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

在所有工业领域，快速和定制的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，在生产过程中很少能直接测量和/或控制零件属性。相反，测量是对已知会影响零件属性的可访问和可测量的主要过程响应进行的。然后通过操纵一组可控工艺参数来控制这些主要工艺响应。这种广泛使用的策略是基于对主要过程响应的适当控制将隐含地产生所需部件属性的假设。当前的工作旨在通过使用新建立的过程模型、过程响应的可用测量和不断更新的数据库中的历史数据对零件属性进行基于模型的显式评估来取代这种隐含的假设。实际上，这种方法意味着对零件所需属性的直接控制，而不是隐式控制，因此，也向快速零件认证迈进了一步。该研究将为分布式网络中的制造平台建立科学和技术基础，该平台将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，由四个在不同时间尺度上运行的控制回路组成。其中两个控制回路在结构上与传统控制器相似，在硬件级起作用，并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部件属性进行基于模型的软件级评估。后两者指示硬件级控制器对其行为进行必要的更改，以达到所需的部件属性。为了实现集成，将建立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，并对从仿真和测量结果中获得的最终部件属性进行编码。这种几何表示非常适合使用通用图形处理单元（GPGPU）快速计算过程模型，以确定零件任意区域的物理过程响应和属性。该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结构定向能沉积机进行验证。

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.