Digital twins (DTs) offer the potential for improved understanding of current and future manufacturing processes. This can only be achieved by DTs consistently and accurately representing the real processes. However, the robustness and resilience of the DT itself remain an issue. Accordingly, this paper offers an approach to deal with uncertainty and disruptions, as the DT detects these effectively and self-adapts as needed to maintain representativeness. The paper proposes an intelligent agent-based architecture to improve the robustness (including accuracy of representativeness) and resilience (including timely update) of the DT. The approach is demonstrated on a case of cryogenic secondary manufacturing.

数字孪生 (DT) 提供了改进对当前和未来制造过程的理解的潜力。这只能通过 DT 一致且准确地表示真实过程来实现。然而，DT 本身的稳健性和弹性仍然是一个问题。因此，本文提供了一种处理不确定性和中断的方法，因为 DT 可以有效地检测这些并根据需要进行自适应以保持代表性。该论文提出了一种基于智能代理的架构，以提高 DT 的鲁棒性（包括代表性的准确性）和弹性（包括及时更新）。该方法在低温二次制造的情况下进行了演示。