Volume 3A: 47th Design Automation Conference (DAC)最新文献_第2页

Reliability Constrained Optimal Design of Distributed Generators in Power System Under Load and Wind Turbine Generation Uncertainty 负荷和风力发电不确定性下电力系统分布式发电机可靠性约束优化设计

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-68199

Zhetao Chen, Zhimin Xi

引用次数: 0

Goal-Oriented Inverse Design (GoID) of Feedstock Filament for Fused Deposition Modeling 熔融沉积模型中原料丝定向反设计(GoID)

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-70503

A. Deka, A. Nellippallil, John Hall

{"title":"Goal-Oriented Inverse Design (GoID) of Feedstock Filament for Fused Deposition Modeling","authors":"A. Deka, A. Nellippallil, John Hall","doi":"10.1115/detc2021-70503","DOIUrl":"https://doi.org/10.1115/detc2021-70503","url":null,"abstract":"\u0000 Additive manufacturing (AM) can produce complex geometrical shapes and multi-material parts that are not possible using typical manufacturing processes. The properties of multi-material AM parts are often unknown. For multi-material parts made using Fused Deposition Modeling (FDM), these properties are driven by the filament. Acquiring the properties of the products or the filament necessitates experiments that can be expensive and time-consuming. Thus, there is a need for simulation-based design tools that can determine the multi-material properties of the filament by exploring the complex process-structure-property (p-s-p) relationship.\u0000 In this paper, we present a Goal-Oriented Inverse Design (GoID) method to produce feedstock filament for FDM process with specific property goals. Using this method, the designers connects the structure and property in the p-s-p relationship by identifying satisficing material composition for specific property goals. The filament properties identified in the problem are percentage elongation, tensile strength, and Young’s Modulus. The problem is formulated using a generic decision-based design framework, Concept Exploration Framework. The solution space exploration for satisficing solutions is performed using the compromise Decision Support Problem (cDSP). The forward information flow is first established to generate the necessary mathematical relationships between the composition and the property goals. Next, the target property goals of the filament are set. The cDSP is used for solution space exploration to identify satisficing solutions for material composition for the target property goals. While the results are interesting, the focus of our work is to demonstrate, and refine, the goal-oriented, inverse design method for the AM domain.","PeriodicalId":204380,"journal":{"name":"Volume 3A: 47th Design Automation Conference (DAC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114714154","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}

引用次数: 1

Proposal for an Adaptive Bone Screw Design Based on FEA Studies Exemplified by Pedicle Screw 以椎弓根螺钉为例，基于有限元分析的自适应骨螺钉设计方案

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-67768

A. Seidler, L. Mehlhorn, P. Sembdner, S. Holtzhausen, R. Stelzer, W. Drossel

引用次数: 0

Understanding the Energy Behavior of Building Occupants Through the Chronology of Their Energy Interactions 通过他们的能源相互作用的年表了解建筑居住者的能源行为

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-69953

Danielle Preziuso, Gregory Kaminski, Philip Odonkor

引用次数: 0

Stochastically-Trained Physics-Informed Neural Networks: Application to Thermal Analysis in Metal Laser Powder Bed Fusion 随机训练的物理信息神经网络:在金属激光粉末床熔合热分析中的应用

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-70557

Justin Pierce, Glen Williams, T. Simpson, N. Meisel, Christopher McComb

{"title":"Stochastically-Trained Physics-Informed Neural Networks: Application to Thermal Analysis in Metal Laser Powder Bed Fusion","authors":"Justin Pierce, Glen Williams, T. Simpson, N. Meisel, Christopher McComb","doi":"10.1115/detc2021-70557","DOIUrl":"https://doi.org/10.1115/detc2021-70557","url":null,"abstract":"\u0000 Modern digital manufacturing processes, such as additive manufacturing, are cyber-physical in nature and utilize complex, process-specific simulations for both design and manufacturing. Although computational simulations can be used to optimize these complex processes, they can take hours or days — an unreasonable cost for engineering teams leveraging iterative design processes. Hence, more rapid computational methods are necessary in areas where computation time presents a limiting factor. When existing data from historical examples is plentiful and reliable, supervised machine learning can be used to create surrogate models that can be evaluated orders of magnitude more rapidly than comparable finite element approaches. However, for applications that necessitate computationally-intensive simulations, even generating the training data necessary to train a supervised machine learning model can pose a significant barrier. Unsupervised methods, such as physics-informed neural networks, offer a shortcut in cases where training data is scarce or prohibitive. These novel neural networks are trained without the use of potentially expensive labels. Instead, physical principles are encoded directly into the loss function. This method substantially reduces the time required to develop a training dataset, while still achieving the evaluation speed that is typical of supervised machine learning surrogate models. We propose a new method for stochastically training and testing a convolutional physics-informed neural network using the transient 3D heat equation- to model temperature throughout a solid object over time. We demonstrate this approach by applying it to a transient thermal analysis model of the powder bed fusion manufacturing process.","PeriodicalId":204380,"journal":{"name":"Volume 3A: 47th Design Automation Conference (DAC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124373580","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}

