposes a research framework that aims to explore the interaction between humans and AI agents in manufacturing setting. Incorporating human considerations properly can enhanced the development of an integrated cyber-physical-social system. The goal of the research methodology presented here is to fundamentally understand a common dimension in CPSS. The objective of the proposed study is to determine how nudging impacts user response on a manufacturing line, both in terms of manufacturing performance and human response. To do so, we introduced the concept of nudging, which can come in the form of audio, visual, and haptic, refers to signals sent by a cyber-physical device to humans to illicit a performance related response. The design of an experiment is discussed in this paper. Quantitative data (assembly time, quality, and assembly errors) and qualitative data (recorded video, pre-experiment questionnaire, and post-experiment interviews) will be used for analysis to determine if and how the types of nudging are observed in cyber-physical-social environments that we see today can impact human response. The results of this research methodology will help inform engineers how cyber physical systems should be implemented in manufacturing environments while considering the impact it has on the human.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130401939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agent-Based Simulation of Optimal Trust in a Decision Support System in One-on-One Collaboration","authors":"Mostaan Lotfalian Saremi, A. E. Bayrak","doi":"10.1115/detc2022-90770","DOIUrl":"https://doi.org/10.1115/detc2022-90770","url":null,"abstract":"\u0000 Intelligent systems that can effectively collaborate with human users can potentially expand human decision-making capabilities in numerous domains. An important factor that determines the effectiveness of these intelligent systems is trust from human users. How much a user should trust an intelligent system to maximize the benefits is an open question. In this paper, we present a quantitative analysis of the impact of trust on the collaboration between a human user and an intelligent decision support system (DSS) in binary classification problems. Using an agent-based simulation model, we represent trust as a static quantity averaged over a set of Monte Carlo simulations calculated based on a user’s self-confidence, confidence in a DSS, and agents’ expertise. Our results show the optimal level of self-confidence and confidence in a DSS needed to maximize the collaboration performance under different problem scenarios. The results indicate that with such an optimal level of confidence, the collaboration performance can exceed the performance of the individual agents alone. Further, our results also show that having a concentrated expertise on particular types of problems is more beneficial than being somewhat knowledgeable in multiple problems given that the expertise of the user and the DSS complement each other.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134461683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Real-Time Artificial Intelligence Process Manager for Engineering Design","authors":"J. Gyory, Nicolas F. Soria Zurita, Jay Martin, Corey Balon, Christopher McComb, K. Kotovsky, J. Cagan","doi":"10.1115/detc2022-88609","DOIUrl":"https://doi.org/10.1115/detc2022-88609","url":null,"abstract":"\u0000 Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team to reap the most impact. In this work, an Artificial Intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from the both the AI and human manager as equally relevant and helpful and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work matches the capabilities of humans, showing potential in automating the management of a complex design process.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127753505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does Design Proficiency Matter in Engineering Design Teams? A Computational Model and Experiments","authors":"Ethan Brownell, J. Cagan, K. Kotovsky","doi":"10.1115/detc2022-89318","DOIUrl":"https://doi.org/10.1115/detc2022-89318","url":null,"abstract":"\u0000 A cognitively inspired, agent-based model of engineering design proficiency is introduced in this work. Proficiency is modeled using move selection heuristics and problem space search strategies, both of which were extracted from a prior human subjects study. Agent behavior in the Proficient Simulated Annealing Design Agents (PSADA) Model is validated against human designer behavior and performance. A multi-agent model of design teams is then produced with Monte Carlo simulation methods and it confirmed the previous finding that the most proficient member of a configuration design team has the largest impact (positive or negative) on team performance. Further experiments are conducted with different team characteristics. It is found that the effect of individual team member proficiency is reduced in larger teams, but the highest proficiency team member likely remains the most influential. Nominal teams, where team members do not interact and where the best design is taken as the team output, were found to outperform interacting teams. This was due to high proficiency agents making poor decisions during interactions by switching away from their designs to the designs of their lower proficiency teammates. Conclusions about the role that individual team member proficiency plays in engineering design teams is discussed.