CPS: Synergy: An Integrated Simulation and Process Control Platform for Distributed Manufacturing Process Chains

CPS：Synergy：分布式制造流程链的集成仿真和流程控制平台

• 批准号: 1646592
• 负责人: Kornel Ehmann
• 金额: $ 70.13万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646592&HistoricalAwards=false
• 关键词: CPS; Synergy; Integrated; Simulation; Process

Rapid and customized part realization in all industrial sectors imposes stringent demands on part attributes, e.g., mechanical properties, microstructure, surface finish, geometry, etc. However, part attributes can very rarely be directly measured and/or controlled in the production process. Instead, measurements are taken of accessible and measurable primary process responses that are known to influence the part's attributes. These primary process responses are then controlled through the manipulation of a set of controllable process parameters. This widely used strategy is based on the assumption that the proper control of the primary process responses will implicitly yield the desired part attributes. The current work aims to replace this implicit assumption by a model-based explicit evaluation of the part's attributes that uses newly established process models, available measurements of process responses and historical data from a data base that is continuously updated. In effect, this approach implies a direct instead of an implicit control of the part's desired attributes and, as such, also moves a step closer to rapid part certification.The research will establish the scientific and technological foundation for a manufacturing platform in a distributed network that seamlessly and efficiently integrates physical processes and numerical simulations in a fast predictive framework. The platform is envisioned as a multi-loop simulation and control environment consisting of four control loops running at different time scales. Two of the control loops, similar in structure to conventional controllers, act at the hardware-level and are devoted to the physical control of the relevant process variables while the other two are devoted to the software-level model-based evaluation of the desired part attributes. The latter two instruct the hardware-level controllers on required changes in their behavior that are necessary to reach the desired part attributes. To enable the integration, a voxel-based geometric model powered by an underlying data structure capable of dynamically generating analysis information, storing experimental information, and encoding the final part attributes obtained from the simulation and measured results will be established. This geometrical representation is well-suited to the use of general purpose graphics processing units (GPGPU) for fast computation of the process models that determine the physical process responses and attributes in arbitrary regions of a part. The researched framework will be validated using the state-of-the art open-architecture Directed Energy Deposition machine at Northwestern equipped with networked real-time sensing and control.

所有工业领域的快速和定制零件实现对零件属性提出了严格的要求，例如机械性能、微观结构、表面光洁度、几何形状等。然而，零件属性很少可以在生产过程中直接测量和/或控制。相反，测量是对已知会影响零件属性的可访问且可测量的主要过程响应进行的。然后通过操纵一组可控过程参数来控制这些主要过程响应。这种广泛使用的策略基于这样的假设：对主要过程响应的适当控制将隐式地产生所需的零件属性。当前的工作旨在通过基于模型的零件属性显式评估来取代这种隐式假设，该评估使用新建立的过程模型、过程响应的可用测量值以及来自不断更新的数据库的历史数据。实际上，这种方法意味着对零件所需属性的直接而不是隐式控制，因此也离快速零件认证又近了一步。该研究将为分布式网络中的制造平台奠定科学和技术基础，该平台在快速预测框架中无缝有效地集成物理过程和数值模拟。该平台被设想为一个多回路仿真和控制环境，由在不同时间尺度运行的四个控制回路组成。其中两个控制回路在结构上与传统控制器类似，在硬件级起作用，致力于相关过程变量的物理控制，而另外两个则致力于对所需零件属性进行基于软件级模型的评估。后两者指示硬件级控制器对其行为进行所需的更改，这些更改是达到所需部件属性所必需的。为了实现集成，将建立一个基于体素的几何模型，该模型由底层数据结构提供支持，能够动态生成分析信息、存储实验信息并对从模拟和测量结果获得的最终零件属性进行编码。这种几何表示非常适合使用通用图形处理单元 (GPGPU) 来快速计算过程模型，从而确定零件任意区域的物理过程响应和属性。研究框架将使用西北大学配备网络实时传感和控制的最先进的开放式架构定向能量沉积机进行验证。

项目成果

A parallelized three-dimensional cellular automaton model for grain growth during additive manufacturing

• DOI: 10.1007/s00466-017-1535-8
• 发表时间: 2018-05-01
• 期刊: COMPUTATIONAL MECHANICS
• 影响因子: 4.1
• 作者: Lian, Yanping;Lin, Stephen;Wagner, Gregory J.
• 通讯作者: Wagner, Gregory J.

Towards smart manufacturing process selection in Cyber-Physical Systems

• DOI: 10.1016/j.mfglet.2018.03.002
• 发表时间: 2018-08
• 期刊: Manufacturing Letters
• 影响因子: 3.9
• 作者: Ebot Ndip-Agbor;Jian Cao;K. Ehmann

Meso-scale modeling of multiple-layer fabrication process in Selective Electron Beam Melting: Inter-layer/track voids formation

• DOI: 10.1016/j.matdes.2017.12.031
• 发表时间: 2018-03
• 期刊: Materials & Design
• 影响因子: 8.4
• 作者: Wentao Yan;Ya Qian;W. Ge;Stephen Lin;Wing Kam Liu;F. Lin;G. Wagner

Deep learning predicts path-dependent plasticity

• DOI: 10.1073/pnas.1911815116
• 发表时间: 2019-12-26
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Mozaffar, M.;Bostanabad, R.;Bessa, M. A.
• 通讯作者: Bessa, M. A.

A conservative level set method on unstructured meshes for modeling multiphase thermo-fluid flow in additive manufacturing processes

• DOI: 10.1016/j.cma.2020.113348
• 发表时间: 2020-12-01
• 期刊: COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
• 影响因子: 7.2
• 作者: Lin, Stephen;Gan, Zhengtao;Wagner, Gregory J.
• 通讯作者: Wagner, Gregory J.