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CPS: Frontier: Collaborative Research: Data-Driven Cyberphysical Systems

CPS：前沿：协作研究：数据驱动的网络物理系统

基本信息

批准号：
1645648
负责人：
Sandipan Mishra
金额：
$ 38万
依托单位：
Rensselaer Polytechnic Institute
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1645648&HistoricalAwards=false
关键词：
CPS Frontier Collaborative Research Data

项目摘要

Data-driven cyber-physical systems are ubiquitous in many sectors including manufacturing, automotive, transportation, utilities and health care. This project develops the theory, methods and tools necessary to answer the central question "how can we, in a data-rich world, design and operate cyber-physical systems differently?" The resulting data-driven techniques will transform the design and operation process into one in which data and models - and human designers and operators - continuously and fluently interact. This integrated view promises capabilities beyond its parts. Explicitly integrating data will lead to more efficient decision-making and help reduce the gap from model-based design to system deployment. Furthermore, it will blend design- and run-time tasks, and help develop cyber-physical systems not only for their initial deployment but also for their lifetime.While proposed theory, methods and tools will cut across the spectrum of cyber-physical systems, the project focuses on their implications in the emerging application of additive manufacturing. Even though a substantial amount of engineering time is spent, additive manufacturing processes often fail to produce acceptable geometric, material or electro-mechanical properties. Currently, there is no mechanism for predicting and correcting these systematic, repetitive errors nor to adapt the design process to encompass the peculiarities of this manufacturing style. A data-driven cyber-physical systems perspective has the potential to overcome these challenges in additive manufacturing. The project's education plan focuses on the already much needed transformation of the undergraduate and graduate curricula to train engineers and computer scientists who will create the next-generation of cyber-physical with a data-driven mindset. The team will reach out to K-12 students and educators through a range of activities, and to undergraduate students from underrepresented groups through year-long research projects. All educational material generated by the project will be shared publicly.

数据驱动的网络物理系统在制造、汽车、交通、公用事业和医疗保健等许多领域无处不在。该项目开发了回答核心问题所需的理论、方法和工具：“在数据丰富的世界中，我们如何以不同的方式设计和操作网络物理系统？”由此产生的数据驱动技术将把设计和操作过程转变为数据和模型以及人类设计师和操作员持续、流畅地交互的过程。这种集成视图带来了超越其各个部分的功能。显式集成数据将带来更高效的决策，并有助于缩小从基于模型的设计到系统部署的差距。此外，它将融合设计和运行时任务，不仅有助于开发网络物理系统的初始部署，而且有助于其整个生命周期。虽然提出的理论、方法和工具将跨越网络物理系统的范围，但该项目重点关注它们对增材制造新兴应用的影响。尽管花费了大量的工程时间，增材制造工艺通常无法产生可接受的几何、材料或机电特性。目前，没有机制可以预测和纠正这些系统性、重复性错误，也没有调整设计过程以包含这种制造方式的特殊性。数据驱动的网络物理系统视角有可能克服增材制造中的这些挑战。该项目的教育计划侧重于本科生和研究生课程已经急需的转变，以培训工程师和计算机科学家，他们将以数据驱动的思维方式创建下一代网络物理。该团队将通过一系列活动接触 K-12 学生和教育工作者，并通过为期一年的研究项目接触来自代表性不足群体的本科生。该项目生成的所有教育材料都将公开共享。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Iterative learning control for power profile shaping in selective laser melting

选择性激光熔化功率分布整形的迭代学习控制

DOI：
10.1109/coase.2019.8843070
发表时间：
2019
期刊：
2019 IEEE 15th International Conference on Automation Science and Engineering (CASE
影响因子：
0
作者：
Shkoruta, Aleksandr;Caynoski, William;Mishra, Sandipan;Rock, Stephen
通讯作者：
Rock, Stephen

Layer-to-Layer Predictive Control of Inkjet 3-D Printing