Modeling and Control of Ceovolutionary Network Formation with Applications to Finishing Processes for 3D Printed Components

计算机进化网络形成的建模和控制及其在 3D 打印组件精加工过程中的应用

• 批准号: 1953694
• 负责人: Ceyhun Eksin
• 金额: $ 43万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953694&HistoricalAwards=false
• 关键词: Modeling; Control; Ceovolutionary; Network; Formation

Network representations allow a deep understanding of the dynamics of natural and technological systems by providing explicit characterization of pairwise relations between entities within the system in consideration. For instance, using networks to represent physical contacts among individuals in a community can provide a more precise representation of an infectious disease outbreak dynamics than standard models where homogeneous mixing of the population is assumed. However, networks do not appear out of thin air and their statistical properties tend to evolve over time given the dynamic nature of the systems. This project addresses the fundamental issues that are at the nexus of fields of network science and control theory, on how real-world networks arise, how they co-evolve with the environment, and how they can be perturbed. Theoretical aspects of this project will be assessed, and in part are motivated, by an experimental thread in controlling localized finishing processes of material surfaces in 3D printing. A material surface at the sub-micrometer level can be thought of as a wrinkled paper with asperities and pores that admits network representations. A finishing process aims to efficiently transition a rough surface (disconnected network) into a smooth surface (highly connected network) through abrasive action. Currently, finishing and post-processing techniques, commonly used to impart desired surface characteristics on 3D printed components, consume 20-70% of the total cycle time. Efficiency gains in and automation of finishing processes can overcome this major impediment to the industrial adoption of this technology. Networks form and change in the real world not just due to the interactions among their internal entities (i.e., nodes) but from their dynamic coupling and coevolution with the environment. The research aims to achieve the following scientific contributions: a) novel network formation models with endogenous dynamical processes and strategic node-level decision-making, and characterization of the effects of latencies and critical feedbacks on emerging network structure; b) theoretical framework for control of network formation that will provide optimal interventions to the decision-making, the dynamic process or the network structure by an external agent in order to shape the arising network features; c) consistent network representations of surface morphology evolution during finishing processes, and automated local finishing processes that are efficient and guarantee desired surface properties. The key technical novelty in modeling of network formation processes is the introduction of latency effects of environmental dynamics and node behavior, which yields a rich set of dynamics, questioning the robustness of fundamental network formation models. We propose to leverage recent works on influence maximization and optimal control with Kullback-Leibler control costs to provide a control theoretic framework for efficiently obtaining desired network structures given the nonlinear dynamics of coevolving networks. Our validation effort promises to show how the proposed theoretical framework can be transformative in novel application areas, e.g., smart finishing of 3D printed components.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

网络表示通过提供所考虑的系统内实体之间的成对关系的明确表征，可以深入理解自然和技术系统的动态。例如，与假设人口均匀混合的标准模型相比，使用网络来表示社区中个体之间的物理接触可以更准确地表示传染病爆发动态。然而，网络并不是凭空出现的，考虑到系统的动态性质，它们的统计特性往往会随着时间的推移而演变。该项目解决了网络科学和控制理论领域的基本问题，包括现实世界的网络如何产生、它们如何与环境共同演化以及它们如何受到扰动。该项目的理论方面将通过控制 3D 打印中材料表面的局部精加工过程的实验线索进行评估，并在一定程度上受到推动。亚微米级别的材料表面可以被认为是一张带有凹凸不平和孔隙的皱纹纸，可以表示网络。精加工过程的目的是通过研磨作用有效地将粗糙表面（断开的网络）转变为光滑的表面（高度连接的网络）。目前，通常用于在 3D 打印部件上赋予所需表面特性的精加工和后处理技术消耗了总周期时间的 20-70%。精加工过程的效率提高和自动化可以克服该技术在工业上采用的主要障碍。网络在现实世界中的形成和变化不仅归因于其内部实体（即节点）之间的相互作用，还归因于它们与环境的动态耦合和共同进化。该研究旨在实现以下科学贡献：a）具有内生动态过程和战略节点级决策的新型网络形成模型，以及延迟和关键反馈对新兴网络结构的影响的表征； b) 控制网络形成的理论框架，它将通过外部代理对决策、动态过程或网络结构提供最佳干预，以塑造所出现的网络特征； c) 精加工过程中表面形态演变的一致网络表示，以及高效并保证所需表面特性的自动化局部精加工过程。网络形成过程建模的关键技术新颖性是引入环境动态和节点行为的延迟效应，这产生了丰富的动态，质疑基本网络形成模型的稳健性。我们建议利用最近关于影响最大化和具有 Kullback-Leibler 控制成本的最优控制的工作，提供一个控制理论框架，以便在给定协同演化网络的非线性动力学的情况下有效地获得所需的网络结构。我们的验证工作有望展示所提出的理论框架如何在新颖的应用领域（例如 3D 打印组件的智能精加工）中实现变革。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。

项目成果

Explainable AI-infused ultrasonic inspection for internal defect detection

可解释的人工智能超声波检查用于内部缺陷检测

• DOI: 10.1016/j.cirp.2022.04.036
• 发表时间: 2022-01
• 期刊: CIRP Annals
• 作者: Karthikeyan, Adithyaa;Tiwari, Akash;Zhong, Yuhao;Bukkapatnam, Satish T.S.
• 通讯作者: Bukkapatnam, Satish T.S.

Approximate Submodularity of Maximizing Anticoordination in Network Games

网络游戏中最大化反协调的近似子模性

• DOI: 10.1109/cdc51059.2022.9993180
• 发表时间: 2023-01
• 期刊: IEEE Conference on Decision and Control
• 作者: Das, Soham;Eksin, Ceyhun
• 通讯作者: Eksin, Ceyhun

Ripple formations determine the heterogeneous microstructure of directed energy deposition (DED)-printed 316L components

波纹结构决定了定向能量沉积 (DED) 打印的 316L 组件的异质微观结构

• DOI: 10.1016/j.matdes.2023.111756
• 发表时间: 2023-03
• 期刊: Materials & Design
• 影响因子: 8.4
• 作者: Balhara, Himanshu;Botcha, Bhaskar;Wolff, Sarah J.;Bukkapatnam, Satish T.S.
• 通讯作者: Bukkapatnam, Satish T.S.

An Incentive Compatible Iterative Mechanism for Coupling Electricity Markets

耦合电力市场的激励兼容迭代机制

• DOI: 10.1109/tpwrs.2021.3100782
• 发表时间: 2021-07
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Garcia, Alfredo;Khatami, Roohallah;Eksin, Ceyhun;Sezer, Furkan
• 通讯作者: Sezer, Furkan

Smart manufacturing multiplex

智能制造多元

• DOI: 10.1016/j.mfglet.2020.08.004
• 发表时间: 2020-08
• 期刊: Manufacturing Letters
• 影响因子: 3.9
• 作者: Botcha, Bhaskar;Iquebal, Ashif S.;Bukkapatnam, Satish T.S.
• 通讯作者: Bukkapatnam, Satish T.S.