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

{"title":"Generative Design of Cyber-Physical-Human System Families: Concepts and Research Issues","authors":"D. Rosen, Young Mi Choi","doi":"10.1115/detc2022-91265","DOIUrl":"https://doi.org/10.1115/detc2022-91265","url":null,"abstract":"\u0000 Cyber-physical-social systems (CPSS) are typically considered as an evolution of cyber-physical systems to include human interactions; further, that interactions among humans through CPSS can lead to emergence of social or community structures and behaviors. Cyber-physical-human systems (CPHS) can be considered as subsets of CPSS that are smart products that offer services to its customer, supported by back-end systems (e.g., information, finance) and other infrastructure. In this paper, initial concepts and research issues are presented regarding the design of CPHS families and generations of these families. Significant research gaps are identified that should drive future research directions. Potential impacts of filling those gaps provide motivation for the development of a design methodology for CPHS. The approach proposed here is a novel combination of generative and configuration design methods with product family design methodology. With this approach, a wide variety of CPHS, including customized CPHS, can be developed quickly by sharing technologies and modules across CPHS family members. Further, the foundation is provided for the evolution of CPHS families; for example, deciding when to add a new family member, when to design a new CPHS family platform, or when the current CPHS family can no longer be supported and a new family developed. The domain of assistive technology is used in this paper to provide an example field of practice that could benefit from a systematic design methodology and opportunities to leverage technology solutions.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"81 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":"132003997","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":"Adapting Consumer Product Design to the Circular Economy","authors":"Vincenzo Ferrero, Katherine C. Morris, B. Hapuwatte","doi":"10.1115/detc2022-89542","DOIUrl":"https://doi.org/10.1115/detc2022-89542","url":null,"abstract":"\u0000 Transition to a Circular Economy (CE) will be facilitated by information transfer between life cycle stages, and, particularly, the transition can be accelerated at the product design stage where the decisions have impact throughout the life cycle. Product design, which is often manual, subjective, and speculative, can be enhanced by data transfer from other stages of product, manufacturing, material, and supply chain life cycles. Mutually, the CE system can benefit from design systems that are real-time, designer-in-the-loop, and dynamic with respect to life cycle information streams. Five identified challenges toward adapting product design to the CE are\u0000 1. Consolidation and identification of DfX areas critical to the CE\u0000 2. Realization of a digital thread to the design activities\u0000 3. Incorporation of designer-in-the-loop and AI design agents\u0000 4. Introduction of CE data-driven design methods\u0000 5. Development of CE design standards\u0000 A literature review in each challenge area identifies future research areas. Finally, a preliminary circular product design convergence model is introduced to illustrate a CE consumer product design system that addresses the challenges and research opportunities. The work contributes a framework within which product design can be improved to aid in the realization of Design-for-the-CE. The circular product design convergence model can be a framework for exploring standards-driven and validated solutions to these challenges.","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":"130613681","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":"An Algorithm for Multi-Objective Efficient Parametric Optimization","authors":"Jonathan M. Weaver-Rosen, R. Malak","doi":"10.1115/detc2022-88567","DOIUrl":"https://doi.org/10.1115/detc2022-88567","url":null,"abstract":"\u0000 Parametric optimization is the process of solving an optimization problem as a function of currently unknown or changing variables known as parameters. Parametric optimization methods have been shown to benefit engineering design and optimal morphing applications through its specialized problem formulation and specialized algorithms. Despite its benefits to engineering design, existing parametric optimization algorithms (similar to many optimization algorithms) can be inefficient when design analyses are expensive. Since many engineering design problems involve some level of expensive analysis, a more efficient parametric optimization algorithm is needed. Therefore, the multi-objective efficient parametric optimization (MO-EPO) algorithm is developed to allow for the efficient optimization of problems with multiple parameters and objectives. This technique relies on the new parametric hypervolume indicator (pHVI) which measures the space dominated by a given set of data involving both objectives and parameters. The novel MO-EPO algorithm is demonstrated on a number of analytical benchmarking problems with various numbers of objectives and parameters. In each case, MO-EPO is shown to find solutions that are as good as or better than those found from the existing P3GA (i.e., equal or greater pHVI value) when the number of design evaluations is limited.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"122 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":"127774789","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 Design Framework for Evolving Cyber-Physical-Social System (CPSS) Based on Force Field","authors":"Ziqing Zhou, Yanwei Sun, Chun Ouyang, Zhongxue Gan, Zhenjun Ming","doi":"10.1115/detc2022-89892","DOIUrl":"https://doi.org/10.1115/detc2022-89892","url":null,"abstract":"\u0000 A Cyber-Physical-Social System (CPSS), as an extension of cyber-physical system (CPS), has received considerable attention in recent years both in academia and industry. Compared to CPS, CPSS has great advantages on efficiency, adaption and robustness due to the existence of self-organizing social system. One of the difficulties in the design of CPSS is anchored in the lack of a control mechanism to achieve self-organizing capability while the system is evolving. To address this problem, we propose a design framework for evolving CPSS based on the force field, which is composed of inner force and task force field. In addition to the framework, we present several performance evaluation indicators to quantify the self-organizing capability of the system, including consumption, emergence, and scalability. We test the performance of the framework using a disaster-relief example, which involves the computational control algorithm (cyber system), robot (physical system), and self-organizing swarm (social system). The result shows that based on our framework, the system can automatically evolve from disorder to order, adapt to the changeable environment and successfully finish the task in a very efficient manner.