引用次数: 1

Value of Information for Continuous Monitoring Systems in Recurrent Maintenance Decision Scenarios 连续监测系统在经常性维护决策场景中的信息价值

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-71021

Xinyang Liu, Pingfeng Wang

{"title":"Value of Information for Continuous Monitoring Systems in Recurrent Maintenance Decision Scenarios","authors":"Xinyang Liu, Pingfeng Wang","doi":"10.1115/detc2021-71021","DOIUrl":"https://doi.org/10.1115/detc2021-71021","url":null,"abstract":"\u0000 Monitoring systems play a crucial role in improving system failure resilience and preventing tragic consequences brought by unexpected system failure and saving the consequential high cost. Continuous monitoring systems have been applied to diversified systems for well-informed operations. Although plenty research has devoted to predicting system states using the continuous data flow, there still lacks a systematic decision-making framework for system designers and engineering system owners to maximize their benefits on adopting monitoring systems. This paper constructs such a decision-making framework, with which system owners can evaluate the operation cost change under specific operation modes considering the effectiveness of continuous monitoring systems in predicting system failures. Two case studies have been conducted to illustrate the value evaluation of the monitoring information and the system maintenance process with the aid of different prognostic results based on the monitoring data. The first case study considers a health-state prediction with fixed accuracy while the second one incorporates the accuracy improvement as the monitoring data accumulates. Results show that the value of monitoring systems will be influenced by the deviation among the equipment group, the accuracy of system-state prediction, and different types of cost involved in the operating process. And the adjustment of maintenance actions based on monitoring and prognosis information will help improve the value of monitoring systems.","PeriodicalId":204380,"journal":{"name":"Volume 3A: 47th Design Automation Conference (DAC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132153263","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}

引用次数: 0

Evaluating Heuristics in Engineering Design: A Reinforcement Learning Approach 工程设计中的启发式评估:一种强化学习方法

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-70425

K. Elsayed, Ilias Bilionis, Jitesh H. Panchal

{"title":"Evaluating Heuristics in Engineering Design: A Reinforcement Learning Approach","authors":"K. Elsayed, Ilias Bilionis, Jitesh H. Panchal","doi":"10.1115/detc2021-70425","DOIUrl":"https://doi.org/10.1115/detc2021-70425","url":null,"abstract":"\u0000 Heuristics are essential for addressing the complexities of engineering design processes. The goodness of heuristics is context-dependent. Appropriately tailored heuristics can enable designers to find good solutions efficiently, and inappropriate heuristics can result in cognitive biases and inferior design outcomes. While there have been several efforts at understanding which heuristics are used by designers, there is a lack of normative understanding about when different heuristics are suitable. Towards addressing this gap, this paper presents a reinforcement learning-based approach to evaluate the goodness of heuristics for three sub-problems commonly faced by designers: (1) learning the map between the design space and the performance space, (2) acquiring sequential information, and (3) stopping the information acquisition process. Using a multi-armed bandit formulation and simulation studies, we learn the suitable heuristics for these individual sub-problems under different resource constraints and problem complexities. Additionally, we learn the optimal heuristics for the combined problem (i.e., the one composing all three sub-problems), and we compare them to ones learned at the sub-problem level. The results of our simulation study indicate that the proposed reinforcement learning-based approach can be effective for determining the quality of heuristics for different problems, and how the effectiveness of the heuristics changes as a function of the designer’s preference (e.g., performance versus cost), the complexity of the problem, and the resources available.","PeriodicalId":204380,"journal":{"name":"Volume 3A: 47th Design Automation Conference (DAC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132249740","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}

引用次数: 0

Point-Cloud Neural Network Using Transfer Learning-Based Multi-Fidelity Method for Thermal Field Prediction in Additive Manufacturing 基于迁移学习的多点云神经网络在增材制造热场预测中的应用