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125634032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Usefulness of Agent-Based Product Social Impact Modeling Through a Systematic Literature Review","authors":"Christopher S. Mabey, C. Mattson, Jon Salmon","doi":"10.1115/detc2022-90001","DOIUrl":"https://doi.org/10.1115/detc2022-90001","url":null,"abstract":"\u0000 A key part of an engineer’s purpose is to create products and services that benefit society, or, in other words, create products with a positive social impact. While engineers have many predictive models to aid in making design decisions about the performance or safety of a product, very few models exist for estimating or planning for the wide range of social impacts an engineered product can have. To model social impact, a model must contain representations of the product and society. Agent-based modeling is a tool that can model society and incorporate social impact factors. In this paper, we investigate factors that have historically limited the usefulness of product adoption agent-based models, and predictive social impact models through a systematic literature review. Common themes of limiting factors are identified and steps are presented to improve the usefulness of agent-based product adoption models and predictive social impact models. The goal of a predictive social impact model is to help an engineer/designer make better decisions. Predictive social impact models can help identify areas in the design space for improving the social impact of products. When coupled with existing design methods, agent-based predictive social impact models can help increase the probability that a product achieves positive social impact.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Envelope Method for Time- and Space-Dependent Reliability-Based Design","authors":"Hao Wu, Xiaoping Du","doi":"10.1115/detc2022-89492","DOIUrl":"https://doi.org/10.1115/detc2022-89492","url":null,"abstract":"\u0000 Deterministic optimization may lead to unreliable design results if significant uncertainty exists. Including reliability constraints in reliability-based design optimization (RBDO) can solve such a problem. It is difficult to use current RBDO methods to deal with time- and space-independent reliability when responses vary randomly with respect to time and space. This study employs an envelope method for time- and space-dependent reliability for the optimal design. To achieve high accuracy, we propose an inverse envelope method that converts a time- and space-dependent limit-state function into a time- and space-independent counterpart, and then the second-order saddlepoint approximation is used to estimate the probability of failure. The strategy is to find an equivalent most probable point for a given permitted probability of failure for each of the reliability constraint. To achieve high efficiency, the new method uses a sequential optimization process to decouples the double-loop structure of RBDO. The overall optimization is performed with a sequence of cycles consisting of deterministic optimization and reliability analysis. The constraints of the deterministic optimization are formulated using the equivalent most probable points. The accuracy and efficiency are demonstrated with four examples, including one mathematical problem and three engineering problems.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134059726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Designer Learning and Performance in Interactive Deep Generative Design","authors":"Ashish M. Chaudhari, Daniel Selva","doi":"10.1115/detc2022-90477","DOIUrl":"https://doi.org/10.1115/detc2022-90477","url":null,"abstract":"\u0000 Deep generative models have shown significant promise to improve performance in design space exploration (DSE), but they lack interpretability. A component of interpretability in DSE is helping designers learn how input design decisions influence multi-objective performance. This experimental study explores how human-machine collaboration influences both designer learning and design performance in deep learning-based DSE. A within-subject experiment is implemented with 42 subjects involving mechanical metamaterial design using a conditional variational auto-encoder. The independent variables in the experiment are two interactivity factors: (i) simulatability, e.g., manual design generation (high simulatability), manual feature-based design synthesis, and semi-automated feature-based synthesis (low simulatibility); and (ii) semanticity of features, e.g., meaningful versus abstract latent features. We perform assessment of designer learning using item response theory and design performance using metrics such as distance to utopia point and hypervolume improvement. The findings highlights a highly intertwined relationship between designer learning and design performance. Compared to manual design generation, the semi-automated synthesis generates designs closer to the utopia point. Still, it does not result in greater hyper-volume improvement. Further, the subjects learn the effects of semantic features better than abstract features, but only when the design performance is sensitive to those semantic features. Potential cognitive constructs, such as cognitive load and recognition heuristic, that may influence the interpretability of deep generative models are discussed.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133517856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}