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自定义的零件实现都对部分属性构成了严格的要求，例如机械性能，微观结构，表面饰面，几何等。但是，很少在生产过程中直接测量和/或控制零件属性。取而代之的是，对可访问和可测量的主要过程响应进行测量，这些响应已知会影响零件的属性。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是基于以下假设：对主要过程响应的适当控制将隐含产生所需的零件属性。当前的工作旨在通过对使用新建立的流程模型的零件属性的基于模型的显式评估来替换此隐式假设，从而可以持续更新的数据库中的流程响应的可用测量以及历史数据。实际上，这种方法意味着直接对零件所需属性的隐性控制，因此，也将更接近快速零件认证的一步。该研究将在分布式网络中为制造平台建立科学和技术基础，在分布式网络中无缝地并有效地整合了物理过程，并在快速预测的框架中整合了物理过程，并在快速的预测框架中进行了数字模拟。该平台被设想为多环模拟和控制环境，该环境由在不同时间尺度上运行的四个控制循环组成。两个控制循环的结构与常规控制器相似，在硬件级别上起作用，并致力于对相关过程变量的物理控制，而其他两个则致力于基于软件级的模型对所需零件属性的评估。后两个指示硬件级控制器对达到所需零件属性所需的行为所需的更改。为了启用集成，将建立一个基于体素的几何模型，该模型由能够动态生成分析信息，存储实验信息以及编码从模拟和测量结果获得的最终部分属性的基础数据结构提供动力。这种几何表示非常适合使用通用图形处理单元（GPGPU）快速计算过程模型，这些过程模型确定了零件的任意区域中物理过程响应和属性。该框架将使用最新的开放式架构的定向能量沉积机在西北航空上进行验证，配备了网络的实时感应和控制。

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

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.

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

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.