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

在所有工业部门中，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。取而代之的是，对已知影响部件属性的可访问和可测量的主要工艺响应进行测量。然后，通过操纵一组可控制的工艺参数来控制这些主要工艺响应。这一广泛使用的策略基于这样的假设，即对主要工艺响应的适当控制将隐含地产生所需的部件属性。目前的工作旨在用基于模型的对零件属性的显式评估来取代这一隐含的假设，该评估使用新建立的工艺模型、可用的工艺响应测量和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控制，而不是隐式控制，因此也向快速零件认证又迈进了一步。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，该网络在快速预测框架中无缝而高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类似于常规控制器，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基于所需零件属性的软件级模型评估。后两者指示硬件级控制器对其行为进行必要的更改，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、存储实验信息并对从模拟和测量结果获得的最终零件属性进行编码的底层数据结构提供动力。这种几何表示非常适合使用通用图形处理单元(GPGPU)来快速计算工艺模型，以确定零件任意区域中的物理工艺响应和属性。所研究的框架将使用西北大学最先进的开放式定向能量沉积设备进行验证，该设备配备了网络化的实时传感和控制。

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.