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"66 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":"126082780","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":"Hierarchical Surrogate Modeling With Multiple Order Partially Observed Information","authors":"Yanwen Xu, Pingfeng Wang","doi":"10.1115/detc2022-89655","DOIUrl":"https://doi.org/10.1115/detc2022-89655","url":null,"abstract":"\u0000 Understanding the input and output relationship of a complex engineering system is an essential task that attracts widespread interests in engineering design fields. To investigate the system performance, surrogate models can be developed based upon a finite set of input-output sample points, and then used to replace expensive black box type performance function and reduce the cost on function evaluations for system design optimization. The finite set of sample points could be obtained from multiple information sources such as experiments with different tests or simulation using different order of computer models. There is a pressing need for an efficient surrogate modeling method that can comprehensively utilize all available information, both fully and partially observed information (POI) collected from sources with different fidelities and dimensionalities. This paper proposes a multi-order system modeling method for partially observed information (MOSM-POI), which takes account of the POI structure and sparseness and uses multiple reduced order models to assist the understanding of the high-dimensional complex system. The Bayesian Gaussian process latent variable model (BGP-LVM) was employed to incorporate POI and a new framework was developed to cope with the high sparseness POI. The numerical experiments demonstrated that the proposed MOSM-POI method provides an accurate solution to take advantage of partially observed information from the multi-order system in developing surrogate models for complex systems.","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":"127479700","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 Multi-Fidelity Approach for Reliability Assessment Based on the Probability of Model Inconsistency","authors":"Bharath Pidaparthi, S. Missoum","doi":"10.1115/detc2022-90115","DOIUrl":"https://doi.org/10.1115/detc2022-90115","url":null,"abstract":"\u0000 Most multi-fidelity schemes rely on regression surrogates, such as Gaussian Processes, to combine low- and high-fidelity data. Contrary to these approaches, we propose a classification-based multi-fidelity scheme for reliability assessment. This multi-fidelity technique leverages low- and high-fidelity model evaluations to locally construct the failure boundaries using support vector machine (SVM) classifiers. These SVMs can subsequently be used to estimate the probability of failure using Monte Carlo Simulations. At the core of this multi-fidelity scheme is an adaptive sampling routine driven by the probability of misclassification. This sampling routine explores sparsely sampled regions of inconsistency between low- and high-fidelity models to iteratively refine the SVM approximation of the failure boundaries. A lookahead check, which looks one step into the future without any model evaluations, is employed to selectively filter the adaptive samples. A novel model selection framework, which adaptively defines a neighborhood of no confidence around low fidelity model, is used in this study to determine if the adaptive samples should be evaluated with high- or low-fidelity model. The proposed multi-fidelity scheme is tested on a few analytical examples of dimensions ranging from 2 to 10, and finally applied to assess the reliability of a miniature shell and tube heat exchanger.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"226 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":"127212737","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":"Two-Dimensional Lineup Design of Intermediate Functional Products With Enumerative Optimization of Combinatorial Mini-Max Optimality","authors":"K. Fujita, Naoki Ono, Yutaka Nomaguchi","doi":"10.1115/detc2022-90899","DOIUrl":"https://doi.org/10.1115/detc2022-90899","url":null,"abstract":"\u0000 As mechanical products have become further complicated and diversified, integrative design of their components, such as motors, fans, and pumps, through commonalization has become essential for shorter lead time and more cost-saving behind the mainstream. This paper names those products as intermediate functional products and proposes their lineup design method. Scale and coefficient identify their performance. Thus, they are arranged over a two-dimensional space of design requirements to minimize the lead time and production cost with maintaining performance optimality. The former cannot be measured quantitatively before actual production. Thus, minimizing the process complexity is demanded. The latter criterion translates into optimizing the worst-case performance across the requirement space. Those understanding leads to a design method by enumerating possible commonalization patterns, exclusively arranging the lineup for each pattern optimally, and investigating the best lineup through trade-off analysis. While the lineup arrangement under a pattern takes a mini-max type formulation, its optimization is performed based on monotonicity analysis efficiently. This paper demonstrates the formulation and procedures through an application to universal motors.