The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its primary purpose is to leverage the process intelligence captured within digital models—or usually their faster-solving surrogates—towards generating increased value from the physical entities. The surrogate models are created using machine learning based on data obtained from the field, experiments and digital models, which may be physics-based or statistics-based. Anomaly detection and correction, and diagnostic closed-loop process control are examples of how a process DT can be deployed. In the manufacturing industry, its use can achieve improvements in product quality and process productivity. Metal additive manufacturing (AM) stands to gain tremendously from the use of DTs. This is because the AM process is inherently chaotic, resulting in poor repeatability. However, a DT acting in a supervisory role can inject certainty into the process by actively keeping it within bounds through real-time control commands. Closed-loop feedforward control is achieved by observing the process through sensors that monitor critical parameters and, if there are any deviations from their respective optimal ranges, suitable corrective actions are triggered. The type of corrective action (e.g. a change in laser power or a modification to the scanning speed) and its magnitude are determined by interrogating the surrogate models. Because of their artificial intelligence (AI)-endowed predictive capabilities, which allow them to foresee a future state of the physical twin (e.g. the AM process), DTs proactively take context-sensitive preventative steps, whereas traditional closed-loop feedback control is usually reactive. Apart from assisting a build process in real-time, a DT can help with planning the build of a part by pinpointing the optimum processing window relevant to the desired outcome. Again, the surrogate models are consulted to obtain the required information. In this article, we explain how the application of DTs to the metal AM process can significantly widen its application space by making the process more repeatable (through quality assurance) and cheaper (by getting builds right the first time).

数字孪生 (DT) 是一个相对较新的概念，在行业中越来越被接受。DT 通常被认为包括一个物理实体、它的虚拟副本和中间的双向数字数据通信。它的主要目的是利用在数字模型中捕获的过程智能——或者通常是它们更快解决的替代物——从物理实体中产生更大的价值。替代模型是基于从现场、实验和数字模型中获得的数据使用机器学习创建的，这些数据可能是基于物理的或基于统计的。异常检测和纠正以及诊断闭环过程控制是如何部署过程 DT 的示例。在制造业中，它的使用可以实现产品质量和过程生产率的提高。金属增材制造 (AM) 将从 DT 的使用中获得巨大收益。这是因为 AM 过程本质上是混乱的，导致重复性较差。然而，扮演监督角色的 DT 可以通过实时控制命令主动将其保持在界限内，从而为过程注入确定性。闭环前馈控制是通过监控关键参数的传感器观察过程来实现的，如果与各自的最佳范围有任何偏差，则会触发适当的纠正措施。纠正措施的类型（例如激光功率的变化或扫描速度的修改）及其幅度通过询问替代模型来确定。由于它们具有人工智能 (AI) 的预测能力，这使他们能够预见物理双胞胎的未来状态（例如 AM 过程），DT 会主动采取上下文敏感的预防措施，而传统的闭环反馈控制通常是被动的。除了实时协助构建过程外，DT 还可以通过精确定位与所需结果相关的最佳处理窗口来帮助规划零件的构建。再次参考代理模型以获得所需信息。在本文中，我们解释了 DT 在金属 AM 工艺中的应用如何通过使工艺更具可重复性（通过质量保证）和更便宜（通过第一次正确构建）来显着拓宽其应用空间。而传统的闭环反馈控制通常是被动的。除了实时协助构建过程外，DT 还可以通过精确定位与所需结果相关的最佳处理窗口来帮助规划零件的构建。再次参考代理模型以获得所需信息。在本文中，我们解释了 DT 在金属 AM 工艺中的应用如何通过使工艺更具可重复性（通过质量保证）和更便宜（通过第一次正确构建）来显着拓宽其应用空间。而传统的闭环反馈控制通常是被动的。除了实时协助构建过程外，DT 还可以通过精确定位与所需结果相关的最佳处理窗口来帮助规划零件的构建。再次参考代理模型以获得所需信息。在本文中，我们解释了 DT 在金属 AM 工艺中的应用如何通过使工艺更具可重复性（通过质量保证）和更便宜（通过第一次正确构建）来显着拓宽其应用空间。