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-67963

Xufeng Huang, Zhen Hu, Tingli Xie, Zhuo Wang, Lei Chen, Qi Zhou

{"title":"Point-Cloud Neural Network Using Transfer Learning-Based Multi-Fidelity Method for Thermal Field Prediction in Additive Manufacturing","authors":"Xufeng Huang, Zhen Hu, Tingli Xie, Zhuo Wang, Lei Chen, Qi Zhou","doi":"10.1115/detc2021-67963","DOIUrl":"https://doi.org/10.1115/detc2021-67963","url":null,"abstract":"\u0000 Melt pool modeling is critical for model-based uncertainty quantification (UQ) and quality control in metallic Additive Manufacturing (AM). Finite element (FE) simulation for thermal modeling in metal AM, however, is tedious and time-consuming. This paper presents a multi-fidelity point-cloud neural network method (MF-PointNN) for surrogate modeling of melt pool based on FE simulation data. It merges the feature representations of low-fidelity (LF) analytical model and high-fidelity (HF) FE simulation data through the theory of transfer learning (TL). A basic PointNN is firstly trained using LF data to construct correlation between the inputs and thermal field of analytical models. Then, the basic PointNN is updated and fine-tuned using the small size of HF data to build the MF-PointNN. The trained MF-PointNN allows for efficient mapping from input variables and spatial positions to thermal histories, and thereby efficiently predict the three-dimensional melt pool. Results of melt pool modeling of electron beam additive manufacturing (EBAM) of Ti-6Al-4V under uncertainty demonstrate the efficacy of the proposed approach.","PeriodicalId":204380,"journal":{"name":"Volume 3A: 47th Design Automation Conference (DAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130175639","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}

引用次数: 1

Potential Energy Surfaces for Conceptual Design and Analysis of Mechanical Systems 机械系统概念设计与分析的势能面

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-70921

C. Manion, M. Fuge

{"title":"Potential Energy Surfaces for Conceptual Design and Analysis of Mechanical Systems","authors":"C. Manion, M. Fuge","doi":"10.1115/detc2021-70921","DOIUrl":"https://doi.org/10.1115/detc2021-70921","url":null,"abstract":"\u0000 Current computational Design Synthesis approaches have had trouble generating components with higher kinematic pairs and have instead relied on libraries of predefined components. However, higher kinematic pairs are ubiquitous in many mechanical devices such as ratchets, latches, locks, trigger mechanisms, clock escapements, and materials handling systems. In many cases there is a need to synthesize new higher kinematic pair devices. To address this problem, we develop a new representation for mechanical systems that extends the capabilities of configuration spaces to consider arbitrary energy storing mechanical devices. The key idea underlying this representation is the use of potential energy surfaces as a generalization of configuration spaces. This generalization enables modelling of mechanical systems in a physics independent manner and captures behaviors such as dynamics. By modeling a device through the lens of a potential energy surface, we demonstrate that differentiable simulation is possible. Differentiable simulation enables efficient calculation of gradients of potential energy surface parameters with respect to an objective function that depends on trajectories taken on the potential energy surface. This allows synthesis of mechanical devices with desired kinematic and dynamic behavior through gradient descent. We demonstrate this through several synthesis examples including positioning devices (e.g., a funnel) and timing devices (e.g., an oscillator).","PeriodicalId":204380,"journal":{"name":"Volume 3A: 47th Design Automation Conference (DAC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115748706","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}

引用次数: 0

Open-Loop Control Co-Design of Floating Offshore Wind Turbines Using Linear Parameter-Varying Models 基于线性变参数模型的海上浮式风力机开环控制协同设计

Volume 3A: 47th Design Automation Conference (DAC) Pub Date : 2021-08-17 DOI: 10.1115/detc2021-67573

Athul K. Sundarrajan, Yong Hoon Lee, James T. Allison, Daniel R. Herber

{"title":"Open-Loop Control Co-Design of Floating Offshore Wind Turbines Using Linear Parameter-Varying Models","authors":"Athul K. Sundarrajan, Yong Hoon Lee, James T. Allison, Daniel R. Herber","doi":"10.1115/detc2021-67573","DOIUrl":"https://doi.org/10.1115/detc2021-67573","url":null,"abstract":"\u0000 This paper discusses a framework to design elements of the plant and control systems for floating offshore wind turbines (FOWTs) in an integrated manner using linear parameter-varying models. Multiple linearized models derived from high-fidelity software are used to model the system in different operating regions characterized by the incoming wind speed. The combined model is then used to generate open-loop optimal control trajectories as part of a nested control co-design strategy that explores the system’s stability and power production in the context of crucial plant and control design decisions. A cost model is developed for the FOWT system, and the effect of plant decisions and subsequent power and stability response of the FOWT is quantified in terms of the levelized cost of energy (LCOE) for that system. The results show that the stability constraints and the plant design decisions affect the turbine’s power and, subsequently, LCOE of the system. The results indicate that a lighter plant in terms of mass can produce the same power for a lower LCOE while still satisfying the constraints.","PeriodicalId":204380,"journal":{"name":"Volume 3A: 47th Design Automation Conference (DAC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121868070","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}

引用次数: 8