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"21 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":"127279182","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":"Self Learning Design Agent (SLDA): Enabling Deep Learning and Tree Search in Complex Action Spaces","authors":"A. Raina, J. Cagan, Christopher McComb","doi":"10.1115/detc2022-89740","DOIUrl":"https://doi.org/10.1115/detc2022-89740","url":null,"abstract":"\u0000 Building an AI agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However, learning over prior data limits us only to solve problems that have been solved before and biases us towards existing solutions. The ultimate goal for a design agent is the ability to learn generalizable design behavior in a problem space without having seen it before. We introduce a self-learning agent framework in this work that achieves this goal. This framework integrates a deep policy network with a novel tree search algorithm, where the tree search explores the problem space, and the deep policy network leverages self-generated experience to guide the search further. This framework first demonstrates an ability to discover high-performing generative strategies without any prior data, and second, it illustrates a zero-shot generalization of generative strategies across various unseen boundary conditions. This work evaluates the effectiveness and versatility of the framework by solving multiple versions of the truss design problem without retraining. Overall, this paper presents a methodology to self-learn high-performing and generalizable problem-solving behavior in an arbitrary problem space, circumventing the needs for expert data, existing solutions, and problem-specific learning.","PeriodicalId":394503,"journal":{"name":"Volume 3B: 48th Design Automation Conference (DAC)","volume":"35 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":"134212085","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":"Multi-Fidelity Reduced-Order Models for Multiscale Damage Analyses With Automatic Calibration","authors":"Shiguang Deng, Carlos Mora, D. Apelian, R. Bostanabad","doi":"10.1115/detc2022-90163","DOIUrl":"https://doi.org/10.1115/detc2022-90163","url":null,"abstract":"\u0000 Predicting the fracture behavior of macroscale components containing microscopic porosity relies on multiscale damage models which typically ignore the manufacturing-induced spatial variabilities in porosity. This simplification is made due to the prohibitive computational costs associated with explicitly modeling spatially varying microstructures in a macroscopic component. To address this challenge, we propose a data-driven framework that integrates a mechanistic reduced-order model (ROM) with a calibration scheme based on latent map Gaussian processes (LMGPs). Our ROM drastically accelerates direct numerical simulations (DNS) by using a stabilized damage algorithm and systematically reducing the degrees of freedom via clustering. Since clustering affects local strain fields and hence the fracture response, we construct a multi-fidelity LMGP to inversely estimate the damage parameters of an ROM as a function of microstructure and clustering level such that the ROM faithfully surrogates DNS. We demonstrate the application of our framework in predicting the damage behavior of a multiscale metallic component with spatially varying porosity.","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":"122583201","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":"Requirements Mapping of a High-Powered Rocket System to Explain Solution Similarities Across Generations","authors":"Lindsey Jacobson, S. Ferguson","doi":"10.1115/detc2022-91348","DOIUrl":"https://doi.org/10.1115/detc2022-91348","url":null,"abstract":"\u0000 Designers have historically used existing solutions as a baseline for modifying and improving the next generation solution. However, it is also possible that certain design solutions are pursued by designers irrelevant of previous design history, suggesting that there is some dominant system architecture for a given set of requirements. While the reason for system architecture similarities can never be determined with certainty without consulting designers, it is possible to speculate about why design decisions might have been made, especially when certain system architectures and design elements have dominated a space for so long. The NASA SL Challenge serves as a compelling area for study, given that NASA SL vehicle and recovery performance requirements are similar each year, while payload integration and payload function requirements differ each year. Student design teams are tasked with designing a new launch vehicle and payload each year to meet all of the requirements. Despite yearly changes in payload requirements, some teams’ vehicle architectures remain largely unchanged, prompting a question of whether these design solutions are inherently used because of the nature of the design problem or if it is a reuse of previous design solutions. To investigate this question, we relate system requirements to vehicle elements using a DSM-style requirements mapping process. These relationships are then translated into tree structures that are centered around a single element/requirement. Finally, the tree structures are analyzed to determine why certain design solutions may have been selected based on entanglement between requirements. These mappings are used to identify areas of tension between requirements and independent variables within system architectures for a NASA SL team. Our analysis of the mappings allows us to suggest that certain elements of system architectures or mission profile may be more dominant, that is more favorable, based solely on the design problem definition. In NASA SL for example, teams could target a lower apogee because it aids in meeting entangled recovery requirements. In future work we will explore how requirements mapping can be used to identify dominant architectures during the conceptual phase of design. Finally, based on observations made in this case study, there is evidence that requirements mappings could help guide the strategic placement of excess in a system. Future work is needed to determine how requirements mapping could support the placement of excess and to explore how to conduct requirements mapping in a streamlined and efficient way.","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":"130312030","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}